Access to spatiotemporal distribution of precipitation is needed in many hydrological applications. However, gauges often have spatiotemporal gaps. To mitigate this, we considered three main approaches: (i) using remotely sensing and reanalysis precipitation products; (ii) machine learning-based approaches; and (iii) a gap-filling software explicitly developed for filling the gaps of daily precipitation records. This study evaluated all approaches over a sparsely gauged basin in East Africa. Among the examined precipitation products, PERSIANN-CDR outperformed other satellite products in terms of root mean squared error (7.3 mm), and correlation coefficient (0.46) while having a large bias (50%) compared to the available in situ precipitation records. PERSIANN-CDR also demonstrates the highest skill in distinguishing rainy and non-rainy days. On the other hand, Random Forest outperformed all other approaches (including PERSIANN-CDR) with the least relative bias (-2%), root mean squared error (6.9 mm), and highest correlation coefficient (0.53).

Water management in arid basins often lacks sufficient hydro-climatological data because, e.g., rain gauges are typically absent at high elevations and inflow to ungauged areas around large closed lakes is difficult to estimate. We sought to improve precipitation and runoff estimation in an arid basin (Lake Urmia, Iran) using methods involving assimilation of satellite-based data. We estimated precipitation using interpolation of rain gauge data by kriging, downscaling the Tropical Rainfall Measuring Mission (TRMM), and cokriging interpolation of in-situ records with Remote Sensing (RS)-based data. Using RS-based data application in estimations gave more precise results, by compensating for lack of data at high elevations. Cokriging interpolation of rain gauges by TRMM and Digitized Elevation Model (DEM) gave 4–9 mm lower Root Mean Square Error (RMSE) in different years compared with kriging. Downscaling TRMM improved its accuracy by 14 mm. Using the most accurate precipitation result, we modeled annual direct runoff with Kennessey and Soil Conservation Service Curve Number (SCS-CN) models. These models use land use, permeability, and slope data. In runoff modeling, Kennessey gave higher accuracy. Calibrating Kennessey reduced the Normalized RMSE (NRMSE) from 1 in the standard model to 0.44. Direct runoff coefficient map by 1 km spatial resolution was generated by calibrated Kennessey. Validation by the closest gauges to the lake gave a NRMSE of 0.41 which approved the accuracy of modeling.

European Center for Medium‐Range Weather Forecasts Reanalysis (ERA), one of the most widely used precipitation products, has evolved from ERA‐40 to ERA‐20CM, ERA‐20C, ERA‐Interim, and ERA5. Studies evaluating the performance of individual ERA products cannot adequately assess the evolution of the products. We compared the performance of all ERA precipitation products at daily, monthly, and annual data (1980–2018) using more than 2100 Iran precipitation gauges. Results indicated that ERA‐40 performed worst, followed by ERA‐20CM, which showed only minor improvements over ERA‐40. ERA‐20C considerably outperformed its predecessors, benefiting from the assimilation of observational data. Although several previous studies have reported full superiority of ERA5 over ERA‐Interim, our results revealed several shortcomings in ERA5 compared with the ERA‐Interim estimates. Both ERA‐Interim and ERA5 performed best overall, with ERA‐Interim showing better statistical and categorical skill scores, and ERA5 performing better in estimating extreme precipitations. These results suggest that the accuracy of ERA precipitation products has improved from ERA‐40 to ERA‐Interim, but not consistently from ERA‐Interim to ERA5. This study employed a grid‐grid comparison approach by first creating a gridded reference data set through the spatial aggregation of point source observations, however, the results from a point‐grid approach showed no change in the overall ranking of products (despite the slight changes in the error index values). These findings are useful for model development at a global scale and for hydrological applications in Iran.

Hamun Lake, the greatest (>8500 km²) freshwater in the Iran plateau, has almost entirely dried over the last 20 years. The flow of the Hirmand (or Helmand) River, the most important feeding river, has decreased from 4.0 to 1.9 km³ in the border of Iran-Afghanistan. In this river basin, the annual water consumption for irrigation is over 90% of the total consumed water. This study aims to calculate the increase in agricultural water consumption in the last two decades. Due to the lack of in-situ data across Afghanistan (including ∼80% of the studied area), this research utilizes remote-sensing. Using Google Earth Engine, land use maps for the years 2002, 2008, 2013, 2017, and 2021 were developed by a supervised classification scheme. Since 2002, it was found that the cropland area has increased from 2008 to 5475 km². Most cropland has been developed around the Kajaki dam. Based on the Penman-Monteith-Leuning Evapotranspiration version 2 (PML V2) actual evapotranspiration (AET) data (our model assumes the irrigation efficiency equal to 0.3), the annual consumed water has increased from 2 to over 6 km³ in the last two decades. The presented framework in this study can be recommended for other ungauged basins.