Sampling Accuracy in Erosion Plot Tanksiirc.narod.ru/4conference/Section/sec4-2.pdf · Proceedings...

Proceedings of The Fourth International Iran & Russia Conference 1245

Sampling Accuracy in Erosion Plot Tanks

Davood Nikkami1 , Mahmood Arabkhrdri 2 , and Peyman Razmjoo 3

1 Assistant Professor, Soil Conservation and Watershed Management Research Institute, P.O. Box: 13445-1136, Tehran, Iran, Tel: (9821) 490-5875, Fax: (9821) 490-5709, e-mail: [email protected]

2 Research Assistant, Soil Conservation and Watershed Management Research Institute (SCWMRI), P.O. Box: 13445-1136, Tehran, Iran, Tel: (9821) 490-1214, e-mail: [email protected]

3 Research Assistant, Soil Conservation and Watershed Management Research Institute (SCWMRI), P.O. Box: 13445-1136, Tehran, Iran, Tel: (9821) 490-1214, e-mail: [email protected]

Abstract Effective factors in soil erosion process and sediment yield are usually evaluated in soil erosion and sediment study plots. Investigating and evaluating effective factors are based on runoff and sediment samples from tanks which are located at the end of these plots. Although these plots and their accouterments are constructed precisely in right places, significant errors on samplings lead the researchers to make wrong decisions. There are limited researches on sampling accuracy from these tanks. In this article that is the result of a research implemented in the Soil Conservation and Watershed Management Research Institute (SCWMRI) during 2003, the sampling accuracy of bottle, pipette, and cylindrical sampler was investigated. Three concentrations of 1.87, 4.68 and 9.36 gr/l of sediment were prepared in 220 liter plot tanks with three mixing periods of 1, 2 and 5 minutes. The samples were taken from center and side of the tanks and from the depths of 20, 40, 60 and 80 centimeter from the water surface for bottles and pipette and the whole depth for cylindrical sampler. The cylindrical sampler showed the least error of 13.04% on concentration compared to two other methods. Sampling with bottle and pipette illustrated that the concentration of sediment increases and becomes more accurate with the depth of the tank. The results showed the most accurate concentration in the last 20 centimeter depth, i.e. 80 centimeters from the water surface. Computed concentration errors in 20, 40, 60 and 80 centimeter from the water surface for bottle sampling were 65.63, 56.13, 45.63 and 32.73 percent and for pipette sampling were 65.48, 57.02, 50.88 and 43.67 percent respectively. Also, there were no significant difference between sampling from center and side of the tanks and between mixing periods of 1, 2 and 5 minutes at 1% level of probability.

Keywords: Sampling, Concentration, Sediment, Erosion plot, Runoff and Sediment Tank

Introduction Soil as one of the basic factors in plant growth, is a fundamental national resource for human needs. In ideal conditions and applying appropriate agricultural rules, it takes about 300 years for generation of 2.5 centimeter of soil which is equal to 12.5 t/ha every year (Bybordi, 1993). Growing population rate and more need for food productions have destructive effects on natural resources causing soil degradation. Conserving generated soil and preventing soil loss need investigating soil erosion and studying sediment yield. The simplest method for studying the management of soil erosion is using soil erosion plots that lead to solve soil erosion and sediment related problems (Mutchler et al., 1994). Erosion plots provide such condition that a factor could be investigated while keeping constant all other factors. These plots are used in small size of one square meter for investigating interill erosion and big size of 11-198 meter long and 2-46 meter width for investigating both rill and interill erosions (Toebes and Ouryvaev, 1970). In small plots, runoff and sediment are collected in a tank, but in big plots a small portion of them are


sampled. Although, developed methods like photogrametry (Cook and Valentine, 1979) and Laser scanner (Flanagan et al., 1995) are used in some researches, sampling by bottle is more common due to its simplicity and least costs. Usually, researchers pay attention on site selection and construction of erosion plots. But the main sources of error which are related to sampling method are not considered. Based on Bagnold’s works (explained by Shfaee Bajestan, 1994), in turbulence flow, sediment particles stay in suspended conditions, where vertical velocity be more than particle’s falling velocity. Point sampling by standard bottles is used for sampling suspended load in rivers (Przedwojski et al., 1995). Using bottles is common when sampling from erosion plot tanks but, they did not show accurate results (Lang, 1992). Zobisch et al., (1996) found significant difference among 5 sampling field staff results. They noticed that the depth of sampling, the velocity of mixing, and experience of samplers are the most important sources of error. To minimize sampling error, a cylindrical runoff and sediment sampler was made in Soil Conservation and Watershed Management Research Institute (SCWMRI) in Iran. It takes a column of water and accompanied suspended sediment from the whole depth of tank (Nikkami, 2002). Three different sampling instruments of bottle, pipette, and cylindrical sampler made in SCWMRI are compared in this research.

Materials and methods Bottle, pipette, and cylindrical sampler were compared for their accuracy on sampling runoff and sediment from erosion plot tanks. Bottle sampling is usually used in erosion plot projects. Their volume differs from 0.5 to 4 liter based on their availability. As their volume increase, the time of sampling increases and more particles will settle in the tank and the accuracy would be affected. In this research, four 330 cc bottles were installed on a vertical metal bar with 30 degree angle and 20 cm far from each other (Nikkami et al., 2004). The bottle taps were opened simultaneously when located in the tank and took samples from 20, 40, 60 and 80 cm from the water surface (Fig. 1). Pipette as the second sampling instrument was used for sampling from the same depths. As shown in Fig. 2, four plastic tubes with 7 mm diameter were installed on a wooden bar with a vertical distance of 20 cm from each other. The another end of these four tubes were interred to one liter bottles which their taps were closed tightly and another tube from those bottles were connected to a 4 to 1 terminal and then with a tube from terminal to a suction (Nikkami et al., 2004).Cylindrical sampler made in SCWMRI, was the third sampler in this research. This sampler was designed to take a column of runoff and sediment from erosion plot tanks and it was consumed that the mixing speed and the amount of technician’s experience does not affect on sampling result (Nikkami, 2002). As illustrated on Figures 3 and 4, this sampler consists of three main parts.

1. Base unit and central shaft: Base unit consists of a 6.5 cm round pan that has a 5 cm diameter hole at the middle. The central shaft with 120 cm height is connected to the center of base unit.

2. Gauged cylindrical unit: Cylindrical unit is made by Plexiglas with 100 cm height and 5 cm diameter. This unit has been gauged to show the volume of sample and height of the collected runoff in the tank.

3. Upper unit: This part has been designed to fasten two other parts together and keep the sample to be replaced.


Usually, a wooden steak is used to mix the runoff and sediment in plot project tanks. In this research, a wooden shovel has been used for this reason to keep the mixing condition constant in the whole research. The 1, 2 and 5 minute mixing period treatments were considered. To have sediment characteristics similar to one in plot projects, one ton of sediment was collected from the hill slopes of Sohrain-Gharachrian flood spreading station in Zanjan province with 40, 40 and 20 percent of sand, silt, and clay, respectively. Comparing grain size of collected sediment with 18 sediment samples from erosion plot tanks of this station showed no significant difference. The sediment has been mixed completely and spread on a lateral surface with 5 cm height. After drying at room temperature, samples of 400, 1000 and 2000 gr were prepared for making appropriate concentrations. The concentrations were 1.87, 4.68 and 9.36 gr/l which were similar to 75% of concentrations in erosion plot tanks of above mentioned station (Nikkami et al., 2004). The research was executed in following steps.

1. Preparing 1.87 gr/l sediment concentration in three 213.5 liter tanks 2. Mixing tank content clock wise with wooden shovel at 1, 2, and 5 minutes periods 3. Sampling 162 times with bottles, pipette, and cylindrical samplers from the center and

side of the tanks. Based on one concentration, 3 mixing periods, 2 sampling location, and 3 replications, 18, 72, and 72 samples were taken by cylindrical sampler, bottles, and pipettes respectively. Because of low volume, 3 replications of bottle samples were mixed together. Therefore, samples of this step changed from 72 to 24 and the total samples from 162 to 114.

4. Computing sediment concentration of samples by passing them through 2.5 µm filters and drying in the oven and considering water volume of samples.

5. Preparing the sediment concentrations of 4.68 and 9.36 gr/l in the tanks 6. Mixing tank content clock wise with wooden shovel at 1, 2, and 5 minutes periods 7. Sampling with cylindrical sampler from the center of the tanks. Based on two

concentrations, 3 mixing periods, 1 sampling location, and 3 replications, 18 samples were taken.

ResultsTable 1 has summarized and shown the results of steps 1 to 3. Analyzing this table by F test showed no significant difference at 1% between results of bottle and pipette, between center and side sampling, and among 3 mixing periods. Sampling errors in 20, 40, 60, and 80 cm from the water surface were 65.63, 56.13, 45.63, and 32.73 percent for bottle and 65.48, 57.02, 50.88, and 43.67 percent for pipette and 11.98 percent for cylindrical sampler respectively. Unacceptable errors in bottle and pipette, and the results for steps 1 to 3 caused to execute sampling for 4.68 and 9.36 gr/l concentrations by cylindrical sampler in the center of tanks and with one minute mixing period. Average achieved concentrations by cylindrical sampler were 3.17 and 8.98 gr/l showing 32.26 and 4.02 percent errors. Computed mean error in whole concentrations was 13.04 percent for cylindrical sampler.

DiscussionSampling results by bottle and pipette in all mixing periods illustrated that the concentration of sediment increases with the depth of the tanks. This complains that making a unique concentration in the tanks is impossible. Cylindrical sampler by taking a complete column of


runoff and sediment with its concentration difference within the depth had the most accurate results compared to two other samplers.

References Bybordi M (1993) Soil physics. Tehran University (1672), 5th Edision. Cook MJ and Valentine WH (1979) Monitoring cut slope erosion by close range photogrammetry. Field Notes U.S. For. Serv. 11(7):5-9 Flanagan DC, Huang C, Norton LD, and Parker SC (1995) Laser scanner for erosion plot measurements. Transactions of the ASAE, Vol. 38, No. 3, pp. 703-710. Lang RD (1992) Accuracy of two sampling methods used to estimate sediment concentrations in runoff from soil-loss plots. Earth surface processes and landforms, Vol. 17, No. 8, pp. 841-844. Mutchler CK, Murphree CE, and McGregor KC (1994) Laboratory and field plots for erosion research. In R. Lal (ed), Soil Erosion Research Methods, 2nd ed., pp.11-37. Soil and Water conservation Society and St. Lucie press, Ankeny, IA. Nikkami D (2002) The problems of sampling runoff and sediment from erosion plot tanks and introducing a new cylindrical sampler. Proceedings of the 1st conference on erosion plot projects, 23 and 24 December, Soil Conservation and Watershed Management Research Institute, Tehran, Iran. Nikkami D, Arabkhedri M, Razmjoo P, and Ahrar M (2004) Investigating sediment suspension and sampling accuracy in erosion plot tanks. Final report of research plan, Soil Conservation and Watershed Management Research Institute, Tehran, Iran.Przedwojski B, Blazejewski R, and Pilarczyk KW (1995) River training techniques, fundamentals, design and applications. Balkema, Rotterdam. Shafaee Bajestan M (1994) Sediment hydraulics. Chamran University, Ahvaz, Iran. Toebes C and Ouryvaev V (1970) Representative and experimental basins: An International guide for research and practice. UNESCO, Henkes-Holland, Haarlem, The Netherlands. Zobisch MA, Klingspor P, and Oduor AR (1996) The accuracy of manual runoff and sediment sampling from erosion plots. Journal of Soil and Water Conservation, 51 (3) 231-233.


Figure Legends

Fig. 1: Bottle sampler Fig. 2: Pipette sampler

Fig. 3: Cylindrical sampler Fig. 4: Cylindrical sampler at work

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.2


The theory of the hydrobiological mechanism of water self-purification in water bodies: from theory to practice

Sergei A. OstroumovHydrobiology Department, Faculty of Biology, Moscow State University, Moscow 119992; Russia; phone: (095) 939-11-48 (Moscow) Email: [email protected]

Abstract. New data on effects of chemicals (surfactants) on water filtration by aquatic invertebrates are reported. The basics of the new theory of the polyfunctional role of biota in self-purification of water in aquatic ecosystems (lakes, rivers, man-made reservoirs) are formulated. The theory covers the following: sources of energy for the mechanisms of self-purification; the main functional blocks of the mechanism of water self-purification; the system of the main processes that are involved; analysis of the degree of participation of the main groups of aquatic organisms; degree of reliability and the main mechanisms providing the reliability of water self-purification; biotic regulation of the processes; the attitude of the mechanism of self-purification towards the external influences/impacts; applications and conclusions relevant to the practice of sustainable use of water resources, including some new approaches in preventing eutrophication and chemical pollution.

Key Words: pollution, water quality, water self-purification, lakes, rivers

Introduction.Sustainable use of aquatic resources is based on the ability of aquatic ecosystems to maintain a certain level of water quality. Water self-purification in both freshwater and marine ecosystems is based on a number of interconnected processes (e.g., Wetzel, 1983; Spellman, 1996; Ostroumov 1998, 2000). It is important to analyze the list of those processes and to find out whether at least some of them are vulnerable to manmade stress (Ostroumov, 2004).

Materials and Methods.We have found and studied some negative effects of several chemical pollutants on the process of water filtration by aquatic organisms (bivalves and rotifers). The process of water filtration is a part of the system of processes leading to water purification in aquatic ecosystems. The details of the methods were described in the paper (Ostroumov, 2002a).

Results.Our data demonstrated that water filtration by bivalves and rotifers was inhibited by several chemicals including surfactants and detergents. Among the surfactants that produced the inhibitory effect were the following:

- sodium dodecyl sulphate (SDS); -tetradecyltrimethylammonium bromide (TDTMA); -Triton X-100.

Discussion.We consider our experimental data within the context of the general theory of the mechanism for water purification that was developed in our publications, e.g., (Ostroumov, 2002b, 2004).

The list of the main processes leading to water purification includes: (1) physical and physico-chemical processes, including: (1.1) solution and dilution of

pollutants; (1.2) export of pollutants to the adjacent land areas; (1.3) export of pollutants to the adjacent water bodies; (1.4) sorption of pollutants onto suspended particles and further


sedimentation of the latter; (1.5) sorption of pollutants by sediments; (1.6) evaporation of pollutants;(2) chemical processes, including: (2.1) hydrolysis of pollutants; (2.2) photochemical transformations; (2.3) redox-catalytic transformations; (2.4) transformations including free radicals; (2.5) binding of pollutants by dissolved organic matter, which may lead to decreasing toxicity; (2.6) chemical oxidation of pollutants by oxygen; (3) biological processes, including: (3.1) sorption, uptake and accumulation of pollutants by organisms; (3.2) biotransformations (redox reactions, degradation, conjugation), mineralization of organic matter; (3.3) transformation of pollutants by extracellular enzymes; (3.4) removal of suspended matter and pollutants from the water column in the process of water filtering by filter-feeders; (3.5) removal of pollutants from the water in the process of sorption by pellets excreted by aquatic organisms; (3.6) uptake of nutrients (including P, N, and organic molecules) by organisms; (3.7) biotransformation and sorption of pollutants in soil (and removal of nutrients), important when polluted waters are in contact with terrestrial ecosystems; (3.8) a network of regulatory processes when certain organisms control or influence other organisms involved in water purification. Aquatic organisms are involved in physical, physico-chemical and chemical processes 1.1-1.6 and 2.1-2.6 directly or through excretion of oxygen or organic metabolites, production of suspended matter, affecting turbidity, temperature of water or other parameters of the ecosystem. As a result, living organisms are the core component of the multitude of processes of the ecological machinery working towards improving water quality. This component performs eight vital functions directly (3.1-3.8) and is involved indirectly in some of the other twelve functions (1.1-1.6 and 2.1-2.6) so that its role is clearly polyfunctional. Our data on the effects of some chemicals on bivalves and rotifers have shown that the chemicals (surfactants) inhibited one oth important processes listed above, i.e. the process 3.4, namely water filtration by invertebrates and the removal of suspended matter from the water column (Ostroumov, 2002a). Living organisms of aquatic bodies (both autotrophs and heterotrophs) are diverse in terms of taxonomy. Among them, autotrophs (phytoplankton; higher plants) generate oxygen that is involved in the processes 2.6 and 2.4 above. Heterotrophs (bacteria, fungi, invertebrates, fish) perform some of processes 3.1, 3.2, 3.4, 3.5 and some others. Virtually all aquatic biodiversity is involved.Given this polyfunctional role of aquatic organisms, in one of our publications we compared aquatic ecosystems to 'large-scale diversified bioreactors with a function of water purification' (Ostroumov, 2000). What is interesting about the biomachinery of water purification is the fact that it is an energy-saving device. It is using the energy of the sun (autotrophs) and the energy of organic matter which is being oxidized in the process of being removed from water by heterotrophs. Some interesting examples of how various organisms are incorporated in that polyfunctional activity were given by authors of the preceding papers in this volume. The importance of aquatic organisms in performing key functions in the hydrosphere provides an additional convincing rationale for protecting biodiversity. The efficiency of the entire complex of those processes leading to water purification in ecosystems is a prerequisite for the sustainable use of aquatic resources. Man-made effects on any of those processes (we have shown effects of surfactants on water filtration by bivalves; some of the experiments were carried out together with Dr. P. Donkin) may impair the efficiency of water self-purification (Ostroumov, 1998; Ostroumov et al., 1998; Ostroumov & Fedorov, 1999; Ostroumov, 2001a, 2001b, 2002a,b, 2004).

Conclusions.1.We postulate and predict that further studies will provide new examples of how important biodiversity is in performing many vital ecological processes leading to upgrading water quality.


By doing so, the multifunctional participation of biodiversity supports the sustainable use of water as one of key resources for mankind. 2.The body of new data and ideas presented in this volume will hopefully serve towards following interconnected and partially overlapping goals: (1) prioritization of efforts on research and management in the area of aquatic resources and aquatic environment; (2) biodiversity studies and protection; (3) sustainable use of aquatic bioresources; (4) advancement of aquaculture and mariculture; (5) decreasing costs and increasing efficiencies in wastewater treatment using ecosystems; combatting eutrophication; (6) understanding the role of biota in biogeochemical flows of chemical elements and in buffering global change.

References.

Ostroumov SA (1998) Biological filtering and ecological machinery for self-purification and bioremediation in aquatic ecosystems: towards a holistic view. Rivista di Biologia/ Biology Forum 91: 247-258.

Ostroumov SA (2000) Aquatic ecosystem: a large-scale, diversified bioreactor with the function of water self-purification (Vodnaja ekosistema: krupnorazmernyj diversifitzirovannyj bioreaktor s funktzijej samoochishchenija vody). Doklady Biological Sciences 374: 514-516 (the Russian edition: Doklady Akademii Nauk 374: 427-429).

Ostroumov SA (2001a) Amphiphilic chemical inhibits the ability of molluscs to filter water and to remove the cells of phytoplankton (Amfifil'noe veshchestvo podavljaet sposobnost' molluskov filtrovat' vodu i udalat' iz nee kletki fitoplanktona). Izvestia RAN. Ser. Biology 1: 108-116.

Ostroumov SA (2001b) Effects of amphiphilic chemicals on marine organisms filter-feeders (Vozdeistvie amfifil'nykh veshchestv na morskikh gidrobiontov-filtratorov). Doklady Akademii Nauk 378: 283-285.

Ostroumov SA (2002a) Inhibitory analysis of top-down control: new keys to studying eutrophication, algal blooms, and water self-purification. Hydrobiologia 469: 117-129.

Ostroumov SA (2002b) Polyfunctional role of biodiversity in processes leading to water purification: current conceptualizations and concluding remarks. Hydrobiologia 469: 203-204

Ostroumov SA (2004) On biotic self-purification of aquatic ecosystems. Elements of theory (O bioticheskom samoochishchenii vodnykh ekoistem. Elementy teorii). Doklady Akademii Nauk 396: 136-141.

Ostroumov SA, Donkin P, Staff F (1998) Filtration inhibition induced by two classes of synthetic surfactants in the bivalve mollusc (Narushenije filtratzii dvustvorchatymi molluskami pod vozdejstvijem poverkhnostno-aktivnykh veshchestv dvukh klassov). Doklady Akademii Nauk 362: 574-576.

Ostroumov SA, Fedorov VD (1999) The main components of self-purification of ecosystems and its possible impairment as a result of chemical pollution (Osnovnyje komponenty samoochishchenija ekosistem i vozmozhnost' ego narushenija v rezultate khimicheskogo zagrjaznenija). Bulletin of Moscow University. Ser. 16. Biology (Vestnik Moskovskogo Universiteta. Ser. 16. Biologija) 1: 24-32.

Spellman FR (1996) Stream Ecology and Self-purification. Technomic Publishing Co., Lancaster, Basel. 133 pp.

Wetzel RG (1983) Limnology. Saunders College Publishing, Fort Worth. 858 pp.


Effects of Soil Conditioner on Amount of Runoff And Erosion In Shahrekord Plain , Iran

Mehdi pajuohesh1 & hosein gholi refahi2 &mehdi shorafa3

1) Senior Researcher of Soil.Sci., Tehran.Univ.,Karaj.Iran, 2) Prof.of soil science., college of agric., Tehran univ., Karaj.Iran 3) Assis. Prof. of soil science., college of agric., Tehran univ., Karaj.Iran

Abstract Forthy five small bare polts (1 meter * 1 meter ) on every slope were analyzed for run off and sediment yield on agricultural field. A rainfall simulatur applied 25 mm/h to each plot after a soil conditioner mix treatment was applied . The following treatments : ( no soil conditioner applied to dry soil (control) , 2 g/m2 , 4 g/m2 , 6 g/m2 , 8 g/m2 ) soil conditioner applied to dry soil . Each treatment was repeated on three plots . When a solution of 8 g/m2 soil condirioner was applied to dry soil in 10% slopes and compared with the control plot , we found an average reduction of 95.4% in run off yield and 85.7% in sediment yield . An redudtion of 74% in run off yield and 88% in sediment yield in 20% slopes . An reduction of 80.6% in run off yield and 84.4% in sediment yield in 30% slopes , when compared to the control . Our results show that the application soil conditioner was caused reduction amount run off and sediment . The easy of application , low maintenance , and relatively low cost associated with soil conditioner make it a partical solution to the costly methods being implemented today.

Key words: Sediment yield , run off, rainfall , soil conditioner , shahrekord plain

INTRODUCTION One effects of rainfall is the initation of the erosion process where individual raindrops impact the soil surface, Soil detachment and particle transport by raindrop splash can lead to serious soil deterioation . Once soil is eroded and transported by surface runoff to lakes , rivers , and stream , a degradation of the aquatic habit occurs . In order to maintain a healthy watershed , it is critical to control erosion and sediment yield . Maintaing soil structure and aggregate stability helps control erosion by increasing infiltration and maintaining less erodible size aggregates . Stable soil structures also help maintain a healthy environment . The use of soil conditioner is a new tool to help maintain soil aggregate stability and reduce erosion caused by surface run off . Soil conditioner can stabilize existing aggregates when the aggregates are saturated with a solution of water soluble soil conditioner mix . Increasing the aggregate stability with polymers reduce the effect of raindrop impact on the soil , thereby reducing erosion . Soil conditioner application to the soil may also retared surface sealing . reduce particle soil detachment , reduce sediment in supension , and compensate low residue . The objectives of this study are to determine the effectiveness of soil conditioner in dry soil condition and in different slope .

Materials and Methods Five soil conditioner treatment were applied to soil test plots : a) no soil conditioner application to dry soil (control) , b) 2 g/m2 soil conditioner application to dry soil c) 4


g/m2 soil conditioner application to dry soil d) 6 g/m2 soil conditioner application to dry soil ) 8g/m2 soil conditioner application to dry soil . In different three slopes and three replications of each treatment were performed using a randomized block design on 1 m* 1m non-vegetated plots on investigation field in Shahrekord plain . The rain intensity of the test site was 25 mm/h. A rainfall simulator was used to rain intensity produce 25 mm/h . Run off from each plot was collected into a container and then amount of the water in each container was estimated at 5 minute intervals during each test . The average trial time was 30 minutes . Sediment samples were extracted by diverting runoff into a collection container during each replication . The samples were dried at 105 c0 for 24 hours and weighed to determine an average sediment load for each trial . Texture soil in three slopes contain 1) sandy clay- sandy clay loam for 10% and sandy clay loam for 20% slopes and sandy clay for 30% slopes . Table1 shows properties of chimophysical of three slope .

Results and Discussion Mean sediment yield , infiltration , and run off depth for the three slopes and 5 concentration soil conditioner are presented in table 2 ,3 ,4 . In the 10% slope of testing , the largest sediment reduction occurred when 8 g/m2 soil conditioner-mix was added to dry soil . The control plot yield 81 g/m2 and in of 8 g/m2 soil conditioner arrived to 12 grams per square meter.Resulting in a reduction of 85.7% in sediment yield and largest reduction run Off occurred when 8 g/m2 soil conditioner-mix was added to dry soil also , the control plot yield 4900ml/m2 and in concentration 8 g/m2 run off volume arrived to 226 ml/m2 . Resulting in a reduction of 95.4% in run off yield . In the 20% slopes the largest sediment and run off reduction occurred 8 g/m2 soil conditioner was added to dry soil . The sediment yield of control 112.09 g/m2 and in 8 g/m2 soil conditioner , arrived to 13.5 g /m2 .

resulting in a reduction of 88% in sediment yield and largest run off reduction occurred when 8 g/m2 soil conditioner-mix was added to dry soil also . The run off yield of control 3504 ml/m2 and in 8 g/m2 arrived to 938.3 ml/m2 , resulting in a reduction of 74% in run off yield . In the 30% slopes , the largest sediment reduction occurred when 8 g/m2 soil conditioner-mix was added to dry soil . the control yield 225 g/m2 and in 8g/m2 to arrived 35.1 g/m2 resulting in a reduction of 84.4% in sediment yield and largest run off reduction occurred when 8 g/m2

soil conditioner-mix was added to dry soil . The control yield 7634.6 ml/m2 and in 8 g/m2 arrived to 1486.3 ml/m2 , resulting in a reduction of 80.6% in run off yield .

Conclusion Results showed that , application of soil conditioner was effective in reducing sediment and run off yields in the test plots . The most effective amount of soil conditioner treatment was 8 g/m2 throughout this study in reducing sediment and run off yields . the easy of application , low maintenance and relatively low cost associated with soil conditioner-mix makes it a practical solution to costly existing methods being implemented . The evidence from the field application in this study reflects that soil conditioner is a tool to reduce soil loss on bare soil until vegetation cover is established . The primary factor that must be considered in future studies is the time of soil conditioner preparation and application . It was noted that the optimal application procedure is to prepare


the solution immediately prior 24 hours to application .This procedure is necessary in order to limit the amount of degradation and maximize the performance of the soil conditioner .

Reference 1) Aase, J. K., Bjorneberg. D.L. and Sojka R.E.(1998).Sprinkler irrigation runoff and erosion control withpolyacryamide-laboratory tests.Soil Sci . Soc. Am.j.62:1681-1687 2) Bubenzer , G.D., patterson, A.E.(1982). Intake rate : spinkler infiltrometer In : Method of soil Analysis , part 1 , Physical and Mineralogical Method , Second Edition , chapter 33,pp . 845870 (Agronomy Monograph Series # 9 ) . 3) khamraev, S.S,. Artybaeva, K, Nizamova , F. and Akhmedova, V.(1983): leaching of salts from takyr soil conditioned by amide containing poly electrolytes. Uzb. Khim. Zh. 4 , 16-20 ( Russian). Chem. Abst. 99, 138730 (1983). 4) Hardar heller and R . keren – Anionic polymers Effect on Rheological BEHAVIOR OF SODIUM – Montmorrillonite suspension- instiute of soil water and Enviromental Science. The volcanicenter . Agriculture Researchorganization (ARO).published in soil sci. soc.Amj-66:19-25(2002) 5) Jhons.Hickman and David A. whitney .soil conditioners . Department of agronomy kansas state university 6) Refahi , H.,(1999).water erosion & conservation . Department of agronomy Tehran Univesity . 7) Roa. A., (1996) .screening of polymers to determine Their potential use in erosion control on construction sites, university of idaho publication No. 101-96,pp.77-83. 8) Vsteven green and stott, D.E Polyacryamide :Areviw of The use, Effecctiveness and cost of a soil erosion control amendement- national soil Erosion Reaserch laboratory pages 384-3894 9)Sojka,R.E.and lentez,R.D.,(1996).A PAM Primer : A Brief History of PAM and PAM-Related issues , university of idaho publication No .101-96, pp. 11-20 .

