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.

As the largest lake within Iranian borders, Lake Urmia's critical conditions have disrupted its primary functions in recent years due to environmental challenges. Considering the virtual water (VW) concept (which takes into account the value of every unit of water volume in addition to the consumed volume of water) as a component of Integrated Water Resources Management (IWRM) can be nominated as an excellent approach for improving the conditions of the Lake Urmia catchment. Here, with the aid of WEAP (Water Evaluation and Planning) software, the region based on IWRM principles is modeled. Then, the necessities and effectiveness of optimizing VW in agricultural activities have been investigated through different cases. A comparison of the cases reveals the unfavorable condition of local agricultural practices at present, both in terms of water consumption and financial revenues. If not remedied, the current situation can aggravate the water scarcity problem in the region. Furthermore, the results suggest that considering multiple objectives (besides water consumption) in making a decision on the cropping pattern can present a more realistic response to the situation, improve the lake's conditions, and resolve the scarcities to certain extents.