Stable diffusion-evolution tendencies of the Lower Yellow River corridor during floods in the 21st century

This study investigates the stable diffusion -evolution tendencies of the Lower Yellow River (LYR) corridor during floods in the 21st century following Xiaolangdi Reservoir's operation. While previous studies have assessed shortterm, cross-sectional channel adjustments in the LYR, our understanding remains limited regarding the longterm, centennial -scale planform morphological responses to Xiaolangdi Reservoir's operation, as field data are limited and multi-spatiotemporal information is difficult to obtain. We integrate multi -source satellite data to extract typical river morphology information. A stochastic channel -forming relationship model based on hydraulic geometry theory links water-sediment inputs with channel adjustments. The data -model integration effectively captures the dynamics of key channel morphology variables. Long-term forecasts of flood period flows and incoming sediment loads are generated using a GradientBoosted Long Short -Term Memory (LSTM) machine learning approach. The channel centerline evolution is numerically simulated by incorporating the forecasted inputs into a physically based meander migration model and representatively analyzed through clustering and gradient descent regression after Monte Carlo simulation. The spatially differentiated analysis and quantification of channel evolution dynamics fill existing knowledge gaps by providing important insights for developing adaptive channel maintenance and corridor management strategies. The integrated methodologies advance understanding of river channel dynamics under complex human -nature interactions in large plains watersheds.