Quantifying hydrological connectivity in inland arid regions using transfer entropy and multi-source data

Hydrological modeling in inland arid regions faces persistent challenges due to the strong spatiotemporal variability of water fluxes and limited availability of high-quality meteorological data. Existing studies often rely on single-source interpolated inputs and conventional evaluation metrics, which constrain the understanding of internal interactions within hydrological subsystems. To address this gap, we employ a multi-source data framework combined with an information-theoretic approach to assess hydrological process connectivity and causal relationships in the Ebinur Basin of northwestern China. We applied the Variable Infiltration Capacity (VIC) model, enhanced with glacier dynamics, using three station-interpolated datasets and one satellite-based reanalysis product. Transfer entropy was utilized to capture directional dependencies between hydrological variables across seasonal and temporal scales. Results indicate that satellite-based and interpolated datasets produce contrasting spatial and seasonal patterns of water fluxes. Evapotranspiration and runoff dominate in summer and autumn, while snow water equivalent exhibits weak causal coupling. Transfer entropy provided more detailed insights than traditional correlation methods, particularly in identifying information flow between runoff and soil moisture. These findings highlight the importance of integrating information-theoretic diagnostics and multi-source data for improving hydrological understanding and prediction in data-scarce, environmentally sensitive arid basins.