Reliability assessment of 17 gridded rainfall dataset for the construction of a daily high-resolution reanalysis (4km) across Senegal for agroclimatic applications

This study focuses on the development of a new high-resolution gridded rainfall dataset for Senegal, which is essential for rainfed agriculture, which is sensitive to climate variability. Given the limited number of rain gauges, the research will evaluate 17 publicly available gridded rainfall datasets (P-datasets) against data from 21 stations of the Senegalese National Meteorological Service (ANACIM) over a 17-year period (2005-2021). The evaluation uses several agroclimatic indices, including rainfall onset and cessation, rainy season duration, and extreme events. The results show that the reliability of the P-datasets varies significantly depending on the metrics used. For total precipitation, ARC2, CHIRPS, ERA5 and RFEv2 were found to be the most reliable datasets. ERA5 achieved the highest Kling-Gupta Efficiency (KGE) value of 0.81 at the daily scale. In terms of agroclimatic parameters, ARC2, CHIRPS and RFEv2 excelled in accurately representing the start (KGE ≥ 0.45) and end (KGE ≥ 0.39) dates of the rainy season. However, the P datasets generally overestimate rainfall events and struggle to identify dry spells. The newly constructed merged dataset (M-dataset) showed over 100% improvement in correlation for daily estimates and a significant reduction in bias of 99.19% for ARC2, 80% for CHIRPS and 90.57% for RFEv2. This research provides critical insight into the selection of appropriate datasets to improve climate information for agricultural decision making in Senegal.