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Correspondence to Author: Achenafi Teklay, 

University of Gondar, College of Agriculture and Environmental Sciences, Natural Resources Management, Gondar, Ethiopia, P. Box 196, Ethiopia

Abstract:

Accurate and reliable rainfall information is crucial for regional water resource management, particularly in developing countries like Ethiopia. Unfortunately, Ethiopia faces challenges with sparse and inconsistent rainfall measurements, as well as a lack of updated data. As a result, the spatio-temporal characteristics of rainfall are poorly understood. In recent years, satellite-derived rainfall products have emerged as an alternative source of rainfall data to overcome these limitations. This study focuses on validating the performance of three satellite rainfall products, namely PERSIANN_CDR, CHIRPS, and TMPA3B42, over the Tekeze basin in Ethiopia. The evaluation involves assessing the prediction accuracy of these products using various statistical measures and spatial comparisons. The study period extends from 2007 to 2017. The results demonstrate that PERSIANN_CDR exhibits a very low percent bias (PBIAS), while CHIRPS and TMPA3B42 significantly overestimate observed rainfall. Moreover, PERSIANN_CDR performs well in capturing rainfall during the kiremt, belg, and bega seasons, with the lowest root mean square error (RMSE) values of 2.9, 1.4, and 0.8 mm/day, respectively. On the other hand, TMPA3B42 performs poorly during these seasons, showing the largest RMSE values of 3.1, 1.9, and 1.1 mm/day, respectively. In terms of detecting observed rainfall, both PERSIANN_CDR and TMPA3B42 exhibit good skills, while CHIRPS has the lowest detection skill across all seasons. Overall, the findings of this validation study highlight the potential of the PERSIANN_CDR product for various operational applications in the Tekeze basin. It can be utilized for studying rainfall patterns and variability in the East African region.

Keywords :Satellite Rainfall Products, PERSIANN_CDR, CHIRPS, TMPA3B42, spatio-temporal, Tekeze basin

1.Introduction: 
2023; Sreelash et al., 2018). Conventionally, rainfall data is derived from rain gauges, believed to be the most reliable method for measuring rainfall (Ayehu et al., 2018; Megersa et al., 2019; Qi, 2020). However, in developing countries, weather stations often face issues like sparse distribution, poor data quality, and lack of updated data, markedly in areas with inaccessible and rugged terrains, where rainfall variability is immense over short distances (Belete et al., 2020; Kimani et al., 2017; Zandler et al., 2019; Mekonen and Berlie, 2020; Ware et al., 2023). As a result, determining rainfall’s spatial distribution over remote areas becomes highly complicated (Ayehu et al., 2018; Belay et al., 2019). Over the years, satellite-derived rainfall estimates, with their long-term and spatially distributed nature, have emerged as a reliable source for overcoming these challenges (Feke et al., 2021; Girma and Berhanu, 2021; Alemayehu et al., 2020; Ayehu et al., 2018). Satellite Rainfall Products (SRPs) offer comprehensive data with fine temporal and spatial resolutions (Park et al., 2017). Therefore, evaluating their performance in different regions is essential for users, scientific communities, and algorithm developers (Belay et al., 2019). This evaluation helps in understanding and quantifying errors and uncertainties and recognizing the best SRPs for site-specific applications (Ayehu et al., 2018; Dinku et al., 2007; Jiang et al., 2016). Several studies have been conducted to evaluate the performance of different satellite rainfall products (SRPs) in various regions of the world, including Asia (Ji et al., 2022; Kesarwani et al., 2023; Masood et al., 2023; Peng et al., 2021; Sharannya et al., 2020; Zhu et al., 2022), Africa (Gebrechorkos et al., 2020; Libanda et al., 2020; Omonge et al., 2022; Polong etal., 2023), south America (Brasil Neto et al., 2021; dos Reis et al., 2017; Ringard et al., 2015) and europea (Aksu and Akgül, 2020; Zandler et al., 2019). These studies have shown that the performance of SRPs can vary significantly depending on the geographical location, topography, and season. For example, Xia et al. (2021) compared four SRPs with rain gauge data in the Pearl River Basin, China. They found that the satellite rainfall products generally performed well over the basin but showed limitations in detecting heavy rainfall events, underestimating high rainfall events, and overestimating the number of rainy days. Similarly, Musie et al. (2019) compared three SRPs over the Nile basin and recommended CHIRPS products due to their superior performance, potentially attributed to their fine spatial resolution. Previous researches still recommended to conduct site-specific validation of SRPs in diverse physiographic settings before using them for operational purposes.

Citation:

Achenafi Teklay. Evaluation of multi-source satellite rainfall products in tekeze basin, Ethiopia. Journal of Environmental And Sciences 2024.