ORIGINAL PAPER

High-resolution spatiotemporal distribution of precipitationin Iran: a comparative study with threeglobal-precipitation datasets

Ali Khalili & Jaber Rahimi

Received: 13 July 2013 /Accepted: 18 November 2013# Springer-Verlag Wien 2013

Abstract High-resolution precipitation datasets are used fornumerous applications. However, depending on the proce-dures for obtaining these products, such as number of obser-vations, quality checking, error-correction procedures, andinterpolation techniques, they include many uncertainties.Therefore, the accuracy of these products needs to be evalu-ated over different regions. In this study, the Iranian NationalDataset (INDS), a new 1×1 km precipitation dataset based onprecipitation data of 1,441 quality-controlled stations for theclimatic period from 1961 to 2005, was constructed using thedigital elevation model, correlation method, and Kriging in-terpolation procedure. Iran's annual precipitation values atgrids and stations were extracted from Climatic Research Unit(CRU) CL 2.0, CRUTS 3.10.01, andWorldClim datasets, anddifferences between corresponding values in each of the threedatasets and INDS were calculated and analyzed. The coeffi-cient of determination (R2) between the national networkstations' data and the CRU CL 2.0, CRU TS 3.10.01, andWorldClim datasets were 0.50, 0.13, and 0.62, respectively.Moreover, R2 values between the grids of each dataset andINDS were 0.51, 0.40, and 0.60, respectively. To determinethe global datasets' efficiency for displaying temporal patternsof precipitation, the monthly values gathered from them at 11stations (as representative of Iran's various precipitation re-gimes) were compared with the real values at these stations.The results showed that in term of temporal patterns, theconcurrences among the three global datasets and the INDSwas more acceptable, especially in the case of CRUCL 2.0. In

general, it is concluded that the global datasets could bedeployed for the primary assessment of the annual precipita-tion distribution; however, for more precise studies, use oflocal data is highly recommended.

1 Introduction

Iran is located in the southern part of the temperate zone in thenorthern hemisphere between the latitudes of about 25° to45° N; therefore, according to general circulation of the atmo-sphere, its major area is located in the region of atmosphericsubsidence. Regarding precipitation amounts, it is consideredas an arid and semi-arid region of the world in such a way thatthese climates have totally covered 85 to 93 % of the area(Rahimi et al. 2013). Based on the findings of several previousstudies, zonal mean of annual precipitation is estimated to be250 to 270 mm across the country. Nevertheless, because ofthe country's geographic condition such as: (a) distance fromthe sea, (b) vast range of height (from −25 to +5,600 m a.s.l.),(c) the Elburz mountain chain in the north and the Zagros inthe west to southwest which act as two great climatic walls(Khalili 1973; Alijani 2008), (d) the large expanse of thecentral desert (Dash-e Lut and Dash-e Kavir deserts), (e) thegeographic-dynamic system of the Caspian Sea and the Elburzmountain chain, and finally (f) the occasional penetration ofthe monsoon currents from the southeast in summer. Thespatial precipitation pattern of the country varies from below50 to above 2,000 mm. The coefficient of variation or distri-bution of annual precipitation ranges between about 20 % inthe Caspian Sea coastal plains and 80 % in the central desertareas (Khalili 1973).

Precipitation in Iran is generally associated with migratinglow-pressure systems, which penetrate from the northwest(12.6 %), west and the Mediterranean Sea (64.5 %), andsouthwest and the Red Sea (22.9 %) (Khalili 2004).

A. Khalili : J. Rahimi (*)Meteorological Division, Department of Irrigation and ReclamationEngineering, College of Agriculture and Natural Resources,University of Tehran, Karaj, Irane-mail: jaberrahimy@ut.ac.ir

