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NMENT AND CULTURE IE MEDITERRANEAN REGION

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p Efe, Georges Cravins, Munir Ozturk and Ibrahim Atalay

CHAPTER TWELVE

SOIL EROSION IDENTIFICATION USING GIS TECHNIQUES IN GOLBA§I (ADIYAMAN)

LAKES BASIN

MURAT KARABULUT, MEHMET GURBUZ AND MUHTEREM KU?UKONDER

Introduction

Soil erosion is a naturally occurring process on all types of land. The agents of soil erosion are water and wind, each contributing to a significant amount of soil loss each year worldwide (Garg and Harrison, 1992). Erosion is the movement of soil particles by the action of water, wind, and gravity. It is a process that is always occurring but it can happen faster if the land is misused. Erosion is a serious problem in many parts of the world as well as in Turkey (Yuksel el al, 2007). On the whole, soil firosJO-o is 3. very .serious /irnWera in arid and .semiarid jegions and the process is greatly accelerated by human activity, specifically when vegetation is destroyed or plant residues are buried through tillage. Farming, deforestation and grazing livestock are all examples of human activity that degrade the natural balance and accelerate the rate of soil loss. Considering the slow formation of soil and the importance of agricultural production to human survival, the severity of this problem can not be ignored.

When natural vegetation is removed from land and soil is disturbed, soil erosion can accelerate and lead to environmental problems such as stream degradation, wetlands damage, lost reservoir capacity, sediment deposition on private and public property, and increased flooding and water pollution. These effects adversely impact both aquatic life and the quality of life. Soil erosion related suspended sediment concentrations in surface water can shorten the useful life of many reservoirs and dams.

174 Chapter Twelve

Suspended sediment concentration also has an effect on the biologic life in those valuable areas. Sediments also have a significant impact on the quality of drinking, recreational and industrial water, because it can serve as a carrier and storage agent of many kinds of pollutants such as phosphorus, nitrogen and other kinds of agricultural chemicals (Brown, 1984; Bhargava and Mariam, 1990; Han et al., 1994, Karabulut, 2004).

An examination and monitoring of soil erosion is very important for sustainable land management. Many studies have been conducted to determine erosion prone areas by traditional methods, which are time consuming, expensive and labor intensive. Because of the dynamic character of erosion and the requirement of large amount of spatial data, G!S techniques have become remarkably effective at the core of studying erosion. The utility of GIS in monitoring, mapping and analyzing erosion problems has increased, due to the fact that this method allows an examination of large amounts of data (Suri and Hofierka, 1994).

In order to assess land conditions and predict the potential impacts of erosion, comprehensive techniques and models need to be created. Creating a model is usually difficult. However, the use of GIS can simplify this task by integrating different types and levels of data, information, and human knowledge. Modeling is used to understand geographic processes and evaluate strategies for efficient operation. Minimum input data for modeling erosion includes hydrological data (rivers and its properties, drainage density), land use (agricultural, natural vegetation, wetlands, residential or other), topographic (slope steepness and length) and soil data (soil types, erodibility) and climatic (rainfall and storms) data. All these data can be used to model erosion problems in such areas by using GIS techniques (Garg and Harrison, 1992; Qullu and Dine,, 1994; Suri and Hofierka, 1994; Desmet and Govers, 1995; Lanza and Siccardi, 1995). Several models have been developed to predict soil erosion such as USLE, RUSLE, WEPP and AGNPS (Mellerowicz et al., 1994; Toy and Osterkamp, 1995; Savabi et al., 2005; Yiiksel et al., 2007).

One of the negative consequences of the steep terrain of Turkey is soil erosion. The causes of soil erosion in Turkey are a function of the steep, mountainous topography and the unique cultural forces that have shaped the country throughout its history. According to the General Directorate of Reforestation and Erosion Control (GDREC, 2007), 86% of the Turkey's land faces significant erosion problems. Indeed, only 14 % of the land represents zero or weak erosion potential. Areas most susceptible to erosion on agricultural land include much of Turkey, such as the Interior, East and Southeastern Anatolia.