Table (1) : Chimophysical properties of three slopeSlope

(%)TEXRURE CACO3

(%)EC PH O.M

(%)Na

Meq/lCa

Meq/lMg

Meq/lb

Gr/cm3Gravel

%

10 Sc-Scl 53 .61 7.7 .08 5.7 6.1 1.5 1.4 2520 Scl 57 .3 7.8 .05 5.1 2.7 2 1.5 3830 SCL 57 .33 7.5 .04 4.5 3.0 2.2 1.6 35

Table (2) :Summary of rainfall ,infiltration , runoff , and sediment yield for 10%slpoe treatment Rainfall

(mm/hr)Infiltration

(cm)Runoff

(ml/m2)Sediment(mgr/m2)

Runoff(%)

Sediment(%)

Gravel(%)

Control 25 4.83 4900 81 100 1002 gr/m2 25 9.3 297 17.87 6 224 gr/m2 25 9.3 280.3 15.5 5.72 19.16 gr/m2 25 10 258 13.55 5.26 16.78 gr/m2 25 9 226 12 4.61 14.3

Table (3) :Summary of rainfall ,infiltration , runoff , and sediment yield for 20%slpoe treatment Rainfall

(mm/hr)Infiltration

(cm)Runoff


Runoff(%)

Sediment(%)

Gravel(%)

control 25 10 3504 112.09 100 100 352 gr/m2 25 9.5 3189.3 62.06 91 55.8 33


4 gr/m2 25 8.83 2460.3 35.54 70.2 31.7 406 gr/m2 25 11 2700 61.24 77 54.6 358 gr/m2 25 10.6 938.3 13.5 26 12 40

Table (4) :Summary of rainfall ,infiltration , runoff , and sediment yield for 30%slpoetreatment Rainfall

(mm/hr)Infiltration

(cm)Runoff


Runoff(%)

Sediment(%)

Gravel(%)

Control 25 10.3 7634.6 225 100 100 352 gr/m2 25 11.66 6405.3 120.7 83.8 53.3 354 gr/m2 25 8 3283 86.6 43 38.4 356 gr/m2 25 11 1641.6 51.5 21.5 22.8 358 gr/m2 25 10.6 1486.3 35.1 19.4 15.6 35


An Algorithm to Mapping Snow, Cloud and Land in NOAA

AVHRR Data, Formulation, Verification and Evaluation Jahangir Porhemmat1, Bahram Saghafian2, Hosain Sedghi3

1-Faculty Member of Soil Conservation and Watershed Management Research Institute ;(SCWMRI) ;Email: [email protected] ;2-SCWMRI, Tehran, Iran;3-Shahid Chamran University, Ahwaz, Iran

KEY WORDS: NOAA AVHRR, snow, cloud, brightness temperature, Karun of Iran

ABSTARCT: Although snow cover extent represents a critical factor in predicting snowmelt runoff, sufficient spatial and temporal resolution is not achieved by field observations. On the other hand, satellite images are readily available and provide exceptional spatio-temporal characteristics needed for snow mapping. In particular, NOAA satellite with AVHRR sensor provides daily images with 1.1-km spatial resolution, which may be quite suitable for snow mapping in large river basins. The snow cover area mapping based on satellite imagery has been mainly practiced using visual interpretation methods. Moreover, the presence of cloud in the images creates an obstacle in snow area delineation. In this article, a two-stage technique relying on feature reflectivity in AVHRR bands 2, 3, and 4 is proposed to classify ground, cloud, and snow. While visual interpretation is not required in this technique, the image classification is performed using image enhancement and multi-spectral analysis. ILWIS GIS served as the spatial data platform for imagery data manipulation and analysis. To examine the capabilities of the proposed technique, two of the available NOAA images corresponding to Jan. 9 and Feb 24, 1997, in an area of roughly 58000 km2 located between (48,05-50,29) E longitude and (31,40-33,29) N latitude were selected. The results conferred overall with those of visual interpretation in areas where snow, cloud, and ground could clearly be identified. Furthermore, the results of applying the proposed technique on a third image dated May 18, 1998, was favorably compared with processing of the same-day LANDSAT/TM image with 28.5 m ground resolution. One major limitation of the technique is the inability to recognize the snow under the cloud cover.

INTRODUCTION Snow cover is one of the most important hydrologic factors affecting energy balance, surface albedo and hydrometeorologic balance. In addition, spatial and temporal variation in snow extent could prove a useful indication of climate change (Simpson et al., 1998). Ground monitoring of snow are normally based on point measurement, which is subjected to numerous problems especially in inaccessible mountainous regions. Therefore, satellite data could be a useful alternative for snow monitoring and snow cover mapping. The Third Symposium on Remote Sensing of Snow and Ice, organized by the International Glaciological Society, affirmed the strong potential of AVHRR (Advanced Very High Resolution Radiometer) images for studying sea ice and snow covered surfaces and recommended its use for long term monitoring of polar processes (Steffen, et al., 1993). Therefore, the NOAA AVHRR data is a suitable alternative for preparing snow map time series. There are a few methods to interoperate satellite imaginary, which are reviewed by Engman and Gurney (1991). The usual method is eye interpretation using factors such as ground features, patterns, uniformity of reflectance, shadows and the consistency of images with time (Engman and Gurney, 1991).


Lucas et al. (1989) used unsupervised multispecteral classification for separation of snow and cloud in AVHRR images by using channel 1, 3 and 4. Cracknel (1997) emphasized on using AVHRR images to determine snow cover extent. He recommended the threshold methods in snow and cloud separation. Simpson and Gobat (1995) used AVHRR channel 2, 3, 4 and 5, to detect cloud. Simpson et al. (1998) used a multispectral-multistage method to separate snow and cloud in AVHRR images. They used channels 2, 3, 4 and 5 and proposed a three-stage algorithm. This method separates snow and cloud in the first stage and then, separate snow from clouds. Some pixels were coding as mixed pixels in tow previous stages and then divided in three parts using linear mixing model. Tow stage algorithm is presented for snow/cloud separation in AVHRR scene by using channels 2, 3 and 4 in this article. While that algorithm is based on Simpson et al.’s method, there are some differences between the methods. Using a ratio of channel 3 to Square of channel 4 reflectance digital number and difference of channels 3 and 4 brightness temperature as second stage are the most difference between Simpson algorithm and our works. Histogram analysis using threshold is the other difference part with cluster analysis of Simpson method.

LOCATION OF STUDY AREA

The study area, Zagross high lands, is located in southwest of Iran. This area is bounded between 48°

to 51° and 29’ estern longitude and 30°, 30' to 33° and 39' northern latitude. This elevation ranges from 700 meters to 4420 meters above mean sea level.

SNOW-CLOUD ALGORITHM

The new multi-spectral, multistage snow and cloud detection has tow stages whit split and merges technique of AVHRR images channels 2, 3 and 4. Albedo of channel 2 (a2), brightness temperature of channel 4 (T4), a parameter, R, based on channel 3 and a2 and finally brightness temperature difference (dT) of channel 3 (T3) and channel 4 ((T4) are the main parameters used in the first stage. Histogram analysis is used to determine thresholds, which are the digital number showing transition from one phenomenon to the other in the images. The threshold conditions are:

≥≥

≤

th

th

th

RR

aa

TT

22

44

Snow and patches of cloud (1)

≥≥

≤

th

th

th

dTdT

aa

TT

22

44

Cloud and patches of snow (2)

⟨

⟨

⟨

⟩

th

th

th

th

dTdT

RR

aa

TT

22

44

Uncovered ground (3)

By application of above three sets of threshold conditions, three images are produced at the end of this stage. Then, the first and second images are merged together and prepared for the second stage.


In the second stage, channels 3 and 4 reflectance images are split based on the merged image of first stage. Then, the new channels 3 and 4 images are used for applying parameters based on T3, and T4differences( dT) and ratio of channel 3 digital number (R3) and channel 4 digital number (R4) as

4

3

R

R(σ ). Threshold conditions are:

≤≤

th

thdTdT

σσ Snow (4)

⟩

⟩

th

thdTdT

σσ Cloud

(5)

APPLICATION OF ALGORITHM IN SNOW SEASON Two scene of NOAA AVHRR images is used for applying algorithm. The first one is Feb. 24, 1997 and the other one is Jan. 9, 1997. Figures 1 and 2 show the research area windows corresponding to Feb. 24, 1997 AVHRR channel 2 images. Figures 3, 4, 5 and 6 respectively, show the frequency curve at a2, T4, dT and R in the first stage. The maps produced by the application of threshold conditions indicated in Equation (1) to (3) are shown on Figure 7, 8 and 9. Figure 7 is snow with the patches of the cloud, Figure 8 is cloud with the patches of the snow and Figure 9 shows the uncovered ground. Figures 7 and 8 are merged to form a new images and channel 3 and 4 are masked for the pixels in this merged images. The parameters dT and σ are calculated from channel 3 and 4 masked images. Figure 10 and 11 show the frequency curves of dT and σ based on these masked images. Figure 12 shows the snow map at the end of second stage.

Figure 2 Zoom of Research Area on Feb 24, 1997 AVHRR channel 2 Image

Figure 1 Windows of Research Area on Feb 24, 1997 AVHRR channel 2 Image

Research Area

Persian Gulf


Figure 4 Brightness temperature frequency curve and the threshold from Feb 24, 1997 AVHRR channel 4 Image

Value

Num

ber

of P

ixel

0100020003000400050006000700080009000

10000

250 256 262 268 274 280 286 292 298 304 310

Figure 3 Albedo channel 2 frequency curve and the threshold from Feb 24, 1997 AVHRR Image

0

2000

4000

6000

8000

10000

12000

14000

16000

0 5 10 15 20 25 30 35 40 45 50

Num

ber

of P

ixel

Value

0

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12000

14000

16000

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20000

-2 2 6 10 14 18 22 26 30 34 38 42 46 50 54 58

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ixel

Figure 5 Parameter R frequency curve and the threshold from Feb 24, 1997 AVHRR Image

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ber

of P

ixel

Value

0

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16000

20000

24000

28000

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36000

0 5000 10000 15000 20000 25000 30000 35000

Figure 6 Brightness temperature difference (T3-T4) frequency curve and the threshold from Feb 24, 1997 AVHRR Images

Figure 7 Snow mixed with the patches of the cloud Figure 8 Cloud mixed with the patches of the snow


The algorithm is applied to January 9, 1997 AVHRR images in the study area. Figure 13 shows the final outcome of the proposed algorithm.

Figure 9 Mixed snow and cloud pixels from first stage (Figures 7 and 8merger) Figure 10 Brightness temperature difference (T3-T4) frequency

curve and the threshold from January 24, 1997 AVHRR Images

0

250

500

750

1000

1250

1500

1750

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2250

0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60

Value

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ixel

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12 16 20 24 28 32 36 40 44 48

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ixel

Figure 11 Parameter σ frequency curve and the threshold from Feb 24, 1997 AVHRR Images

Figure 12 Snow cover area

Figure 13. Final result applying from January 9, 1997 AVHRR snow map

Snow Map

Channel 2


5. VALIDATION OF ALGORITHM

Validation of the algorithm is performed by comparing the result of snow and cloud maps produced by eye interpretation and on screen digitizing method of both NOAA and Landsat TM images of the same dates. In Figure 14 the 24 Feb. 1997 snow maps produced by eye interpretation method and the algorithm (model) are compared. Overlay of the two maps in GIS resulted in 93.6 percent agreement.

Figure 15 shows the cloud-free 18 May 1998 Scene No. 164-038 Landsat TM and corresponding 17 May 1998 AVHRR images. In Figure 16, maps prepared by the algorithm and eye interpretation Landsat image. Overlay of the two maps in GIS resulted in 88 percent agreement.

Figure 14 Comparison of snow maps on 24 Feb. 1997

Eye interpretation method

Model

Figure 15 Scene No. 164-038, 18 May 1998, Landsat TM and corresponding 17 May 1998 AVHRR images

Landsat NOAA AVHRR BAND 2 DATED 17 MAY 1998

Snow Parcel

Study Area

Cloud Parcel

Cloud

AVHRR

Study Area

Figure 16 shows the comparison maps prepared by models in 17 May 1998 with maps produced using 18 May 1998 Landsat TM images

Landsat TMAVHRR


CONCOLUTION The following conclusion can drawn from this study: 1-The two-stage algorithm using AVHRR channel 2, 3 and 4 can separate snow, cloud and uncovered ground. 2- Validation of the algorithm in comparison with eye interpretation shows 6.4 percent difference. 3- Validation of the algorithm in comparison with Landsat TM shows 12 percent difference in an image of end of snowmelt season.

REFFRENCES 1): Cracknell, P., 1997. The Advanced Very High Resolution Radiometer. Taylor and Francis Pub. 2): Engman, E.T., and Gurney, R.J., 1991. Remote Sensing in Hydrology. 1st Edit, Chapman and Hall, p.

225.3): Lucas, R.M., Harrison, A.W. and Barrett, E.C., 1989. A Multispectral Snow Area Algorithm for

Operational 7-Day Snow Cover Monitoring, Remote Sensing and Large Scale Global Processes. Proc. IAHS 3rd Int. Assembly, MD, IAHS Pub. No. 186, pp. 161-166.

4): NOAA (National Oceanic and Atmospheric Administration of U.S.A.), NOAA POD Guide, Online on Internet.

5): Simpson, J.J., and Stephan, R.Y., 1994. Reduction of Noise in AVHRR Channel 3 Data with Minimum Distortion. IEEE Transactions on Geoscience and Remote Sensing, Vol. 32, No. 2, pp. 315-328

6): Simpson, J.J., and Gobat, J.I., 1995. Improved Cloud Detection for Daytime AVHRR Scenes Over Land. Remote Sensing of Environment, No. 55, pp. 21-49.

7): Simpson, J.J., Stitt, J.R., and Sienko, M., 1998. Improved Estimates of the Arial Extent of Snow Cover from AVHRR Data. Journal of Hydrology, No. 204, pp. 1-23.

8): Steffen, K. Bindschalder, R., Casassa, G. Cosimo, J., Eppler, D., Fetterer, F., Hawkins, J., Key J., Rothrock, D., Thomas, R., Weaver, R., Welch, R., 1993. Snow and Ice Applications of AVHRR in Polar Regions: Report of a Workshop Held in Boulder, Colorado, 20 May 1992, Ann. Glaciol., 17, pp. 1-16.


Canola Response to Different Irrigation Regimes

Mohammad Hossein Rahimian, Hamidreza Zabihi,Majid Foroohar 11Soil & water Research Department ,Khorasan Agriculture and Natural Research Center ,Mashhad ,Iran .e_mail: Rahimian45 @ yahoo.com

Abstract In a randomized complete block design, the effect of deficit irrigation on canola yield and water use efficiency(WUE)were investigated ,also the sensitive phonological stages of canola to water application were determined. Treatments were as I1 : without irrigation after germination(only rain received); I2: irrigation at stem elongation, bud formation, flowering and pod development stages;I3:irrigation at bud formation ,flowering and pod development stages; I4: irrigation at stem elongation, flowering and pod development stages; I5: irrigation at bud formation and pod development stages; I6: irrigation at flowering and pod development stages . Results showed that irrigation treatments had significant effect on grain yield and oil content percentage(OCP) of seed. Maximum grain yield(2337 kg/ha)and WUE (0.531) were obtained for I4 treatment (irrigation at stem elongation, flowering and pod development stages).Grain yield and OCP were minimum for I1 treatment .Irrigation treatments had no significant effect on leaf mineral element concentrations namely :N,P,K,Na,Fe, and Mn

Key words: canola , deficit irrigation , water use efficiency , yield

IntroductionIn arid and semi arid regions crops are fully dependent on irrigation for high yield production .The shortage and coincidence of final irrigation of one winter crop such as canola with first irrigation of another crop is a serious problem in the region that affects yield drastically. Canola as an oil seed crop has recently received much attention and its acreage is increasing steadily. The seasonal water requirements for canola depends on variety, target yield and crop management. varieties that are earlier in maturity consume less water. Susceptibility of the crop to irrigation regime varies at different growth stages. The most critical times for irrigating canola are during late vegetation/spiking and throughout the flowering period. Moisture stress during these periods can cause major yield reduction ( 2,4 ).Sims et al. (1993) reported that canola yield in Montana was increased greatly with increasing availability of water, but that increased water lowered mean oil content .Nielsen (1996) reported that 7.73 kg/ha of seed are produced for every mm of water used after the first 158mm of water usage. He mentioned that water stress during the vegetative growth stages limited early leaf area development but plants recovered and produced more leaf area as water become available later in the growing season. Hang and Gilliland(1991) indicated that under water stress conditions ,irrigation at flowering and pollination stages leds to the most beneficial results . Mackenzie(1996) suggested that watering should be carried out when 50% of available water is depleted from root expansion zone. He also stated that the end of late vegetative growth ,onset of flowering bud development and flowering stages are critical and sensitive stages in which water deficiency leds to sever decrease in canola yield. Farahnia et al (unpublished data)indicated that application of 3000 m3 irrigation water along with 240kg/ha N is needed for 4000kg grain production per hectare.

Materials and Methods A field experiment was conducted to determine water use efficiency and susceptible stages of canola to moisture stress in Torogh Agricultural and Natural Resource Center, Mashhad,


during the years 2000 – 2001. The experiment was laid out as randomized complete block design with three replication. The irrigation treatments were as (I1, without irrigation after germination(only rain received); I2: irrigation at stem elongation, bud formation, flowering and pod development stages;I3:irrigation at bud formation ,flowering and pod development stages; I4: irrigation at stem elongation, flowering and pod development stages; I5: irrigation at bud formation and pod development stages; I6: irrigation at flowering and pod development stages) . Before planting, soil samples (0-30cm) were taken for fertilizer recommendation. Urea (46%, N) was applied at pre planting and booting stages while potash as (SOP) and phosphorus in the form of triple supper phosphate in accompany with micronutrients were applied pre planting. Plot dimensions were 3 × 8 m and each plot had 5 furrow. At early flowering, leaf samples

were taken and analyzed for leaf mineral composition

ResultsThe soil properties are presented in Table1, accordingly the soil under experiment had silt loam texture, without salinity limitation with pH= 7.7and Ec=1.7 dS/m Water as an important input has vital effect on yield and oil percent of canola and elimination of water at each stage can be critical for its yield. The results of this study showed that irrigation treatments affected seed yield significantly (at 1% level) and the greatest yield was obtained from I4 treatment with 2337 kg /ha, this treatment received 440 mm water in three times at stem elongation, flowering and pod development. It seems that these stages are susceptible to water shortage. I1 treatment produced the lowest seed yield equal to 883 kg /ha. Rain was the only source of water for this treatment(Table 2).Rain distribution in the season was not good and we experienced a dry spring with high temperatures in the last month of growing season. Irrigation treatments had no effect on plant height ,the amount of seed per pod, and the amount of pod per plant .But irrigation treatments affected seed yield (at 1%level) and seed oil content at 5% level. The highest water use efficiency in both years occurred in(I4 treatment) without water stress during stem elongation, flowering and pod development stages . The lowest water use efficiency in both years occurred in I1 treatment with water stress at all growth stages but germination. It seems that watering at stem elongation is necessary because in this stage the growth rate increases exponentially and water demand is high. At bud formation, watering may be omitted due to sufficient precipitation and low evapotranspiration. Watering is essential at pod development stage because nutrient translocation from source(leaves)to sink(pods and seeds)occurs at this stage.

CONCLUSION Irrigation at increased seed yield and crop oil percent but did not affect Nutrient concentration in leaves as a consequence water most be applied at this stages and any water stress will decrease yield.

REFERENCES 1- Balestrini, s., Vartanian, N. and Rollier, M. (1983) Variabilite genetique dans les reactions adaptives du colza a la secheresse . In:Proceedings of the Sixth International Rapeseed Congrees, Paris pp.64-71. 2- Hang, A.N. and Gilliland, G.C. (1991) Water requirement for winter rapeseed in central Washington. In: McGregor, D.I.(ed.) Proceedings of the Eighth International Rapeseed Congress, Saskatoon, Canada.


3- MacPherson, H., Scarth , R., Rimmer , s.r. and MacVetty, P.B.E.(1987) The effect of drought stress on yield determination in oilseed rape. In: Proceedings of the Seventh International Rapeseed congress, Poznan, Poland. The Plant Breeding and Acclimatizatoin Institute , Poznan, p.822-827.4- Mckenzie, R. H. 1996. Fertilizing irrigated grain and oilseed crops. Alberta Agriculture. Food and Rural Development, Edmonton, AB, Canada.5- Miller, Perry,. Herb cutforth, Brian Meconkey , and Martine Entz. How thirsty are canola and Mustard . Semiarid Prairie Agricultural Research Center. RESEARCH NEWS LETTER. No. 4.6- Potfer ,J.P.(1987) Etude du systeme racinaire du colza de printemps en conditions de secheresse . Unpublished compte rendu dexperrimentation . CETIOM, paris, France, 28 pp. 7- Saskatchewan. Agriculture and food… Growing8 canola in nontraditional Areas "farm fact" Canada.

TABLES

Table 1. Soil properties Clay% Silt% Sand% K(mg/k) P(mg/k) TOTAL

N%O.C% T.N.V% pH Ec(dS/m) depth

33 45 22 165 13.6 0.057 0.38 12.6 7.9 1.83 0-30

Table 2- Yield(ton/ha) and depth of water applied (mm) in different irrigation regimes Treatment I1 I2 I3 I4 I5 I6 Number of irrigation 1 6 5 5 3 4 Irrigation water depth(mm) 200 510 460 440 340 390 Yield(ton/ha) 0.88 d 1.71 bc 1.68 bc 2.34 a 1.23 cd 1.87 b Duncan test at 1% level

Table 3- Water use efficiency(WUE) and Seed oil percent in different irrigation regimes Treatment I1 I2 I3 I4 I5 I6 Water use efficiency 042 0.335 0.365 0.531 0.362 0.479 Seed oil percent 37.5 b 43.1a 42.7a 42.0a 40.9a 43.5a Duncan test at 5% level


Role of Karst formation in Mazandaran province water resources, Iran

S. L. Razavi1 , S. J. Sadatinejad2 & Karim Solaimani3

1- M.Sc. student, Tarbiat Modarres University, [email protected];2- Department of Natural Resources, Shahrekord University, [email protected] 3- Assistant professor, Watershed Management, Natural Resources College, Mazandaran University, Sari,[email protected]

Abstract Interaction between climate factors, carbonate formations and water resources as Karst hydrology issues have been increasingly implemented in recent years in several parts of the world. Approximately 11 percent of the territory of Iran is covered by carbonate rocks, so the knowledge of karst systems is important in water resources. The present study was conducted to determine the karstic features distribution in the Mazandaran province leading to compare runoff coefficient of karstic and nonkarstic watersheds with the same climate. The results indicated that the most karstic features are located in Sari-Neka region in the eastern part of Mazandran province. In the period, the runoff coefficient of Neka karstic watershed was 1.2 times higher than Behshahr nonkarstic watershed in the most eastern part of Mazandaran. Specific discharge for Neka karstic and Behshahr nonkarstic watersheds was 0.0024 m³/s/ km2 and 0.0014 m³/s/ km2 respectively.

Key words: Karst , Watershed , Discharge, Run off coefficient, Water resources

Introduction Iran is an arid and semi arid country with about 250mm mean annual precipitation, which is less than a third of world average (Mahdavi, 1988). In spite of the shortage of water through the country, studies have shown that management of water resources has not been carried out in a proper manner and most of rainfall is lost without being used. The current situation indicates the importance of water management issues in Iran. In this case karstic water resources can be mentioned as high renewable resources that usually can be found as hidden reservoirs. Karst hydrology can answer some questions about water needs (Afrasyyabiyan, 1994). Karstic features affect on both rate of surface runoff and outlet flow of watershed. Larijani and Khezli (1993) have investigated two karstic and nonkarstic watersheds in Zagros ranges to understand their hydrological behavior. They have compared runoff coefficient and output hydrograph and concluded that annual flow hydrograph for non karstic watershed would be more sharped and afterwards calculated the runoff coefficient for karstic watershed, which was 4 to 5 times higher than non karstic watershed. Ghazavi (1996) compared the rate of surface runoff and discharge of karstic and nonkarstic watersheds in Zagros ranges and found that runoff coefficient and specific discharge was 3.6 and 3.7 times of nonkarstic ones, respectively. In the United State, Filton (1994) analyzed a karstic watershed and concluded that existing relations in karstic watersheds is not useful to be used in non karstic watersheds. In order to distinguish the distribution of karstic features in Mazandaran province and to compare the karstic and nonkartic watersheds the present project was carried out that stated with the better understand of karstic process in Mazandaran province, comparing the karstic and nonkarstic watersheds in respect of runoff coefficient, discharge rate and specific discharge and finally finding out the effect of karstic features in runoff coefficient were the main goals of the study.

Materials and methods Study area In order to compare runoff coefficient of karstic and non karstic watersheds, two similar watersheds were selected with the same climatologic conditions and area. The first watershed namely Neka at the Gelevard station was selected. It lies between 36˚ 35 19˝ to 36˚ 55 32˝ N latitude and 53˚ 47 28˝ to 53˚ 52 16˝ E longitude, with the area of 1518 km2 and mean altitude of 682 meter amsl and formation material of limestone. The second watershed namely Behshahr at the Rigcheshme station was also considered. It lies between 36˚ 35 6.5˝ to 36˚ 43 15˝ N latitude and 53˚ 35 19.6˝ to 53˚ 41 32˝ E longitude. It covers 1042 km2 in area and its mean altitude is 521 meter amsl, with material formation of conglomerate, marl and silty marl. MethodologyAfter choosing watersheds, using existing information the precipitation rate, temperature variation and other climatic conditions of the watersheds were considered. Then mean annual discharge and mean annual precipitation and maximum mean daily discharge of each watershed were calculated during water year, precipitation volume calculated using mean annual precipitation and annual runoff volume calculated using discharge data. These values were then compared with each others. The change of mean annual discharge and maximum mean daily discharge, in the watersheds were also drawn and compared. To conside the state of karst and its distribution in the Mazandaran province, geology map and Aerial photographs of the province was considered. With the field investigation the extension of limestone materials in different regions, distribution pattern of karst was understood. At the end the boundaries of limestone were recognized with mixing the above parameters.

ResultsRefering to the findings of the study, out of 25637 km2 of watersheds in Mazandaran province, 1665 km2 (76%) is covered by thick formation. Limestone covers 4760 km2 of the area (25%), and has the most appropriate conditions for water storage. From this area the maximum extensions of limestone has been occured in limited studies areas of Sari - Neka that are equivalent to 1577 km2 and the lowest area is in near Ghaemshahr – Jooybar that are equivalent to 124 km2 (Table 2). From the comparison of the runoff coefficient and the special discharge in two karstic and nonokarstic watersheds, they were obtained that the runoff coefficient for the Neka watershed with the mean annual precipitation of 905.6 mm was 0.086, and runoff coefficient for the Behshahr watershed with the mean annual precipitation 632 mm was 0.071 (Table1). In the other word, runoff coefficient in the Neka watershed in the Gelevard station is 1.2 times of the Behshahr watershed in the station of Rigcheshme. Specific discharge in the karstic watershed (Neka) was estimated to be 0.0024 m³/s/ km2 , and specific discharge of the non-karstic watershed (Behshahr) was estimated as 0.0014m³/s/ km2 (Table1). In the other word, the specific discharge of the Neka watershed is 1.71 times of the Behshahr watershed. The range of mean annual discharge for both watersheds is nearly the same, In 1998-1999 they both had the maximum mean annual discharge for the statistical period, the mean annual discharge value of the Neka watershed in these years have been 4.58 m³/s and for the Behshahr watershed have been 1.66 m³/s (Figure 2). The least mean annual discharge in the both watersheds in 1995-1996 have been happened, the mean annual discharge in these years for the Neka was 3.01 m³/s and for the Behshahr have been 0.5 m³/s (Figure2). In all years, maximum mean daily discharge of karstic watershed of Neka have been more than the maximum mean daily discharge of non-

karstic watershed of Behshahr ( in the both watersheds the highest variation of maximum mean daily discharge have happened in 1992-1993 and the least one have happened in 2000-2001). Of course in the interval of 1992-1993 we have seen the maximum mean daily discharge of Neka watershed increased in comparison with the maximum mean daily discharge of Behshahr watershed which was due to flood in this watershed (Figure1).

DiscussionIn this study, both watersheds have had similar climatic records and the mean temperature and precipitation distribution in two watersheds were similar, thus with bearing in mind that, formation of the land in common condition is subjected to the volume of outlet, Neka has to have less outlet than Behshahr watershed, because of one hand their area are the same, and on the other hand the Neka watershed karstic form are more extended. But as it mentioned in the results, runoff coefficient of the Neka watershed is 1.2 times of runoff coefficient of the Behshahr watershed, thus it is possible to conclude that beside of the rain, there are other resources, which here is karstic resources. Therefore in spite of the extensive karst especially in high elevations (Figure 3, 4) and water detention in these regions, which appears as numerous karst springs in the lower watersheds (Figure 5, 6) have paved away this shortcoming, and results in higher discharge and higher runoff coefficient in this watershed in comparison with the nonkarstic watershed of Behshahr .Also bearing in mind that the major extension karstic area is in the Sari-Neka. It can be better possible with correct programming, solution findings. AcknowledgmentsWe thank and appreciate Dr Sadeghi's sincere and precious guidance in this project.

