Comparison of geostatistical interpolation methods to map annual rainfall in the Chéliff watershed, Algeria

Abstract

The Cheliff watershed has one of the most spatially diverse pluviometric regimes in northwestern Algeria. Understanding these regimes is essential for managing water resources and identifying the most vulnerable regions to climate change. Mean annual rainfall data (1972–2012) for 58 meteorological stations and their corresponding elevation were used. Maps were produced using three geostatistical interpolation algorithms: ordinary kriging (OK), regression-kriging (RK), and kriging with external drift (KED); the first algorithm uses only rainfall while the other two use also elevation. Interpolation methods were compared using statistical indicators of cross-validation. Results indicate that KED is the least biased interpolator with limited number of strong underestimates or overestimates and limited relative importance of this strong underestimation or overestimation, followed by RK and finally OK. The best match between measured and predicted values was for KED (correlation coefficient of 0.82), followed by RK (0.79), while OK is far from them (0.70). KED can be considered the best model because it gives the lowest values of mean error, mean absolute error, and root mean square error (− 1.9, 35.4, and 49.5 mm, respectively) and the highest values of Willmott agreement index, Lin concordance coefficient, and Nash–Sutcliffe efficiency coefficient (0.89, 0.80, and 0.67, respectively), results of RK are intermediate, while those of OK are the worst. There is clearly significant improvement in the prediction performance taking into account the elevation, in particular by KED. Results show that KED is the most appropriate to produce map of mean annual rainfall in the Cheliff watershed, Algeria.