Performance Assessment of Satellite-Based Rainfall Products in the Abbay Basin, Ethiopia
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| Publicado en: | Remote Sensing vol. 18, no. 1 (2025), p. 2-30 |
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| Autor principal: | |
| Otros Autores: | , |
| Publicado: |
MDPI AG
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| Materias: | |
| Acceso en línea: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| Resumen: | <sec sec-type="highlights"> What are the main findings? <list list-type="bullet"> <list-item> </list-item>In the Gojjam sub-basins, satellite rainfall products capture broad rainfall patterns but exhibit distinct strengths and limitations. <list-item> These products consistently overestimate light rainfall and underestimate heavy rainfall, with systematic errors becoming more dominant as intensity increases. </list-item> What are the implications of the main findings? <list list-type="bullet"> <list-item> </list-item>Product-specific calibration is required to correct characteristic biases, particularly reducing missed light rainfall in CHIRPS and false alarms in MSWEP/TAMSAT. <list-item> Sub-basin scale evaluation underscores the importance of localized calibration for reliable hydrological modeling and climate assessments in Ethiopia’s complex highland terrain. </list-item> Satellite-based rainfall products (SRPs) are indispensable for hydro-climatological research, particularly in data-limited environments such as Ethiopia. This study systematically evaluates the performance of three widely used SRPs: Climate Hazards Group InfraRed Precipitation with Station data version 2 (CHIRPS), Tropical Applications of Meteorology using Satellite and ground-based observations version 3.1 (TAMSAT), and Multi-Source Weighted Ensemble Precipitation version 2.8 (MSWEP) across the North and South Gojjam sub-basins of the Abbay Basin. Using ground observations as benchmarks, spatial and temporal accuracy was assessed under varying elevation and rainfall intensity conditions, employing bias decomposition, error analysis, and detection metrics. Results show that rainfall variability in the region is shaped more by the local climate and topography than elevation, with elevation alone proving a weak predictor (R2 < 0.5). Among the products, MSWEP v2.8 demonstrated the highest daily rainfall detection skill (≈ 87–88%), followed by TAMSAT (≈78%), while CHIRPS detected only about half of rainfall events (≈54%) and tended to overestimate no-rain days. MSWEP’s error composition is dominated by low random error (~52%), though it slightly overestimates rainfall and rainy days. TAMSAT provides finer-resolution data that capture localized variability and dry conditions well, with the lowest false alarm rate and moderate random error (~59%). CHIRPS exhibits weaker daily performance, dominated by high random error (~66%) and missed bias, though it improves at monthly scales and better captures heavy and violent rainfall. Seasonally, SRPs reproduce MAM rainfall reasonably well across both sub-basins, but their performance deteriorates markedly in JJAS, particularly in the south. These findings highlight the importance of sub-basin scale analysis and demonstrate that random versus systematic error composition is critical for understanding product reliability. The results provide practical guidance for selecting and calibrating SRPs in mountainous regions, supporting improved water resource management, climate impact assessment, and hydrological modeling in data-scarce environments. |
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| ISSN: | 2072-4292 |
| DOI: | 10.3390/rs18010002 |
| Fuente: | Advanced Technologies & Aerospace Database |