Reference Afrasyyabian, A. (1994). Investigation applied karst in Maharloo watershed, Journal of Water and

Development, 1: 11-14 Larijani, H. and Khezli, D. (1993). Investigation two karstic and non karstic watershed hydrological behaviors in Zagros, 43-45p Ghazavi, Gh. (1996). Compared rate of surface runoff and flow discharge of two karstic and non karstic watersheds in Zagros, M-Tech Dissertation, Tarbiat Modarres University, 65-80 p Mahdavi, M. (1998). Applied Hydrology,volume two, 56-60 p Felton, G.k. (1994). Hydrologic response of a karst watershed, Journal of American society of Agricultural Engineers, 27: 11-19

Watersheds

station area

(km²)

Meanannual

precipitation (mm)

Meanelevation

(m)

Meanannual

discharge

(m³/s)

precipitation volume

(MCM)

Meanannual runoff volume (MCM)

Runoffcoefficient

(Cr)

Specific discharge

(q)m³/s/ km2

Behshahr Rigcheshme

1042 632 521 1.5 658 55.18 0.071 0.0014

Neka Gelevard 1518 905.6 628 3.77 1373 118.88 0.086 0.0024

Table 1. calculation runoff coefficient and special discharge in Behshahr and Neka watersheds using observed data in 1992-2001

Non hydrophill carbonate(km2)

Hydrophill carbonate(km2)

Study boundries

478 597 Ramsar-Chaloos 792 393 Noor - Noshahr 789 976 Amol - Babol 129 124 Gooybar - Ghaemshahr 852 1577 Sari- Neka 30 156 Gaz - Behshahr

Table2. study boundries of Mazandaran province and limestone classes in them

01020304050607080

92-93 93-94 94-95 95-96 96-97 97-98 98-99 99-2000

2000-2001

water year

max

mea

n da

ily d

isch

arge

( m

3 / s

) Neka

Behshar

Figure 1. variation of maximum mean daily discharge in investigation watersheds

0

1

2

3

4

5

6

92-93 93-94 94-95 95-96 96-97 97-98 98-99 99-2000

2000-2001

water year

mea

n an

nual

dis

char

ge (

m3

/ s )

Behshahr

Neka

Figure 2. variation of mean annual discharge in investigation watersheds

Figure 3, 4: karst spring in Neka watershed

Figure 5, 6: surface karst in Lar formation limestone in Neka watershed

Figure 7. one Dooline in Neka watershed


Regional flood frequency analysis based on L-moment approach (case study Halil-River basin)

Rahnama M. B. 1, Rostami R.,21-Assistant Prof. Irrigation Dept., Shahid Bahonar University, Kerman, Iran. Phone:+98-913-140-4423 Email: [email protected] 2- Master of science, Irrigation Dept., Shahid Bahonar University, Kerman, Iran. Phone: +98-482-2563417 Email: [email protected].

Abstract

Flood estimation with certain frequency is one of the fundamental factors for design of hydraulic structures, flood plain, river coastal stabling, basin management, etc. Accurate estimation of flood frequency discharge increases safety of the structures. L-moment approach was used for flood frequency analysis in Halil-River basin. For identifying homogeneous regions, the Ward hierarchical cluster method was used. Site data were used for independent testing of the cluster of the station for homogeneity. The Halil-River basin was divided into two regions (region A and B). In these regions parameters of the regional frequency distribution were evaluated by L-moment ratios. The L-moment diagram, goodness of fit test, and plotting position methods were used for the selection of appropriate distributions. In Halil-River basin, Generalized Pareto distribution for region A, Generalized extreme values, Pearson type III, Lognormal, Generalized Logistic, and Generalized Pareto for region B, were selected as appropriate distributions. The relative Root Mean Square Error (rRMSE) between observed and estimated data in all stations was calculated. The results show a good agreement between observed and estimated data. Keywords: Halil-River, homogeneity, L-Moment, regional frequency

Introduction

An important practical application of hydrology is the estimation of extreme events, especially because the planning and design of water resource projects and flood-plain management depend on the frequency and magnitude of peak discharges. Information on flood magnitudes and their frequencies is needed for design of hydraulic structures such as dams, spillways, road and railway bridges, culverts, urban drainage systems, flood plain zoning, economic evaluation of flood protection projects etc. (Kumar et al. 2003). Regional flood frequency analysis is usually applied when no local data are available at a site of interest or the data are insufficient for a reliable estimation of flood quantiles for the required return period. Regional flood frequency analysis has three major components, namely, delineation of homogeneous region, determination of appropriate probability density function (or frequency curves) of the observed data, and the development of a regional flood frequency model (i.e., a relationship between flows of different return periods, basin characteristics, and climatic data). The study includes identification of homogeneous regions based on cluster analysis of site characteristics, identification of suitable regional frequency distribution and development of regional flood frequency models.

L-moments

Recently, Hosking (1990) has defined L-moments, which are analogous to conventional moments, and can be expressed in terms of linear combinations of order statistics. Basically, L-moments are linear functions of probability-weighted moments (PWMs) (Sankarasubramania and Srinivasan 1999). Procedures based on PWM and L-moment are equivalent, but L-moment is more convenient because it is directly interpretable as measure of the scale and of the shape of probability distributions. L-moments are robust to outliers and


virtually unbiased for small samples, making them suitable for flood frequency analysis (Adamowski 2000). Similar to conventional moments, the purpose of L-moments and probability-weighted moments is to summaries theoretical distribution and observed samples. As mentioned in Schulze and Smithers, 2002, paper Greenwood summarizes the theory of PWM and defined them as (Schulze and Smithers, 2002):

})]([{ rXr xFXE=β (1)

where r is the rth order PWM and FX(x) is the cumulative distribution function (cdf) of X. Unbiased sample estimators (bi) of the first four PWMs are given as:

∑=

==n

jjX

nm

10

1β ∑−

= −−=

1

1)(1 ]

)1([

n

jjX

nn

jnβ ∑−

= −−−−−=

2

1)(2 ]

)2)(1()2)(([

n

jjX

nnn

jnjnβ

∑−

= −−−−−−−−=

3

1)(3 ]

)3)(2)(1()2)(1)(([

n

jjX

nnnnjnjnjnβ (2)

where x(j) represents the ranked Annual Maximum Series (AMS) with x(1) being the highest value and x(n) the lowest value, respectively. The first four L-moment are given as follow:

01 βλ = 012 2 ββλ −= 0123 66 βββλ +−= 01234 123020 ββββλ −+−= (3) Unbiased sample estimators of the first four L-moments are obtained by substituting the PWM sample estimators from Eq. (2) into Eq. (3). The first L-moment 0 is equal to the mean value of X. Finally, the L-moment ratios are calculated as:

2

33 λ

λτγ ==−L2

44 λ

λτ ==− kL1

22 λ

λτ ==− VCL (4)

Sample estimates of L-moment ratios are obtained by substituting the L-moments in Eq. (4) with sample L-moments (Hosking and Wallis 1997).

Index flood

The T-year event XT is defined as the event exceeded on average once every T years. When the annual maximum floods are distributed according to a specified frequency distribution with (sfd), the T-year event can be calculated as (Cadman et. al. 2003):

XT=F-1(1-1/T) (5) Regional frequency analysis methods, such as the index flood method, include information from nearly stations exhibiting similar statistical behavior as at the site under consideration in order to obtain more reliable estimates (Schulze and Smithers, 2002). Regional methods can also be used to obtain estimates at ungauged sites, which is important in region such as Halil-River basin, where the flow gauging network density is relatively low. Consider a homogeneous region with N sites, each site i having sample size ni and observed AMS xij,j=1,…,ni. The AMS from a homogeneous region are identically distributed except for a site-specific scaling factor, viz., the index flood. At each site the AMS is normalized using the index flood as:

iijij Qq µ/= (6)Where µi is the Mean Annual Flood (MAF) at site i, which is often used as the index flood. The sample L-moment ratios are estimated at each site and the regional record length weighted average L-moment ratios are calculated as:

∑∑==

=N

ii

N

i

iri

Rr nn

11

)( /λλ))

(7)

where )(irλ

)is the rth order sample L-moment ratio at site i , and R

rλ)

is the rth order regional average sample L-moment ratio. The parameters of a regional frequency distribution can be estimated using the method of L-moment ratios, as shown, for example, by Hosking and Wallis (1997). Finally, the T-year event at site i can be estimated as:


TiiT qQ)))

µ=, (8)where iµ) is the MAF at site i, and Tq

) is the regional growth curve. The regional growth curve is the (1-1/T)-quantile of the regional distribution of the normalized AMS as defined through Eq. 8 (Hosking and Wallis 1997).

Identification of homogeneous regions

In Halil-River basin six hydrometric sites, which have sufficient length record of data and are important for frequency analysis, were selected. For identification of homogeneous regions, Hosking and Wallis recommended using Ward’s method, which is a hierarchical clustering method based on minimizing the Euclidean distance in site characteristics space within each cluster. The site characteristics selected in this study for each site included: latitude (LAT) and longitude (LON) of the flow gauging weir, Mean Annual Flood (MAF), station area (AREA), altitude (ALT) and design storm intensity (ID). Table1 shows the site characters for six stations in Halil-River basin. Using this method Halil-River basin divided to two regions (A and B). After identification of homogeneous regions, using Hosking’s method discordancy measure (Di) of the sites was determinate in each region. Table 2 shows the L-moment ratios and discordancy measure for region A and B stations. Fig.1 shows location of gauging sites and homogenous regions in Halil-River basin.

Heterogeneity test

Hosking and Wallis (1997) proposed a statistical test based on L-moment ratios for testing the heterogeneity of the proposed regions. The test compares the between-site variation in sample L-CV with the expected variation for a homogeneous region. The method fits a four parameters kappa distribution to the regional average L-moment ratios. The estimated kappa distribution is used to generate 500 homogeneous regions with population parameters equal to the regional average sample L-moment ratios. The properties of the simulated homogeneous region are compared to the sample L-moment ratios as

VVVH σµ /)( 11 −= (9)

where µV is the mean of simulated V values, and V is the standard deviation of simulated V values. For the sample and simulated regions, respectively, V is calculated as:

∑ ∑= =

−=N

i

N

ii

Rii nttnV

1

21

1

2)( }/)({ (10)

where N is the number of sites, ni is the record length at site i, t(i) is the sample L-CV at site I, and tR is the regional average sample L-Cv. If H<1, the region can be regarded as ‘acceptable homogeneous’, 1 H<2 is ‘possible homogeneous’, and H 2 is ‘definitely heterogeneous’ (Hosking and Wallis, 1997). Table 3 shows the heterogeneity measure for identified regions in Halil-River basin.

Goodness-of-fit test

The goodness-of-fit test described by Hosking and Wallis (1997) is based on a comparison between sample L-kurtosis and population L-kurtosis for different distributions. The test statistic is termed ZDIST and given as follows:

4444 /)( στ BtZ RDISTDIST +−= (11) where DIST refer to a candidate distribution. DIST

4τ is the population L-kurtosis of selected distribution, Rt4 is the regional average sample L-kurtosis, and 4σ is the standard deviation of regional average sample L-kurtosis. A four-parameter kappa distribution is fitted to the regional average sample L-moment ratios. The kappa distribution was used to simulate 500


regions similar to the observed regions. From these simulated regions 4B and 4σ are estimated. Declare the fit to be adequate if ZDIST is sufficiently close to zero, a reasonable critererion for selection of suitable being 64.1≤DISTZ (Hosking and Wallis 1997). The test

described above was applied to the four homogeneous regions. For each region the data were tested against the General Logistic (GLO), General Pareto (GPA), General Extreme Value (GEV), General Normal (GNO) and Pearson Type 3 (PE3) distribution. Table 4 shows the results.

Regional flood frequency distribution

Several methods are available for selecting appropriate regional distributions. In this study the regional frequency distributions were selected based on the results of L-moment ratios as described by Hosking and Wallis (1997). Additional probability plots (plotting position) were used to verify that the selected regional distributions provided a satisfactory description of the observed AMS.

L-moment ratio diagrams

An L-moment ratio diagram of L-kurtosis versus L-skewness compares sample estimates of the dimensionless ratios with their population counterparts for ranges of statistical distributions include GLO, GEV, GNO, PE3 and GPA. L-moment diagrams are useful for discerning grouping of sites with similar flood frequency behavior, and identifying the statistical distribution likely to adequately describe this behavior. Fig.2 shows the L-moment ratio diagram for homogeneous regions in Halil-River basin (A and B). As the sample L-moments, are unbiased, the sample points should be distributed above and below the theoretical line of a suitable distribution (Hosking and Wallis 1997). From the above L-moment diagrams, it appears that the GPA distribution for region A and the GPA, GLO, GEV, PE3 and GNO for region B are appropriate.

Plotting Position

As pointed out by Hosking et al. (1985), comparison of different regional frequency distributions against observed data cannot be used to discriminate between different distributions, as the observed data represents only one of an infinite number of realizations of the ‘true’ underlying population (Schulze and Smithers, 2002). However, the probability plots may reveal tendencies such as systematic regional bias in the estimation of the extreme events. To assess how well the proposed regional frequency distribution fit to the observed AMS, the calculated XT-T relationships for Konarueyeh station in region B are shown in Fig.3. The empirical exceedance probability for the ordered observations x(i) were calculated using the median probability plotting position as described by Hosking, and shown bellow:

nixXP i /35.][ )( −=> (12) From above three methods, goodness-of-fit test, L-moment ratio diagram and plotting position, the GPA distribution for region A and the GNO, GEV, GLO, GPA and PE3 distributions for region B were selected as regional frequency distributions.

Quintiles estimation

After the regional distributions selected, using these distributions the quintiles with different nonexceedance probability estimated for regions A and B in Halil-River basin. Table 5 shows the estimated value using GPA distribution in regions A and B. The accuracy of estimated values (regional and at-site estimations) was determinate using relative Root Mean Squire Error (rRMSE). Fig. 4 shows the rRMSE in Konarueyeh station. From these charts the rRMSE values in high return period are low. This indicates that both at-site and regional estimation procedure in high return period give accurate results.


Regional Model

Different methods are available to obtain regional models with hydrologic and basins parameters which can estimate AMS values in ungaged regions. In this study we obtained the regional models for regions A and B in Halil-River basin using multiple regression and stepwise method. In these models QP, A, L and Id are average AMS, basins area, latitude and design storm intensity respectively.

Conclusion

In this study using site characteristic and Ward’s method, hierarchical clustering method based on minimizing the Euclidean distance in site characteristics space within each cluster, the Halil-River basin divided into two acceptably homogeneous regions. The heterogeneity measures based on H1 were -1.67 and 1.88 for regions A and B respectively. The identification of suitable regional distribution for each of two regions was based on the L-moment diagram, a goodness-of-fit test and evaluated using probability plots. The GPA distribution for region A and PE3, GNO, GLO, GPA and GEV distributions for region B were suitable and selected. The rRMSE values between computed and observed data were obtained. These values in high return period were low and indicate that both at-site and regional estimation procedure in high return period give accurate results. Regional models for homogeneous regions was obtained using the multiple regression and stepwise method and with catchments and hydrologic characteristics.

References

Adamowski k., (2000), Regional analysis of annual maximum and partial duration flood data by nonparametric and L-Moment method, Journal of Hydrology 229: 219-231. Cadman D., Zaidman M. D., Keller V., and Yong A. R., (2003), Flow-duration-frequency behavior of British rivers based on annual minima data, Journal of Hydrology 277: 195-213. Hosking J. R. M., (1990), L-moment analysis and estimation of distributions using linear combinations of order statistics, Journal of the Royal Statistical Society, series B 52: 105-124. Hosking J. R. M., and Wallis J. R. (1997), Regional frequency analysis: An approach based on L-Moment, Cambridge University Press, London, UK. Hosking J. R. M., Wallis J. R., and Wood E. F., (1985), Estimation of the generalized extreme value distribution by the method of probability weighted moment, Technometrics 27: 251-261. Kumar R., Chatterjee C., Panigrihy N., Patwary B. C., and Singh R. D., (2003), Development of regional flood formulae using L-moments for gauged and ungauged catchments of North Brahmaputra river system, IE (I) Journal 84: Lee S.H, and Meang S. J., (2003), Frequency Analysis of extreme rainfall using L-moment, Irrigation and Drainage 52: 219–230. Sankarasubramania A., and Srinivasan K., (1999), Investigation and comparison of sampling properties of L-Moments and Conventional Moments, Journal of Hydrology 218: 13-34. Schulze R. E., and Smithers J. C., (2002), Regional flood frequency analysis in KwaZulu-Natal province, South Africa, using the Index-Flood method, Journal of Hydrology 255:194-211.

TablesTable 1. Site characteristic for Halil-River stations

Station name LAT LON ALT (m) MAF (m3/s) AREA (km2) ID(mm/hr)

Soltani 56.32 29.02 2070 79.24 935 6.2

Meydan 56.59 29.08 1880 97.24 631 7.5


Pol baft 56.37 29.14 2270 33.33 300 8.1

Henjan 56.57 29.15 2150 130.39 261 8.1

Konarueyeh 57.15 28.53 1400 427.87 7600 5.4

Cheshm Aroos 56.52 29.18 2460 24.37 83 10.5

Table 2. L-moment ratios and discordancy measure for region A and B stations

Table 3.

Heterogeneity measure for identified regions in Halil-River basin Values Sl. No. Heterogeneity measures

Region A Region B 1. Heterogeneity measure H (a) Observed standard deviation of group L-CV 0.0123 0.0855 (b) Simulated mean of standard deviation of group L-CV 0.0413 0.0352 (c) Simulated standard deviation of standard deviation of group L-CV 0.0174 0.0268 (d) Standardized test value H -1.67 1.88 2. Heterogeneity measure H (2) (a) Observed average of L-CV/L-Skewness distance 0.0391 0.0927 (b) Simulated mean of average L-CV/L-Skewness distance 0.0761 0.0731 (c) Simulated standard deviation of average L-CV/L-Skewness distance 0.0267 0.0496 (d) Standardized test value H (2) -1.22 0.4 3. Heterogeneity measure H (3) (a) Observed average of L-Skewness/L-Kurtosis distance 0.0549 0.0495 (b) Simulated mean of average L-Skewness/L-Kurtosis distance 0.081 0.0955 (c) Simulated standard deviation of average L-Skewness/L-Kurtosis distance 0.028 0.0663 (d) Standardized test value H (3) -0.93 -0.69

Table4. Test statistic ZDIST of regional distributions Table 5. Regional parameters for the various distributions for region B

Table 6. Regional models for Halil-River basins.

Table 7. Estimated discharge (m3/s)

Region A

Station name Record length (year) L-Cv L-Skew L-Kurt Di

Soltani 29 0.4968 0.2401 0.0441 0.44

Meydan 29 0.5078 0.3236 0.1558 1.0

Pol baft 29 0.4804 0.2608 0.0774 0.5

Henjan 29 0.0228 0.2105 0.5116 0.64

Region B

Konarueyeh 29 0.6127 0.506 0.2232 1.0

Cheshm Aroos 29 0.4417 0.4344 0.2915 1.0

Distribution Region A Region B

GLO 4.37 0.88

GEV 3.48 0.75

GNO 3.01 0.29

PE3 2.18 -0.5

GPA 1.23 0.16

Parameters of the distributionGEV =0.455 =0.426 k=-0.420 GLO =0.634 =0.355 k=-0.470 GNO =0.596 =0.605 k=-1.019 PE3 µ=1 =1.178 k=2.87 WAK =-0.093 =0 =0 =0.654 =0.279

R2Regional Model Region

0.97121.651151.99953.0236.0 −++= IDELAQP

A

0.99602.16094.0)( += ApQB


from GPA distribution in region A and B Nonexceedence probabilityStation name

0.5 0.8 0.9 0.95 0.96 0.98 0.99 0.995 0.998 0.999Soltani 59 132 181 225 239 277 311 341 376 400 Meydan 73 162 223 276 293 334 380 418 462 490 Pol baft 25 56 76 95 100 116 130 143 158 168 Henjan 106 235 321 399 421 489 584 602 665 708

Konarueyeh 261 612 941 1335 1476 1977 2572 3281 4437 5515 Cheshm Aroos 14.9 35.3 54.1 77 85 114 148 189 255 317

Figures

Fig. 1. Homogeneous regions and Halil-River stations Fig. 2. L-moment diagrams for regions A and B

Fig. 3. Probability plot for Konarueyeh station in region B

Fig. 4. rRMSE between computed and observed data for Konarueyeh station

Soltani Polbaft Region

Region

Cheshm Aroos

MeydaHenjan

Konaroeyeh

Jiro

ftJi

roft

dam

-0.3

0

0.3

0.6

0.9

1.2

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

L-skewness

GPA GLOPE3 LN3GEV Region ARegion B

L-k

urt

osi

s

0

0.04

0.08

0.12

0.16

0.2

44.6 17.6 10.9 5.1 3.0 2.5 2.0 1.3

Return period (year )

rRM

SE

At-site GEV distribution

Regional GEV distribution

0

0.04

0.08

0.12

0.16

0.2

44.6 17.6 10.9 5.1 3.0 2.5 2.0

Return period (year )

rRM

SE

At-site GPA distribution

Regional GPA distribution

0

500

1000

1500

2000

2500

0 10 20 30 40 50

Return period (year)

Observed dataAt-site GEV distributionRegional GEV distribution

Dis

char

ge

(CM

S)

0

500

1000

1500

2000

2500

0 10 20 30 40 50

Return period (year)

Observed dataAt-site GPA distributionRegional GPA distribution

Dis

char

ge

(CM

S)


Temporal Variations of Rainfall Erosivity Factor in Mazandaran Province, Iran

S.H.R. Sadeghi1, M. Behzadfar2

1-Head and Assistant Professor, Department of Watershed Management Engineering, College of Natural Resources and Marine Sciences, Tarbiat Modarres University, Noor, Mazandaran, Iran. E-mail: [email protected] 2- M.Sc. Student, Department of Watershed Management Engineering, College of Natural Resources and Marine Sciences, Tarbiat Modarres University, Noor, Mazandaran, Iran. E-mail: [email protected]

Abstract The rainfall erosivity factor indicated as R in Universal Soil Loss Equation (USLE) is one of the best quantitative indices for the evaluation of potential erosion risk in an area. The erosivity factor describes the ability of rainfall to erode the soil. Studying the magnitude of erosivity factor in different times and spaces is an important task which can be used for managerial purposes of soil erosion control. In order to study the temporal variation of R-factor, the Mazandaran Province located in the northern part of Iran was selected. The entire 25 years data of 4 stations in the Mazandaran province and one station in the Golestan province were collected for the study. The R values were then calculated with the help of equation applied in USLE. The average annual rainfall erosivity of the study area was found to be 46.62 ton.m/ha.h with the maximum and the minimum values of 14.77 and 0.40 ton.m/ha.h respectively belonging to months October and May. The highest and the lowest values of R factor also occur in autumn and winter, respectively. The crop management treatments can therefore be easily suggested to protect the land against the rain drops based on the results obtained from the study.

Key words: Erosivity, Iran, Mazandaran Province, Temporal variation, USLE

Introduction Annually on protection against natural dangers, it is spent about 2% of the gross world product. At growth rate of the gross world product of 3.5% in year, growth of damage from natural hazards is about 10% one year (Blagovechshenskiy et al., 2004). Iran also has no exception and thus losses lots of resources owing to different causes. On the basis of 1991 statistics, 300m2 of forests and 400m2 of rangelands are being continuously depleted per each second (Rahmani and Jalaliyan, 1993). Among miscellaneous destructive factors, erosion stands in top priority and needs to be considered well. Water, wind, glacier and gravity are the main causes of soil erosion. The extension of water erosion is more and its consequent results is also much complicated than other types of erosion in Iran. In water erosion, detachment of soil grains seriously is controlled by rainfall splash. The magnitude of kinetic energy of rain drops is described by an index known as rainfall erosivity factor and shown as R factor in Universal Soil Loss Equation (Wischmeier and Smith, 1958). Duration, intensity, rain drop diameter, elevation, and spatial and temporal variability of rainfall influence the rain erosivity. Wischmeier and Smith (1958) studied 183 storms in Zanzoil region in USA and showed that soil loss has strong relation with rainfall intensity (I30) which itself varies with time. Atre (1997) estimated rainfall erosivity in Rahuri, India, in three periods of pre-monsoon, monsoon and post-monsoon and highlighted the differences among the studied periods. Posch and Seppo (2003) computed rainfall erosivity factor for Finland. They pointed the noticeable monthly and seasonally difference of erosivity. Till date no study has been conducted in Iran to emphasize the temporal variation of erosivity. Therefore, in the present paper, the temporal variability of rain erosivity has been considered for one of the northern


provinces in Iran facing with serious rate of land degradation and having high potential for agricultural utilization.

Materials and Methods The Mazandaran province with an area of 23756.4 km2 laid in 50°34' to 54°10' E-longitude and 35°37' to 37° N-latitude. Mean rainfall of the province is 750mm and classified as semi-humid climatic. Figure 1 shows the location of the study area in Iran (Management and Planning Organization of Mazandaran Province, 2001). In order to conduct the study, at first, the climatological stations equipped with rain gage recorders and located in the study area were selected. The location of the selected stations i.e. Ramsar, Nowshahr, Babolsar and Kasiliyan has also been shown in Fig. 1 and their coordinates have been summarized in Table 1. Because of lack of station in eastern part of the study area, another station was then selected in neighboring province of Gorgan. The collected rainfall hytographs of 25 common years were precisely analyzed for daily, monthly, seasonal and annual basis. The rainfall kinetic energy for each storm in the aforesaid periods was calculated using the following equation:

iLogI7.893.210E += (1) where E is kinetic energy of rainfall in j/m2/cm rain and Ii is rainfall intensity in cm/h for any time step. The rainfall erosivity factor (R) was then computed with the help of the following equation (Wischmeier and Smith, 1958):

R= (2)100

30EIn

1i∑

=

where R is rainfall erosivity factor in ton.m/ha.h; E= storm energy from step i=1 to i=n and I30 is maximum rainfall intensity in 30 minutes of an individual storm in cm/h. After computing the storm-wise rainfall erosivity, the monthly, seasonal and annual erosivity values were then respectively computed by sum of daily, monthly and seasonal values. The concept of Thiessen method was also used to obtain an average values for the study area. The temporal variation of rainfall erosivity was ultimately investigated throughout the study province.

Results and Discussions The monthly and seasonal values of rainfall erosivity in selected stations calculated as per the procedure explained above have been summarized in Tables 2 and 3 and their graphical variations have been depicted in Figs. 2 and 3. The annual rainfall erosivity for the Mazandaran province was also found to be 46.62 ton.m/ha.h. As it is seen in Tables 2 and 3 as well as Figs. 2 and 3, the variation of rainfall erosivity in different time scales is high and necessary attention is therefore seriously required. The prioritization of different months in seasonal and annual rainfall erosivity contribution in the study area has been sorted in Fig. 4 and shows that October, and April and May have the maximum and the minimum rainfall erosivity, respectively. To analyze the reasons behind such results, the storm characteristics were scrutinized. The frequency of storm occurrence has been demonstrated in Fig. 5.Fig. 6 shows that the maximum numbers of storms occurred in months February and October while their erosivity is quite different. Comparing Figs. 5 and 6 shows that number of storms can not describe the potential rainfall erosivity of the associated month. The mean maximum 30 minutes intensity of storms occurred in October is 0.6 cm/h which is much higher than 0.24 cm/h belonging to February. This result which has also been incorporated by


Wischmeier and Smith (1958) may be supposed as one of the main reasons. In addition, the maximum mean rainfall occurs in October with the amount of 9.83mm. Prioritization of the seasons, demonstrated in Fig. 6, showed that autumn and winter have the highest and the lowest rainfall erosivity, respectively. Analyzing the storm characteristics in different seasons showed that storms occurred in autumn have high maximum 30 minutes rainfall intensity equal to 0.5 cm/h, the highest rainfall duration as well as mean rainfall of 7.96mm.

ConclusionThe results of the study verified the significant variation of rainfall erosivity during different months as well as seasons in the Mazandaran Province in southern part of Caspian Sea. The average annual rainfall erosivity of the study area was found to be 46.62 ton.m/ha.h with the maximum and the minimum values of 14.77 and 0.40 ton.m/ha.h respectively belonging to months October and May. The highest and the lowest values of R factor also occur in autumn and winter, respectively. The crop management treatments can therefore be easily suggested to protect the land against the rain drops based on the results obtained from the study. Study on the main sources and reasons of temporal variations in the study area and extension of such type of studies in different parts of the country and providing necessary agricultural issues are strictly advised.

Acknowledgments The financial and administrative supports of the Management and Planning Organization of Mazandaran Province are timely acknowledged.

References Atre AA, Damale AS and Bangal GB (1997) Estimation of erosion index at Rahuri (Maharashtra), Journal Maharashtra Agricultural University, 22(2):221-222 Blagovechshenskiy VP, Gulyaeva, TS and Kokarev, AL (2004) Natural hazards estimation and mapping in the Dzunger Alatau range (Kazakhstan), In: 10th Congress Interpraevent 2004 Proceedings, May 24-28, Italy, I: 27-36. Management and Planning Organization of Mazandaran Province (2001) Natural features of Mazandaran Province, http://www.mpom.org/fa/index.aspx Posch, M and Seppo R (2003) Erosivity factor in Universal Soil Loss Equation estimated from Finnish rainfall data, Journal of Agricultural and Food Science in Finland, 2(4):271-279 Wischmeier WH and Smith, DD (1958) Predicting rain-fall-erosion losses from cropland east of the Rocky Mountains, Agricultural Handbook, 282, Washington DC.