Soil Rrosion Identification Using GIS Techniques in Golbasi 175

The aim of this study is to investigate erosion problems in the Golbasj Lake (Adiyaman) Basin by using a Universal Soil Loss Equation (USLE) model with the aid of GIS.

Study Area

The area selected for this study is located in a small part of Aksu River watershed, including the Gdlbasi lakes depressions and their surrounding areas. This area is approximately 100 kilometers away from the city of Kahramanmaras and roughly 80 kilometers from the city of Adiyaman (Fig. 12-[). The elevation ranges from 830 m to 1480 meters. The mean annual precipitation is around 734 millimeters and average temperature is 14° C. Vegetation types consist of oak, juniper and terebinth. The dominant agricultural products are wheat, barley and Antep pistachio (Pistacia vera). The general soil types consist of brownish and reddish brownish soils. The land is partially cultivated, while the rest is covered with scattered vegetation and wetland.

Materials and Methods

The most widely used soil erosion mapping tool is a Universal Soil Loss Equation (USLE) that is an empirical equation derived from various investigations (Ndunda, 2005; Akyuvek and Okalp, 2006). In general, USLE model estimates soil erosion by rain drop impact and surface runoff. In this study, USLE was used in an ArcGIS 9.1 environment to calculate the soil erosion index for Gdlbasi (Adiyaman), Turkey. A model was developed to execute the USLE formula using data from the study area. The USLE is defined as follows (Dogan and Gucer, 1976, Wijesekera and Samarakoon, 2001, Ndunda, 2005, Irvem et al., 2007):

A^RLSKCP Where: A - average annual soil loss in tons per acre, R. = rainfall and runoff erosivity factor, L = slope length factor, S = slope steepness factor. K. = soil erodibility factor, C = cover and management factor, P = supporting and conservation practices factor.

176 Chapter Twelve

Fig. 12-1: Location of the study area.

Despite some limitation of this equation, it is still one of the best techniques which can be used to estimate soil losses as average annual erosion per unit area as a function of the major factors of sheet and reel erosion. In this study, each factor was used as a thematic layer in G1S model. Rainfall data, digital topographic map, soil and land use maps at 1:25,000 were used. GIS files were created for each factor and integrated into ArcGIS 9.1 Spatial Model (Fig. 12-2). Determinations of the various erosion factors are explained in following sections.

Soil Erosion [dentification Using GIS Techniques in Golba i 177

Soil Erodibility Factor (K). K factor defines the ability of soils to resist detachment and transport, based on the physical characteristics of each soil type. The K factor map was generated from a soil map at a scale 1:25.000 provided by the Ministry of Agriculture (1981). Soil slope 9% and slope length 22.1 meter were the main criteria for K factor determination (Table 12-1 and 12-2, Fig, 12-3) (Dogan and Gucer, 1976).

Table 12-1. K values and their erosivity levels (Dogan and GOcer, 1976) K values Erosivity levels 0-0,05 Very Low

0,05-0,10 Low 0,10-0,20 Moderate 0,20-0,40 High 0,40-0,60 Very High

Slope length and slope gradient factor (LS). Naturally, soil erosion tends to increase with the steeper slope of a field due to running greater amount of soil off by water erosion. Similarly, soil erosion by water also increases when the slope length increases due to the greater accumulation of runoff. The Digital Elevation Model (DEM) for this research was developed from vector contour tines using a topographic map scale 1/25.000 and the LS factor layer is then generated from DEM using the following formula (Figure 12-4) (Hudson, 2005).

LS=1.6 *POW(([flowacc]*resolution)/22.1. Q.6)*POW(Sin([slope]*Q.Q1745)/Q.Q9>L3)

Table 12-2: Soil groups with their K values used in this study (irvem and Tuliictt, 2004).

Soil Groups K Values

Brown soil 0.20 Colluvial 0.18 Non calcic brown forest 0,15 Brown forest 0.20 Red brown Mediterranean 0.15 I lydromorphic alluvial 0.15 Red brown soil 0.20 Residential area/ Riverbed 0.001

178 Chapter Twelve

Cover and management factor (C). Soil loss from the land will increase if the land has no vegetative cover or very little vegetative cover and/or crop residues because vegetation intercepts rainfall, reducing their kinetic energy before hitting the ground. In this study the C factor was generated from digital soil maps at a scale of 1:25.000 provided from the Ministry of Agriculture and TM images. C factor classification procedures were completed based on previous works in Turkey (Table 12-3, Fig. 125) (Dogan and Giicer, 1976).

