Quality Assessment of Satellite Data for Rainfall Estimation using Streamflow Simulation in Hydrological Modeling (Case Study: the Tighsiah Catchment)

Background: Precipitation is one of the most critical inputs in hydrological models.Due to its significant spatiotemporal variability, high-resolution temporal and spatial data are required for accurate hydrological modeling.Precise precipitation measurements are generally obtained from conventional meteorological stations.However, sparse rain gauge stations can lead to poor spatial representation of precipitation.This spatial inaccuracy can have a significant impact on modeling results, especially in areas with significant geographic variations, such as mountainous regions.The lack of high-resolution precipitation data can result in low-quality hydrological simulations and inadequate solutions for water resource problems.In developing countries such as Iran, financial and technical limitations result in sparsely and unevenly distributed rain gauge networks, creating major challenges in water resource prediction and management.To address this gap, precipitation, temperature, and evapotranspiration data required for hydrological modeling are provided by various organizations using satellite-based remote sensing products.Gridded precipitation products on a global or quasi-global scale with various temporal and spatial resolutions have been produced in recent decades.These products are compiled from various sources and processed using complex models to generate high-accuracy precipitation maps.Therefore, evaluating the quality, suitability, and accuracy of these products in different regions is necessary before their use in hydrological modeling and water resource decision-making.Methods: This study evaluates the quality of four widely used high-resolution satellite-based precipitation estimation products: CMORPH, 3B42RT, 3B42, and PERSIANN, for simulating water flow using the Soil and Water Assessment Tool (SWAT) hydrological model in the 312 km 2 mountainous Tigh Siah catchment in southeastern Iran.The unique geographic and climatic features of the Tigh Siah watershed provide an appropriate environment for assessing the accuracy and efficiency of satellite data in hydrological simulations.Two different calibration approaches for the SWAT model were examined to assess the quality of satellite-based precipitation estimates.The first approach involved calibration using measured precipitation data from rain gauge stations as model inputs.These measured data served as a reference for evaluating the accuracy and precision of the satellite data.The second approach involved calibration using each of the satellite precipitation products as model inputs.In this method, satellite data were directly input into the SWAT model, and the simulated water flow results were compared with those obtained using measured data.This comparison aimed to determine the accuracy and efficiency of each satellite product in simulating water flow in the Tigh Siah watershed.The results of this study can help identify the strengths and weaknesses of each satellite-based precipitation product and provide strategies for improving the accuracy of hydrological simulations. Results:The results indicate significant errors in the estimates from satellite-based precipitation data, despite variation in the performance of each satellite product.Specifically, 3B42RT and CMORPH demonstrated better quality in precipitation estimation than 3B42 and PERSIANN.These differences in the accuracy of satellite data can significantly affect hydrological simulation outcomes.The model calibrated with satellite-based precipitation estimates performed better in simulating flow than that calibrated with rain gauge station data.However, the model calibrated with satellite data led to the overestimation of the Curve Number (CN), suggesting caution when using parameter values calibrated with satellite-based inputs.To improve simulation accuracy, the error correction of satellite precipitation estimates was conducted in two stages.First, the error in each satellite data group was estimated in each pixel by dividing the monthly precipitation estimate by the corresponding rain gauge precipitation.Then, the monthly precipitation of each satellite data was multiplied by the estimated monthly error to eliminate errors in all satellite