Abstract. The basin-scale river channel migration, driven by multiple factors such as hydrometeorological conditions, tectonic movements, and human activities, exerts a profound influence on regional morphological features, water resource, and ecosystem over long-term evolution. Conventional river dynamics approaches struggle to quantitatively characterize basin-scale channel migration due to difficulties in incorporating factors like basin hydrological processes and tectonic activities. This study proposed a novel technique for the numerical simulation of river channel migration, integrating a fully coupled multi-processes landscape evolution model (e.g., hydrological, geomorphic and tectonic processes) with channel extraction. Furthermore, to address model parameter uncertainty, a Markov chain Monte Carlo (MCMC) method with a modified likelihood function is used for parameter uncertainty quantification. Simultaneously, a computationally efficient Long Short-Term Memory (LSTM)-based surrogate model for channel migration is developed to overcome the computational bottleneck in uncertainty analysis. Applied to the Kumalake River Basin within China's Tarim Basin, the study employs the Landscape Evolution-Penn State Integrated Hydrologic Model (LE-PIHM) to construct the landscape evolution model. Combined with channel extraction, it simulates historical (2000–2021) and future (2021–2100) landscape evolution and channel migration processes. Results demonstrated that the developed river channel migration model, aided by parameter uncertainty analysis, reliably captures the dynamics of channel migration in the study area during 2000–2021. Additionally, the LSTM-based surrogate model achieves high accuracy, effectively resolving computational challenges in parameter uncertainty analysis. Predictions under different climate scenarios reveal significant variations in future channel evolution, indicating that climate change will profoundly reshape basin geomorphic features and river patterns.
Wu et al. (Thu,) studied this question.