Abstract
The reference evapotranspiration (ET0) is an essential variable in the agrohydrological systems and its estimation on a regional scale is limited to its spatial variability. This study compares two approaches for preparation of spatial distribution maps of ET0 in Mazandaran province of Iran. In the first approach, ET0 was calculated using climatic data and Hargreaves-Samani equation in weather stations locations and then were interpolated. In the second approach, the components of the Hargreaves-Samani equation were interpolated and then ET0 maps were prepared by applying the Hargreaves-Samani equation and suitable commands in GIS. The 10-year climatic data for 51 weather stations (46 stations for preparing ET0 maps and 5 stations as validation station) were gathered over Mazandaran province. Semivariograms were calculated and the best semivariogram model was selected on the basis of the least value of Residual Sums of Squares (RSS). The spatial correlation of the data was compared on the basis of Nugget to Sill ratio. The data were interpolated using Ordinary Kriging method and the interpolation error was computed by cross validation technique based on Root Mean Square Standardized Error (RMSSE). The predicted ET0 values were compared to the computed ET0 in validation stations and sensitivity analysis was conducted. Results show the second approach had better spatial correlation and lower interpolation error and the difference between these two approaches were not significant. Therefore, the accuracy of the ET0 maps is more related to the method of computing ET0 than the type of climatic data is being interpolated.
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References
Ahmadi SH, Sedghamiz A (2008) Application and evaluation of kriging and cokriging methods on groundwater depth mapping. Environ Monit Assess 138(1–3):357–368
Allen RG, Pereira LS, Raes D, Smith M (1998) Crop evapotranspiration. Irrigation and drainage paper no.56. F.A.O, Rome
Ashraf M, Loftis JC, Hubbard KG (1997) Application of geostatistics to evalu-ate partial weather station networks. Agr Forest Meteorol 84:255–271
Assaf H, Saadeh M (2009) Geostatistical assessment of groundwater nitrate contamination with reflection on DRASTIC vulnerability assessment: the case of the upper Litani Basin, Lebanon. Water Resour Manag 23(4):775–796
Barca E, Calzolari MC, Passarella G, Ungaro F (2013) Predicting shallow water table depth at regional scale: optimizing monitoring network in space and time. Water Resour Manag 27(15):5171–5190
Basistha A, Arya DS, Goel NK (2008) Spatial distribution of rainfall in Indian Himalayas—a case study of uttarakhand region. Water Resour Manag 22(10):1325–1346
Bechini L, Ducco G, Donatelli M, Stein A (2000) Modelling, interpolation and stochastic simulation in space and time of global solar radiation. Agric Ecosyst Environ 81:29–42
Cambardella CA, Moorman TB, Novak JM, Parkin TB, Karlen DL, Turco RF, Konopka AE (1994) Field-scale variability of soil properties in central Iowa soils. Soil Sci Soc Am J 58:1501–1511
Caruso C, Quarta F (1998) Interpolation methods comparison. Comput Math Appl 35(12):109–126
Creutin JD, Obled C (1982) Objective analyses and mapping techniques for rainfall fields: an objective comparison. Water Resour Res 18(2):413–431
Cuenca RH, Amegee KY (1987) Analysis of evapotranspiration as a regionalized variable. In: Hillel D (ed.). Advances in Irrigation. 4:181±220
Dalezios NR, Loukas A, Bampzelis D (2002) Spatial variability of reference evapotranspiration in Greece. Phys Chem Earth A B C 27(23–24):1031–1038
Delbari M, Khaiat Kholqi M, Mahdian MH (2004) Evaluation of geostatistical methods for soil hydraulic conductivity assessment in Shibab and Poshtab-paein in Sistan plain, Olume keshavarzi Iran magazine. 35(1)
Delhomme JP (1978) Kriging in hydrosciences. Adv Water Resour 1:251–266
