Remote Sensing Data Efficacy for Hydro-Climate Characterisation of Rainfall in Data Scarce Areas: A Case of Notwane Sub-Catchment, Botswana

Technological advancements provide remote sensing (RS) data as a viable option for hydro-climate analysis. In Water resources management, it is important to establish the efficiency and effectiveness of application of the various data products especially in data scarce areas where reliability may contribute significantly to decision making. The study therefore aims to assess the effectiveness of remote sensing products in characterizing the hydro-climate of a data scarce area. In Notwane Sub-Catchment (NSC), the study analyses Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS-v2), Climate Research Unit (CRU), fifth generation of the European Centre for Medium-Range Weather Forecasts (ECMWF) Atmospheric Reanalysis (ERA5) and TERRACLIMATE monthly rainfall data from 1990- 2022 against ground-based gauge data using the Kling-Gupta Efficiency (KGE) for assessing temporal dynamics (r), biasness (β) and variability (γ); and scatterplots were applied to compare the two datasets conformity and reliability. Spatio-temporal analysis was performed using the CUSUM and non-parametric Mann-Kendall (MK) test at α=0.05 level. KGE results averaged at 0.52 with r, β and γ at 0.67,1.00 and 0.81 respectively. Mann Kendall test picked some natural fluctuations consistent with CUSUM, of wet and dry seasons without any significant changes. CHIRPS-v2 overestimated low and underestimated high rainfall at 55% and 45% respectively. Two distinct wet and dry seasons were observed similar to the seasonality observed with gauge data. The study effectively characterised the spatio-temporal patterns of rainfall using remote sensing data and validated CHIRPS-v2 data as an alternative remote sensing product for data scarce areas. The study contribute knowledge to data scarce area on application of remote sensing data.