Table 1. Some characteristics of the stations No. Station Long.(E) Lat.(N) Elevation(m, ams) 1 Ramsar 50° 40' 34° 54' -20 2 Nowshahr 51° 30' 36° 39' -20.9 3 Babolsar 52° 39' 52° 39' -21 4 Kasiliyan 53° 11' 36° 6' 1100 5 Gorgan 54° 16' 36° 51' 13.3

Table 2 Monthly variation of rainfall erosivity factor (ton.m/ha.h) station

MonthRamsar Nowshahr Babolsar Kasiliyan Gorgan

Weighted mean

April 0.75 0.22 0.44 0.30 0.47 0.40 May 0.36 0.46 0.38 0.13 0.52 0.40


June 1.70 6.63 0.96 0.97 6.21 3.57 July 5.73 1.78 2.49 4.03 1.84 2.62 August 0.94 2.78 2.71 1.31 2.35 2.33 September 3.89 18.29 6.90 0.55 2.79 7.65 October 6.42 37.28 11.10 0.39 7.30 14.77 November 4.74 4.93 11.90 0.29 12.26 8.30 December 2.23 6.42 3.57 0.66 12.26 5.76 January 1.41 1.24 1.79 0.72 3.52 1.89 February 0.71 1.63 1.87 0.05 0.91 1.28 March 0.86 0.55 1.31 0.31 2.21 1.17

Table 3. Seasonal variation of rainfall erosivity factor (ton.m/ha.h) station

seasonRamsar Nowshahr Babolsar Kasiliyan Gorgan

Weighted mean

Spring 2.80 7.37 1.78 1.4 8.9 4.76 Summer 9.61 7.34 12.10 1.95 8.18 8.60 Autumn 14.71 48.62 26.57 1.34 31.82 28.93 Winter 2.98 3.42 4.97 1.08 6.64 4.33

Fig. 1 The location of study area in Iran

0

2

4

6

8

10

12

14

16

Apr. May June July Aug. Sep. Oct. Nov. Dec. Jan. Feb. Mar.

Rai

nfa

ll er

osi

vity

fac

tor

(to

n.m

/ha.

h)

Fig. 2 Monthly variation of rainfall erosivity in Mazandaran Province


0

5

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15

20

25

30

35

Spring Summer Autumn Winter

Rai

nfa

ll er

osi

vity

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h)

Fig. 3 Seasonal variation of rainfall erosivity in Mazandaran Province

0

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Oct. Nov. Sep. Dec. June July Aug. Jan. Feb. Mar. Apr. May

Rai

nfa

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osi

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h)

Fig. 4 Monthly prioritization of rainfall erosivity in Mazandaran Province

Fig. 5 Frequency of monthly storm occurrence in Mazandaran Province


0

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Autumn Summer Spring Winter

Rai

nfa

ll er

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Fig.6 Seasonal prioritization of rainfall erosivity in Mazandaran Province


Seasonal Precipitation-Runoff model for Eastern South Part of Caspian Sea, Iran

S. H. R. Sadeghi1 and H. Razaghian2

1- Head and Assistant Professor, Dept. of Watershed Management Engineering, College of Natural Resources and Marine Sciences, Tarbiat Modarres Univ., Noor, Mazandaran, Iran, E-mail: [email protected] 2- Master Student, Dept. of Watershed Management Engineering, College of Natural Resources and Marine Sciences, Tarbiat Modarres Univ., Noor, Mazandaran, Iran.E-mail:[email protected]

Abstract The precipitation and the runoff are the important components of watershed hydrologic system and therefore their modeling is much necessary for managerial purposes. In the present study, an attempt has been made to find out the appropriate models for estimating the outflow discharge resulted from seasonal precipitation rates in Goorganrood watershed. The study watershed is located in eastern south of Caspian Sea, Iran, and recently faced with two catastrophic floods occurred in 2001 and 2002.In the present study, the 26 years daily precipitation data set i.e. 1974-2000 belonging to four climatologic stations viz. Robat-e-Gharabil, Cheshmeh Khan, Till Abad and Tangrah in Golestan Province were collected and then prepared for modeling processes. The precipitation data were ultimately modeled with the discharge data recorded for the same period at Tangrah hydrometric station for different seasons with the help of multi regression approach. The results of the study verified that the precipitation-runoff models are much different during the studied seasons.

Key words: Caspian Sea, Goorganrood Watershed, Iran, Precipitation-Runoff model

Introduction The flood is one of the typical outputs of the watershed system occurred repeatedly and has a complicated nature. Some 390000 were tolled in the globe during 1988 to 1997 out of which 59% belongs to flood. According to the recorded reports more than 120000 Iranians have been killed till date among which 13% belongs to flood occurrence (www.Iranrivers.com, 2003). During 1980s, at least 60 floods were reported throughout the world having more than 100 tolls in each and in 10 cases the number of deaths was beyond 1000 (Najafinezhad, 2003). Since the precipitation is the most important as well as the most accurate measurable input to the watershed system, distinguishing the relationship between precipitation and runoff and studying its probable temporal variations is a necessary prerequisite for sound management of the watershed. The precipitation-runoff modeling is therefore one of the common studies made for a watershed to predict the output flood of the watershed corresponding to a particular precipitation (Alizadeh, 2002). Two consequent floods occurred in the Golestan province located in eastern-south part of Caspian Sea during years 2001 and 2002. The first flood killed more than 300, missed some 27 and caused more than $600 million (Hosseini and Moradi, 2002), only. Besides that most of the Golestan National Park and downstream villages were flooded during the above mentioned floods (Sadeghi, 2004). The aforesaid problems simply justifies conducting more accurate and rapid studies in the area. Thus, in the present study an endeavor has been made to study the probable variation of the precipitation-runoff models in different seasons throughout the year.

Materials and Methods The study has been conducted for the Tangrah watershed as a part of Goorganrood basin located in Golestan Province. The watershed finally drains into Caspian Sea. It comprises an


area of 1791.2 km2 with compactness coefficient of 1.59 and is also extended in a semi-dry area between 55˚44 and 56˚26 E-longitude as well as 36˚47 and 37˚56 N-Latitude. The perimeter of the watershed is about 240km and the minimum and the maximum elevation of the study area is respectively 330 and 2578 m above mean sea level. The watershed runoff is discharged in east-west direction through Doogh or Madarsoo River having 53.75km length. The mean slope of the watershed and the main river is 3.60 and 1.76, respectively (Golestan Water General Office, 1994 and Boomabad Consultant Engineering Group, 2003).The study was conducted in two stages viz. collection and classification of data and statistical analysis. The daily precipitation data collected during 1974-2000

. , - -

. The average daily discharge data recorded at Tangrah hydrometry station was also used for modeling process as dependent variable. The entire data were then categorized into 4 separate seasons viz. March 20th to June 20th, June 20th to September 20th, September 20th to December 20th and December 20th to March 20th as per Iranian calendar. The general feature of the study area and the location of stations have been shown in Fig. 1 (Iran Water Resources Research Organization, 2001). In the next step, the entire collected data was arranged in chronological order and the corresponding precipitation and runoff data were all set together. The precipitation-runoff relationship was then analyzed using bivariate and multivariate regression and with the help of SPSS package. The normality of distribution was also checked and different type of transformations were tried to minimize the skewness of data belonging to 4 mentioned seasons. The reliability of models was then evaluated based on coefficient of determination, R2(Mahdavi, 1998), relative error (RE) and root mean square of error (RMSE) using the following equations (McCuen and Synder, 1983):

(Eq. 1)

(Eq. 2)

where Qo and Qe are observed and estimated discharges, respectively and n is number of data. The applicability of developed models was also verified with the help of error of estimation for those data not used for calibration stage i.e. 2001-2002 including unordinary floods. The acceptable limit of below 40% for error of estimation and the magnitude near to zero for RMSE (Mozayyan, 2003) was used for finalization of models.

Results and Discussions About 2340 precipitation-runoff data sets were analyzed for the Tangrah study area. Different procedures of modeling processes including forward, backward and stepwise multi regression analysis as well as different type of transformation viz. logarithmic, exponential, root and fractional forms were applied to get most reliable models as per the manner explained earlier. The resulted seasonal models and their specifications have been summarized in Table 1. As it is seen in Table 1, the precipitation-runoff relationships i.e. Eqs. 3 to 6 in the study area are not linear and the logarithmic transformation equations could define the relationship better than other types of transformations. The least and the highest value of coefficient of determination was found to be corresponded to summer and spring seasons, respectively, and

n

QQRMSE

i

neo∑

=

−=

12)(

×100


it is thoroughly meaningful in all the seasons at the level of 99% probability. It is also implied from the table that the equation developed for each particular season is quite different from others and it simply verifies the seasonal variations in precipitation-runoff models for the Tangrah watershed. The minimum and maximum relative error in calibration as well as verification stages respectively belong to autumn and spring and varies within the acceptable range of below 40%. The small values of RMSE near to zero also justify the models accuracy. The significant variations found in seasonal precipitation-runoff models in the study area may be because of temporal and spatial variations in precipitation patterns throughout the year and watershed area as well. The climatological study (Boomabad Consultant Engineering Group, 2003) has confirmed the existence of different precipitation patterns in the area during different seasons. The partial effectiveness of each area affected by a particular climatological station and determined by Thissen method have been calculated based on beta ( ) values in multivariate regression analysis and summarized in Table2. Scrutinizing Table 2 shows that each station plays different roles in generation of daily runoff during different seasons e.g. Tangrah station has a crucial role in first three seasons whereas its effectiveness is comparatively less than Robat-e-Gharehbil and Til Abad stations during winter. It is also implied from the table that the effectiveness of Tangrah station during summer when the recent serious floods have been occurred is much more than other stations and therefore has to be precisely monitored during the summer.

ConclusionAn attempt has been made to study the seasonal variation of precipitation-runoff relationship in Tangrah watershed where two ruinous floods happened in recent years. The study revealed that the reflection of the watershed to the precipitation during different seasons is not alike that it may be because of precipitation or watershed characteristics. The precipitation-runoff modeling in different time steps to increase the accuracy and reliability of the models is therefore suggested for the study area and throughout the country as well. Installation of flood warning systems based on the results obtained from the present study may be very beneficial to the people who are in close contact with the watershed under study. The extension of such study to find out the main reasons behind temporal variation of precipitation-runoff relationship is recommended.

References

Alizadeh A (2002) Principles of Applied Hydrology, Imam Reza University, 735 pp. Boomabad Consultant Engineering Group (2003) Climatological report of Goorgan and Gonbad Plain. Golestan Water General Office (1994) Doogh River statistical reports Hosseini SM and Moradi HR(2002) Evaluation of anthropogenic and natural factors in creation of

Golestan flood (Under publication) Iran Water Resources Research Organization (2001) Goorganrood statistical reports 1971- 2001. Mahdavi M (1998) Applied Hydrology, Tehran University Publications, 362pp. McCuen RH and Synder WM(1983) Hydrologic modeling, statistical methods and applications,

Prentice Hall, 563 pp. Mozayyan M(2003) Study on Relationships between different components of rainfall and runoff in

Kasilian watershed, Master Thesis, College of Natural Resources and Marine Sciences, Tarbiat Modarres University, Iran, 81 pp.

Najafinezhad A (2003) Analysis of Flood Occurrence in Golestan Province, www.turkmenstudents-researches-floodgol.blogspot.com

Sadeghi SHR (2004) Study on Consequent Flood Occurrence in a Part of Northern Iran, In: Proceedings Interpraevent2004, Italy, May 24-28, 2004, 1(III):251-258

www.iranrivers.com,2003. /new%20rivers /newin81 /azar/disaster%20line.htm


Table 1 Tangrah seasonal precipitation-runoff models Relative Error (%)

Season Model

([Log 10 (Qe+1.1)]+1.0=.......) R2

Calibration Verification RMSE Eq.

Spring 0.01053R+0.00054C+0.00379Ti+0.006837Ta+1.544 0.28 13.62 33.00 0.40 3

Summer 0.00214R -0.00118C+0.001025Ti+0.002849Ta+1.165 0.14 7.62 11.48 0.35 4

Autumn 0.00211R+0.00374C+0.0007434Ti+0.002928Ta+1.167 0.24 5.53 9.59 0.11 5

Winter 0.008198R+0.002387C+0.00166Ti+0.00169Ta+1.306 0.16 11.69 13.98 0.17 6

Where Qe is the estimated discharge, and R, C, Ti and Ta respectively stand for Robat-e-Gharabil, Cheshmeh Khan, Till Abad and Tangrah precipitation amounts

Table 2 Partial effectiveness of each station on seasonal precipitation-runoff relationship Season Robat-e-Gharehbil Cheshmeh Khan Til Abad TangrahSpring 0.113 0.001 0.094 0.155

Summer -0.036 -0.022 0.047 0.128Autumn 0.038 0.081 0.043 0.157Winter 0.093 0.034 0.058 0.052

Fig. 1 General feature of and location of stations in Tangrah watershed

Robat-e-GharabilTangrah

Till Abad

Cheshme Khan

N Hydrometry Station Climatological Station

(Scale less)


South Teheran Sewerage Plant as a Water Resource for Agricultural Activities across Varamin Lands

Sayed Amirodin Sadrnejad1

1- Civil Eng. Dept., K.N.Toosi Univ. of Technology, Teheran, Iran Phone:+98-21-2937010 Email : [email protected] and Head Principal of Shahid Rajaii University, Teheran, Iran

Abstract Tehran city is situated at the foot hills of Alborz Mountain in the north, surrounded by Damavand Mountain in the north east, Karaj road in the south west & Varamin road, Tehran refinery and Saveh road in the south. Sewer facilities are urban necessities not only in the improvement, but also in the preservation of water quality and in protection from inundation. Indeed, sewerage facilities are very vital to the society and the degrees of dependence of the inhabitations to such facilities are extremely high. The decentralized approach is a new means of addressing wastewater management needs of sewerage area in a comprehensive fashion. The basic idea of that is to treat the wastewater (possibly together with refuses) on a centralized site by means of fairly low-cost treatment systems, and make direct use the treatment products (water, compost, and biogas). This alternative can meet a sustainable wastewater management requirement and has a promising future, especially for great city of Tehran, where the water and sanitation issues are becoming a more and more important issue as a water resource for agricultural activities. Such an Integrated Waste Water Management plans demand all aspects of the water cycle are considered and our awareness and expertise in all the aspects enable us to apply individual capabilities effectively, economically and in context. For example, the design of individual process schemes takes in the wider implications of supply and demand of agricultural water. The development of treatment schemes can include quantitative environmental risk assessment. The project will cause a significant improvement in the chemical, biological and microbiological quality of polluted surface waters in Tehran, and this will be lead to considerable public health benefits for the population of Teheran. Furthermore, the discontinuation of the use of sewage wells will prevent further contamination of ground water resource with nitrates and micro-organisms. Ground water levels within the city are expected to fall, reducing consequent damage to buildings and services. Also, the use of treated effluent and sludge for agriculture on the Varimin plane will lead to increased crop production, a reduction in dependence on artificial fertilisers and a more assured supply of irrigation water, allowing an expansion of the cropped area to 50000 hectares. The treatment plant effluent will be transferred to Varamin agricultural lands by pre-constructed Tehran Varamin canal in capacity of 8 cubic meters per second to be used in agricultural irrigation. The selected biological treatment process is based on the activated sludge process. The possibility of monitoring of discharges to the environment from the sewage treatment plant and industry, treated slates, surface water quality, and soil quality in the Varamin plane set procedures in the event of non-compliance with standards.Presentation of an optimum sewerage plant design certainly is in need of proper estimation of all affecting parameters in operating mechanism during the life of system. In this paper the best choice of affecting parameters within the defined operating range of agricultural activities is obtained in a rationalized and justified manner and upon providing adequate margin and safety factor is presented.

Keywords: water resource for agricultural activity, low-cost treatment systems, Eco-toxicological studies.


Introduction Tehran city is situated at the foot hills of Alborz Mountain in the north, surrounded by Damavand Mountain in the north east, Karaj road in the south west & Varamin road, Tehran refinery and Saveh road in the south. The city is located between latitudes of 35o-32’ & 35o-50’

and longitudes of 51o-4’ & 51o-33’. The sewerage project area is of steep slope 5% in the north but relatively flat in the south 1.3%. The difference in ground elevations at northern and southern end of project area is approximately 810 meters in a horizontal distance of over 29 km. The total sewage generated in all Tehran areas will be distributed in southern treatment plant and southwestern treatment plant in accordance with the respective capacities. Due to congested areas requiring long lengths of sewers, steep slope, large quantity of sewage requiring large size of sewer and obstructions such as other underground utility lines and metro-crossing, the entire eastern trunk sewer and many lengths of other sewers are to be constructed as tunnels. This is planned to convey flows to the treatment plants along the shortest possible routes. The present population living in the project boundary is 9 million (2003) and it is projected to grow to 11 million in the year 2016 for that the project is planned. Southern sewage treatment plant is located at south of Shahr-Ray and it is out of development limit of Tehran city in the next 25 years and an area of 110 hectares is owned by the client. Four modules of the plant proposed to be constructed in current phase having capacity of 1.3 cubic meters per second, each which will serve population of 2.1 million in this phase. The plant is to be extended up to 8 modules. The coverage area for southern treatment plant is app. 32000 hectares. Later on another plant called southwestern treatment plant will be constructed that will cover about 40000 hectares. The treatment plant effluent will be transferred to Varamin agricultural lands by pre-constructed Tehran Varamin canal in capacity of 8 cubic meters per second to be used in agricultural irrigation.The selected biological treatment process is based on the activated sludge process having following Electrical/Mechanical components: This project has been planed to cater for the need of the city boundary expected to be in 30 years (up to year 2029). The city divided into 22 municipal regions, is estimated to cover 70000 hectares, having estimated population of 11 million. Sewage collection system comprises reinforced concrete sewers, and tunnels will be constructed in 5 phases. The first phase will cover an area of 16500 hectares serving the population of about 2 million. Figure 1 shows Teheran area map including city area and Varamin zone. It is supposed that 4 modules of sewage plants will be constructed during first phase, covering 20% of population and the other 4 modules will be constructed in the next phase (2016); increasing the coverage up to 60%. The phases 4th and 5th will be planned to provide 100% coverage to the population. The treatment plan of 4 module is shown in Figure 2. The total availability of water after the completion of the above projects will be 3216917 cubic meters per day, which will meet the maximum day demand of the year 2003. Once operational, most of the impacts of the project will be positive. The project will cause a significant improvement in the chemical, biological and microbiological quality of polluted surface waters in Tehran, and this will be lead to considerable public health benefits for the population of Teheran. The discontinuation of the use of sewage wells will prevent further contamination of ground water with nitrates and microorganisms. Ground water levels within the city are expected to fall, reducing consequent damage to buildings and services. Also, the use of treated effluent and sludge for agriculture on the Varimin plane will lead to increased crop


production, a reduction in dependence on artificial fertilisers and a more assured supply of irrigation water, allowing an expansion of the cropped area to 50000 hectares. Completion for the water of Jaj-e-Rud River between Tehran’s potable water demands and irrigation water demands will be eased. Furthermore, there will be a possibility, given the proposed sludge use application rate; the limit values for sludge use in agriculture for cadmium could be exceeded. Recharge of ground water on the Varamin plane will increase total water resources, redressing the water imbalance between this and the Tehran plane, and is not expected to cause any deterioration in the quality of Varamin plane ground water. The environmental protection guards of EPO will establish and implement formal programmes for the monitoring of discharges to the environment from the sewage treatment plant and industry, treated slates, surface water quality, and soil quality in the Varamin plane, which set procedures in the event of non-compliance with standards. It will develop a system of controls on discharges into Firouzabad and Sorkhe-Hessar canals. The EPO will review and develop legislation and standards for the control of industrial discharges to the environment, and implement monitoring procedures and licensing of discharges.

Capacity of Treatment Plant Tehran sewerage project is a comprehensive project that covers in an area of about 70,000 hectares. This includes 15,000 hectares in northern region with impenetrable, rocky land; 25,000 hectares in southern region of the city with high level of underground water and fine soil; 30,000 hectares in central region with complex urban texture that includes commercial centers. This project is comprised of about 9,000 km interceptors and laterals network and main trunks, having 250 mm upto 3,200 mm inner diameters. Regarding topographic situation of Tehran, they will carry sewage to two locations of large treatment plants with final average flow ( 25 m3/sec). One of these plants is located in south of Shahre Rey and the other one will be in south-west of Tehran in Robatkarim region and their nominal capacities are 4 and 6.5 million P.E., respectively (10.5 million people totally). Considering the vastness and volume of operations, planning and classification, the scope of objective is followed on the basis of defined priorities and citizens needs, in five operational phases within a 20 to 25 year period. The plant has the capacity to treat sanitary sewerage of a maximum flow of 859 million gallons per day (mgd) and a capacity to treat up to 1,520 mgd of a combination of sanitary and storm flow while consistently meeting or exceeding permit requirements for effluent quality. The plant also produces approximately 1,000,000 wet tons of wastewater residuals each year which are either incinerated in compliance with applicable air pollution control laws or transported to commercially operated landfills in south. Figure 3 shows the treatment plant flow chart. The Sewerage Disposal System’s primary role is to convey and treat the sanitary and combined sewage collected throughout the service area in accordance with applicable service agreements so that the public health is protected and the treated effluent discharged to Varamin canal and the emergency by pass to Firoozabasd canal is in compliance with the limits established by the Department’s water and Sewerage of country System Permit and any other applicable laws, rules and regulations imposed by Department of health in the country . The Sewerage Disposal System will be implementing a Pre-treatment Information Management System to booster the database of our 15,000 industrial customers. Tehran Sewerage Co. (TSC) will implement a revision of divisional business and operational plans and the data migration for the database is competed. TSC will continue the expansion and


improvement of the Wastewater Treatment Plant to provide a cost and energy efficient treatment facility and protect and preserve the local environment of the great city of Tehran. DWSD is now looking to the next 50 years of development for the city and the surrounding area. The vision for this project is to develop a “Comprehensive Wastewater Master Plan” which will provide guidance and direction for the continuing, orderly expansion of the sewerage disposal system to meet those needs.

Sewerage System The justified technical data accepted as initial specification boundary values are as follows: Procedure for the design of a wastewater treatment plant (Knowledge needed as Environmental Engineer) • Collection of basic data and effluent standards • Evaluation of basic data, future changes • Selection of appropriate processes and combinations for wastewater and sludge treatment • Design of the respective processes • Detailed design of construction and equipment • Site surveillance (correct construction) • Guidance in start up of operationKnowledge needed as Environmental Engineer in design of a WWTP • Knowledge of the overall system sewerage, rainwater collection and treatment, Storage tanks, WWTP and receiving waters • Overview over the whole range of processes and combinations • Advantages / Disadvantages of processes, designs and equipment (colleagues!) • Balance of investment and operation costs • Consideration of future development (e.g. extension) • Consideration of operational demands (e.g. at least 2 lanes), stable process and control • Emissions of odour and noise • Operation under extreme conditions: Freezing, WW temperatures above 30°C (nitrate formation), water shortages in droughts Parameter Hydraulic retention time [g/PE/d] Raw Wastewater in Primary Sedimentation 0,5-1,0 h 1,5-2 h-BOD5 60 45 40-COD 120 90 80-SS 70 35 25-N-tot 11 10 10-P-tot 1,8 1,6 1,6 Additional loads from sludge treatment can raise these values up to 20%, Chemical precipitation can remove far more BOD/COD and SS. Figure 3 shows the treatment plant flow chart.

Typical values for preliminary design by BOD sludge loading: Sludge loading: Bx,BOD = 0.3 kg BOD5/kg SS/d for BOD Elimination = 0.15 kg BOD5/kg SS/d forNitrification = 0.05 kg BOD5/kg SS/d simultaneous stabilisation Sludge concentration: SSAT = 3.0 kg SS/m3 with primary clarifier = 3.5 kg SS/m3 with simultaneous P precipitation = 4.5 kg SS/m3 without primary clarifier (stabilisation) Hydraulic retention time (qAT = VAT / Q): qAT = 3 - 18 hours (consequence of volume calculation)

Calculate the volume of a nitrification-reactor based on sludge loading Qin = 15.000 m³ (equals 100.000 P at 150 l/P/d)


BOD5,in = 300 mg/l before primary clarifier Way of design: Assumption: BOD5 after primary clarifier is 200 mg/l (if no measurement is available) Nitrification: BX,BOD = 0,15 d-1 (temperatures above 12°C) XAT = 3 g/l = kg/m³ VNitrification Reactor = Qinx BOD5,in/ BX,BOD / XAT = 6667 m³ (check units always) VNR chosen = 7.000 m³ check plausibility with volume per capita and hydraulic retention time: 7.000 m³ / 100.000 x 1.000 = 70 l/P [t hydr,NR = 7.000 m³ / 15.000 m³/d = 0.47 d = 11.2 h [30% of total reactor for Pre-Denitrification: (1-0.3)xVAT = 7.000, AT=10.000m³

ConclusionsWith continuing advancement in wastewater treatment technology and increasingly stringent wastewater discharge requirements, treated wastewater effluents produced by South Teheran treatment plants have planned as a consistent high water quality and are increasingly being considered for nonpotable reuse. In the semiarid and arid Varamin area, treated wastewater has been used as a new source of water to help alleviate shortages faced by water-deficient communities. The chemical composition of most treated effluents is within the range defined by accepted irrigation water quality criteria and is comparable to that of water commonly used in crop and landscaping irrigation. At present, treated municipal wastewater probably accounts for much less than one percent of national irrigation water requirements, and it is likely that the level of agricultural use will not significantly increase. Effective barriers to increased use include the limited availability of irrigated agricultural land near plant, and the competition with more cost-effective, higher-value urban uses for reclaimed water. Treated sewage sludge is an end product of municipal wastewater treatment and contains many of the pollutants that are removed from the influent wastewater during treatment. The nutrients and organic matter in treated sludge resembles those in other organic waste-based soil amendments such as animal manure and organic composts. The use of sludge as a soil conditioner serves to improve soil physical properties in a manner similar to other organic-based soil amendments. While sewage sludge has been land applied since it was first produced, most of the early operations were carried out with little regard for possible adverse impacts to soil, crops, or ground water. In the past two decades, more emphasis has been placed on applying treated sludges to cropland at agronomic rates. The financial incentive for farmers to use sewage sludge in crop production is debatable.

References Anderson, T.J. and G.W. Barrett. (1982), Effects of dried sludge on meadow vole (microtus pennsylvanicus) population in two grassland ecosystems. J. Applied Ecol. 19:759-772. Asano, T., R. G. Smith, and G. Tchobanoglous. (1985), Municipal wastewater: treatment and reclaimed water characteristics in Irrigation with reclaimed municipal wastewater, a guidance manual, G. Pettygrove and T. Asano, eds. Chelsea, Mich.: Lewis Publishers. Bajwa, R. S, W. M. Crosswhite, J. E. Hostetler, and O. W. Wright. (1992), Agricultural irr-igation and water use. Washington, D.C.: U.S. Department of Agriculture Economic Research Service. Ag. Info. Bull. No. 638.


Barrett, G. W. (1992), Landscape ecology: design of sustainable agricultural landscapes. Pp. 83-103 in Integrating Sustainable Agriculture, Ecology, and Environmental Policy, R. K. Olsen, ed. New York: Haworth Press. Sutton, S. D., G. W. Barrett, and D. H. Taylor. (1991), Microbial metabolic activities in soils of old-field communities following eleven years of nutrient enrichment. Environ. Pollut. 73:1- 10. Westcot, D. W. and R. S. Ayers. (1985), Irrigation water quality criteria in Irrigation with Reclaimed Municipal Wastewater, A Guidance Manual, G. Pettygrove and T. Asano, eds. Chelsea, Mich.: Lewis Publishers. White-Stevens, R. (1977), Perspectives on fertilizer use, residue utilization and food production. Pp. 5-26 in Food, Fertilizer and Agricultural Residues, Proceedings of the 1977 Cornell Agricultural Waste Management Conference. R. C. Loehr ed. Ann Arbor, Mich.: Ann Arbor Science.

Figure 1 Tehran area map


Figure 2 General plan of treatment plant

Figure 3 The treatment plant flow chart


Water Resources Development and Utilization in the Zayandeh Rud basin, Iran

Hamid R. Salemi1, Nader Heydari2, Hammond M. Rust3

1-Isfahan Agricultural Research Center, Isfahan, Iran. Phone +98-311-7760061 Email: [email protected]; 2- Iranian Agricultural Engineering Research Institute, Karaj, Iran. Phone +98-261-2705320 Email: [email protected]; 3- International Water Management Institute (IWMI), Colombo, SriLanka. Phone +94-1-787404 Email: [email protected]

Abstract Zayandeh Rud is the most important river in Isfahan Province in central Iran. For many centuries it has provided the basis for a rich and prosperous region based around the ancient city of Isfahan. Analysis of water supplies and demand over the past 50 years in the Zayandeh Rud basin indicates that despite large investments in water resources development, the basin remains just as vulnerable to drought as it always has been. During the period of analysis two trans-basin diversions and a storage reservoir have been constructed which have more or less doubled the annual supply of water to the basin. But with each water resource development, extractive capacity for irrigation, urban and industrial use has increased by the same amount, so that all new water is allocated as soon as it is available. The most recent developments, since 1980, have actually increased vulnerability to drought because extractive capacity is greater than average flow into the basin. Whenever demand exceeds supply all water is extracted from the basin and the tail end dries up. During the past 50 years flows into the salt pan at the downstream end of the basin have been negligible for more than half the time. Prospects for the future are bleak because once the current phase of water resources development is completed no further water supplies are likely, but demand continues to rise at a steady rate. Ultimately, agriculture will have to concede water to urban, industrial and environmental demands. The Zayandeh Rud provides an excellent example of how a chronically water-short basin has tried to match supply and demand over the past fifty years. The need for a more integrated approach to basin management is required, as well as a set of longer term plans for reallocation of water among sectors to cope with the anticipate water deficits that will arrive in or around 2020. This article attempts to arises some issues related to water supply and demand in the Zayandeh Rud basin and to provide the current situation and scope of future water scarcity as an early warning to the management authorities of water resources in the basin.

