Spatiotemporal dynamics of evapotranspiration and implications for agricultural water management in the Lake Tana Basin, Ethiopia: a cloud-computing approach

Abstract

Evapotranspiration (ET) monitoring is essential for many aspects of regional water resource management. The Surface Energy Balance Algorithm for Land (SEBAL) is used in this work to examine the spatiotemporal fluctuations of actual ET in Ethiopia’s Lake Tana Basin. To estimate ET, SEBAL was used together with the Google Earth Engine (GEE) platform. Key remote sensing variables such as LST, NDVI, and albedo were extracted using custom JavaScript code. The accuracy of SEBAL-based ET was validated using FAO Penman-Monteith estimates from meteorological station data of 2021 and 2022 and further compared with results from previous studies in similar climatic regions. The daily actual evapotranspiration using the SEBAL model in the Lake Tana Basin ranged from 0 to 7.1 mm day⁻¹, with a daily mean value of 4.96 mm day⁻¹. Our results also showed spatial variability in ET over different land covers; it was established that areas covered by trees had a high evapotranspiration rate, followed by shrubland, built-up land, cropland, and grassland, respectively. Similarly, open water bodies had high evaporation rates. The comparison of SEBAL against the FAO Penman-Monteith method resulted in a strong correlation (R2 = 0.83), a root mean square error (RMSE) of 0.22, and a mean absolute error (MAE) of 0.18. The SEBAL model successfully represented seasonal ET fluctuations (3.0–4.3 mm day⁻¹), with associated uncertainty limits of (± 0.39–0.44 mm day⁻¹). Furthermore, the model differentiated ET across various land covers (1.97–4.08 mm day⁻¹) in a very realistic manner. These SEBAL results demonstrate that the model can reliably estimate actual ET in regions with limited or scarce ground-based hydrological data, providing useful information for managing water in the Lake Tana Basin, Ethiopia.