Diagnosing Cryospheric Runoff Dynamics: A Distributed Differentiable Hydrological Model With Global Transfer Learning

Abstract

Accurate hydrological prediction in alpine regions remains challenging due to complex cryospheric processes and limited observational records. Traditional hydrological models are often subject to structural uncertainty, whereas purely data‐driven deep learning (DL) models may lack physical interpretability under non‐stationary climate conditions. Here, we present a distributed differentiable framework for alpine basins that combines bottom‐up physical constraints with top‐down global learning to test frozen‐ground structural hypotheses while mitigating the effects of local data scarcity. Specifically, we developed differentiable Coupled Routing and Excess STorage model (dCREST), a distributed differentiable physics‐informed model that enables the joint diagnosis of model structure, parameter dynamics, and spatial transferability. Using the Headwater Area of the Yellow River (HAYR) as a test bed, we investigated the hydrological role of frozen‐ground processes and the value of global pretraining for runoff simulation in data‐scarce alpine regions. Results indicate that conventional single‐layer soil structures are unable to adequately capture permafrost‐controlled hydrological dynamics. In contrast, an explicit dual‐layer soil structure coupled with dynamic cryospheric parameterizations substantially improves the simulation of seasonal runoff partitioning and long‐term hydroclimatic responses. Furthermore, pre‐training on globally distributed catchments enables dCREST to learn transferable rainfall‐runoff relationships, enhancing modeling performance despite limited local observations. This transfer learning strategy increases the average Nash–Sutcliffe efficiency (NSE) from 0.35 in the current operational system to 0.63. Our findings demonstrate that differentiable hydrological modeling provides a powerful framework for simultaneously diagnosing model structural deficiencies and improving runoff prediction in data‐scarce alpine basins through physically constrained transfer learning.