Keywords: Consumption, Demand, Supply, Water resources, Water short basin, Zayandeh Rud Basin

Introduction The Zayandeh Rud river flows out of the Zagros Mountain range into the arid inland basin of central Iran (Fig.1). The Zayandeh Rud is chronically water short, and has been for the past 50 years. During the second half of the 20th century the age-long balance between economic growth and the water resources available to support that growth has dramatically changed. Expansion of the irrigated area through major investments in modern irrigation systems, the establishment of large scale industries which require significant volumes of water, and the continuing rapid growth of Isfahan with a current population over 2 million people has all depended on the fragile water resources of the Zayandeh Rud basin.


Since 1950 a series of measures have been taken to increase natural water sources, both through trans-basin diversions and reservoir construction, but by 2000 it is clear that demand has continued to grow faster than it is possible to develop water resources. As a result there is increased pressure on both water and soil resources, tail end areas showing the greatest stress with reduced water availability, deteriorating ground water quality, increased soil salinity and declining agricultural production and little water now reaches the Gavkhouni swamp at the tail end of the river (Fig. 3). Salemi et al. (2000), Sally et al. (2001), Murray Rust et al. (2000), Salemi and Murray Rust (2002) studied different water resources and water management issues of the Zayandeh Rud basin.There are serious concerns that it will be extremely difficult, if not impossible, to meet expected demands for water over the next 20 years, and this has grave implications for economic growth in the basin, particularly for agriculture that remains the main user of water. The objective of this paper is to discuss water resources development and utilization status in current situation and to take some lessons from the past and to provide forecasting and early warnings about water deficit in the basin in future.

Materials and Methods In every river basin there is a sequential development of water resources that should ideally keep peace with demand or attempt to anticipate future demand (Fig. 2). In many cases the initial developments are relatively small scale, meeting local needs, and are often constructed by communities rather than central government. This is the phase of acquisition defined by D. Molden (Molden, 2001) where the level of water resources development is only a small fraction of potentially available water. The second stage of development is one that occurs when it is no longer possible for communities acting in isolation to construct new water acquisition infrastructure (Fig. 2). The responsibility for water resources development passes to government agencies, and over time the concern changes more from water acquisition to management of water. While resources are not in particularly short supply, it gradually becomes necessary to focus on management to ensure that as the supply/demand ratio becomes smaller, water is used as effectively as possible. The final phase of basin development occurs when supply/demand ratio approaches parity, and the basin starts to close. At this point the main concern with basin management is allocation between sectors and for improving water productivity within each sector within the total amount allocated. In this phase there is almost no spare water available (Fig. 2). Ideally this should be a smooth progression. However, examination of the development of water resources in the Zayandeh Rud basin show that the sequence of development can be much more complex. Data are available for analysis of water utilization in the basin for several key locations. It allows us to assess overall water utilization at basin level. The most important pieces of information are discharges at Pol-e-Kalleh river gauging station the upstream reach of the Zayandeh Rud between Chadegan reservoir and the first diversions, and at Varzaneh which is the final river gauging station before Gavkhouni Swamp (Fig. 3). The difference between these two stations tells us the total water extraction along the Zayandeh Rud because there is not any local inflow between these two points. The Pol-e-Kalleh data set starts in 1949, and the Varzaneh one in 1952. In addition there are annual totals for releases from Chadegan from 1972 onwards, and


precipitation data for Kuhrang from 1966. The entire data sets for Pol-e-Kalleh and Varzaneh river gauging stations are presented in Fig. (4). In this paper the attempt is to explain the water resources development and utilization phases of the Zayandeh Rud basin.

Results and Discussions

1. Water Resources Development in the Zayandeh Rud basin The Zayandeh Rud, has been the basis for a long and diverse culture based around the city of Isfahan. Fed primarily by snowmelt in the Zagros mountains, the river runs eastwards into increasingly arid areas, finally culminating in the Gavkhouni swamp 150 km east of Isfahan city (Fig 3). Geologically the Zayandeh Rud basin is always a closed basin as the swamp is an inland salt pan, but functionally as long as water flows into the swamp we can treat the basin as an open basin. A more detailed description of the hydrology of the basin is provided by Murray-Rust et al. (2000). The development phases of basin are as follow:

a) Phase I: Water resources development before 1953

Until 1953 water resources development were confined primarily to small diversion structures that provided irrigation water to riverine irrigation systems in the central part of the valley. Irrigation was primarily confined to the spring and early summer when snowmelt provided sufficient discharge, but was of minimal importance in full summer and autumn. Cropping patterns reflected water availability, with wheat, barley and fruit trees being the main crops grown.

b) Phase II: First Trans-basin Diversion

The first major water resources development was completed in 1952 when a tunnel was constructed from the Kuhrang river west of the Zayandeh Rud watershed into the Zayendeh Rud itself. This tunnel has a capacity of approximately 337 MCM per year, or about 40% of the normal annual yield of the Zayandeh Rud itself. The Kuhrang River is fed through Karstic springs and snowmelt and eventually flows into the Persian Gulf. It is therefore a suitable option to divert flows into the arid interior of the country to supplement eastward flowing rivers. However, it also has significant seasonal variations and thus cannot provide full discharge through the tunnel in the water-short summer season. Much of the additional water is therefore available in winter and spring when the Zayandeh Rud itself has relatively favorable water conditions.

c) Phase III: Chadegan Reservoir

In some years there is significant snowfall in the Zagros mountains that results in serious spring flooding. To minimize flooding hazards along the Zayandeh Rud the government decided to construct a multipurpose flood control-hydropower-irrigation reservoir at Chadegan at the point where the Zagros mountains meet the plains (Fig. 3). The dam was completed in 1972. The reservoir itself has only a modest storage capacity (1500 MCM) which is less than twice the annual inflow. While this provides only a modest capacity to store water from one year to the next, it is sufficient to capture most of the spring floodwater and release it more gradually


throughout the summer. This has permitted expansion of summer cropping to include rice and maize as important crops. The reservoir by itself does not really allow an increase in annual volumes released into the basin, but it is able to store flows from the Kuhrang diversion when demand for water in the basin is low.

d) Phase IV: Second Trans-basin Diversion

By the early 1980’s it was clear that demand for water was again exceeding available supplies and a second trans-basin diversion was completed from the Kuhrang river in 1985. This second tunnel is smaller than the initial one, with an annual yield of about 250 MCM.

e) Phase V: Future Developments

At present the basin is still in Phase IV of development but two new developments are underway and will be completed before 2010. A third tunnel from the Kuhrang river is under construction. When completed it will provide an additional 280 MCM per year. This means that the three diversion tunnels will provide as much water as the natural flow of the Zayandeh Rud itself. In addition there are numerous springs and local water sources that can be tapped from the limstone foothills of the Zagros mountains. It is estimated that the total yield of these springs and local sources will be approximately 150 MCM. In summary, therefore, we can see a gradual increase in available water resources in the basin over the past 50 years, as shown in Fig. (5). The overall result of these developments is that average annual yield, equivalaent to available water reosurces, has risen from about 850 MCM to 1487 MCM at present and will eventually reach 1917 MCM by the year 2010. Through a sequence of planned developments total water available has more than doubled. However, to put these developments into perspective it now becomes necessary to look at actual water utilization over the same period.

2. Water Utilization in Zayandeh Rud basin, 1949-2000 Figure 4 presents a comprehensive picture of the relative balance between supply and demand during each phase of development of the basin water resources. Throughout the last 50 years there is considerable variation in annual flows at Pol-e-Kalleh, both before and after reservoir construction. These fluctuations are almost entirely related to variations in rainfall and illustrate that average water availability estimates are of little utility for actual management purposes. It is instructive to examine conditions in each of the main phases of basin development. There are insufficient data available to make any assessment of conditions before the construction of the Kuhrang Tunnel #1 (Phase I). Therefore, the phases for water utilization are identified from phase II as below:

a) Phase II: 1953-1971

In Phase II (1953-1971) there were only two years when water availability exceeded the planned level of supply. Immediately after the construction of Kuhrang Tunnel #1 water supply exceeded demand, and there were good flows recorded at Varzaneh. This means that all demands for water were fully met, or more precisely, that water diversion strcutures took as much as they could but there was still water left over. From 1955 onwards, however, discharges at Pol-e-Kalleh began to fall while abstractions remained more or less constant. By 1960 all water was used up before Varzaneh and apart from floods in 1967-68 and 1968-69 total annual


discharges were less than 40 MCM, or an annual average discharge at Varzaneh of less than 1.25 m3 /sec (most of which comes in the winter months). It appears that during this phase the extractive capacity was in the order of 750 MCM between Pol-e-Kalleh and Varzaneh, when flows exceed this amount, water reaches Varzaneh, but when flows are less than 750 MCM flows into Gavkhouni Swamp were negligible.

b) Phase III: 1972-1985

The construction of Chadegan by 1972 did not significantly increase overall water availability, but it did enable storage of flood waters for releases later in the year. There was parallel upgrading of major irrigation systems at Nekouabad and Abshar at this time, which increased the irrigated area and allowed more water to be abstracted for irrigation. Opening of the reservoir coincided with an increase in irrigation abstractions so that although flows increased somewhat no water reached Varzaneh. It was not until improved inflow into the reservoir in 1975-76 that water supply conditions exceeded demand, and from 1976 to 1982 there was sufficient water not only to meet demand but also to have substantial flows into Gavkhouni Swamp. After 1982, however, a decrease in rainfall and continued high levels of abstractions meant that the Zayandeh Rud dried up again in 1982 and remained dry for the next five years. With the increase in water resources infrastructure the extractive capacity rose to about 1000 MCM.

c) Phase IV: 1986-2001

The current phase of basin development was marked by the opening of Kuhrang Tunnel #2 in 1986. Rainfall was plentiful in the next couple of years and water abstractions immediately rose to about 1500 MCM per year. There was still sufficient excess that flows to Varzaneh increased to over 550 MCM for two consecutive years. Within three years of the opening of the second tunnel, however, water supplies dropped below 1500 MCM and immediately Varzaneh water supplies dried up again. For three years very little water reached the Gavkhouni Swamp. Floods in 1992-93 and 1993-94 gave two years of good flows at Varzaneh, and water abstractions rose to over 1500MCM for the first time. At this point in time catastrophe struck the Zayandeh Rud basin. Rainfall at Kuhrang fell to historic lows for six of the next seven years, water supplies fell below 1300 MCM for the next three years, and from 1998-2001 water supplies more or less disappeared. Irrigation was curtailed in the summer of 2000 and no surface water was delivered in 2001. All surface water was reserved for urban and domestic uses, and any irrigation relied solely on groundwater extraction. The stages of basin development shown in Fig. (2) imply that there could be a relatively smooth transition between the Development, Utilization and Allocation stages of basin development. The experience of the Zayandeh Rud shows a much less encouraging picture. Each phase of development of water resources within the Zayandeh Rud basin led to an increase in potentially available water, but each increase in supply was matched almost immediately be increases in demand. The implication of this is that the basin will remain vulnerable whenever supplies are less than demand.


ConclusionsThe Zayandeh Rud basin provides an excellent example of how a chronically water-short basin has tried to match supply and demand over the past fifty years. As potential demand grows, new water supplies have been developed, primarily by transbasin diversion, so that total water avaialbility is now double that of the natural flow of the river. Despite these increases in supply, demand rises almost immediately after commissioning of the new systems, so that for most of the past 50 years the basin remains under stress. The basin became completely closed in 1960, and has only discharged water into the salt pan at the lower end when rainfall is significantly above average. We can therefore redraw Fig. (2) to better reflect the conditions in Zayandeh Rud (Fig. 6). Demand frequently exceeds available supply, a situation that is possible due to groundwater mining, and we believe that at present groundwater is being mined at an unprecedented rate due to the current dry conditions. It would be naive to think that demand should be curtailed to provide a cushion in times of drought, but responses to shortfalls in supply appear to be ad-hoc and uncoordinated. Irrigation systems, particularly head end ones, extract their design discharges irrespective of basin level water conditions, and there seems little early-warning mechanism that will reduce water to different sectors in water-short years. The need for a more integrated approach to basin management is required, as well as a set of longer term plans for reallocation of water among sectors to cope with the anticipate water deficits that will arrive in or around 2020.

References Molden D (2001) Stages of basin development and implications for water management. Proceedings of

the Workshop on Institutional Support Systems for Transferred Irrigation systems, Loshop Dam, September 2000, IWMI.

Murray-Rust H, Sally H, Salemi HR, Mamanpoush A (2000) An overview of the hydrology of the Zayandeh Rud basin. Iran-IWMI Collaborative Research Project, Research Paper no.3.

Salemi HR, Mamanpoush A, Miranzadeh m, Akbari m, Torabi N, Toomanian N, Murray-Rust H, Droogers, P, Sally, H, Gieske, A (2000) Water management for sustainable irrigated agriculture in the Zayandeh Rud basin, Isfahan province, Iran. Iran-IWMI Collaborative Research Project, Research Paper no.1.

Salemi HR, Murray-Rust H (2002) Water supply and demand forecasting in the Zayandeh Rud basin, Iran. Iran-IWMI Collaborative Research Project, Research Paper no.13.

Sally H, Murray-Rust H, Mamanpoush, AR, Akbari, M (2001) Water supply and demand in four major irrigation systems in the Zayandeh Rud basin, Iran. Iran-IWMI Collaborative Research Project, Research Paper no. 8.

Yekom Consulting (1998) Supplemtary studies of surface water resources within the Zayandeh Rud basin. Department of Budget and Planning, Ministry of Agriculture, Isfahan (in Farsi).

Zahabsanei A (2000) Investigation of data summary of water situation in the Zayandeh Rud. Isfahan Regional Water Authority (in Farsi).


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Fig. (3) The main regulatores in the Zayandeh Rud basin

Fig. (1) The Zayandeh Rud basin, Iran


The study on watershed management plan for Karoon River

Kenichi Sasaki1

1- Forests, Rangeland and Watershed Management Organization, Ministry of Jihad-e-Agriculture, Tehran, Iran Phone: + 98 -21-2446546 E-mail: [email protected]

Introduction There are many flood damages in Iran for each year. Some of them are occasionally in so sever situation that regional socio-economic activities are devastated. One of the cause of the flood damages are mentioned to inappropriate watershed development or overuse of the land by human activities. This study has taken Karoon river basin as the sample for breaking a ‘vicious circle’ between land overuse and flood damages and for recommendation about countermeasures on socio-economic activities and infrastructures.

1. Situations of Karoon River basin The Karoon River is a tributary of the Tigris-Euphrates River and has the largest drainage area

(about 70,000 ) in the Islamic Republic of Iran (hereinafter referred to as Iran). The upstream basin occupies the highly elevated Zagros mountain range where natural disasters such as debris flows, landslides, and floods are prevailing. Most of the area is located in a high altitude from 1000 to 4000m in Zagros mountain range and mountain area occupies about 76% of the entire area. Mountains are steep and Karoon River makes deep valleys between mountains are alluvial fans, river terraces, and alluvium deposits which occupy 19% of the entire and are used for the farmland, residential area and another utilized area.

Fig.1 Location of the study area Average annual rainfall is about 650mm in the study area, though it varies from 250mm to

1700mm depending on location, elevation, slope aspect, etc. In general, rainfall amount has trend to reduce toward south east.

In the southwest part of the study area, flood occurs frequently in November and December, while, the snow-melting period of March to May is predominant in the northeast part. There are many causes of floods in this area such as heavy rainfall and snow melting as meteorological condition, lack of channel capacity for discharge due to sedimentation and river bed rise caused by soil erosion as natural condition. The channel capacity of the Karoon River at Ahwaz is estimated around 12,000 m3/s, however, the capacity is decreasing because the sediment from upper reaches raises the riverbed, which is said to be the main cause of floods.

At altitude more than 4,000m in places where there are weathered materials, and rocks provide protection and some cold resistance grasses grow for a short time. At 3,500 in altitude scattered trees such as juniper, and shrub can be seen. Owing to their inaccessibility, the vegetation growing at these altitudes have no pasturage benefit. The 2,500m is the altitude where the vegetation is most concentrated. In general, the natural vegetation of the study area have poor condition, decreasing trend, and a carrying capacity of 155 Animal Unit in Month (AUM)/ . One AUM is equivalent to 60kg of dried forage. This disappointing situation can be attributed to over exploitation and disordered utilization of natural resources.

In the study area, sheep and goats are grazed widely in the rangeland. It is suffered that the area is over grazed beyond its carrying capacity. Consequently, present grazing situation is studied roughly based on the following conditions.

-Animal unit is calculated for the adult female goat/sheep weighing 40kg.


-Cow and horse are equivalent to 5 animal units and sheep is equivalent to 1 animal unit. -Number of livestock is converted into the number of goat. -AUM is adopted based on values mentioned in inventory of natural vegetation. -Total daily nutrient requirement and coarse protein requirement are calculated based on

“Japanese Feeding Standard for Sheep(1996). Under this very rough estimation, following results can be pointed out. -Total number of livestock is estimated at above 4 times of the adequate number which can be

fed in the area, and the area is over grazed as a whole. -Total feed of the study area is mostly (83%) composed of alfalfa that is mainly planted in

irrigated area.

2. Selections of master plan study areas 2.1 Selection criteria

In this study, the following items as criteria for selecting master plan study areas are taken into consideration; (1) Natural-disaster damage

Master plan areas are to be selected from the sub-basins where the certain scale of natural disaster hit and sizable damage occurred in recent years. For the comparison of candidates of the master plan area, damages of the floods were estimated.

*Here, the disaster is confined to flood including debris flow, and landslides. *Flood including debris flow occurred in the latest 10 years and landslides occurred within 50 years are to be evaluated.

(2) Evaluation for soil erosion The extent of soil erosion which is shown in the rate of eroded area in each sub-basin is

classified to 5 grades for the evaluation. (3) Necessity of structural or non-structural countermeasures Necessity of these kinds of countermeasures is obtained from field survey and interview from provincial officials and the residents in each sub-basin. (4) Possibility of development To evaluate the possibility of each sub-basin, easiness of obtaining water resources is used. (5) Land capability Land capability is examined from the viewpoint of topography and soil conditions in connection with the land use. (6) Accessibility and propagation This index is evaluated with the distance from province capital or district center, the distance from main road, the road conditions, etc. (7) Existence of established plan for development In case of the existing plan which is to be done by the government for regional development, the area is given superiority.

2.2 Selected master plan areasAccording to the criteria described above, five sub-basins shown in the following table were

selected.

Name No. Area (km2) Province

1. Vastegan K4-1-9 67.0 Chaharmahal


2. Chaman Goli-Bazoft K3-1-16 113.1 Chaharmahal

3. Sarbaz K7-0-19-1 154.5 Esfahan

4. Tang Sorkh K7-48 65.4 Kohkilouyeh

5. Zeras K8-28 63.7 Khuzestan

Location of the master plan areas is shown below. Fig.2 Location of the master plan areas 3. Plan Formulation 3.1 Overall Goal and Project Purpose

The Study area locates in the Zagros mountain range with average altitude of approx. 3,000 m. The area has been degraded by decrease of the vegetation and the forest area due to overgrazing, deforesting for fuels and reclamation of the new cultivation area. In case of heavy rainfall or rapid snow melting, many types of disasters such as debris flow and flood are anticipated because of degradation of land. That is, the area is suffering from a vicious circle of natural-social environment: “Decrease of farm income (Poverty) -Further exploitation of land - Degradation of natural environment -Natural disasters and damage to farmland -Decrease of productivity of land -Decrease of farm income (Poverty)”. The vicious circle is schematically shown below, and the regional society is facing the danger of collapse.

Deforesting for fuel collection Decrease of vegetation of rangeland by overgrazing Unsuitable dry farming

Acceleration of soil erosioIncrease of surface runoff

Figure 3 Vicious Circle of Environment Degradation and PovertyThe overall goal of the master plan is to break through the above vicious circle at two nodes of

the “Degradation of natural environment” and “Decrease of farm income. In order to realize the overall goal, following five project purposes are proposed.

(1) Mitigation of flood, debris flow, and landslide damages (2) Control of soil erosion and conservation of water (3) Restoration and improvement of rangeland vegetation (4) Improvement of living standard (5) Improvement of agricultural product/inputs marketing and

extension of agricultural technology

Population increaseOver exploitation of land

Intensified flood and debris flow damage farmland Degradation of farmland and decrease fertility

Decrease of farm income (Poverty)


The achievement if those project purposes could be expressed in terms of following indexes. (1) Frequency and scale of damages by flood, debris flow, and landslide (2) Sediment amount, and number and scale of gully erosion (3) Vegetation coverage (4) Income job opportunity (5) Number of corporative members, and number of attendance to extension services

Several approaches to each project purposes are considered, and each approach forms an individual project. Implementation of each approach (project) will produce an effect (effects) on each project purpose.

These project purposes, indexes and approaches are the results of PCM (Project Cycle Management) analysis for each master plan area. The workshop for PCM was held several times with members of Ministry of Jihad-e-Agriculture.

3.2 Implementation Schedule Implementation of the proposed projects was to have been commenced in 2002, after

completion of the Master Plan Study, and will be completed in 2020. The respective periods required for the detailed design, and financial procedures are included in the duration of the implementation schedule. Table 1 is an example of proposed schedule.

Table 1 Implementation Schedule (K4-1-9 Vastegan)

Target Year Project

2002 2005 2010 2015 2020

1 Construction of check dam

2 River treatment

3 Rangeland vegetation improvement

4 Orchard terracing

5 Groundwater monitoring

6 Increase of irrigated agriculture

7 Diversification to milk cow

8 Rural water supply improvement

9 Rural road improvement

10 Establishment of cooperative

11 Community enhancements


4. Project Evaluation Proposed projects are evaluated from the viewpoint of economic aspect, social aspect, and natural

environmental aspect. In the economic aspect, income improvement, generation of job opportunity, and improvement of productivity, etc. is discussed. The effects and impacts on understanding of conditions/ issues, improvement of social bond, and social cooperation, etc. is included in the social aspect. The aspect of natural environment is divided into four impacts/effects, which are prevention of natural disasters, stabilization of water resources, improvement in security, and improvement in sanitation/ health.

The project will give rise to many kinds of tangible and intangible, direct and indirect benefits. Tangible benefits are those that can express in monetary terms. In the financial and economic analysis, only tangible benefits are assessed by three criteria, NPV, B/C Ratio and IRR. Following is one of the results of economic analysis.

Table 2 Result of economic analysis (K4-1-9 Vastegan)

Project NPV B/C IRR 1. Construction of check dam -1,280,354 0.42 1.9% 2. River treatment -1,284,283 0.50 4.7% 3. Rangeland vegetation improvement 562,841 13.21 - 4. Orchard terracing 6,290,655 10.60 97.6% 5. Groundwater monitoring Intangible Intangible Intangible 6. Increase of irrigated agriculture 244,927 1.96 83.5% 7. Diversification to milk cow 1,194,769 1.18 162.5% 8. Rural water supply improvement -167,670 0.52 - 9. Rural road improvement -350,114 0.63 4.8% 10. Establishment of cooperative 1,862,699 1.36 93.9% 11. Community enhancement Intangible Intangible Intangible

Conclusion As described above, the overall goal of the master plan is to break through the vicious circle at

two nodes of the “degradation of natural environment” and “decrease of farm income”. For this purpose, various kinds of projects are proposed. Overall economic evaluation for each master plan area is as follows.

Area B/C EIRR


1. Vastegan 1.12 17.1 2. Chaman Goli-Bazoft 1.20 24.9 3. Sarbaz 1.56 50.0 4. Tangsorkh 1.51 31.0 5. Zeras 1.15 15.1

As a result, the projects are sound in engineering aspect and recommendable from economic point of view. To confirm these projects are effective, it was recommended to select experimental pilot project area. Also, instead of reforesting or vegetation, plantation for profitable products was recommended to gain resident’s participation and promote their income.


Reservoir flood routing using one-dimensional flow model through rockfill dams

Mohammad Shayannejad Assistant Professor of Irrigation Dept., Shahrekord Univ., Shahrekord, Iran,Tel:(0098)0381-4424532 E-mail: [email protected]

Abstract For the sustainable management of watersheds and flood control ,a model that can simulate the hydraulics of flow through rockfill dams is valuable. This paper describes a model of flow through rockfill dams. In the developed model an exponential relationship between Reynold’s number (Re) and Darcy-Weisbach coefficient (f) is suggested. Using real field data and a nonlinear optimization technique, the relationship coefficients are obtained. By introducing inflow hydrograph and rockfill characteristics as an input data and utilizing the above relationship with one dimensional continuity equation, flow rating curve of rockfill dam can be identified and employed in a storage flood routing model. Outflow hydrograph of rockfill dam is the objective of the developed model. The accuracy of the numerical solution has been shown to be reliable wen compared to an exact analytical solution. The parametric sensitivity analysis demonstrated that the larger the rock, the less sensitive the water surface elevation will be.

Key words: Flood routing, Rockfill dam

Introduction When rock is available, rockfill dam is an economical and fast tool for flood detention and control purposes. Rockfill dam can be designed satisfactorily when hydraulics of flow through rockfill dam is known. As this type of dam consists of coarse particles, the flow will deviate from Darcy’s law and mostly is turbulent. This means that the relationship between the flow velocity, V, and its hydraulic gradient, i, is a nonlinear one. Different researchers proposed the following nonlinear relationships:

i AV B= (1) i A V B V= ′ + ′ 2 (2)

where A A B B, , ,′ ′ are coefficients dependent on the rock and fluid characteristics. Equation (1) was proposed by Prony in 1804 and Equation (2) by Forcheimer in 1901 (Li et al., 1998). Other researchers, to explain the hydraulics of flow through rocks, suggested relationships between Reynold’s number (Re) and Darcy-Weisbach coefficient (f) in the following forms:

f a b= Re (3)

fa

b= ′ + ′Re

(4)

where a a b b, , ,′ ′ are also coefficients dependent on the rock and fluid characteristics. If the Reynold’s number is written in terms of v, the Darcy-Weisbach can be expressed in the form of Equations (1) and (2), respectively. The following shows some of the relationships proposed by different researchers. In 1952, Ergun proposed the following relationship (Ergun, 1952):

in

gn dV

n

dgnV= − + −

1501

1 7512

3 2 32( )

..( )ν

(5)


where n = rock porosity,ν = kinematic viscosity, d = average size of rock material and g = acceleration due to gravity. Wilkins introduced the following relationship(Wilkins, 1956):

iR n

Vh

= 0 04650 925 1 85

1 85.. .

. (6)

where Rh is the hydraulic radius of the rock material. He considered Rh equal to d/10 for the range 0 4 2. ≤ ≥d meters. Ward used the following (Ward, 1964):

igk

Vc

g kVw= +ν 2 (7)

where cw = a coefficient and k = intrinsic permeability. Leps introduced the following (Leps, 1973):

v k R ih= o0 5 0 54. . (8)

where ko is a coefficient. McCorquodal used the following relationship (McCorquodal et al., 1978):

ignR

Vgn R

Vh h

= +70 0 812 0 5

2ν ..

(9)

Stephensen proposed a relationship in a form of (Stephenson, 1979):

f kt= +800

Re (10)

where Re = Vd

nν. kt for a laminar flow through rock material is equal to zero and it takes

different values when the rock shape changes. For turbulent flow ( Re >10000), f is almost equal to kt . Herrera and Felton introduced the following (Herrera and Felton, 1991):

f = +385817 6

Re. (11)

They defined Re as V d( )− σ

ν where σ is the standard deviation of rock size

distribution. Li and his colleagues used the following relationship (Li et al., 1998):f = −8 75 0 17. Re . (12)

in which d considered equal to 4Rh . Rh and Re are defined as:

Re

Ahs

=ν

(13) Re =vR

nh

ν (14) where e and A sν are the void ratio and particle surface area per unit volume, respectively.

According to the above review of the different relationships it is concluded that the hydraulic gradient may be defined from Darcy-Weisbach equation considering f in a form similar to Equation (3).

Materials and Methods Model Development To develop the model, either of Equations (3 ) or (4) can be combined with Darcy-

Weisbach equation. When Equation (3) is used, ( )d − σ instead of d, and ih

L= ∆

where L is

the base length of the rockfill dam, Darcy-Weisbach equation becomes


( )ia

d

V

g

b

=−Re

σ

2

2 (15)

If Re is defined as V d( )− σ

ν Equation (15) becomes

V i b= +α1

2 (16) where

( )α ν

σ=

−

−

+21

1

2g

a d

b

b

b

(17)

Equation (16) is similar to Darcy’s law except i exponent is not equal to unity. Combining

Equation (16) with the continuity equation and defining i as − dh

dx, yields

Qdh

dxh w

b= −

+α

1

2. (18) where

Q = flow rate, h = water level inside the rockfill dam, w= width of flow cross section, x = the longitudinal coordinate in flow direction. Integrating Equation (18) between the limits H2 to H1 for h and zero to D for x, the following equation is resulted:

( )( )Q

D

w

bH H

b

b

b b b=

+−

+

+

+ + +1

3

1

1

1

2

13

23

1

2α

(19)

where D is defined according to Sharma (1979): D L S L H= − = −0 7 0 71 1 1. . cot β (20)

β 1 is the angle of the upstream face of the dam with the horizontal direction, and H2 and H1

are the downstream and upstream water depths, respectively. Similarly, if Equation (4) is considered instead of Equation (3), the following should be obtained,

( ) ( )QM

MH H

b M

MH H

b M

M

M H b

M H b2 1

212

22 1

22 1 2

21

23

2 1

2 22= − −

′− +

′ + ′+ ′

ln (21)

where( )

Mg d w

D1

22=

− σ and ( )M

a w

Q d2 = ′−νσ

. Therefore, Equations (19) and (21) can be

used as flow rating equations for one dimensional flow through rockfill dams.