A. Khalilie-mail: akhalili@ut.ac.ir

Theor Appl ClimatolDOI 10.1007/s00704-013-1055-1

Several researchers have studied spatial distribution ofannual precipitation in Iran so far (Ganji 1955; Adle 1960;kaviani 1988; Jawadi 1966; Mousavi 1971; Ghayur andMasoudiyan 1996; Asakereh 2007; Masoudiyan 2008;Modarres and Sarhadi 2011). In these studies, the number ofstudy stations varied from 40 to about 300, and the mappingmethods were almost based on interpolationmethods and theirexpert experiences. Khalili (1991) presented Iran's precipita-tion map in the IntegratedWater Plan of Iran (with the scale of1:250,000) for a period of 30 years using data collected from1,100 stations and the precipitation-elevation gradient methodfor all watershed basins of the country. A point-to-point com-parison of this map with the previous maps showed that thedifferences among estimations for different places of thecountry range between ±75 %. Thus, a revision in the net-work, climatic period, and the mapping method for creatingIranian National Dataset (INDS) appeared to be essential.Moreover, some efforts have been made for providinghigh-resolution precipitation datasets at the global level(Ropelewski et al. 1984; Legates and Willmott 1990; Xieet al. 1996; Huffman et al. 2001; Dai and Del Genio 1997;New et al. 2002; Chen et al. 2002; Mitchell and Jones 2005;Beck et al. 2005; Hijmans et al. 2005; Brohan et al. 2006),which have been used in many studies (Cramer and Fischer1996; Nicholls 1997; Booth and Jones 1998; Rahimi 2012).The main differences between the components of thesedatasets stemmed from the features of the climatic data usedin generating these sources such as the number and quality ofobservations, data quality control method, the length of theclimatic period, and different interpolation methods as well.Therefore, evaluating these datasets in different places in theworld is significantly important for both those who expandthis data collection and those who use them. Although severalstudies have been conducted at the global level comparingthese datasets (Rudolf and Schneider 1997; Chen et al. 2002;Qian et al. 2006), it appears to be essential to assess theefficiency of these datasets at a local scale according to thegeographical/climatic conditions of different places (Dinkuet al. 2008). Eventually, the assimilation of the internationaland local datasets can lead to estimate precipitation in differentplaces more precisely.

According to the remarkable topographic changes in Iranand the importance of isohyetal map in the country, the mainobjectives of this study are as follow:

(a) Introducing height-precipitation relations to more preciselyestimation of spatiotemporal precipitation distribution inareas with no stations, and ultimately, generating Iran'snumerical precipitation field.

(b) Discussing, analyzing, and assessing the spatiotemporaldifferences between the current findings and major globalprecipitation datasets (Climatic Research Unit (CRU) CL2.0, CRU TS 3.10.01, and WorldClim).

2 Materials and methods

2.1 Study area

The study area is located in southwest of Asia between thelongitudes of 44° and 64° E and the latitudes of 25° and 40° Nwith an area of 1,648,000 km2. The Elburz and Zagros moun-tain chains are stretched in the north and west of the country asthe main topographic features. Iran consists of several closedbasins that collectively are referred to as the Central Plateau.The eastern part of the plateau is covered by two salt deserts,the Dasht-e Lut and the Dasht-e Kavir.

2.2 Data analysis methods

2.2.1 Global datasets

In this study, three spatially interpolated precipitation datasetswere used including CRU CL 2.0 (New et al. 2002), CRU TS3.10.01 (Mitchell and Jones 2005; Harris et al. 2013), andWorldClim (Hijmans et al. 2005). The first and seconddatasets include monthly gridded data, provided at the CRUof the University of East Anglia and covers all land areas ofthe earth. These datasets are related to the periods of 1961–1990 and 1901–2009, respectively. In addition, the resolutionof the CRU CL 2.0 and CRU TS 3.10.01 datasets are 10 and30 arcmin, respectively, while the third dataset includes themonthly value with spatial resolution of 30 arcsec for theperiod of 1950–2000.

2.2.2 National network stations data

There are two major precipitation-measuring networks in Iran,first is associated with the Islamic Republic of Iran Meteoro-logical Organization and the second one belong to theMinistryof Energy. Among all 1,411 available stations, the once whichhad enough statistical coverage reconstructable coverage wereselected in the climatic period (1961–2005).

2.2.3 Data quality control

Quality control analysis were undertaken by comparing annu-al precipitation values of each station with concurrent data ofreference stations which are stations with long records, andconfirmed by the abovementioned organizations.

