A framework for developing a spatial high-resolution daily precipitation dataset over a data-sparse region

This paper outlines a framework in order to provide a reliable and up-to date local precipitation dataset over Sistan and Baluchestan province, one of the poorly rain gauged areas in Iran. Initially, the accuracy of GPCC data, as the reference dataset, was evaluated. Next, the performance of eight gridded precipitation products (namely, CHIRPS, CMORPH-RAW, ERA5, ERA-Interim, GPM-IMERG, GSMaP-MVK, PERSIANN and TRMM3B42) were compared based on the GPCC observations during 1982-2016 over the study area. The evaluation was done by using eight commonly used statistical and categorical metrics. Then, among the products, the most suitable ones on the basis of their better performance and least time delay in providing data, were utilized as the constituent members of the proposed hybrid dataset. Using several statistical/machine learning approaches (namely, NSGA II, ETROPY and TOPSIS), daily weights of the chosen datasets were estimated, while the correlation coefficient and the estimation error of the data were maximized and minimized, respectively. Finally, the efficiency of the proposed hybrid precipitation dataset was investigated. Results indicate that the developed hybrid dataset (2014-present), using the estimates of the chosen ensemble members (GPM-IMERG, GSMaP-MVK and PERSIANN) and their respective weighting coefficients, provides accurate local daily precipitation data with a spatial resolution of 0.25°, representing the minimum time delay, compared to the other available datasets.