Reservoir Flood Routing In this investigation, it is assumed that the flow upstream the rockfill dam has no significant velocity and it is acting as a reservoir and the basic equation for its flow routing is

Q QdS

dti o− = (22)

whereQi = reservoir inflow rate,


Qo = reservoir outflow rate, dS

dt = storage changes with respect to time.

The finite difference form of Equation (22) is going to be as: Q Q Q Q S S

tin

in

on

on n n+ − + = −+ + +1 1 1

2 2 ∆ (23)

where n and n+1 indicate two following time steps with ∆t difference. Qon+1 can be obtained

from combining Equations (23) with (19) or (21).

ResultsModel Calibration To determine the different calculation goals of this investigation, a computer program is developed. The model parameters which are indicated as the coefficients of Equations (3) and (4) are required. They are determined by application of a nonlinear optimization process. In the optimization process, the coefficients are determined in such a way where the sum squares of the differences of the calculated outflows , Qoc , and measured ones , Qom , as an objective function is minimized. In this optimization process the objective function to be minimized is the difference between the calculated and measured outflow rates. The experimental data used in this calibration was Hansen’s data (Hansen et al.,1995). The data includes flow rates, upstream and downstream water levels for different rock sizes, 25 to 130 mm. The optimized result for Equation (2) was

f = −54 0 077Re . (24) By substituting Equation (24) in Equation (16) yields:

V d i= −1027 500 56 0 52. ( ) . .σ (25)

where d50 is the average size of the rockfill material.

Model Validation To check the accuracy of the model the following control steps were conducted:

Real field data were employed to check the validity of the model. In this regard, the data collected at the Jehad Sazendagi watersheds management research center in Tehran/Iran, were used. The data includes flow rates, upstream and downstream water levels for different rock sizes, 41 to 94 mm. As Tablet 1 indicates good agreement between the calculated and measured values. In addition, the results of the model were compared to other equations introduced by other researchers. Figure 1 has shown much more agreement with Wilkins’ Equation results than the other equations.

An example of applications In this regard, the model has been used to introduce flow rating curve and flood routed hydrograph for the following input data: L=3 m, dam width, w=5 m, , σ=0, d50=50 mm, β β1 2 90= = o where β 2 is the angle of the downstream face of the dam with the horizontal direction, (Mannings’ coefficient) for downstream channel = 0.014, slope of downstream channel = 0.001, and the reservoir length = 2000 m. Figures 2 and 3 show samples of the model flow rating curve and flood routing, respectively.

Conclusions


1. The optimized relationship, f = −54 0 077Re . , is obtained for rockfill material in the range of 25 to 130 mm. For sizes larger than 130 mm, the above relationship is applicable with caution for two reasons. First, the above coefficients are comparable to Wilkins’ coefficients where Wilkins’ relationship is applicable for sizes up to 2 meters. Second, as Table 1 shows, the model sensitivity to large d50 is insignificant.

2. The model sensitivity to the different related parameters is different. Its sensitivity to band d (smaller than 0.2 meter) more than the other parameters.

3. The model has the capability of prediction with good accuracy, the flow rating curve, outflow hydrograph for specific inflow hydrograph and rockfill characteristics (see Figure 1 and 2).

4. Determining the rockfill characteristics needed for the case of known inflow hydrograph and downstream flow rating curve.

References 1.Ergun, S., (1952). Fluid flow through packed columns. Chemical Eng. Progress, 48. 2.Hansen, D., V. K. Garga, and D. R. Townsend, (1995). Selection and application for a one-dimensional non-Darcy flow equation for two-dimensional flow through rockfill embankments. Can. Geotech. J., 32: 223-232. 3.Herrera, N. M., and G. K. Felton, (1991). Hydraulics of flow through a rockfill dam using sediment-free water. Transactions of the ASAE, 34(3): 871-875. 4.Leps, T. M., (1973). Flow through rockfill. Embankment dam engineering. R. C. Hirschfeld and S. J. Povlos, eds., John Wiley and Sons, Inc., New York, 87-105. 5.Li, B., V. K. Garga and M. H. Davies . (1998). Relationships for non-Darcy flow in rockfill. J. of Hydraulic Eng., 124( 2): 206-2 . 6.McCorquodal, J. A., A. A. Hannoura and M. S. Nasser, (1978). Hydraulic conductivity of rockfill. J. Hydr. Res., Delft. The Netherland, 16( 2): 123-137. 7.Sharma, H. D., (1991). Embankment dams. Oxford and IBH publishing Co. Pvt. Ltd., New Delhi. 8.Stephenson, D., (1979). Rockfill in hydraulic engineering. Elsevier science publisher, New York.9.Ward, J. C., (1964). Turbulent flow in porous media. J. Hydr. Div. ASCE, 92(4): 1-12. 10.Wilkins, J. K., (1956). Flow of water through rockfill and its application to the design of dams. Proc., 2nd Australian New Zealand Conf. on soil mech. and foundation

Table1-Model results versus experimental data

ObservedObserved1-D

Q(l/s)d50(cm)σ ( )cmL(cm)W(cm)β 1β2H2(cm)H1(cm)H1(cm)

14.15.70806090904.130.132

21.36.90806090904.437.139.2

26.39.41806090905.938.241.9

27.36.91806090904.944.848.8


28.14.10696090904.851.554.1

29.56.90696090906.452.746.1

215.71796090904.642.644.5

23.68.2281.56090907.346.543.9

19.75.71806090904.938.942.9

27.38.21816090906.643.945.6

0

10

20

30

40

50

60

0 2 4 6 8 10 12

Q (l/s)

H1(

cm)

real data

1-D

Stephensen

Willkins

Li

Fig.1-model and other reseacher’s results

0

0.5

1

1.5

2

2.5

3

3.5

0.01

0.21

0.41

0.61

0.81

1.01

1.21

1.41

1.61

1.81

Q(cms)

H1(

m)

Fig.2-Rating curve calculated by the model


0.5

1

1.5

2

2.5

3

0 2 4 6 8 10 12 14

T(h)

Q(c

ms)

QI(cms)

QO(cms)

Fig.3-Flood routing calculated by model


Flood routing in rivers by Muskinghum’s method with new adjusted coefficients

Mohammad Shayannejad Assistant Professor of Irrigation Dept., Shahrekord Univ., Shahrekord, Iran,Tel:(0098)0381-4424532 E-mail: [email protected]

ABSTRACT For determining of Muskinghum model coefficients, requires to a output hydrograph. Such hydrograph is not available in most rivers. In this research, the Muskinghum’s new coefficients are determined by the method that which is not require to output hydrograph and its accuracy is high. This coefficient is determined based on kinematic wave model with suitable scheme. The comparison between results of Muskinghum model with new coefficients and dynamic wave model, showed that the new coefficients, are valid at special conditions. The new coefficients were adjusted by optimization technique for all conditions. The new adjusted coefficients are function of bed slop, bottom width and Manning’s coefficient of river.The results of this coefficients were validated by dynamic wave model.

Keywords: Flood routing, Kinematic wave,Muskinghum

INTRODUCTION The flood flow in rivers is an unsteady flow and its characteristic is varied with time. This variations is made by human or natural factors. The flow variations is described by a hydrograph in hydrology. The flood routing investigates the variations of depth and discharge flow in rivers or channels. The methods or models of flood routing is different. The full dynamic model is the most accurate of them , in which the continuty and momentum equations are solved completely. In the other methods as kinematic wave, the continuty equation and summarized form of momentum equation are solved. This methods were compared by Samani and Shayannejad(2000). The kinematic wave method is valid if the local and convection accelerations be negligible and slopes of surface water and bed be same (Li et al 1975). The generated error in results of kinematic wave model is due to basic assumptions and finite difference numerical solution (Weinmann and Laurenson 1979). An usual and simple method for flood routing in rivers is Muskinghum’s method. This method was based on continuty equation and its equation is following:

1312212 ICOCICO ++= (1)

where 1I and 2I =input discharges at 1t 2t time steps ; 1O 2O =output dischage at 1t 2t

time steps. 321 ,, CCC =constant coefficients are which determined by a given input and output hydrograph. The disadventages of this method are:

1. It requires to a output hydrograph for calculating of its constant coefficients. 2. It determines a output hydrograph only at one point of river. 3. The applied assumptions in this method causes low its accuracy.

The many of researchers have presented coefficients for Muskinghum’s method to remove above disadventages. For example Cunge (1965), Ponce (1986) and Bowen and Koussis (1989) presented a series of coefficients, but the accuracy was low still. In this paper the Muskinghum’s method with new adjusted coefficients have been derived of kinematic wave and then they have been adjusted by full dynamic model. This model valided the results of the new method.


MATERIALS AND METHODS The kinematic wave method is combination of continuty equation and a equation for flow resistance as Chezy-Manning equation. This equations are:

0=∂∂+

∂∂

t

A

x

Q (2)

BQKA .= (3)

where Q =discharge; A =area cross-section; x =distance; t =time ,5

3=B and:

5

3

0

3

2

=S

nPK (4)

where P =wetting perimeter ; n =roughness Manning’s coefficient ; 0S =bed slope.K in equation 4. is determined by considering a given discharge (base flow) and calculation of wetting perimeter for this discharge.The derivative of equation 3. is:

t

QQBK

t

A B

∂∂=

∂∂ −1.. (5)

The combination of equations 2. and 5. is:

0.. 1 =∂∂+

∂∂ −

t

QQBK

x

Q B (6)

For solving equation 6. by numerical method, its terms are discreted following form (Chow et al 1988):

x

QQ

x

Q Ji

Ji

∆−

=∂∂ ++

+11

1 (7)

t

QQ

t

Q Ji

Ji

∆−

=∂∂ +

++ 1

11 (8)

21

1 Ji

Ji QQ

Q ++ +

= (9)

where i =local step number; J =time step number; t∆ =time between two sequential time step; x∆ =distance between two sequential local step. By substituting equations 7. , 8. and 9. into equation 6. gives following equation:

Ji

Ji

Ji QCQCQ 12

11

11 . +

+++ += (10)

where:

xCBKt

tC

∆+∆∆=

01 ..

(11)


xCBKt

xCBKC

∆+∆∆

=0

02 ..

.. (12)

1

11

0 2

−

++

+=

BJi

Ji QQ

C (13)

The equation 10. is Muskinghum’s method with new coefficients and a coefficient is equal to zero. In spite of old Muskighum,s method, this new coefficients are not constant during time step calculations, because the coefficients depend on 0C and it is not constant. Besides for calculating of the new coefficients is not require to a given output hydrograph and flood routing can be carried out at any point of river. The grid size calculation must choose so that the Courant number be equal or less than one. This number is:

x

tCC K

∆∆

=.

(14)

where C =Courant number; KC =celerity(velocity of wave transport) . It is determind from equation 3. :

1..

1−=

∂∂=

BK QBKA

QC (15)

The results of this new method is compared with the results of full dynamic model that its accuracy have been validated by real data.The equation 2. and following equation(momentum) constitute full dynamic model:

( ) 00 =−+∂∂+

∂∂+

∂∂

SSgx

yg

x

VV

t

Vf (16)

where V =flow velocity; y =depth of flow; g =gravitional acceleration; fS =slope of energy

grade line. The equations 2. and 16. are Saint –Venant equation and are solved by numerical method. For this work, they are discreted by Preissman scheme(Chaudhry,1993). Then for each reach of river, two equation with four unknown variables are constituted. This variables are depth and velocity of flow at two ends of any reach. Thus for M reach (with M+1 nodes), 2M nonlinear equation with 2m+2 unknown variables are constituted. Thus two equations are required, that are gained from boundary conditions. For example ,the downstream boundary condition is rating curve and upstream boundary condition is a input hydrograph. The nonlinear equation is linearized by Newton-Raphson method. Finally depth and velocity of flow(and then discharge) are determined at any node and any time. In this paper, the coefficients of equation 10. were adjusted in order to increasing of the new method accuracy, by nonlinear optimization technique with using full dynamic model. Firstly, this coefficiets were changed to following form:

111 CKC =′ (17)

222 CKC =′ (18)

030 CKC =′ (19)


Instead of 1C , 2C and 0C at equation 10. 1C ′ , 2C ′ and 0C ′ were applied. Secondly 1K , 2K and

3K were determined by nonlinear optimization technique according to following objective function:

( )∑=

−=N

ttt QcQmF

1

2 (20)

where F =objective function; Qm =output discharge calculated by full dynamic wave; Qc =output discharge calculated by equation 10. with new adjusted coefficients(equation 17. ,18. and 19.) and N =number of time step.Thirdly were determined relationship between 1K , 2K , 3K and characteristic of river.

RESULTS AND DISCUSIONFigure 1. shows the output hydrograph at a distance one kilometer , calculated by full dynamic model and Muskinghum’s method with new coefficients( 1C , 2C and 0C ) for a hypothetic input hydrograph and following input data: n =0.035 ; 0S =0.001 ; bottom width= 20m ; min1=∆t ; mx 100=∆Figure 1.shows there is different between results of two methods. In this research was concluded that with increasing of bed slop and decreasing of bottom width and slope of input hydrograph, the results of two methods were similar. On the other hand , in this cases, the kinematic wave is dominated. For increasing of accuracy of equation 10. its coefficients were adjusted by optimization technique and values of 1K , 2K and 3K were determined. This coefficients were not constant and were depended on characteristic of river. K (equation 4.) was chosen as presentative of characteristic of river, because it depends on bed slope, wetting perimeter(depends on bottom width) and roughness Manning’s coefficient. Figure 2. shows the relationship between 1K , 2K , 3K and K . The statistical analysis gives following equation:

0289.10391.01 +−= LnKK (21)

956.00577.02 += LnKK (22)

4705.159961.03 +−= LnKK (23)

Figur 3. validates Maskinghum method with new adjusted coefficient. The following data were used in this figure are: n =0.02 ; 0S =0.003 ; bottom width= 30m ; min1=∆t ; mx 100=∆

The accuracy of the new adjusted coefficients is more than Cunnge, Ponce and Bowen ones. Figure 3. shows results of Cunge coefficients for example.Thus Muskinghum’s method with new adjusted coefficients is acceptable in rivers.

REFERENCES 1.Samani, J.M.V. and Shayannejad, M.,(2000),Comparison of kinematic wave and matched diffusivity methods with dynamic wave method in flood routing of rivers,International J. of Eng. Science, Iran Univ. of Science and Technology,3(11),29-43 2.Chaudhry, M. H.,(1993),Open channel flow,Prentic Hall ,Englewood,Cliffs, N.J.


3. Bowen, J.D., and Koussis, A.D.(1989).Storm Drain Design: Diffusive Flood Routing for PCS , J. Hydr. Eng. Div. ASCE, 115(8): 1135-1149 4. Chow, V. T., Maidment, D.R., and Mays, L.W.(1988).Applied Hydrology. McGraw Hill Publishers.5.Cunge, J.A.(1965). On The Subject of a Flood Propagation Computation Method(Muskingum Method). J. Hydr. Res. , 79(2): 205-230. 6.Li, R.M., Simons, D.B., and Steven, M.A.(1975). Nonlinear Kinematic Wave Approximation For Water Routing. Water Resource Res. 11(2): 245-252. 7.Ponce, V.M.(1986). Diffusion Wave Modeling of Catchment Dynamics. J. Hydr, Eng. Div. ASCE, 112(8): 716-727. 8. Weinmann, P.E., and Laurenson, E.M.(1979). Approximate Flood Routing Methods: a Review. J. Hydr. Eng. Div. ASCE .105(12):1521-1535.

Fig.1- Comparison of results of full dynamic and Muskinghum with new coefficients methods

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Fig.2- Variations of 1K 2K 3K related to K

Figure 3. Comparison of output hydrograph by different methods

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Water resources management in Japan - our experience -

Kenji Someya1

1- JICA Expert (Japan International Cooperation Agency) of Water Affairs / Water Policy, Phone +98-21-223-9485 E-mail:[email protected]

Abstract This paper introduces water resources and their management in Japan. Although Japan is one of the rich countries in precipitation, the large population makes Japan a poor country in respect to annual water resources per capita. There have been a lot of problems in Japan concerning water resources management, such as flood control, water use, regulation of underground water use, and environment. The Japanese government has established or revised laws and systems in order to meet the needs at times, hence has handled those matters successfully.

Key Words: Japan, law, water balance, water resources management, water use

Introduction The international interest in water issues has been raised. The international efforts on water resources affairs have become more important and strenuous, e.g. the 3rd world water forum which was held in Japan in March 2003. While the Middle East is poor in water resources, due to increase of population and importance in the world, the necessity for appropriate water resources management in this area has been raised. Each country has made its own effort for appropriate water resources management, which is based on its geographical and social features and historical background. Here, is mentioned water resources management in Japan, where water resources per capita is short in spite of fairly rich precipitation. General information regarding water resources and water use is introduced. Then, laws concerning water resources management, that is to say how the government of Japan has worked on it, is mentioned. It is hoped that this paper will be useful for future reference.

Precipitation in Japan Japan is geographically located in the Asian monsoon zone, one of the few parts of the world with extensive rainfall. Annual precipitation and annual precipitation per capita are shown in figure-1. Average annual precipitation is given as being around 1,730mm. This is about double the annual precipitation level for the whole world, which is recorded as 970mm. But, the total annual precipitation per capita is only about 5,300m3. Since the average rainfall per capita for the world is said to be 22,000m3, it is clear that the Japanese per capita average is mere one fourth of that. Therefore, it is not possible to consider Japan as a country with large water resources when compared with other countries. Moreover, the topographical condition, that mountains are fairly steep, and the concentration of rainfall to rainy season have made water resources management more difficult. The statistics over 100 years show that the annual precipitation has been decreasing gradually. In addition, recent increase in differences in annual precipitation levels between low precipitation years and high precipitation years can be seen.

Water balance Water balance in Japan is shown in Figure-2. Annual precipitation in Japan is approximately 650 billion m3, of which approximately 230 billion m3 is lost through evaporation. The remaining


420 billion m3 is theoretically the maximum amount that can be used by human and is referred to as the inventory of water resources. The amount of water actually used is approximately 87.7 billion m3, which is equivalent to roughly 21% of the mean inventory of water resources. Of the 87 billion m3, around 76 billion m3 (approximately 87%) is obtained from rivers, lakes and marshes, and around 11 billion m3 (approximately 13%) is obtained from groundwater. The situation regarding water use breaks down as approximately 57 billion m3 (66% of total sage) for agriculture, 13.4 billion m3 (approximately 15%) for industry, and 16.4 billion m3 (19%) for domestic purpose. 1. State of Water use 1.1 Domestic water In Japan, water supply system was expanded rapidly from the latter half of the 1950s to the 1960s. By the end of the fiscal year 2001, the water supply system coverage ratio had reached 96.7%, which means 122.98 million of 126.90 million of Japan’s population had water supply system. The trend of domestic water usage and the daily amount of domestic water per capita are shown in figure-3. The amount of domestic water usage in 2000 totaled approximately 14.4 billion m3 in terms of effective water. Because of the population increase and expansion of economic activities in the period between 1965 and 2000, domestic water use increased by roughly three times, but it has been static in recent years. The daily amount of domestic water per capita in 2000 was 322 litres, which roughly doubled in the period between 1965 and 2000 due to changes in lifestyle. It has also been almost stable in these ten years. The domestic water is mainly consumed for baths (approximately 26% of all household water), toilets (approximately 24%), cooking (approximately 22%) and laundry (approximately 20%). 1.2 Industrial water The trend of industrial water usage is shown in figure-4 with its water recycling ratio. In 2000, 55.5 billion m3 of water was used for industrial purposes. Industrial water use increased roughly three times between 1965 and 2000. However, due to advances in water recycling, the amount of water actually taken from rivers or underground has been gradually decreasing after peaking at 15.8 billion m3 in 1973. The chemical industry, iron and steel industry, and pulp, paper and paper goods industry (hereinafter referred to as “the three most water-consuming industries”) are the main industries that use industrial water. The amount of water used by those industries corresponds to approximately 70% of the total. Water recovery efficiency, which was about 78% in 2000, has not increased dramatically in recent years. Among the three most water-consuming industries, the chemical and steel industries have maintained high levels (80 – 90%) of water recycling ratio. On the other hand, the pulp, paper and paper goods industry shows low water recycling ratio (40%). 1.3 Agricultural water In 2000, the amount of water used for agriculture, in terms of volume of water intake, was approximately 57.2 billion m3. Water for agricultural use can be roughly classified into three; 1) water for paddy fields (paddy rice), 2) water for irrigated fields (fruits and vegetables), and 3) water for livestock (cattle, pigs, and chickens). Water for paddy fields comprises most of the agricultural water usage. Although the area of paddy field under cultivation has been declining, demand for water has not decreased. It is forecasted that the amount of water used for irrigated fields especially for greenhouses will increase. Regarding water for livestock, no significant change is expected, as the number of animals being raised has remained steady in recent years.


2. water resources management in Japan (history) In the course of its rapid modernization, Japan has faced a variety of water issues, each time overcoming them through tireless efforts of its people. Specifically, laws concerning water resources management have been introduced or reformed to meet the needs and restrictions each time. The period since the modernization until now can be divided in respect of water resources management. First; the period for land preservation and industrial promotion (1896 – 1945), second; the period for reconstruction after World War Second and establishing the basis of dramatical economic growth (1946 – 1964), third; the period for stable growth and improvement of lives (1965 – present). Main topics are following. 2.1 1896 - 1945In the 1880s, rivers were improved for navigation. Since flood disasters occurred one after another along major rivers and railway networks took a role of conveyance in the 1890s, fundamental flood control measures were needed. “The former River Law” was enacted in 1896, and “the Sabo Law” and “the Forest Law” in 1897. These laws made the foundations for modern flood and erosion control in Japan. The River Law was designed to control surface water in rivers, the Sabo Law was designed to prevent sediment outflow into the rivers, and the Forest Law was designed to prevent soil outflow and conserve watershed forests through the establishment of a protected forest system and the like. The River Law at that time was mainly focused on flood control and rarely referred to utilization of river water. On the other hand, as Japan began active trade with foreign nations, cholera outbreaks and other waterborne infectious diseases occurred seriously. In response, relevant parties began to clamor for the installation of modern waterworks and sewage facilities as sanitation facilities, primarily in the port cities that were most at risk for the entry of waterborne infectious diseases. In 1887, the first modern waterworks in Japan were completed in Yokohama where a number patients suffering from cholera hit the worst in Japan. Other waterworks were subsequently completed in Hakodate (1889) and other cities. The result was a dramatic decrease in the number of fatalities caused by waterborne infectious diseases. 2.2 1946 – 1964 Little progress was made in river and water management during the war. In the period from the years immediately following the World War II to the 1950s, a series of large typhoons struck war-torn Japan and caused major disasters. The Ise Bay Typhoon which caused great damage to the country in 1959 led to the enactment of the Erosion and Flood Control Emergency Measures Law and the Flood Control Special Account Law in 1960. Thus, for the first time a legal basis was established for long-term flood control plans (10- or 5-year plans). In the 1950s, with the aim of rehabilitating the war-ravaged land and economy, utilization of river water was noted. In 1950, “the Comprehensive National Land Development Law” was enacted. “The Electric Power Development Promotion Law” was enacted to meet the growing demand for electricity in 1952. For the urgent necessity of water resources for industry and municipality accompanying rapid economic growth, Multi-purpose dams, designed for both flood control and water utilization, were constructed. In “the Specified Multipurpose Dam Law” enacted in 1957, the construction and administration of dams were centralized under the control of river administrators. Beginning in the mid-1960s, Japan entered the hyper growth period, and population, assets and industry rapidly concentrated on the plains at river estuaries (floodplains). As a result, the supply of municipal water was unable to keep up with the urgent demand in urban areas, and the excessive use of ground water led to ground settlement. Figure-5 shows how ground settlement


progressed. At the same time, the delay of taking measures against drainage, which followed rapid increase of water demand, led to severe water pollution problems in rivers. In order to meet the urgent increase of water demand, “the Water Resources Development Promotion Law (1961)” was enacted. This law has been based on comprehensive management of river system. In the designated river systems, long-term water demand and supply plans were made up and water resources development projects were to be executed under the plan. Water Resources Development Public Corporation was also established in order to construct and manage water resources development facilities which were designated under the Water Resources Development Promotion Law. “The Specified Multipurpose Dam Law” was completely revised in 1964 to manage rivers for both flood control and water utilization. To deal with the ground settlement problem, subsidy programs were introduced under “the Waterworks Law (1957)” and “the Industrial Water Supply Business Law (1958)”, which has promoted to shift from ground water use to surface water use. Moreover, in order to prevent ground settlement, “the Industrial Water Law (1956)”, which established a permission systemfor well water pumping, and “the Law Concerning the Regulation of Pumping-up of Underground Water for Use in Buildings (1962)” were enacted. “The Water Quality Conservation Law (1958)” and “the Factory Effluent Control Law (1958)” were enacted to manage industrial waste water, also “the Sewerage Law (1958)” was revised. “The Basic Law for Environmental Pollution Control (1967)”, which set up water quality standards for public water bodies, was enacted. “The Water Pollution Control Law (1970)” was enacted to regulate the concentration of effluent from each factories and total pollutant load in enclosed water areas, and to promote measures against domestic effluent. 2.3 1964 – present This is the stage when Japanese society shifted from highly economic growth to stable economic growth. The policy was focused on infrastructure which improved the quality of citizens’ lives or environmental conservation which reduces environmental pollution. As for flood control and land preservation, various comprehensive flood control measures were executed steadily. People who experienced highly economic growth have paid attention to the quality of life and environment. Rivers have been paid attention as the place for life and as the place for regional climate and culture. Therefore, the River Law was revised to add the role of environmental conservation to flood control and water utilization. Moreover, the regional opinions are to be reflected in river improvement plan in this revision. “The Land Improvement Law” was also revised in 2001 so that the land improvement projects should be in harmony with the environment and regional public opinions. “The Basic Environment Law (1993)”, which was expansion of “the Basic Law for Environmental Pollution Control” was enacted and the idea of “healthy hydrological cycle for environmental conservation” was introduced. The present stage is to secure a stable supply of safe and good quality water. 2.4 for future development

As for water demand and supply, since no more sudden increases in water demand are expected, providing that construction of facilities will advance according to schedule, stable water supply in normal year and water shortage year should be possible. Therefore, situations on water resources development have been changed from construction of facilities to effective use of existing facilities.

Decrease of annual precipitation and increase of difference between precipitation of water shortage year and water abundant year, which are caused by global warming, are expected to be


more remarkable. Appropriate management for frequent flood and water shortage should be indispensable.

In order to use water effectively and realize sustainable development, healthy hydrological cycle should be applied more affirmatively.

Public opinion must not be neglected for appropriate water resources development and management. Relationship with citizens would be more and more important.

ConclusionThe issues of water resources development or management have greatly changed. The

government of Japan has introduced or reformed laws concerning water resources and have executed them appropriately.

The problem of urgent demand for water resources was overcome; as a result it led to dramatic economic growth in Japan.

There were some problems in ground settlement and river water pollution. The laws regarding groundwater conservation and drainage regulation to rivers have been applied, and therefore overcome those problems successfully.

The issues now are to ensure “a stable supply of safe and good quality water” and “a promotion of healthy hydrological cycle management”. References 1. 2003 Water Resources in Japan; water resources department, land and water bureau, Ministry of land, infrastructure and transport 2. A Review of Comprehensive Water Resources Management in Japan 3. Toshikatsu Omachi; the River Law with commentary by article

Figure-5 Ground settlement


55557777 13131313 16161616

((((4444))))((((4444))))

Agricultural UseAgricultural UseAgricultural UseAgricultural Use ((((55554444))))

IndustrialIndustrialIndustrialIndustrial UseUseUseUse ((((9999))))

DomesticDomesticDomesticDomestic UseUseUseUse ((((11113333))))

River waterRiver waterRiver waterRiver water 77776666

GroundwaterGroundwaterGroundwaterGroundwater 11111111

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((((333333333333))))Annual useAnnual useAnnual useAnnual use 88887777

PrecipitationPrecipitationPrecipitationPrecipitation 650650650650 Inventory ofInventory ofInventory ofInventory of

water resourceswater resourceswater resourceswater resources 420420420420

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Figure-2 Water balance in Japan

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Figure-4 Industrial water use

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Simulation model for seasonal variation of infiltration in cracking soils with two crop residue management

S. H. Tabatabaei1, H. Fardad2, M. R. Neyshabory3, and A. Liaghat4

1- Irrigation Department, Agricultural faculty, Shahrekord University, Shahrekord, Iran, Phone: +98 381 4424401-6, Email: [email protected] , 2- Irrigation Department, Agricultural faculty, Tehran University, Karaj, Iran, 3- Soil science Department, Agricultural faculty, Tabriz University, Tabriz, Iran, 4- Irrigation Department, Agricultural faculty, Tehran University, Karaj, Iran

Abstract: To determine the water application efficiency in furrow irrigation more accurately, consideration of seasonal and spatial variation of infiltration properties is needed. In addition, the effectiveness of different farm management on infiltration is significant. The main objective of this research was to simulate the seasonal variation of infiltration coefficients in Kostiakov-Louise equation (KLE) in a heavy soil under two traditional farm managements (soil with wheat residue and soil without wheat residue). Field studies carried out in a clay-loam soil in Karaj. There were 22 furrows with 0.75 m width in the farm. KLE infiltration coefficients were measured using inflow-outflow and two point’s methods in six furrows. The results of this study showed that the seasonal variation of coefficients (a and k) are not significant, but variation of f0 is significant which was simulated with a logarithmic model. The effectiveness of seasonal variation of cumulative infiltration (Z) was also evaluated and shown to be significant. The Z parameter was simulated with a logarithmic model too. Finally, some dimensionless parameters called Z* were developed to predict Z parameters at different infiltration opportunity times, irrigation events and different residual managements.