This method (Khalili 1991) is based on the comparison ofstandardized annual precipitation (ZSi) of under control sta-tion (S ) in the i th year with standardized annual precipitation(ZRi) of the nearest reference station (R) (Khalili 1991).

The R , which is chosen among those have nearest distancefrom under S , should fulfill the following conditions: thecorrelation between the under S and the selected R should besignificant at least at 5% level of significance, and precipitation

A. Khalili, J. Rahimi

regime of both stations should be similar. ZSi and ZRi can bedefined as:

ZRi ¼ PRi − PR

SDPR

ZSi ¼ PSi − PS

SDPS

where Pi is the precipitation of i th year, P is mean, SD isstandard deviation, Zi is the standardized precipitation of Rand S stations. It should be noted that Zi values in Iran rangesbetween (−3.2) and (+3.5) in Iran.

ZSi and ZRi values enable us to compare, rank, and cate-gorize the precipitation data of different years (i ) for thestudied stations. Dryness and wetness levels for each yearhave been identified by given benchmarks numbered inTable 1.

Basic assumption of this method is that two neighborstations located in a given precipitation zones, which havethe similar precipitation regime, could not be very different interms of dryness and wetness in a given year. Conversely, onecannot experience severe dryness and the other one be in anextremely wetness condition. In this regard, differences be-tween normalized precipitation classes of the R and under theS could be fair criteria for assessing. The criteria used in thisstudy are presented in Table 2.

It should be mentioned that the use of aforementionedmethod was satisfactory for annual precipitation value whileit did not have always adequate accuracy for monthly valuesand it is not recommended for daily values

2.2.4 Data reconstruction

Reconstruction of the suspicious or lost data was done by usingthe inverse square distance ratio method with 2, 3, or 4

surrounding correlated stations. In this method, to reconstructthe annual precipitation data (Po) of a station named O , first jstations were selected among a number of surrounding stationswhich satisfy the following conditions: (a) annual precipitation(Pj) has the greatest significant linear correlation coefficient,(b) distance (Dj) is the closest to station O , and (c)common historical years with O is the longest. Therefore,Po for the considered year is calculated by the followingequation:

Po ¼X

1

j W j

W⋅Pj

Where, W j ¼ 1D2

j, W =∑Wj and j =2,3 or 4.

R (Pj, Po) should be significant at least at 5 %.This method gives a better estimate, especially for dry and

wet years, comparing the direct use of the single- or multi-variable correlation method (Khalili 1991).

The years that was not possible to reconstruct data withsignificant correlation coefficients, the method of normal ratiowith the nearest station was employed. Statistical coverage ofthe stations' data in the climatic period and the reconstructionmethod are given in Table 3. The aim of this operation was tocreate a collection of precipitation data in the climatic period(1961–2005) which is used to build the INDS.

The altitudinal distribution of the studied weather stations isalso heterogeneous. The altitudinal classes of stations are givenin Table 4. As shown, 89% of the stations are located below thealtitude of 2,000 m, while the majority of water resource ofIran's plateau are derived from the highlands, especially thehigh areas of the western (Zagros) and northern (Elburz)mountains. It appears that to have a much more preciseestimation of the country's water resource, it is necessary

Table 1 The grading criteria ofdry and wet years Z value −1.5 −0.75 −0.25 0 0.25 0.75 1.5

Severity Classes Dry Year Normal Wet Year

Severe Moderate Slight Slight Moderate Severe

Benchmark 1 2 3 4 5 6 7

Table 2 Criteria of data quality control

Difference between benchmark Data evaluation

0, 1, 2 Acceptable

3, 4 Suspect, should be checked

5, 6 Unacceptable, should be rejected

Table 3 Statistical coverage of the data in the climatic period, 1961–2005

Reconstruction methodusing

Number ofstations

Statisticalcoverage (%)

4 correlated stations 292 ≥953 correlated stations 373 90–95

2 correlated stations 646 70–90

Normal ratio 130 50–70

Total 1,441

High-resolution spatiotemporal distribution of precipitation

to have reliable estimation of the precipitation amount inmountainous areas which have no stations. The spatial distri-bution of the research network stations and Iran's topographyare shown in Fig. 1.