DEM1 DEM

Flow c i«^= Direc. S I ° P e

+ + r. n. Slope FlowDir D e £

T T

Flow Map Accym. Algebra Flow SIpNot Ace. Deg.

M a p ' Sin* Algeb.3 ^ I n

Flowac Slope Div. ^ Sin

Pow2 MaP / Algeb.2/

Const. \ *Slope 0,4 \ SinDiv/

\ / L

DEM: Digital Elevatior BTG : Soil Groups SAK: Landuse/lanc POW: Power SIN: Sine DIV: Division DEG: Degree

R Factor B T G S A K Factor

To Rast. To R

\

R K

Mult Const. 1,3

•* Pow

S

Multiply

A / L S R

i Model

\ cover

ast. To R 2 i

C

K

Multiply 3

ast. To Rast. 1 1

: p

/ ipiy

LSRK PC

Multiply 5 *

RISK

Fig, 12-2: Created spatial analysis model for erosion risk assessment of Golbasi.

Soil Erosion Identification Using GIS Techniques in Golbasi 179

Fig. 12-3: K factor map of the study area.

Residential Aiai Roads Stream

I 1 t j k . .

Fig. 12-4: LS factor map of the study area.

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+

Resifla filial Area Roads

— Straam

CZ1 l * ~

Fig. 12-5: C factor map ofthe study area.

Table 12-3: Land use and associate^ C values (Jrvem and Tiiliicu, 2QQ4). Landuse C values

Dry farming (fallow) 0.38 Dry farming (non-fallow) 0.07 Irrigated farming 0.28 Forest / Orchard 0.05 Scrub / Vineyard / Grassland 0.09 Residential area / River bed 1.00

Rainfall and runoff erosivity factor (R). Both rainfall and runoff factors must be considered in the determination of water erosion risk areas. The impact of raindrops on the soil surface can break down soil aggregates and disperse the aggregate material- Thus, R factor represents the erosive power of rain drops and run-off water. In this, the study R factor was determined by using works <jf Dogati and Denli (1999) (Fig. 12-6).

Soil fc'rosion Idemification Using GIS Techniques in Golbasi 181

Support practice Factor (P). Because no information in regard to the P-factor is available for this area, a value of 1 was assigned to the model presented in this study (Fig. 12-6).

Results and Discussions

In this study, soil erosion risk was modeled in the Golbasi Lake Basin by integrating the USLE with GIS. The soil erosion map resulting from the spatial overlay of USLE factors in the study area is presented in Fig. 12-7. Table 12-4 presents corresponding quantitative soil loss, in addition to spatial information. Figure 12-7 shows erosion risk in the study area, which is expressed in five classes, ranging from very low risk to very high risk.

The result of this study gives an erosion range of 0 to 25 tons per hectare per year (tons/ha/yr). Variation in the results is due apparently to variations in the values of each of the factors used. It is found that very severe erosion risk areas cover only around 1.38 % in the area. The study shows that only a 386.5 hectare area is under high and very high erosion risk. An area of 4.83% (1344.90 hectares) has a moderate risk of erosion. These areas require adaptation of suitable conservation measures to protect the present valuable soil. The rest of the land (11525.80 hectares 41.41%), represents a weak or very weak erosion risk (14574.50 hectares, or 52.36% of the total land), due to valuable vegetation cover and topographic conditions. Large percentages of these areas are located in regions with moderate to high erosion potential, where the inappropriate cultivation practices or crop rotation result in accelerated soil erosion. The higher erosion values are concentrated in steeper parts of slopes wilh accelerated movement of vvater, indicating medium or high erosion ri&k. The lower erosion values are located in less steep parts of the land, with slowed movement of water, indicating the areas with low erosion risi. Relatively flat areas, which are occupied mostly by wetland, are affected only by non significant erosion. A soil loss from these parts is not critical. Most probably, these low lying slopeless areas cause a slow movement of water, indicating the areas of medium to high sediment deposition. In this study, the above-mentioned areas are located in the bottom of the Golbasi depression, a depression which was shaped by tectonic activities.

