Google Earth Engine‐Based Estimation of the Spatio‐Temporal Distribution of Suspended Sediment Concentrations in a Multi‐Channel River System of the Yangtze River Basin

Abstract

Lowland multi‐channel alluvial river systems are highly variable in frequency and magnitude of floodplain inundation and are vulnerable to human activities such as damming. In the Yangtze River Basin, the Three Gorges Dam (TGD) has trapped >80% of upstream sediment supply, causing downstream scouring and rapid geomorphic changes in river and its floodplain lakes. Suspended sediment concentration (SSC) is widely used to monitor river morphodynamics, but traditional measurements of SSC are time consuming, costly and difficult to quantify SSC in a large spatial scale. Using Google Earth Engine and in situ observed hydrological data, we created a multiple linear regression model to map SSC in the multi‐channel system Songzi River of the Yangtze. The new SSC predictive model achieved high accuracy (R2: 0.87) and showed opposite downstream trends in SSC during peak flood years and normal flood years. For the first time, we found that in the peak flood year of 1998 the study rivers exhibit a downstream increase trend of SSC with an abrupt increase in their middle reach, while SSC in normal flood years experiences a downstream decline with minimal changes. A prominent difference in SSC is also revealed after the TGD with a reduction of 60%. Furthermore, SSC in the closely hydrologically connected lakes is more dynamic than the less connected lakes. Our study demonstrates that the proposed method enables to rapidly quantify the spatio‐temporal SSC distribution in the multi‐channel systems on the floodplain of large alluvial rivers and highlights the importance of connectivity in regulating SSC dynamics.