2.2.5 Structure of INDS

The study of geographical distribution of annual precipitationin different basins of Iran has shown that in small regionsreaching a linear relationship between annual precipitationand elevation is generally possible which helps to estimatehighlands precipitation values. These relationships were esti-mated for 68 basins in all parts of the country based on 20-yeardata (1964–1984) (Mohammad Pour 1994). In the present

study, a new effort was made to find the gradient zonesboundaries using 1961–2005 data. Having consideredthe natural boundaries of the basins, and having de-ployed the trial and error method, it was attempted todistinct the zones so that there was a significant linearregression relationship between altitude (Z ) and amountof precipitation (P ) as P =A +BZ . Based on climatolog-ical principles, increase of precipitation corresponding toheight has an upper threshold, which is considered to be∼3,000 m for mountainous regions of Iran (Khalili1973). In addition, this increase has been seen in INDSup to 3,250 m. As regions with elevations higher than3,250 m is about 0.2 % of country area, the error stemfrom lacking stations higher than 3,250 m would benegligible. Finally, the amount of precipitation at eachpoint will be calculated by the equation: P =A +BZ +ε;where ε is the estimated error that has a random distribu-tion on each zone and it could be evaluated by Krigingmethod (Khalili et al. 2004). This study led to categoriza-tion of Iran into 17 gradient zones which are named inalphabets in Fig. 2.

There is a significant linear gradient relationship at each ofthese zones, except for the zone in the south of the CaspianSea (zoneM) and the zone in the east (zone O). It was found tobe a quadratic type of relationship in the zone M, i.e., precipi-tation will decrease once the altitude is increased, up to 1,500 m

Table 4 Altitudinaldistribution of thestudied stations in thenational network

Elevation classes (m) Number ofstations

<500 287

500–1,000 185

1,000–1,500 426

1,500–2,000 384

2,000–2,500 147

>2,500 12

Fig. 1 Spatial distribution of theresearch network stations, Iran'stopography, and typical stationsintroducing precipitation regimes(stars)

A. Khalili, J. Rahimi

Fig. 2 Gradient zones of Iran'sannual precipitation

Table 5 Regression equations of precipitation (millimeters) in proportion to altitude (kilometers) for different gradient zones of Iran, and the comparisonbetween zonal mean precipitations gathered from three global datasets and INDS

Zone Number of stations R Regression equationP=A+BZ

Zonal mean

INDS WorldClim CRU 3.10.01 CRU CL 2.0

Abs. Relative (%) Abs. Relative (%) Abs. Relative (%)

A 233 0.33** 228.6+87.7 Z 386.2 385.8 −0.1 383.0 −0.8 454.6 17.7

B 54 0.70** 324.4+222.8 Z 510.5 462.2 −9.5 463.5 −9.2 406.5 −20.4C 136 0.61** 22.7+172.5 Z 280.2 278.5 −0.6 308.1 9.9 340.1 21.4

D 105 0.76** 34.9+245.2 Z 386.9 287.4 −25.7 359.8 −7.0 459.1 18.7

E 86 0.63** 1.1+175.9 Z 147.6 152.3 3.1 170.8 15.7 177.3 20.1

F 67 0.61** 62+324.1 Z 689.4 383.7 −44.3 442.3 −35.9 400.1 −42.0G 51 0.66** −337.7+303.8 Z 483.2 271.8 −43.8 337.6 −30.1 291.0 −39.8H 92 0.64** −41.1+105.8 Z 112.8 119.3 5.8 149.1 32.1 148.8 31.8

I 64 0.75** −2.9+108.6 Z 111.1 108.4 −2.4 146.0 31.5 131.1 18.0

J 91 0.81** 229.1+353.4 Z 348.7 255.7 −26.7 287.1 −17.7 238.2 −31.7K 65 0.45** 210.1+70.7 Z 214.2 159.4 −25.6 221.0 3.1 187.9 −12.3L 52 0.71** 107.8+88.6 Z 164.2 126.5 −22.9 140.4 −14.5 147.5 −10.2M 116 0.61** 306.8 Z2–928.1 Z+1,123 801.4 528.6 −34.0 191.0 −76.2 701.2 −12.5N 82 0.46** 207.3+78.1 Z 328.9 267.2 −18.8 245.4 −25.4 281.1 −14.5O 51 0.18 491.4+189.8 Z 475.3 283.5 −40.4 202.8 −57.3 220.1 −53.7P 35 0.79** 306.6+573.1 Z 494.3 344.2 −30.4 369.9 −25.2 337.3 −31.8Q 61 0.58** −115.1+327.9 Z 254.2 185.5 −27.0 229.3 −9.8 209.6 −17.5Entire country 1,441 – – 254.0 211.0 −16.9 224.0 −11.8 242.0 −4.7

