A spatiotemporal framework to calibrate high‐resolution global monthly precipitation products: An application to the Urmia Lake Watershed in Iran

Abstract

AbstractImproving precipitation accuracy over a watershed is one of the highest priorities in water resources studies and management. Several global precipitation datasets are available for estimating precipitation over any region in the world. However, local or regional application of these datasets should account for and correct potential errors in the original products. This article presents a novel spatiotemporal calibration framework to improve the accuracy (bias and correlation) of global precipitation datasets in regional applications. The proposed methodology consists of two steps. First, gridded global precipitation datasets are regressed pointwise against rain gauge data. This yields downscaled and bias‐corrected precipitation values at the point scale. Second, the resulting point‐scale regression parameters are used to build a geostatistical model that predicts the regression parameters across the region of interest, allowing for bias‐correcting the precipitation datasets at the regional scale. The framework is applied to the Urmia Lake Watershed in northwestern Iran. Eight global high‐resolution monthly precipitation datasets (CHIRPS, ERA‐5, IMERG, PERSIANN, PERSIANN‐CCS, PERSIANN‐CDR, TRMM and Terra) are evaluated and three downscaling approaches including linear, Q‐Q and Linear Scaling (LS) regression methods are used to calibrate the precipitation datasets based on a regional network of rain gauge observations. Ordinary kriging is subsequently used to predict the regression parameters at ungauged locations. Out of all combinations (i.e., eight datasets and three methods), downscaled IMERG using linear and Q‐Q regression methods showed the best performance in estimating the spatiotemporal variations of monthly precipitation across the watershed of interest. The original IMERG dataset overestimated the monthly precipitation by approximately 20% compared to the precipitation from rain gauges. After applying the proposed methodology in this article, the IMERG bias was reduced by 93%, with an additional 26% decrease in the RMSE.