Evaluating Satellite Precipitation Products in Capturing the Spatio-temporal Rainfall Variability Across North Darfur State, Sudan

Remote Sensing in Earth Systems Sciences https://doi.org/10.1007/s41976-025-00219-2 RESEARCH Evaluating Satellite Precipitation Products in Capturing the Spatio‑temporal Rainfall Variability Across North Darfur State, Sudan Mohammed B. Altoom1,4 · Elhadi Adam1 · Khalid Adem Ali1,2 · Colbert M. Jackson1,3 Received: 12 January 2024 / Revised: 17 October 2024 / Accepted: 18 March 2025 © The Author(s) 2025 Abstract Accurate rainfall measurement is vital when investigating spatio-temporal precipitation variability, especially in arid lands. However, there are regions worldwide where only a few ground-based observations are made. This research evaluated the applicability of six satellite precipitation products (SPPs) in detecting rainfall variability in North Darfur State, Sudan. The SPPs were Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA), African Rainfall Climatology (ARC), Climate Hazards Group Infrared Precipitation with Station Data (CHIRPS), Integrated Multi-satellitE Retrievals for Global Precipitation Measurements (GPM) Final Run (GPMIMERG), Precipitation Estimation from Remote Sensing Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), and the Tropical Applications of Meteorology using SATellite and ground-based observations (TAMSAT). The SPPs were assessed at daily, monthly, and annual timescales for 2000–2019. Four categorical indices, i.e., the probability of detection (POD), probability of false alarm (POFA), bias in detection (BID) and Heidke skill score (HSS), and four continuous indices, i.e., the Pearson correlation coefficient (r), the root mean square error (RMSE), the per cent bias (Pbias), and the Nash–Sutcliffe model efficiency coefficient (NSE) were used to evaluate the accuracy of the SPPs. Results of the statistical analysis showed that (1) at the daily timescale, the SPPs underestimate daily rainfall by 6.53–17.61%, and CHIRPS was the best for detecting rainy days, while PERSIANN-CDR performed poorly; (2) monthly and annual scales performed better than daily timescale, and TAMSAT and CHIRPS portrayed better performance than the other SPPs. Therefore, the two could reasonably estimate rainfall amounts in North Darfur State. Keywords ARC· CHIRPS · GPMIMERG · PERSIANN-CDR · TMPA · TAMSAT · North Darfur State 1 Introduction Rainfall is vital to how water moves through Earth's water cycle, which connects the land, atmosphere and oceans [1]. Therefore, rainfall is essential in Earth’s climate system [2, 3]. Rainfall is * Mohammed B. Altoom ; Elhadi Adam Khalid Adem Ali Colbert M. Jackson 1 not evenly distributed worldwide—some places receive more than normal, while others become prone to droughts [4]. The variable distribution, rate and amount of rainfall determines Earth’s ecosystems [5, 6]. Reliable measurement of rainfall data is essential for understanding the spatio-temporal variability 2 Department of Geology and Environmental Geosciences, College of Charleston, Charleston, SC 29424, USA 3 Department of Geography, Faculty of Natural and Agricultural Sciences, University of the Free State, Bloemfontein 9300, South Africa 4 School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, Westville Campus, Durban 4000, South Africa Faculty of Science, School of Geography, Archaeology and Environmental Studies, University of the Witwatersrand, Johannesburg 2050, South Africa Vol.:(0123456789) Remote Sensing in Earth Systems Sciences of rainfall at various timescales and its effects on hydrology, agriculture, and ecosystem functioning [6–8]. Traditionally, rain gauge observations have been used to characterize the spatio-temporal variability of rainfall [9]. However, in developing countries, due to various reasons, rainfall data is often unavailable, and even if it does exist, the quality are poor or inaccessible [10]. For example, the Meteorological Department of Sudan does not receive sufficient funding from the government; this has hampered the recruitment and retention of trained and volunteer observers and the ability to rehabilitate malfunctioned and/or abandoned meteorological stations [11, 12]. In addition, the second Sudanese Civil War between 1983 and 2005 interfered with measuring and reporting meteorological data, especially in the southern region [13–15]. Therefore, political unrest and conflict significantly influenced the distribution, density and quality of rain gauge measurements. It is particularly true in the Darfur region, which often shows gaps in temporal rainfall data [12, 16]. The limitations associated with rain gauge observations have necessitated alternative methods to measure rainfall [4]. Fortunately, remote sensing provides an alternative approach to ground-based rainfall measurement and monitoring methods. Rainfall data can be gathered from satellite-based products [6]. A variety of satellite precipitation products (SPPs) are produced by the passive microwave (PMW)- and thermal infrared radiation (TIR)-based sensors onboard both geostationary and LEO satellites. The TIR-based method uses algorithms to assess rainfall from cloud-top temperature [17]. The PMW technique can penetrate clouds and produce accurate rainfall amounts compared to the TIR-based technique, which has difficulties identifying rain-producing clouds [18, 19]. However, the poor temporal resolution of low Earth-orbiting satellites makes the PMW technique not helpful in assessing accumulated rainfall over a longer duration [20]. Therefore, uncertainties may originate from the temporal resolutions of satellites, the algorithms used to assess rainfall from cloudtop temperature, and satellite instruments themselves—these reduce the accuracy of satellite rainfall estimates [21]. Other techniques, e.g., combined PMW/TIR [22] or merged PMW/ TIR technique with rain gauge observations or numerical weather models [23] were developed to improve the accuracy of satellite rainfall assessment. Several studies, e.g., Huffman et al. [24], Xie and Arkin [25], Boushaki et al. [26], Ebert et al. [27], Katiraie-Boroujerdy et al.[28], Sanogo et al. [29], Dezfuli et al. [30], Bai et al. [31], Dembélé and Zwart [32], and Ayugi et al. [33] have been conducted using the latter technique at different spatial and temporal scales. The SPPs play a vital role in accurately mapping the spatiotemporal variability of rainfall, which is essential in designing climate change adaptation strategies. Sanogo et al. [29] evaluated the spatio-temporal characteristics of rainfall in West Africa using the gridded African Rainfall Climatology Version 2 (ARC 2.0). The results were consistent with ground observation data. Dezfuli et al. [30] showed that the diurnal precipitation cycle was better captured by the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG)-Final v6 (IMERG6) than the TRMM (Tropical Rainfall Measuring Mission) Multi-satellite Precipitation A (...truncated)