**Significant at 1 %

High-resolution spatiotemporal distribution of precipitation

(the effect of distance from the sea), and from that pointabove, precipitation increases with height (the effect ofaltitude). This increase and decrease was previouslyconfirmed by Adle (1960) through flora aspects of theregion. Later, that matter was confirmed by the statisticalmethod (Khalili 1973).

A significant relationship between P and Z cannot beachieved in zone O. In this zone, the effect of altitude isignored and the estimation is made by the inverse squaredistance method which was recently approved by Nadi(2010). The number of stations used for obtaining regressionequation of precipitation versus altitude and correlation coeffi-cient are presented in Table 5. As seen in Table 5, the altitudinalgradient of annual precipitation in Iran is different in variouszones and ranges from 71 to 573 mm/km.

The aim of this research was to build Iranian annualprecipitation dataset using the equations in Table 5 andthen applying them to Iran's digital elevation model andcorrection of ε for each zone's pixels. This dataset is thebasis for mapping isohyets of Iran and the point-to-pointcomparison of precipitation with the mentioned globaldatasets.

Figure 3 depicts Iran's spatial distribution of precipitationbased on INDS data. The minimum value of annual precipi-tation in Iran occurs in the Dasht-e Lut desert (13mm), and themaximum amount is related to the southeast of the CaspianSea (2,030 mm).

3 Results

The INDS (with 30 arcsec resolution) has made it possibleto compare the efficiency of precipitation data provided bythree global datasets including CRU CL 2.0 with 10 arcminresolution, CRU TS 3.10.01 with 30 min resolution andWorldClim with the resolution of 30 arcsec.

3.1 Comparison of the zonal means of precipitation

The zonal mean of precipitation at each of the 17 zones of Iranwas studied based on the three global datasets and INDS,which are shown in Table 5. The study of the values showedthat the absolute and relative differences between themean precipitation values in different regions are notice-able. These relative differences vary between −53.7 and31.8 % for CRU CL 2.0, between −76.2 and 32.1 % for

Fig. 3 Spatial distribution ofprecipitation in Iran (1961–2005)

Table 6 Range of relative differences between INDS and datasets values(percent)

Dataset WorldClim CRU TS 3.10.01 CRU CL 2.0

Individual stations −421.8 to 83.0 −496.5 to 74.7 −463.8 to 85.0

Precipitation zone −44.3 to 5.8 −76.2 to 32.1 −53.7 to 31.8Entire country −16.9 −11.8 −4.7

A. Khalili, J. Rahimi

CRU TS 3.10.01 and between −44.3 and 5.8 % forWorldClim. In general, the differences show an underes-timation of the global precipitation datasets in comparisonwith INDS. Comparing the relative differences betweendatasets and INDS, it can be concluded that as the area ofstudied region is broader, the zonal means of precipitationare closer to each other in such a way that the differencesare large for individual stations, but comparably small forthe entire country. Table 6 summarizes range of differencesfor individual stations, precipitation zones, and the wholecountry.

3.2 Comparison of the observed data with collected onesfrom the global datasets

The evaluation of the mentioned global datasets in thecountry was in such a way that the amount of precipita-tion at each one of the 1,441 national network stationswere extracted from the three mentioned global datasetsand then comparison was made. The amount of precipita-tion obtained from the global datasets and the real amountof the observation of the studied stations network areshown in Fig. 4. Distributions of points specify theirrandomness. The comparison of the outlet points in thesefigures identifies that the difference of the estimations atmost points reaches many times greater than the observedvalue of precipitation. However, in general, the correlationsbetween the observed and estimated values are significant.

