Performance Assessment of Satellite-Based Rainfall Products in the Abbay Basin, Ethiopia

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出版年:Remote Sensing vol. 18, no. 1 (2025), p. 2-30
第一著者: Terefe, Gashaw Tadela
その他の著者: Melesse, Assefa M, Abate Brook
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MDPI AG
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024 7 |a 10.3390/rs18010002  |2 doi 
035 |a 3291810055 
045 2 |b d20250101  |b d20251231 
084 |a 231556  |2 nlm 
100 1 |a Terefe, Gashaw Tadela  |u Africa Center of Excellence for Water Management, Addis Ababa University, Addis Ababa P.O. Box 1176, Ethiopia; tadela.terefe@aau.edu.et 
245 1 |a Performance Assessment of Satellite-Based Rainfall Products in the Abbay Basin, Ethiopia 
260 |b MDPI AG  |c 2025 
513 |a Journal Article 
520 3 |a <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. 
651 4 |a Africa 
651 4 |a Ethiopia 
653 |a Benchmarks 
653 |a Environmental assessment 
653 |a Accuracy 
653 |a Bias 
653 |a Water resources management 
653 |a Datasets 
653 |a Performance evaluation 
653 |a Calibration 
653 |a Rainfall 
653 |a Elevation 
653 |a Topography 
653 |a Basins 
653 |a Mountainous areas 
653 |a Hydrology 
653 |a Rainfall intensity 
653 |a Error analysis 
653 |a Mountain regions 
653 |a Precipitation 
653 |a Climate change 
653 |a Error detection 
653 |a Resource management 
653 |a Systematic errors 
653 |a Random errors 
653 |a Spacecraft recovery 
653 |a Drought 
653 |a Performance assessment 
653 |a False alarms 
653 |a Environmental hazards 
653 |a Climate models 
653 |a Land use 
653 |a Ground-based observation 
653 |a Satellite observation 
653 |a Composition 
653 |a Ground stations 
653 |a Rain 
700 1 |a Melesse, Assefa M  |u Department of Earth and Environment, Florida International University, Miami, FL 33199, USA 
700 1 |a Abate Brook  |u Department of Civil Engineering, Addis Ababa Science and Technology University, Addis Ababa P.O. Box 16417, Ethiopia; brook.abate@aastu.edu.et 
773 0 |t Remote Sensing  |g vol. 18, no. 1 (2025), p. 2-30 
786 0 |d ProQuest  |t Advanced Technologies & Aerospace Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3291810055/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch 
856 4 0 |3 Full Text + Graphics  |u https://www.proquest.com/docview/3291810055/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch 
856 4 0 |3 Full Text - PDF  |u https://www.proquest.com/docview/3291810055/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch