Open AccessSpatial Estimation of Reference Evapotranspiration in Andalusia, Spain
Author(s) -
Karl Vanderlinden,
Juan Vicente Giráldez,
Marc Van Meirvenne
Publication year - 2008
Publication title -
journal of hydrometeorology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.733
H-Index - 123
eISSN - 1525-755X
pISSN - 1525-7541
DOI - 10.1175/2007jhm880.1
Subject(s) - kriging , elevation (ballistics) , evapotranspiration , spatial variability , standard deviation , range (aeronautics) , environmental science , multivariate interpolation , geostatistics , interpolation (computer graphics) , spatial analysis , variogram , correlation coefficient , statistics , mathematics , computer science , animation , ecology , materials science , geometry , computer graphics (images) , composite material , bilinear interpolation , biology
Knowledge of the spatial and temporal distribution of reference crop evapotranspiration (ET0) is of interest for regional water resources management, especially in areas of the world where fine-tuning of agricultural water demands over large areas is required. This study provides a strategy for mapping ET0 in regions with low meteorological data availability. For Andalusia, Spain, it involves estimating ET0 from temperature data using a locally calibrated version of the Hargreaves equation and the application of geostatistical interpolation techniques that take into account elevation as secondary information. Average annual ET0 at 191 observatories (with elevation between 0 and 1260 m) ranged from 954 to 1460 mm, with an average of 1283 mm, a standard deviation of 99 mm, and a correlation coefficient with elevation of −0.86. Simple kriging with varying local means (SKlm) and kriging with an external drift (KED)—two methods that take into account elevation as secondary information—increased spatial model efficiency by 30% as compared to ordinary kriging. SKlm was used for mapping ET0 since it better reproduced the descriptive statistics of the point data and yielded slightly smaller root-mean-squared estimation errors than KED. The spatial correlation of annual and monthly ET0 was well structured and anisotropic. Short-range variability, for separation distances up to 20–40 km, showed a strong linear increase with distance while long-range variability, up to 130–250 km, increased more gently with distance. The results of this structural analysis are relevant for the spatial optimization of a recently installed automated ET0 observation network, while obtained maps constitute a valuable tool for regional water resources evaluation, planning, and management and contribute to optimizing water use in local irrigated agriculture.