Coefficient of determinations revealed that the WorldClimestimates are closer to real values in comparison to CRU CL2.0 and the later dataset estimations are better than CRU TS3.10.01.

3.3 Comparison of the precipitation values in grid pointsof the global datasets with INDS

The amounts of mean annual precipitation of global datasetsgrids are illustrated versus their corresponding amounts of theINDS that are shown in Fig. 5. It is seen that although thedensity of the points in this case is much more than theprevious case, the general shape of the cloud points of scatterdiagram has maintained the general picture of the stationcomparison case. In addition, the order of the efficiency ofthe global datasets is the same as the previous order, but the R2

values are greater in this case.

3.4 The altitude and latitude effect on the estimates

In Table 7, the efficiency of the global datasets against thenational dataset are evaluated at various latitudes and altitudesusing R2. This Table shows that in all altitude classes below 2,000 m, WorldClim estimations are better. As far as latitudeeffects concern, the most appropriate estimates are seen in 28−35° N class.

Fig. 4 Comparison of real values of the mean annual precipitation of 1441 national network stations with their similar values estimated from variousdatasets (millimeters per year)—the illustrated line is the regression line

Fig. 5 Comparison of mean annual precipitation values of the global datasets' grid points with INDS

High-resolution spatiotemporal distribution of precipitation

3.5 Comparison of area of differences between INDSand the global datasets

The relative area of classified differences between globaldatasets estimations and INDS values are shown in Fig. 6.This figure illustrates that the most differences are seen in –10to –500 mm class but in some cases differences exceed500 mm which covers an area of about 53 % of the countryfor CRU 3.10.01, 34 and 35 % for WorldClim and CRU CL2.0, respectively.

3.6 Comparison of the temporal pattern of precipitation

The temporal regimes of precipitation in Iran are mostlyMediterranean type during the year, i.e., the dry season isconcentrated in the summer and the rainy season correspondsto the cold half of the year so that, in general, 89 % ofprecipitation throughout the country occurs during the6 months (December to May). Hence, the precipitation pat-terns and the importance of precipitation amount in differentmonths of the year vary from area to area. In this respect, somemajor regimes are distinguishable throughout the countrywhich is listed below. Furthermore, examples of these regimesare depicted in Fig. 7.

(a) Caspian Sea Regime; includes the southern coastal plainsof the Caspian Sea. At this regime, dry season literallydoes not exist and precipitation occurs in all seasons.Maximum precipitation in western regions occurs in fall;in central regions, it takes place in the winter and fall, andfinally in eastern regions, it happens in the winter andspring.

(b) Spring Regime; in this regime, maximum precipitationoccurs in spring and includes northwest of the countryand the Elburz highlands.

(c) Mediterranean-Monsoon Regime; this regime has theMediterranean origin with the winter precipitation, butin the summer, it is affected by the Monsoon regime ofthe Indian Ocean and summer showers occur. Addition-ally, a second and less important maximum is observedin July.

(d) Winter regime; this regime covers a large part of Iranianplateau and precipitation occurs in the winter. In thisregime, the slope of variation is steep around maximum.

(e) Winter–spring Regime; in this regime, winter and springprecipitations are of the same importance. This regimeincludes the west of the country and some northern partof Khorasan.

To compare the temporal pattern of precipitation in stationsidentifying Iran's different regimes, the monthly means of thestations introducing the above typical regimes are extractedfrom INDS and studied datasets as well which are illustratedin Fig. 7. As it is shown, the concurrence of yearly precipita-tion regime is almost acceptable. To quantify this concurrence,the correlation between the percentages of monthly precipita-tion of all typical stations against the values extracted fromtheir similar points has been calculated. The results are pre-sented in Table 8.