Our erosion prediction model indicated that erosion response is much more sensitive to slope steepness and cover management factors than to other environmental variables. Therefore, in the area which was studied, priority must be given to protection of natural vegetation on steep slopes to reduce the erostvity effects on soil loss.

182 Chapter Twelve

Fig. 12-6. R and P factor maps of the study area

+

Leg»nd

Erosion risk areas — " ' Very low

Mode rats

Very high ResicJantlaJ Area

L Z D L*k.t

Fig. 12-7: The soil erosion map resulting from the spatial overlay of USLE factors in the study area.

Soil Erosion Identification Using GIS Techniques in Golbaji 183

Table 12-4. Area and percentage erosion hazard. Erosion Risk Area (ha) Percent (%)

Very Low 14574.50 52.36 Low 11525.80 41.42 Moderate 1344.90 4.83 High 312.30 1.12 Very High 7JL20 0.26 Total 27831.70 100.00

Conclusions

The USLE model which is used in this study is a useful technique for predicting soil erosion. This study showed that the combination of USLE and GIS presents a promising way for identification, mapping and interpretation of the spatial pattern of eroded soil. The results indicate that the use of GIS techniques can improve the mapping accuracy of USLE when applied to affected areas. Preliminary field investigations confirm those results. On the other hand, infonnation gathered from field works can be used for accurately assessing mode! data and can provide reliable parameters for the USLE model.

Erosion risk maps provided here can be used in several ways. Firstly, these maps could help to identify natural and accelerated erosion prone areas. Variation of erosion density on the map can be compared with topographic conditions, ecologic status and cultural information to determine the nature of the problem. Secondly, the maps not only provide information on erosion risk areas, but also represent sediment deposition areas. Overall, these maps can produce valuable information about the condition of land to support governmental decision making.

In conclusion, it must be kept in mind that modeling can sometimes produce inappropriate results due to erosion factor values. However this study proved that it is possible to analyze multi-layers of data spatially and quantitatively within a watershed using GIS.

References

Akyurek, Z.and K. Okalp. 2006. A fuzzy-based tool for spatial reasoning: A Case study on soilerosion hazard prediction, 7th International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences. M. Caetano and M. Painho (eds.), 5-7 July, Portugal.

184 Chapter Twelve

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Brown, L.R. 1984. The Global Loss of TopsoiJ", Journal of Soil and Water Conservation 39:162-165.

Cullu, M.A. and U. Dine. 1994, Cografi Bilgi Sistemleri Yardimiyla Simdiki ve Potansiyel Toprak Erozyon AlanJarmin Belirlenmesi, /. Ulusal CBS Sempozyumu Bildiri Kitabi, 18-20 Ekim 1994, Trabzon.

Desmet, P.J.J, and G. Govers. 1995. GIS-Based Simulation of Erosion and Deposition Patterns in an Agricultural Landscape: A Comparison of Model Results with Soil Map Information, CATENA 25:389-401.

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Han, L., D.C. Rundquist, L.L. Liu and R.N. Fraser. 1994. The Spectral Responses of Algal Chlorophyll in Water With Varying Levels of Suspended Sediment, International Journal of Remote Sensing 15:3707-3718,

Hudson, P.F. 2005. Soil Erosion Modeling Using the Revised Universal Soil Loss Equation (RUSLE) In a Drainage Basin in Eastern Mexico, Environmental G1S Lab Notes, (http://www.utexas.edu/depts/grg/hudson/grg3 60g/EGIS/labs_04/Lab9 /Iab9_soil erosion_05.htm).

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Soil Erosion Identification Using GIS Techniques in Golba^i 185

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Yuksei, A., A.E. Akay, M. Reis and R. Gtindogan. 2007. Using the WEPP Model to Predict Sediment Yield in a Sample Watershed in Kahramanmaras Region, International Congress on River Basin Management, 22-24 March, Antalya.