Diodato N, Tartari G, Bellocchi G (2010) Geospatial rainfall modelling at eastern Nepalese highland from ground environmental data. Water Resour Manag 24(11):2703–2720
FAO (1992) Expert consultation on revision of FAO methodologies for crop water requirements. FAO, Rome, p 60
FAO (1994) Water for life. World Food day 1994, Rome
Farshi AA, Shariati MR, Jarollahi R, Ghaemi MR, Shahabifar M, TavallaeiMM (1997) An estimate of water requirement of main field crops and orchards in Iran. Nashre Amuzesh Keshavarzi press. 2:629
Ganjizadeh R, Borumandnasab S, Soltani mohammadi A, Ganjizadeh H (2013) determination of reference evapotranspiration using interpolation methods and comparison to experimental methods (A case study in Golestan province), first conference of water crisis, Isfahan
Hargreaves GH, Samani ZA (1985) Reference crop evapotranspiration from temperature. Appl Eng Agric 1(2):96–99
Isaaks EH, Srivastava RM (1989) An introduction to applied geostatistics. Oxford University Press, NewYork, p 561
Kite GW, Droogers P (2000) Comparing evapotranspiration estimates from satellites, hydrological models and field data. J Hydrol 229:3–18
Manghi F, Mortazavi B, Crother C, Hamdi MR (2009) Estimating regional groundwater recharge using a hydrological budget method. Water Resour Manag 23(12):2475–2489
Mardikis MG, Kalivas DP, Kollias VJ (2005) Comparisonof interpolationmethods for the prediction of reference evapotranspiration—an application in Greece. Water Resour Manag 19:251–278
Marofi S, Tabari H, Zare Abyaneh H (2011) Predicting spatial distribution of snow water equivalent using multivariate non-linear regression and computational intelligence methods. Water Resour Manag 25(5):1417–1435
Martinez-Cob A (1996) Multivariate geostatistical analysis of evapotranspiration and precipitation in mountain terrain. J Hydrol 174:19–35
Masoud AA (2014) Groundwater quality assessment of the shallow aquifers west of the Nile Delta (Egypt) using multivariate statistical and geostatistical techniques. J Afr Earth Sci 95:123–137
Matheron G (1963) Principles of geostatistics. Econ Geol 58:1246–1266
Nalder IA, Wein RW (1998) Spatial interpolation of climatic normals: test of a new method in the Canadian boreal forest. Agric For Meteorol 92:211–225
Noshadi M, Sepaskhah AR (2005) Application of geostatistics for potential evapotranspiration estimation. Iran J Sci Technol Trans B Eng 29(No. B3)
Phillips DL, Marks D (1996) Spatial uncertainty analysis: propagation of interpolation errors in spatially distributed models. Ecol Model 91:213–229
RahimZadeh F, Khoshkam M (2003) Moisture series changes in country synoptic stations, the third regional conference and the first climate change national conference, 53–62. Isfahan University of Technology
Rana G, Katerji N (2000) Measurement and estimation of actual evapotranspiration in the field under Mediterranean climate: a review. Eur J Agron 13:125–153
Ventura F, Spano D, Duce P, Snyder RL (1999) An evaluation of common evapotranspiration equations. Irrig Sci 18:163–170
Weber D, England E (1994) Evaluation and comparison of spatial interpolators II. Math Geol 26:589–603
Webster R, Oliver MA (2001) Geostatistics for environmental scientists. Wiley, Chichester, p 271
Zhang X, Kang S, Zhang L, Liu J (2010) Spatial variation of climatology monthly crop reference evapotranspiration and sensitivity coefficients in Shiyang river basin of northwest china. Agric Water Manag J 97(2010):1506–1516
Zhao C, Nan Z, Cheng G (2005) Methods for estimating irrigation needs of spring wheat in the middle Heihe basin, China. Agric Water Manag 75(2005):54–70
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Kamali, M.I., Nazari, R., Faridhosseini, A. et al. The Determination of Reference Evapotranspiration for Spatial Distribution Mapping Using Geostatistics. Water Resour Manage 29, 3929–3940 (2015). https://doi.org/10.1007/s11269-015-1037-4
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DOI: https://doi.org/10.1007/s11269-015-1037-4