Keywords: Crop residue management, Furrow irrigation, Infiltration coefficients, Seasonal variation and Simulation model

Introduction: Application efficiency of irrigated lands in Iran was estimated 13.6-36% where as 97 % of the irrigated lands are under surface irrigation [ 19, 20]. Efforts to achieve high application efficiency in furrow irrigation are limited, because of very large spatial and temporal variation in infiltration characteristics. However, the efficiencies of 85 to 90% are periodically reported from studies incorporating careful soil moisture monitoring and automation [ 6]. Eq.1 shows the Kostiakov-Louis infiltration model. Where Z is cumulative infiltration (m3/m/min), t irrigation time (min), f0 soil basic infiltration rate (m3/m/min), k and a are constant empirical coefficient.

tfktZ a0+= (1)

The parameters of k, a and f0 are important for designing and evaluation of furrow irrigation systems [ 19]. The temporal and spatial variation of infiltration is important in terms of water runoff and deep percolation. Shafique and Skogerboe [ 17] investigated soil infiltration variation on a clay-loam soil and two crops (Annual and Perennial) in Colorado, USA. They reported a significant variation in infiltration between two consecutive irrigation events and during the season. Hunsaker et al [ 9] reported a considerable variation in infiltration on 13 irrigations. Raghuwanshi and Wallender [ 15] presented that the design, scheduling and net income of irrigation is sensitive to variation of infiltration and this variation should be considered in designing furrow irrigation. Malekpur [ 13] did a sensitive analysis on different parameters of furrow irrigation. He reported that a 20% error in estimation of k, a and f0

causes 18.4, 2.8 and 32.4% error in calculation of advance time, respectively. However, soil


variability has been found to be the major contributor as compared to intake opportunity time [ 10, 4]. Janes and Hunsaker [ 11] observed a large quantity of infiltration variation in border irrigation. Childs et al [ 4] measured infiltration with flow-through infiltrometer in a 21 ha cotton field in the northern region of the San Joaquin valley of California. Variability contributed by soil infiltration characteristics and intake opportunity time was quantified for preplant irrigation as well as for three postplant irrigations. The mean and variability of infiltration were greater for the preplant than the postplant irrigations, and soil variability contributed more to total infiltration variability than did the variability in intake opportunity time. Since the infiltration pattern is correlated temporally between post plant irrigations, representative sites rather than complete field irrigation could be used to estimate mean and variability of infiltration. Childs et al [ 4] concluded that this variability can play a more important role in the variability of infiltrated water than the factors governing the intake opportunity time. Christensen et al [ 5] showed the effect of cultivation management on infiltration in a sandy clay loam soil. Generally mulch or crop residue is used in three forms. It may uses as soil surface covering or sometimes mixed with soil by cultivation and finally may be removed from soil surface or burned to access the next cropping season more rapidly. The two later are used in arid and semiarid regions of Iran such as Isfahan. Cultivation of soil with crop residue increases soil organic matter. Unfortunately, most of studies have been done on the first form and the two others remained virgin. Crop residues on the soil surface prevent rainfall drop to hit the soil aggregates and prevent destroying of the soil structure. In this case, Pasture and Alfalfa covering the soil, increase stability of the soil and infiltration [ 1, 16]. Studies show that 30 percent error in estimation of infiltration causes 50 percent error in estimation of advance time. This error has not been considered in furrow irrigation design, scheduling and evaluation. These factors use the first infiltration equation. Walker and Skogerboe [ 22] present a graph show considerable variation on infiltration in first couple infiltrations. In case that furrow infiltration is predicted from a model for each irrigation event, furrow design, scheduling and evaluation will be determined more accurate than the two equations. By the way, the effect of residue management on furrow infiltration has not been considered in furrow design, scheduling and evaluation. The main objective of this research is to model a seasonal variation of furrow infiltration in two traditional residue managements in arid and semiarid region in a clay loam soil.

Materials and methods: Materials: The experiment was performed in a clay loam soil with 40 % clay at research filed station of Tehran University in Karaj at summer 2002. The crack has seen after the first irrigation in soil surface clearly as shown in Fig.1. There were two crop residue treatments with three replicates; 1- tillage without wheat residue (CL-NR) and 2- tillage with wheat residue (CL-WR). The wheat residue (450-500 g/m2) was added to the soil surface and it was spread on a strip with 11 m width and 60 m length. Then, the wheat residue was mixed with soil, up to 35 cm depth, using cultivation. Other processes of land preparation (disk, leveling, fertilization and making furrows done as tradition) were performed in the field. Twenty two furrows were made with 0.75 m width and 60 m length and planted with corn. Measurements were made in six furrows at the field during cropping season (June-September) in 12 irrigations.

Method: Soil moisture characteristic curves from depth 0-60 cm were determined by Pressure plate method [ 3] which is shown in Fig. 2. Bulk density of the soil was measured at the beginning, middle and end of the season. Soil bulk density at the beginning of the season was 1.38 g/cm3


(See Fig. 7). Furrow geometries were measured using profilemetry method. These measurements were made at 5, 30 and 55 m from furrow top stream before and after each irrigation event. The measured data were analyzed by software that written in VB (Visual Basic) and finally all the geometric and hydraulic coefficients were determined.

Caly loam soil- avrage samples

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Irrigation time was determined base on initial soil moisture content before each irrigation event. Maximum allowable depletion (MAD) factor was chosen to be 50% and irrigation depth calculated base on MAD and root depth. The root depth was measured to be 50 cm at the middle of growing season. The maximum root depth was considered 60 cm base on FAO note [ 8]. Soil moisture contents were measured at two depths (0-30 and 30-60 cm) by Notronmetery technique.Soil moisture contents at the field capacity and wilting point were determined base on PF curves (Fig. 2). To eliminate the effect of soil moisture content on infiltration coefficients, it tried to irrigation starts when soil moisture content reaches 15.7%. Furrow inflow discharge was measured by volumetric methods. Outflow discharge measured by W.S.C. flume type two. Calibration’s equations are presented in Eq. 2 where Qout is outflow discharge (m3/min) and H is depth of water on the flume (cm).

64.20002244.0 HQout ×= (2)

Infiltration coefficients were measured by volume balance method. Therefore, the two-point method of Elliot-walker was used for this purpose [ 6]. To determine the soil basic infiltration rate (f0), which is needed by two-point method, the final infiltration rate was measured by inflow-outflow method [ 22]. Eq. 3 shows f0 measurement where L is length of furrow (m).

L

QQf outin −

=0 (3)

To check the results of two-point method, the advance time was simulated with the kinematics wave, zero inertia and hydrodynamic models [ 22]. Fig. 3 shows the result of a sample. The result shows no significant difference between zero inertia, hydrodynamic model output’s and observes data. The difference between kinematics wave model and observed data may be exist because of the assumption in the model. This means that the infiltration coefficients, predicted by two-point method, agreed well with the field data. Statistical software such as MSTATC (Mathematic Statistic Computer) and SPSS (ver 9.0) were used to compare the treatments.

Result and discussion: In this study the infiltration parameters such as k, a, f0, and Z were determined during growing season for two treatments. Figure 4 shows variation of k parameter for all irrigation events for two treatments. Total irrigations were 12 during growing season.

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Figure 3- Advance time simulated by three model Figure 4- Seasonal variation of “k”

As can be seen from fig. 4, seasonal variation of the k parameter does not show any trend. Therefore, it cannot be predicted easily during growing season. The results of this study agreed well with the results obtained by Esfandiari and Maheshwari [ 7] that they stated variation of k parameter does not show any specific trend during the five irrigation events. They reported that the existence of cracks in the soil may cause such trendless. In this research, cracks observed in the soil too (fig. 1). However, the result of this study is not agreed with the result of Hunsaker et al [ 9] that they found a trend for k factor. They applied Kostiakov infiltration equation (KIE), but in this research the Kostiakov-Louis infiltration equation (KLIE) was used. Existence of soil basic infiltration rate (f0) in KLI equation compare to KI equation may cause disagreement and causes that the effectiveness of seasonal variation transfers to other factors. Table 1 shows the result of t test for k parameter which compares the two treatments. There is no significant difference for k factor for two treatments.

Table 1- Analysis of variance of infiltration coefficients and parameters in the treatments 99% Confidence Interval of

the Difference Treatment

no

Infiltration parameter

Initial value

T value df Sig. Mean difference

Lower Higher

1 k 0.00340 -.721 35 0.476 -7.50E-05 -2.86E-04 1.36E-04 1 a 0.14 -5.946 35 0.120 -4.66E-02 -6.26E-02 -3.07E-02 1 f0 0.000138 -16.580 35 0.000 -5.23E-05 -6.086E-05 -4.37E-05 1 Z 0.063 -11.545 23 0.000 -2.641E-02 -3.284E-02 -1.999E-02

2 k 0.00328 -1.932 35 0.061 -2.803E-04 -5.747E-04 1.418E-05 2 a 0.15 -4.919 35 0.060 -4.861E-02 -6.867E-02 -2.85E-02 2 f0 0.000154 -17.773 35 0.000 -6.31E-05 -7.28E-05 -5.35E-05 2 Z 0.06 -11.545 23 0.000 -2.641E-02 -3.284E-02 -1.999E-02

Table 1 also indicates that the k parameter could be considered constant during growing season. The KLI equation is analogous to Philip infiltration equation (PIE). Soil basic infiltration rate (f0) in KLI equation has a very close meaning with transmisivity of soil (A) in PI equation. In fact, k parameter is an empirical coefficient, but it is similar to Sorptivity parameter (S) in PI equation. Physically every parameter that affect A and S will affect f0 and k too. Some studies have shown that the soil texture and initial soil water content are the essential parameters that affect S factor [ 3, 14]. Since the initial soil water content was tried to be constant for all irrigation events, it could be concluded that the S and k parameters also remain constant in every irrigation event.


Figure 5 shows seasonal variation of “a” parameter. This parameter doesn’t follow a trend too. This result is agreed with Esfandiari and Maheshwari results [ 7], but it is not agreed with Hunsaker et. al. results [ 9]. The no trend on a parameter could be due to existence of crack in the heavy soil. Hunsaker et. al. [ 9] also reported that the trend of a parameter is neither linear nor logarithmic.

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CL-NR CL-WRLog. (CL-WR) Log. (CL-NR)

Figure 5- Seasonal variation of “a” Figure 6- Seasonal variation of “f0”

Figure 6 shows seasonal variation of f0 for the two treatments. Table 1 also shows that the variation of f0 is significant at 1% level. Figure 6 shows that the f0 value decreases during the irrigation events. Its rate is high at the beginning of growing season and low at the end. Its trend fit a logarithmic model. This result agreed with Esfandiari and Maheshwari [ 7] findings. They reported 40% reduction in f0 during the season compare to the initial value. Some of the soil physical properties such as porosity, aggregate stability decreased during the season. These properties affect the soil hydraulic coefficient and soil basic infiltration rate directly. If the soil properties change with a distinct trend during the season, it will be expected that the dependent variable (f0) show a distinct trend too. Figure 6 confirms this explanation. At the beginning of growing season, soil porosity is high because of cultivation (Fig.7) and the f0

value is high too (Fig. 6). After the first and second irrigation events, soil porosity decreases (Fig. 7). In addition, water movement over the soil surface destroys soil aggregates. These two functions causes that f0 decreases rapidly (Fig. 6). After irrigation three, soil aggregate destruction and soil porosity reduction go slower and finally tend to be constant at the end of season. Logically it is expected that f0 reduction rate be small too. The results of this study show that the variation of f0 is going to be constant during the irrigation no. 4 and forward.

1.37

1.38

1.39

1.4

1.41

1.42

1.43

0 2 4 6 8 10 12 14Irrigation event (Ie)

Bd

(g/c

m3 )

46

46.5

47

47.5

48

48.5

49

Pros

ity

(%)

Bulk density Prosity

0.000

0.010

0.020

0.030

0.040

0.050

0.060

0.070

0.080

0 2 4 6 8 10 12 14Irrigation Event (Ie)

Z (

m3 /m

in/m

)

CL-NR CL-WRLog. (CL-WR) Log. (CL-NR)

Figure 7- Seasonal variation of bulk density Figure 8- Seasonal variation of “Z”


The f0 trend simulated by several model. Logarithmic model is a simply model that has a good correlation with the observed data. Equation 4 and 5 show simulation models of f0 for treatment 1 and 2 respectively, where Ie is irrigation event number.

( ) **20 61.0ln0000201.0000119.0 =×−= rIef (4)

( ) **20 74.0ln000025.0000133.0 =×−= rIef (5)

Predication of cumulative infiltration (Z) is an important parameter for irrigation scheduling. Predication of Z is depend on predication of infiltration coefficient (k, a and f0). According to the obtained results, there is no significant seasonal variation trend for k and a parameters. For solving this problem it was tried to simulate the Z. The value of Z was calculated for two specific infiltration opportunity time (200 and 400 minutes) and then the results were analyzed. The t test shows that seasonal variation of Z is significant at 1 % level (Table 1). Its trend fit a logarithmic model with a good correlation. Fig. 8 shows the seasonal variation of Z during irrigation 1 to 12. Eq. 7 is used to predicate Z in every desired irrigation event (Ie) at 400 minutes in treatment 1.

( ) **2 65.0ln00934.0055.0 =×−= rIeZ (7) As can be seen in Fig. 8, the Z variation in clay loam soil and without wheat residue is the same as f0 trends. It means that the soil properties which affect f0, affect Z with the same manner. All the two parameters (f0 and Z) can be simulated by logarithmic model significantly. Eq. 9 presents simulation model for seasonal variation of Z in the clay loam soil with wheat residue management (treatment 2).

( ) **2 76.0ln0118.0061.0 =×−= rIeZ (9) Linderman and Stegman [ 12] showed that the major variation in infiltration occur in the first and second irrigation events. They reported that the surface seal and soil surface softness make this variation. Shepard et. al. [ 18] compared five infiltration measurement methods for different soil texture. Their results showed that the average infiltration decreases between 44 to 78% depend on the method of experiment. In this research seasonal variation of Z were calculated to be 48% averagely. Esfandiari and Maheshwari [ 7], Hunsaker et. al. [ 9] reported similar results too. For evaluation of the effect of wheat residue on infiltration, correlation test was used for pair samples which are presented in table 2. The result (Table 2) shows that the parameters are correlated significantly at 1% level. This means that wheat residue did not affect the trend of infiltration parameter in a clay loam soil.

Table 2- Paired samples correlation (PSC) test of infiltration parameters in the treatments Infiltration Coefficient

and Parameter N Correlation Sig.

f0 36 .916 .000 Z 28 .889 .000

The other important aspect is whether the value of the parameter is the same for both treatments in every irrigation events or not? In the other hand, although the wheat residue does not affect the trend of parameters but it may affect the quantity of infiltration coefficient. For this purpose a Paired difference sample test (PDST) was used. Table 3 shows that the value of f0, and Z are significantly different in every irrigation event between treatments. Table 2 and 3 show that the value of f0 and Z parameters are significantly different at 1% level in treatment no. 1 and 2. It means although the trend of f0 is similar in treatment 1 and 2 but there is a significant difference between the f0 value in clay loam soil with residue and


without residue. It is feasible that the wheat residue increases soil porosity and consequently increases infiltration in compare with a non residue soil.

Table 3- Paired difference sample test (PDST) of infiltration parameters in the treatments Paired Differences

95% Confidence Interval of the Difference

Infiltration Coefficient And

Parameter Mean Std.

DeviationStd. Error

MeanLower Upper

t df. Sig.

k 3.25E-04 9.39E-04 1.56E-04 7.46E-06 6.43E-04 2.078 35 .055 a -8.06E-03 5.52E-02 9.25E-03 -2.67E-02 1.06E-02 -.875 35 .387 f0 -5.12E-06 8.58E-06 1.43E-06 -8.02E-06 -2.2E-06 -3.582 35 .001 Z -4.80E-02 3.64E-02 6.88E-03 -6.71E-02 -2.89E-02 -6.981 27 .000

Comparison of seasonal variation of Z in treatment 2 is presented in Fig. 8. The same discussion is valid for clay loam with residue management. The main factor that causes these differences is variation on soil properties. Fig. 6 and 8 show that the soil with residue causes a difference in value of f0 and Z in compare to a soil without residue. For instant fig. 6 shows that the average value of f0 in a clay loam soil with residue is 10 percent grater than the clay loam soil without residue. In another aspect, f0 is decreasing during the season in every treatment but this difference is going to be constant until the end of period. At the end of season the difference phase tend to be zero. In irrigation scheduling, depth of infiltration is used generally instead of infiltration coefficient. So it is very important to find a relation between Z and irrigation event (Ie) [ 8]. The developed model for predication of seasonal variation of Z is base on cutoff time at 400 minutes. For extending the developed model for every desire irrigation cutoff time a non-dimensional approach used as Eq. 11 where Z1 is cumulative infiltration in irrigation no.1 at t=400 min, Zn is similarly for irrigation no. m and Z* is a non-dimensional parameter.

1

*

Z

ZZ n= (11)

By using non-dimensional value of Z data and analyzing, two non-dimensional equations for both treatments developed as below.

7045.08741.0)(148.0 2* =+−= RIeLnZ (12) 7772.08794.0)(1722.0 2* =+−= RIeLnZ (14)

The above equation coefficients have no dependency to irrigation cutoff time. Cumulative infiltration can be calculated for each cutoff time in every irrigation event, base on Z in irrigation event no. 1.

Conclusion:It is concluded that infiltration parameter such as soil basic infiltration rate and cumulative infiltration are not constant during a season. The research showed that it varies at least 48 % from the first irrigation to irrigation no.12. The reference shows that it is a considerable value and should be considered in design, implementation and evaluation of furrow irrigation both in soil with residue and not residue.

Reference: 1. Angers D.A., and G. R. Mehuys, 1989, Effects of cropping on carbohydrate content and water-

stable aggregation of a clay soil. Can. J. Soil Sci. 69:375-380. 2. Baybordi, M., 1993, Fundamental of irrigation engineering: soil and water relationship, sixth

edition, Tehran University press, p.692.


3. Baybordi, M., 1993, Soil physics, Fifth edition, Tehran University press, p 591. 4. Childs, J.L., Wallender, W.W. and Hopmans, J.W. 1993, Spatial and seasonal variation of furrow

infiltration. J. Irrig. Drain. Eng. 119(1): 74-90 . 5. Christensen, N. B. Jones,-T.L. Kauta, G.J., 1998, Infiltration characteristics under no-till and clean-

till furrow irrigation, ASCE, 58(5), 1495-1500. 6. Elliott, R. L., and Walker, W. R., 1982, Field evaluation of furrow infiltration and advance

functions, Transactions of the ASAE, 25(2):396-400. 7. Esfandiari M. and B. L. Maheshwari, 1997, Field values of the shape factor for estimating surface

storage in furrows on a clay soil, Irrigation Science, 17(4):157-161 8. F.A.O., 1998, Cropwat, software for calculating water requirement and irrigation scheduling, FAO

press. 9. Hunsaker, D. J., J. Clemmens and D. D. Fangmeier, 1993, Cultural and irrigation management

effects on infiltration, soil roughness and advance in furrow level basins, Trans of the ASAE, 42 (6):1753-1762

10.Izadi, B. and W.W. Wallender. 1985. Furrow hydraulic characteristics and infiltration. Trans of the ASAE 25(6): 1901-1908.

11.Jaynes, D.B. and D. J. Hunsaker, 1989, Spatial and temporal variability of water content and infiltration on a flood irrigated field, Trans of the ASAE, 32(4): 1229-1238

12.Linderman, C.L. and E.C. Stegman, 1971, Seasonal variation of hydraulic parameters and their influence upon surface irrigation application efficiency, Trans ASAE, 14 (5):914-923.

13.Malekpur, A., 1994, mathematical model of water flow in furrow, Ms.c. thesis, Irrigation and reclamation department, Agricultural faculty, Tehran University.

14.Philip, J. R., 1957, The Theory of Infiltration: 4. Sorptivity and algebraic infiltration equations. Soil Science 84: 257-264.

15.Raghuwanshi, N. and W. W. Wallender. 1997. Economic optimization of furrow irrigation. J. of Irrig. & Drain. Eng., ASCE, 123(5): 377-385.

16.Rowel, D.L., D. Payne, and N.Ahmad.1969. The effect of the concentration and movement of clay in saline and alkali soils. J. Soil Sci. 20:176-188.

17.Shafique, M. S., V. Skogerboe, 1983, Impact of seasonal infiltration function variation on furrow irrigation performance, Advance in Infiltration, ASAE, PP:292-301.

18.Shepard, J. J., W. W. Wallender & J. W. Hopmans, 1993, one point method for estimating furrow infiltration, Trans. Of the ASAE, 36(2), 395- 404

19.Tabatabaei, S. H., H. Fardad, M. R. Neyshabori and A. Liaghat, 2004, Seasonal and spatial variation of furrow cross section, Journal of agricultural science and natural resource, ISSN: 1028-3099, Vol. 11, No. 2.

20.Tabatabaei, S. H., H. Fardad, M. R. Neyshabori and A. Liaghat, 2004, Simulation model for seasonal variation of infiltration in heavy soils with two crop residue management, International soil congress, Ataturk university, 7-10 June 2004, Erzurum, Turkey.

21.Tarboton, K. and W.W. Wallender. 1989, Field-wide furrow infiltration variability. Trans. ASAE 32(3): 913-918.

22.Walker, W. R., and Skogerboe, G. V., 1987, Surface Irrigation: Theory and Practice. Prentice Hall, Inc, Englewood Cliffs, NJ, P.489.


Agroclimatic zoning of Azarbayjan-Sharghi province for rainfed almond using GIS

Hojjat Yazdanpanah1 and Gholamali Kamali,2

1-Ph.D student of Agricultural climatology, Teacher Training University, Tehran,Iran,phone:+98-311-2676218 , E-mail:[email protected] ;2-Atmospheric Science and Meteorological Research Center (ASMERC), IRIMO, P. O. Box 14965-114, Tehran, I. R. of Iran ) ,Phone:+98-21-6004048

Abstract Limitations in soil and water resources together with irregular rate of population increasing , cause that we choose a usefull landuse in our available resources. In order to do this,climatic investigations are necessory.The objective of this study was classification of Azarbaijan Sharghi Province in aspect of climatic potential of Almond in dryland farming.For this purpose ,the precipitation and evaporation data as well as temperature paramameters of ten meteorological stations of area were collected and analysed.Following indices were selected for Almond in dryland farming: • Probability of chilling occurrence on bud and flower of Almond • Probablity of rainfall greater than 250mm • Spring and summer precipitation to annual precipitation ratio • Probablity of occurrence of growing degree days greater than 3500 G.D.D (base temperature 0 celsius) • Amount of available moisture index For each of above parameters a coverage layer was prepared in GIS environment,in the second stage five mentioned coverage layers were crossed and overlapped to obtaining the agroclimatic map of area .Finally agroclimatic map reclassed to highly favorable,favorable,weak and not suitable area. key word:Agroclimate + GIS+Almond

Introduction Precision farming aims to optimize the use of natural resources. Geographical information systems (GIS) are systems for the storage, analysis and presentation of spatial data. A combination of GIS and simulation models is highly relevant for precision farming.Agroclimatic classifications have proved to be of great utility for planning and management of various agricultural and forestry activities. Several climatic conditions may affect annual yield of deciduous fruit trees.Air temperature and rainfall are the most important climatic factors for grown and development of plant species. The objective of present work is to use GIS, along with the model obtained, to find suitable areas for rainfed Almond. Material and methods Azarbaijan sharghi with 45480 Km2 area is one of the northwest provinces of Iran. Daily mean ,maximum and minimum temperature data and evapotranspiration as well as precipitation information were obtained from the synoptic and climatic stations of the area for a 25 year period(1971-1995) (Table 2-1).The phenological (flowering date) and effective rainfall data also were collected from ministry of Agriculture for the same period.

Tab.(2-1): List of meteorological station of area


Num. Station Elevation(m) Latitude Longititude

1 Sarab 1651 37 56 47 32

2 Bostanabaf 1750 37 50 46 50

3 Mianeh 1094 37 25 47 42

4 Tabriz 1361 38 05 47 16

5 Maragheh 1420 37 24 46 16

6 Sharafkhane 1302 38 11 45 29

7 Khodaafarin 300 38 08 46 56

8 Azarshahr 1400 38 26 45 45

9 Jolfa 704 38 57 45 38

10 Ahar 1300 38 29 47 03

Climatic requirements of AlmondAlmond (Prunus amygdalus) is one of the desicious fruits with low water requirement but very sensible flowers,buds and young fruits to chilling(Vezvai 1999). The threshold amount of rainfall for dryland farming of Almond is annual precipitation greater than 250 mm.Not only the amount of precipitation but the distribution of it (summer and spring precipitation to annual precipitation ratio) is very important.The growing season is another factor influences the yield of almond. The most important limitation factor for almond planting is chilling on flowers, buds and young fruits .The threshold temperature for chilling injury is –1 degree centigrade.The above mentioned climatic parameters are the main factors that limitate almond growing specially in dryland farming.In present investigation five information layers were calculated as following: • Probability of chilling occurrence on flowers, buds and young fruits(PCO). • Probability of occurrence annual precipitation greater than 250mm(POAP) • Summer and spring precipitation to annual precipitation ratio(SSPAR). • Moisture Available Index(MAI). • Probability of occurrence of growing season greater than 3500 degree days (POGS).

Information layers preparation in GIS

Digital elevation model(DEM)Digital elevation model(DEM) was prepared using topographic map.For this purpose Arc/Info software and a Digitizer were used. Considering zone 38 as a base zone the digited map converted to UTM project system. By exporting the layer prepared in Arc/Info to IDRISI software the format of the map was converted from vector to raster base.DEM of area is illustrated in Fig.(2-1) .


Probability of annual precipitation greater than 250mm map

Rainfall is very important in dryland farming so we prepared a coverage for it.First of all the best statistical distribution for rainfall data was determined using HYFA software, probability of occurrence annual precipitation greater than 250mm was calculated then for each station.Regression equation between elevation and POAP was calculated(Table(2-2)). Finally this equation was applied on DEM of area and obtained map was classified based on the following conditions (Fig.2-2): Areas in which POAP is more than 0.80 is known as the firs class. Areas in which POAP is between .60 to 0.80 is known as the second class. Areas in which POAP is between .40 to .60 is known as the third class. Areas in which POAP is less than 0.40 is known as the fourth class.

Table( 2-2): Correlation equations between studied parameters and elevation(m)

Parameter Correlation equation R2

1-PCO 1-PCO=0.0006-.0005124Z -0.644*

POAP POAP=0.023+0.00043 Z 0.624*

POGS POGS=0.99001-0.00038Z -0.731*

SSPAR SSPAR=0.071+0.00053Z 0.656*

MAI MAI=0.0094+0.00031Z 0.699*

Fig. (2-2): Precipitation map of Azarbaijan shargi province


Probability of chilling occurrence map:Similar to precipitation layer after fitting a good statistical distribution to minimum temperature the PCO was calculated for each station.in order to draw geographical distribution of PCO and elavation was determined for the area(table(2-2)). Existing map was classified to 4 zones(Fig(2-3)) as following: Areas in which the PCO is less than 0.25 are known as the first class zone. Areas in which the PCO is between 0.25 to 0.50 are known as second class. Areas in which the PCO is between 0.50 to 0.75 are known as third class. Areas in which the PCO is more than 0.75 are known as fourth class. Geographical distribution of MAIMoisture available index(MAI) is one of the most important factors in dryland farming.MAI obtained by effective rainfall devided by evapotaranspiration according to: MAI = Pe/ETC Where Pe is the sum of effective rainfall and ETC is total crop (Almond) evapotranspiration in the growing season. Geographical distribution of MAI was obtained in same approach as PCO.Four distingushed areas can be recognized from MAI map(Fig(2-4)) as following: Areas in which MAI was more than 0.60 are known as the first class. Areas in which MAI was between 0.40 to 0.60 are known as the second class. Areas in which MAI was between 0.20 to 0.40 are known as the third class. Areas in which MAI was less than 0.20 are known as the fourth class.Fig. (2-3): Chilling occurence probability map


Fig. (2-5): Geographical distributon map of POGS

Table(4-1): The criteriaes determined suitability of area to Almond dryland farming

Class Parameter PCO POAP MAI SSPAR POGS

Very suitable <0.25 >0.80 >0.60 >0.60 >0.85

Suitable 0.25- 0.50 0.60 - 0.8 0.40 -0.60 0.40 - 0.60 0.65- 0.85

Weak 0.50- 0.75 0.40 –0.60 0.20- 0.40 0.20 - 0.40 0.45- 0.65

Not suitable >0.75 <0.40 <0.20 <0.20 <0.45


Geographical distribution map of POGS:

Geographical distribution of POGS(fig2-5) determined in same method as previous elements.Distinguished areas can be ranked as following: Areas in which POGS was between 0.65- 0.85 are known as the first class.

