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Assessing the suitability of gridded precipitation products for hydro-climatic applications in a sparsely Gauged arid basin of Iran | ||
| Journal of Applied Research in Water and Wastewater | ||
| مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 04 آبان 1404 اصل مقاله (2.55 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22126/arww.2025.12654.1400 | ||
| نویسندگان | ||
| Ali Nasirian* ؛ Mahsa Mardani | ||
| Department of Civil Engineering, Faculty of Engineering, University of Birjand, Birjand, Iran. | ||
| چکیده | ||
| Hydro-climatic research and water resource management in arid, data-scarce regions depend fundamentally on precise precipitation data. This study presents the first comprehensive, multi-scale evaluation of four prominent gridded precipitation products (GPPs)—TRMM, CHIRPS, GSMaP, and ERA5—in the climatically challenging and sparsely gauged South Khorasan province of Iran (2010–2019). Using ground-based observations as a reference, GPP performance was evaluated across multiple timescales with a suite of statistical metrics. The evaluation framework leverages diagnostic visualizations, such as Taylor and performance diagrams, to provide deeper insights into error structures than can be achieved through traditional map-based assessments. The analysis revealed a clear performance ranking: the satellite-based TRMM and GSMaP consistently performed best, showing higher accuracy (median RMSE ≈ 2.91–3.05 mm/day), stronger correlation (median CC ≈ 0.63–0.65), and a more balanced detection skill (median CSI ≈ 0.43–0.45). In contrast, the ERA5 reanalysis product, despite achieving the highest probability of detection (POD ≈ 0.78), suffered from notable systematic biases and the largest random errors. Performance for all products degraded during the arid summer, and estimation errors systematically increased in wetter regions. We conclude that the gauge-adjusted satellite products, GSMaP and TRMM, provide the most dependable precipitation estimates for the study area. These findings offer a critical, evidence-based guide for selecting appropriate GPPs in this vulnerable environment and provide insights for future algorithm development. | ||
| کلیدواژه‌ها | ||
| Complex topography؛ Data-scarce region؛ Error characterization؛ Multi-scale؛ Seasonal performance | ||
| مراجع | ||
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