This table shows that the temporal concurrence of annualprecipitation regimes throughout a year is acceptable based onthe findings obtained from all three global datasets except forfew cases. It stemmed from the fact that temporal precipitationregimes cover a vast area of the continents. Therefore, invarious stations, monthly precipitation regimes throughoutthe year are comparable with global pattern. The best concur-rence of the monthly data is related to CRU CL 2.0, CRU TS

Table 7 R2 values between the global precipitation datasets and INDS atdifferent altitudinal and latitudinal points

Classes Global precipitation dataset

WorldClim CRU 3.10.01 CRU CL 2.0

Elevation classes (m)

<500 0.88 0.12 0.79

500–1,000 0.71 0.35 0.54

1,000–1,500 0.63 0.58 0.44

1,500–2,000 0.52 0.48 0.49

>2,000 0.23 0.28 0.5

Latitude classes (degree)

≤28 0.43 0.27 0.57

28–35 0.79 0.58 0.58

≥35 0.51 0.04 0.44

0

5

10

15

20

25

30

<-500 -500 , -50 -50 , -10 -10 , 10 10 , 50 50 , 500 >500

Per

cent

age

area

of t

he c

ount

ry

Difference in precipitation (mm)

CRU 3.10.01 Dataset

CRU CL 2.0 Dataset

WorldClim Dataset

Fig. 6 Comparison of area ofdifferences between INDS andthe global datasets

A. Khalili, J. Rahimi

3.10.01, and WorldClim, respectively. Moreover, it should bementioned that the precipitation estimated by WorldClim inNovember has the highest difference in all typical stations.

4 Conclusions

Considering the fact that in most climatic studies, water re-source management, hydraulic models, flood forecasting, cli-mate change studies, water balance estimation, irrigation

programming, etc., the existence of precipitation informationand its spatial distribution are of great importance. Moreover,the zonal estimation of precipitation in places without stationsand providing the precipitation maps are essential. As thespatiotemporal variation of precipitation consist the main fea-tures of Iran's climate, the present study tried to provide anational precipitation dataset for Iran to study the efficiency ofthe three major global precipitation datasets in mapping Iran'sspatial distribution of precipitation. The most important find-ings of the comparative study of the three global precipitation

Fig. 7 Comparison of monthly precipitation regimes at typical stations of INDS with their similar values from the three global precipitation datasets

High-resolution spatiotemporal distribution of precipitation

datasets in illustrating the spatiotemporal distribution of pre-cipitation in the country during the climatic period (1961–2005) can be summarized as follow:

& These data provide just an estimation of spatial changespatterns. Therefore, it is recommended that one should becautious deploying these data for climatic studies.

& Considering the findings of the study, it is recommendedthat local data should be applied as the main reference instudies in which the quantitative evaluation of water re-sources is of considerable importance.

& Considering the knowledge gained from the country'snetwork stations, it is recommended that INDS data couldbe used for studies relating the country's water resourcesand for improving the estimation of the global precipita-tion datasets as well.

& Among the studied datasets, it appeared that WorldClimmatches more properly to the local data with regard to theannual precipitation. This privilege is even seen at differ-ent altitudinal points (from below 500 m to above 2,000 m) and at various latitudes.

& Concerning the monthly precipitation regime, a concurrencebetween all datasets and the national precipitation dataset isnoticeable. This means that once proper estimation of theannual precipitation is made, the monthly distribution ofprecipitation would be more precise. The main reason forthis agreement is that the climatic precipitation regime isdominant over a vast area of the world. For instance, thespring regime in northwest of Iran has high agreement withthe Anatolian Plateau and the neighboring areas.

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Table 8 Correlation between the relative monthly precipitation of thetypical stations of INDS and their corresponding values from the threeglobal precipitation datasets

Station Global precipitation dataset

WorldClim CRU 3.10.01 CRU CL 2.0

Bandar-Anzaly 0.91** 0.34 0.96**

Gharakheil 0.87** 0.80** 0.80**

Salian Tapeh 0.72** 0.95** 0.96**

Tabriz 0.86** 0.69* 0.85**

Mashhad 0.83** 0.82** 0.86**

Sanandaj 0.91** 0.96** 0.97**

Abadan 0.92** 0.98** 0.98**

Shahre Kord 0.72** 0.97** 0.95**

Sabzevar 0.90** 0.87** 0.85**

Iranshahr 0.60* 0.96** 0.98**

Namarestagh 0.90** 0.86** 0.85**

All stations 0.79 0.81 0.89

*Significant at 5 %

**Significant at 1 %

A. Khalili, J. Rahimi

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High-resolution spatiotemporal distribution of precipitation