Unraveling the hydrology and sediment balance of an ungauged lake in the Sudano-Sahelian region of West Africa using remote sensing

Abstract

Abstract. The presence of ephemeral ponds and perennial lakes in the Sudano-Sahelian region of West Africa is strongly variable in space and time. Yet, they have important ecological functions and societies are reliant on their surface waters for their lives and livelihoods. It is essential to monitor and understand the dynamics of these lakes to assess past, present, and future water resource changes. In this paper, we present an innovative approach to unravel the sediment and water balance of Lac Wégnia, a small ungauged lake in Mali near the capital of Bamako. The approach uses optical remote sensing data to identify the shoreline positions over a period of 22 years (2000–2021) and then attributes water surface heights (WSHs) to each observation using the lake bathymetry. We then present a novel methodology to identify and quantitatively analyze deposition and erosion patterns at lakeshores and in lake beds. The method therefore represents a significant advancement over previous attempts to remotely monitor lakes in the West African drylands, since it considers not only changes in water depth to explain recent declining trends in lake areas, but also changes in the storage capacity. At Lac Wégnia, we recognize silting at the tributaries to the lake, but overall, erosion processes are dominant and threaten the persistence of the lake because of progressive erosion through the natural levee at the lake outlet. This factor contributes 66 %±18 % to the decreasing WSH trend, while 34 %±18 % of the dry-season lake level changes are explained by increasing evaporation from the lake and by possibly falling groundwater tables. Due to the decreasing reservoir capacity of the lake, WSHs are declining even in the wet season in spite of positive rainfall patterns.

Integrating Open-Source Datasets to Analyze the Transboundary Water–Food–Energy–Climate Nexus in Central Asia

“…These datasets can be integrated with cloud-computing systems such as Google Earth Engine (GEE), thereby facilitating data analysis for large areas and extended periods. This data integration can provide vital information to fill existing data gaps, such as identifying areas of deposition and erosion in surface waters [161] or alterations of the river course [162].…”