Can Turbidity Data from Remote Sensing Explain Modelled Spatial and Temporal Sediment Loading Patterns? An Application in the Lake Tana Basin

• Published in: Environmental modeling & assessment 2024-10, Vol.29 (5), p.871-882
• Main Authors: Nkwasa, Albert, Getachew, Rediet Esayas, Lekarkar, Katoria, Yimer, Estifanos Addisu, Martínez, Analy Baltodano, Tang, Ting, van Griensven, Ann
• Format: Article
• Language: English
• Subjects: Applications of Mathematics; Earth and Environmental Science; Environment; Fluvial sediments; Hydrologic models; Inlets; Lake basins; Lakes; Math. Appl. in Environmental Science; Mathematical Modeling and Industrial Mathematics; Operations Research/Decision Theory; Quality management; Remote monitoring; Remote sensing; Rivers; Seasonal variations; Sediment load; Sediment transport; Soil water; Spatiotemporal data; Turbidity; Water inflow; Water quality; Water quality management
• ISSN: 1420-2026; 1573-2967
• DOI: 10.1007/s10666-024-09972-y

Understanding the spatial and temporal patterns of sediment loading in water bodies is crucial for effective water quality management. Remote sensing (RS) has emerged as a valuable and reliable tool for monitoring turbidity, which can provide insights into sediment dynamics in water bodies. In this study, we investigate the potential of turbidity data derived from RS to explain simulated spatial and temporal sediment loading patterns in the Lake Tana basin, Ethiopia. Utilizing existing RS lake turbidity data from Copernicus Global Land Service (CGLS) and simulated seasonal and multiyear trends of river sediment loadings into Lake Tana from the Soil and Water Assessment Tool (SWAT + model), we estimate correlations at different river inlets into Lake Tana. The results reveal a strong positive correlation ( R 2  > 0.66) between the multiyear monthly average sediment load from inflow rivers and RS lake turbidity at most river inlets. This indicates that the simulated river sediment loads and lake turbidity at river inlets exhibit similar seasonal patterns. Notably, higher turbidity levels are observed at the river inlet with the highest sediment load export. These findings highlight the potential of RS turbidity products in characterizing temporal and spatial patterns of sediment loadings, particularly in data-scarce regions, contributing to a better understanding of water quality dynamics in such areas.