Areas in which POGS was between 0.65 to 0.85 are known as the second class. Areas in which POGS was between 0.45 to 0.65 are known as the third class. Areas in which POGS was less than 0.45 are known as the first class.

Geographical distribution map of SSPAR:

Althought the amount of annual precipitation is a main factor in dryland farming but its distribution during the year is very important too, in order to draw geographical distribution of SSPAR regression equation between elevation and SSPAR was determined(table( 2-2) and applied to DEM.Major classified zones are as following (Fig(2-6)): Areas in which SSPAR was more than 0.65 are known as the first class. Areas in which SSPAR was between 0.45 to 0.65 are known as the second class. Areas in which SSPAR was between 0.25 to 0.45 are known as the second class. Areas in which SSPAR was less than 0.25 are known as the second class. ResultsTo producing the Agroclimatic map of Azarbaijan province for Almond dryland farming the 5 above mentioned maps(PCO,POAP,MAI,SSPAR and POGS maps) were overlaid and crossed.resultant map can be classified to four distinguished zones as following (refer to table( 4-1) and fig(4-1)):

Fig. (4-1): Agroclimatic map of Azarbaijan for AlmondVery suitable zones(first class)There is a high correspondence between climatic conditions of area and the climatic requirement of Almond in this zone.The areaofth is zone is about 4852.9 km2 ndincludes: Hurand, Kalaleh and Ghareaghaj regions.


Suitable areas(second class)Although there is aweaker correspondence between climatic conditions and requirements of Almond in these areas compare to first zone it is possible to make condition better with supplemental irrigation and other Agricultural activities(such as planting tolerant specious to drought and chilling).The area this zone is about 2833 km2 and includes:Azarshahr,Maraghe,bostanabad,Sarab,Zenuz and Varzaghan.

Weak areas(third class)These areas have low potential and low yield for dryland farming of Almond ,The area of this zone is about 10052 km2 and includes malekan,sharafkhane,kalibar,Bonab,Marand and Tabriz regions.

Not suitable areas (fourth class)This zone consider as areas that are not suitable for Almond dryland farming because of a non-corresponding between climatic conditions and requirement of Almond.The most important factor that limits planting of Almond in these areas is chilling injury on flowers ,buds and young fruits .Area of this zone is about 2242.1 km2 and includes:Tasuj,Khomarloo,and Haris regions.

References• Bazgir,S.,1999,Agroclimatic zoning of Kordestan province for rainfed Wheat.,MSc. Thesis, University ofTehran . • Huang,S.B,1990,Agroclimatology of the major fruit production in china,Agric. For. Meteorol.,53:125-142• Khambete-NN,1992,Agroclimatic classification for assessment of crop potentioal of kharantaka.Mausam,43:1,91-98 • Lamba-Bs,1991,Agroclimatic zoning of Panjab and Haryana on the basis of MAI,Mausam,42:211-213 • Rumayor-R,Zegbe,A,1996,Use of GIS to describe suitable production aeaes for Pear,Acta-Horticulturea,471:175-182 • Yazdanpanah,2001,Agroclimatic zoning of Azarbaijan Province for rainfed Almond,MSc. thesis,Tehran university.


Remote sensing of environmental changes in Central Alborz, Iran

F. Bayat1, K. Solaimani1 and S.R. Mousavi1

1- Dept. of Watershed Management, University of Mazandaran ;PoBox 737 Sari-Iran; [email protected]

Abstract The region under investigation covers an area of approximately 1000 square kilometres, located to the east of the Lar Dam, between Tehran in the south and the city of Amol to the north. This study investigates an area of the upper Haraz catchment, located to the Central Alborz mountainous. Its an area of high to moderate relief consisting of sedimentary rocks of Quaternary, Tertiary Cretaceous and Jurasic formations. The study evaluates the utility of stereoscopic KFA-1000 image in erosion processes analysis. Field work techniques and image interpretation were Investigated. Field investigation is important because the presence and orientation of the surface features can have great significance, and correlations may exist between them and regions of weakness characterized by erosion, sedimen concentration and stream activity. Key words: Central Alborz, erosion, remote sensing and Iran.

1. IntroductionThe range of the Central Alborz can be subdivided into different stratigraphic-structural units, as recorded by Solaimani (2001). From north to south these units are; the Caspian Depression, the Northern Mesozoic Border Zone, Paleozoic Central Range, the Tertiary Central Zone, the Southern Paleozoic/Mesozoic Zone, the Southern Tertiary Zone and the Southern Frontal Depression. Quaternary, Post-orogenic deposits are fairly widespread in this area for example, in the Lar at Polour and the Haraz valley (Figure 1). This study is concerned with the emploing of KFA-1000 photographs for investigation of lava flowes and it,s major effects on the Upper Haraz basin. The region under investigation located to the north of Tehran between 51 50 to 52 E. and 35 55 to 36 N. The purpose of this study is an investigate of the morphologic factors and their role in erosion and sediment. Therefore geometric characteristics of the study area can be one of the major factor in morphological changes. Because of ground data problem and mountainous area in most catchment of Iran remote sensing technique can be useful to gain our porpuse in this study. Solaimani in 2001, Lilesand (1994), Townshend (1981), Barnard (1965) and Way (1982) have used digital and stereoscopic satellite data for the catchment morphological changes and sediment estimation wich show the ability of this technique for the montainous area.

Figure 1: The study area in relation to Central Alboz on ASTER Research method

Caspian Sea

Tehran

Amol

Sari

Study area


In this study remote sensing technique was an effective tool for geology and geomorphology survey, and it was particularly useful in inaccessible summit area where the volcano Damavand based investigation is difficult. The primary technique employed was stereoscopic analysis of the airphotos in different scales. For this aim two sample areas with different physical condition have been selected for study; the Lar Dam site from south and Lassem in the east side of summit. For identification and updating of morphological changes from remote sensing data their extent was established from; � stero KFA-1000 at 1:270000; � geology map of the Central Alborz at 1:100000; � aerial photographs at 1:50000 scales, and �� the morphological changes detected from KFA-1000 and aerial photos were then

compared with the field investigation.

ResultsThe Alborz mountain ranges are the northern branch of the Alpian-Himalayan orogen in Iran. The Zagros mountain ranges form it,s southern branch. The centeral part of the Alborz changes it,s trend from north-west southeast to east-west and then to northeast-southwest. Alam kuh, which is located in the western part of Centeral Alborz, is the hightest, 4840 m, non-volcanic peak of the Alborz ranges. The highest volcanic peak of the range is Damavand summit in the Lar Dam basin. The age of the volcanon is 38500 years. It is lacated almost at the point where the trend of folds changes from west-northwest to east-notheast. From the Combrian to the Middle Triassic, continental deposits covered the whole Iranian region including the Alborz. The epicontinental marin conditions were ended by early Kimmerian movements. Marin conditions prevailing in the Middle Triassic resulted in thick-bedded carbonates. In Centeral Alborz longituding and differential uplift set in. A depression formed to the north of Central Alborz. During the Upper Triassic and Jurassic rapid changes of the swell and trough structures occured. Continental sedimentation including coal-bearing horizons were deposited in the northern trough. At the end of the Jurassic, in response to late Kimmerian orogeny, there followed uplift and southward thrusting of Central Alborz. During the Cretaceous a southern trough formed south of Central Alborz, while the northern trough survived. A prototype of the Alborz began to form in the Laramian orogeny as the northern trough reversed and formed and early Alborz range in the Tertiary, (Stoklin 1964). Figure 2 shows the relationship of the area and altitude as extracted from topography map which is classified in figures 3 and 4 as slope classes and long profile of the catchment. Some morphological characteristics of the study area as the maior geometric which are influncing the morphological changes classified in table 1. The volcanic cone of Damavand, rising to an altitude of 5671 m. above the Persian Gulf level, not only dominates the Lar down stream but also has exerted a major influence on the development of drainage patterns and sedimentation in the basin. It was the early eruptions of the volcano, some 60,000 years B.p., that resulted in the bloking of the original channel of the Lar and first led to the creation of the natural lake. Although no volcanic eroptions from Damavand have occured during geologic times, the volcano is not completly extinct. Sulphur emanations are still found within the creater on top of the cone and imports to the summit it,s characteristic yellow colour when free of snow. At Ab-e- Garm village on the eastern slopes of the Damavand, hot springs still emerge and a lower elevation on the same slopes, at Ab-e-Ask, warm water spring are found. A further reminder that the volcano is only dormant is the comment of De Morgan (1905) who stated that parts of the cone were warm enough to melt snow during his visit to the area in mid-winter 1889. The recent nature of Damavand on the KFA-1000 can clearly be interpreted from the presence of many primary volcanic structures preserved around the cone and effectively unmodified by sub-aerial erosion. Individual lava


flows can be identified on the KFA-1000 in plan, particularly by the use of stereographic vieow , as can remnants of numerous lava tunnels.

Figure 2: Hypsometric curve of the study area.

The upper surfaces of the lava flows in down stream after recent Dam undulate extinsively. This can be clearly be seen on the colour steriopair KFA-1000 of the lavas along the left bank of the River Lar and has also been recognised beneath the sedimentary cover. Adjacent to the limestone gorge through which the Lar river flow, the undulation in the lava flows appear to reflect similar undulation in the lava flows appear to reflect similar undulation in the underlying bedrock surface. However, elsewhere the undulations are apparently unrelated to the form of the bedrock surface and to a large extend simply reflect the irregular way in which succeeding tongues of lava were deposited on top of each other. The undulation in the upper surface of the lava are likely to be largely primary depositional features, although at least some modification of the surface is likely to have accured as a result of movements in the lacustrine sediments, where these underlie the lava. The base of the trachyandesite lavas to the north of Lar gorge has acquired a dip to the north of between 9 and 10 degrees. The undulations in the bured lave surface are in part, at least, reflected by the precent topography, despite the cover of later sediments. The horse-shoe shaped depression to the north-east of Lar gorge is underline by a similar depression in the lava surface. A C14 dating of plant material from the terraces of the lower Lar valley gave a minimum age of 38500 years, (Allenbach p.40, 1966) indicating a probable young Pleistocene age (early wurm) for the Lar terraces. The natural lake became completely infilled with sediment and subsequently water spilling over the lava dam breached it and the present Lar valley was created. The base lavel of the valley was lowered in a series of sporadic episodes, each temporary base level being marked by the creation a river terrace level in the upper Lar valley. This stage in the development of the area is continuing at present. A similar situation has occured at the junction of Verarud and Dalichai and at the end of the Lassem valley, where the sediments, (mixed with ashes) are very coars (Solaimani, 1995). The alluvium of the Lassem valley is widespread and extends to a fairly high level.

010

203040

5060

2550

2650

2750

2850

2950

3050

3150

3250

3350

3450

3550

3650

3750

H/m

A/K

m


Figure 3: Altimetric characterstic of Sefid-Ab basin.

Figure 4: Slope classes of the study area.Table 1: Geometric charactrestics of the Sefid-Ab basin.

Area (km2) 54 Perimetre (km) 40

Tc 1/88 Drainage density 1/189

50% h/m 3250 Kc 1/52

Miller 0/03 Form 0/319

Characteristics of Russian satellite photography

0

4

8

12

16

2550

2650

2750

2850

2950

3050

3150

3250

3350

3450

3550

3650

3750

H/m

A/K

m2

0

5

10

15

20

> 30 20-30 10--20 0-10

A/Km

S%


A.photos and space photography from Russian satellite data was used for this project. The KFA-1000 camera system acquires colour spectrozonal photography surveyd at an average height of 270 kilometers with an average acale of 1:270,000 (Almer, 1990). The frame size is 30×30 cm. and cover an area of 6400 square kilometrs with 60% longitudinal overlap of photographs (Jacobson, 1993). The ground resolution of this photography is stated 5 meters and has a spectral range of 570-680nm and 680-810nm. The camera number, focal length and exposure number appear in each frame as well (Tale 2, Sollner R. 1993).

Table2: Technical data of high resolution satellite systems

Type Altitude/km Rsolution/m Spectral reso. Dim. of sc./km SPOT 830 10 Pan. 60×60MOMS 300-600 4-5 Pan. 75×75KFA-1000 220-350 5-10 CIR 66×66, 105×105

4. Conclusion Physical charactristics of the study area is an important factor in morphological changes and dynamic features. Remote sensing data is an important application for detection changing in the study area as mountainous catchment. Ongoing research clearly show the termendous potential of the aerial photographs and KFA-1000 images in black and white and colour to updating geomorphology map than the other methods. Firstly, this paper outlines the potential use and benefit of combining existing technology in a way that has not been done before in order to creat geomorphology map. Secondly is a comparison report of high resolution KFA-1000 imagery of the Upper Haraz basin lava flows with airial photographs. The stereo-pairs KFA-1000 images used operationally in the completion of study area geomorphologic map. The stereoscopic interpretation of black and white and colour KFA-1000 images revealed many previously unmapped features and led to the discovery of several formerly undetected active land surface changes possible responsible for much of the sediment supply in the Upper Haraz. These lava flows are the results of previous orogenies and play an important role in Damavandb eruption in the study area. It can be said that the KFA-1000 which have been available for certain time provide a cost-saving alternative to the digital data of the LANDSAT and SPOT system in the field of geology sciences applications.

References Allenbach, P. (1966). Geology and petrology of Mt. Damavand and its environment. G.S.I. Almer A. et al. (1990), Digital mapping with high resolution SOJUZ KFA-1000, images.Monograph: in proceedings of the Tenth Earsel Symposium, p.404. Barnard, P.D. (1965). Flora of the Shemshak Formation. Ital. Paleont. Strat., 71(4), Milano, pp. 1123-1168. Jacobson K. (1993). Comparative analysis of the potential of satellite images for mapping, ISPRS Symposiom Commission, Hanover. Lillesand, T.M. and Kiefer, R.W. (1994). Remote sensing and image interpretation. John Wiley and Son. New York, 1994. Morgan, J.D. (1905). Note sur La geologie de La Perse et sure les travaux paleontologiques de M.H. Douville sur cette region. C.R.S.G.F. 5 (4e): 170-189. Short N.M. (1986). Volcanic landform. in: Short Nm, Blair RWJr (eds) Geomorphology from space. NASA sp-486 US Govt printing office, Washington, DC, pp 185-254. Solaimani K. (1995). Application of satellite photographs in a morphotectonic study of the Lar Dam basin Iran. Presented in 17th BRG meeting (PG) at Leeds University.


Flood Occurrence Hazard Forecasting Based on GIS (Case Study: Kasillian Watershed, Iran)

K. Solaimani1, H. Mohammadi Domirchi2 and M. Z. Ahmadi3

1- Assistant Professor, Faculty of Natural Resources, University of Mazandaran, Sari, Iran, [email protected] 2-Former Master of Sciences student in Watershed Management Department, University of Mazandaran, Sari,

Iran 3-Professor, College of Agriculture, University of Mazandaran, Sari, Iran

ABSTRACT The phenomenon of flood causes substantial human and financial damages in different parts of the world. In Iran, the losses are huge, as the rainfall is unsuitable temporally and spatially. Most of Iranian watersheds either do not have hydrometry stations or the stations are not complete. Installation of flood forecasting systems in these watersheds may reduce the flood-induced damages. Using Geographical Information system (GIS) with its high ability to up to date the watershed data and estimation of SCS model parameters are considered for real time flood forecasting recently (1, 2). The main aim of this paper is to investigate the possibility of the linkage between GIS with a comprehensive hydrologic model, especially HMS. The use of GIS could produce a suitable agreement between observed results with the calculated results of the hydrological model. The obtained results from rainfall-runoff process simulations of the model in this research showed that submergibility of the main watershed, Kasillian, does not depend on the outlet discharge rate of each one of its watershed independently, but it is related to how those two outlet hydrographs from main river watershed are combined. The model is capable of showing the flood characteristics temporally and spatially in each cross section of the channel network.

Key words: Flood, Geographical Information System (GIS), Kasillian Watershed

INTRODUCTION With increase in constructions along rivers and concentration of population around submergible areas, the flood-induced damages are in increasing trend. The complete flood protection with installation of great flood control structures like flood dams are not justified due to its high cost. It is not environmentally, socially and economically an optimum idea either. For this reason, the flood forecasting system can have a considerable role in flood management through logical utilization of weir gates and dam reservoirs. In this direction, different systems have been innovated in different countries of the world, but lack of equipment and tools and also high cost of installation are the limiting factors in our country (3). Recently the flood return period has decreased in northern Iran, so a suitable method of decreasing the flood-damages is required, by flood forecasting. The study area is an ideal location for this purpose. The overall aim of this study is to find out the efficiency of GIS to create the main inputs to simulate a comprehensive hydrological model. So that the main requirement of a hydrological model is the description of flow channel characteristics and land surface as input to watershed model. The flood forecasting is in fact the development and perfection of the applied engineering hydrology and its aim is to obtain real time data of rainfall and river flow by short wave, radio and satellite network and using them in rainfall-runoff models to forecast floods in consecutive time and space intervals (4). The quality of flood forecasting systems depends mainly on the quality and the amount of basic collected data about hydrology and the hydrological yield of the corresponding watershed (5).


The Kasillian watershed is located from 530 18/ to 520 60/ 30// east longitudes and 360 7/ to 350 58/ 30// north latitude, in northern Iran and north of Alborz Mountain. The basin is limited in north to 1100m and 1790 m Haraz altitudes, in south to 2700 m and 3349 m altitudes, in west to 2043m altitude and in east to 1613 m altitude. The total area of the Kasillian watershed id 67.8 Km 2 , with mean main channel slope of 13%. This watershed has mountainous regions in the south and forest areas in the north. It has the vegetation kinds of forest, rangelands and agricultural lands. From geological points of view it belongs to the second, third and fourth era and sediment of the region is from Jurassic, Pelican and quartner era.The mean annual rainfall of the region is 791 mm by Isohyet method and 816.2 mm by Theissen method. The precipitation is 55% rainfall and 45% snow. The main part of the basin has very humid climate (with Orimlek and Sangedeh stations) and the northern part of the basin with lower elevations has humid climate by De marten method. Based on Emberger method, the middle and northern part has cold humid and southern part of the basin with higher elevations has mountain climates. This study which combines GIS with hydrologic model is based on some of the previous research works. Smith (1995) created a hydrological information development system, by using GIS and hydrological watershed parameters such as design storm, soil hydrology, time of concentration, runoff coefficient, etc (6). He used the system for rainfall-runoff model management of north Sulphurs River and confirmed that the accuracy of hydrological models based on data obtained from GIS was very high in flood management and gave accurate results. OLivera and Maidment (1992) used CRWR-PREPRO program, obtained primary stages data of GIS including the elevations, separated the reach network and sub-basins, recognized the hydrological elements, created continuity among them and finally input them in the hydrological model (7). The results obtained from using CRWR-PREPRO showed that determination of spatial parameters are easy by hydrological model systems and the results can be extendable. Hellweger and Maidment (1997) combined GIS with hydrological model and through CRWR-PREPRO found the required parameters in the basin model, like the boundary of the basin and channels as ASCII file from elevation data and put it through a conversion table to hydrological model (8).

MATERIALS AND METHODS To recognize the Kasillian watershed, the vegetation, geology, soil and other information are prepared in the form of maps. Then through GIS and evaluation of the effect of the application of pre-processor of GIS for hydrological system, the channel vegetation of the basin is created to obtain a regional model of channel and watershed characteristics. Information from early model of elevations is prepared from GIS processor and is transferred into hydrological model. To create a rainfall model in GIS medium, it has been tried to practically evaluate it in the hydrological model. To compare the results of hydrological model with observed flow data for calibration of the model, the evaluation of the correctness of the input to the model and the modeling system have been performed. The curve numbers of the SCS (CN) and water velocity in different reaches of channel network in GIS medium were obtained for three cases of (I, II, and III), runoff production of low and medium and an almost saturated soil. Krepcho (1997) calculated CN through GIS in northwest watersheds of Turkey (9). The same method was used for Kasillian basin. i.e. the hydrological maps of land use which were prepared before and their accuracy was confirmed by field observations, have been converted to elevation files and were put in the GIS medium and then a new layering was obtained from them. To determine the water velocity in the selected reaches, a cross


section was surveyed between the highest and lowest main channels in each hydrological unit. These sections should be regular and constant in shape. After estimation of roughness coefficient by Chow (1988) method, the water velocity was calculated for the main river and each tributary using Manning equation. The lag time of the sub-basins was calculated from US-SCS formula described by Chow et al. (10):

Tc =L 0.8 W[(1000/CN)]-9]0.7 31.68S 0.5 (1)Where Tc is the lag time in minutes or the time between the center of effective rainfall to peak time of hydrograph, Lw is the length of the longest channel in sub-basin in ft, CN is the average curve number and S is the slope of the longest channel (%). Reach parameters like length, routing method, K and X of Muskingum in sub-basins and water velocity are given to the model as input.

K= L/V (2) Where K is storage coefficient, which is part of output discharge rate, L is the length of the reach and V is the average water velocity. X is between 0-5 and is calculated from following equation:

X=S 1/2 NP 2/3 (3)Where S is the river slope in m/m N is the Manning roughness coefficient and P is the wetted perimeter. After creation of all information layers in Avcview and Avcinfo, all errors are investigated so that they can be executed in GIS medium. To prepare hydrological model of rainfall, some floods of Valikben station, which had the same –duration and single rainfall have been selected. Hyetographs of daily rainfall of Sagdeh and Valikben stations were fitted statistically and the rainfall-runoff events of the watershed have been chosen. In this research, the calibration of the model was performed automatically. For this purpose, after putting all information into the model, the simulation was started for Kasillian basin, and the results were optimized after comparisons with the observed hydrograph in the outlet. The creditability for 2/3 of the events in each of the antecedent moisture (I,II) was used, so that the model could test for creditability in each antecedent moisture content separately.

RESULTS AND DISCUSSION After inputting the data into GIS medium and obtaining the CN information, water velocity in the reaches, calibration and creditability of the model and the results from simulation of the rainfall-runoff events, the submergibility of the sub-basins were considered. There was one flood event for the medium moisture condition of (II) and 3 flood events in dry condition of (I). The rainfalls and flow rates used in simulation of flood discharge in Kasillian watershed are shown in Tables 1 and 2.

Table 1. The rainfall data used for simulation of flood discharge in Kasillian watershed.

Rai

nfal

l 5

days

be

fore

/mm

Rec

orde

d R

ainf

all/m

m

Star

ting

time/

hr

End

ing

time/

hr

Star

ting

date

End

ing

date

10 18 13 19.30 7.10.92 7.10.92 39.5 20.75 14 16.15 16.6.95 16.6.95 19 28.91 14.45 15.45 13.10.95 13.10.95 26 17.99 10 23 7.10.96 7.10.96


Table 2. The flow rates used for simulation of flood discharge in Kasillian watershed V

olum

e of

w

ater

reco

rded

in

Val

ikbe

n/m

3

Vol

ume

of

floo

dre

cord

ed in

V

alik

ben/

m3

Star

ting

time/

hr

End

ing

time/

hr

Star

ting

date

End

ing

date

34.02 67.8 17 24 7.10.92 9.10.92 26.93 51.8 11 21 16.6.95 17.6.95 70.22 390.71 1 23 13.10.95 14.10.95 31.27 103.34 15 23 7.10.96 9.10.96

Figures 1 & 2 show the hydrological elements after execution of GIS model in Kasillian watershed. This model was inputted in hydrological model as a watershed model with ASCII format.

The results of creditability tests of the model in dry (I) and medium moisture (II) conditions: The amounts obtained in (I) condition for events 2, 3 and 4 are shown in Figures 3, 4 and 5 as simulated and observed hydrographs in Valikben hydrometry station.

CN values in sub – basins After calibration of model for moisture conditions (I) and (II) and testing their creditability, the CN and primary retention (Ia) are summarized in Tables 3 & 4.

Table 3. Values of CN and Ia after calibration in condition (I)

S1 S3 S4 S7 S8 S10

S11

S12

S13

Hyd

rolo

gic

al

Cha

ract

eris

tics

AM

C

2.7575 74 74 80.4 77.8 74 77.3 80 CN

76.16 16.61 17.8 17.8 12.3 14.5 17.8 14.9 12.7 0.2S I

Table 4. Values of CN and Ia after calibration in condition (II). S8 S12 S13 Hydrological

Characteristics AMC

87 90.2 86 CN 7.59 5.519 8.27 0.2S II

In Figures 6,7 and 8 the forecasted amounts of flood in different times are shown. The upper part is for upstream hydrograph of watershed and the lower part is related to Valikben hydrometry station.

To determine the flood water stage, the discharge rating equation of Valikben hydrometry station was used:

Y = 10.929X + 12.178 (4)


R 2 = 0.9481 CONCLUSIONS As most Iranian watersheds are submergible and the water resources plans are in progress, use of flood forecasting models and techniques like GIS became more important than before. That is because the method is a combination of management and structures issues which makes the system more efficient and decreases the flood –induced damages. This research which is to investigate the efficiency of using GIS as a preprocessor for a comprehensive hydrological models and to improve the flood forecasting systems in Kasillian watershed, is a new gate in comparison to other research works on this subject and in Iran and other neighboring countries. The results of this research can be summarized as follows:

- HECPRERO is an effective tool for simulation and creation of a geographical information bank from watershed levels and transferring it into an acceptable format by hydrological model system. - The hydrological model system with two inputting files enables the users not only to forecast the effects of future storms, but also informs them of the past events, so that the losses will decrease effectively. - To generalize the model, more studies are required, because the regional results should be compared with results of other submergible locations, the area is limited and the duration of data collection is short. For complete calibration, the discharge for the total flood duration should be determined and compared with the measured flow rates. The beginning of the work in times with dry or normal moisture makes the situation that the observed and simulated hydrographs start in the same level, so that the future calculations will not have any skews. In addition to flow rates, the accuracy of the evaluated parameters to obtain better results will also increase. - At present time there are methods for determination of CN of SCS in GIS and ways to evaluate Muskingum coefficients. These parameters are the main parameters used in this research and it has been recognized that use of GIS determines them accurately. This accuracy makes the calibration methods faster. - In this study evaluation of the efficiency and use of GIS in hydrological model system was successful. GIS as a suitable technique prepares the great and accurate files of the watershed and is favorable for locations with ground data available. For example, the rainfall distribution and conversion to readable format by hydrological model system which combines these components in an applied hydrological models. - The results of simulation of rainfall-runoff process in this investigation showed that the submergibility of the main watershed does not depend to flow rate of each of its watershed independently, but it is related to combination of the outlet hydrographs from the main river basin. The model can determine flood characteristics spatially and temporally in each point of the channel network. - Use of hydrological model system based on GIS is suitable for simulation of the watershed and conversion of rainfall to runoff and prediction of peak flow and flood volume. The obtained graphs and data are dependable and the model can be used for dam management if necessary. - The results showed that CN is more effective than any other factors in submergibility determination and is affected by soil and vegetation. So, improvement of vegetation and soil physical conditions is to be considered in watershed management. Besides, change in CN is easier than changes in other factors.


REFERENCES [1] Williams, P.B. (1994). Flood control Vs. flood management. Civil Eng. Pp. 51-54. [2] Xiaoliu, Y. and C. Michel. (2000). Flood forecasting with a watershed model: a new model of paprametre updating. Hydrological Sci. Journal. [3] Olivera, F.S. and D. Maidment (1998). HEC-Prepro v.2: An Arcview pre-processo for Hec,s Hydrological Modeling System. Centre for research in water resources Austin, Texas. [4] Cabal, A. and M. Erlich (1992). Design development and implementation of hydrological data base management system for the purpose of real-time flood forecasting. 4th Int. Conf. on Hydraulic Eng. Software Hydrosoft/92. Billerica, USA, pp. 395-406. [5] Ammentorp, H.C., Havno, K., Refsgaard, J.C. (1992). Real time flood forecasting. Int. Symp. On Dams and extreme floods, Granada, Spain, pp. 103-111. [6] Smith, P. (1995). Hydrologic Data Development System, Master Thesis, Department of Civil Engineering, University of Texas at Austin. [7] Olivera, F and Maidment, D. (1999). Developing a Hydrologic Model of the Guadalupe Basin / Center for Research in Water Resources, Austin, Texas. [8] Hellweger, F. and D.R.Maidment (1997). Definition and Connection of Hydrologic Elements Using Geographic Data, accepted for publication in the ASCE - Journal of Hydrologic Engineering. [9] Kupcho, K. (1997). Obtaining SCS Synthetic Unit Hydrograph By Gis Techniques. [10] Chow, V.T.,D.R Maidment. and L.W Mays (1988). Applied Hydrology. / New York.

Fig.1. End of GIS model execution and formation of hydrological model system files.


Fig.2. The elements of watershed model in GIS medium with ASCII format.


Fig.3. The simulated and observed hydrographs in Valikben hydrometry station for the event 2 in (II) condition(after optimization).

Fig.4. The simulated and observed hydrographs in Valikben hydrometry station for the event 3 in (I) condition (after optimization).


Fig.5. The simulated and observed hydrographs in Valikben hydrometry station for the event 4 in (I) condition (after optimization).

Fig. 6. Simulated flood hydrograph of 7.10.92 event in upper and lower (Valikben) stations.





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