Regionalization of Hydrologic Behavior and Pothole Water Storage Dynamics in the Prairie Pothole Region

Abstract In pothole‐dominated catchments, such as those in the Prairie Pothole Region (PPR), potholes strongly influence catchment hydrologic behavior through complex and dynamic fill–spill–connection mechanisms. This complexity—combined with the predominance of ungauged catchments and the lack of high‐resolution pothole inventories—poses challenges for both traditional hydrologic models and purely data‐driven deep learning approaches. To address this, we developed the δHBV‐Pot model within a differentiable modeling framework (δ). This physics‐informed deep learning model integrates the conceptual HBV model with a probabilistic algorithm that emulates the aggregate effects of pothole fill–spill–connection processes. Applied to 98 PPR catchments, δHBV‐Pot achieves stronger predictive accuracy and physical realism than a purely data‐driven Long Short‐Term Memory (LSTM) model and two conceptual hydrologic models. The PPR‐scale regional δHBV‐Pot model successfully simulates hydrologic behavior for the majority of pseudo‐ungauged (test) catchments withheld during model development, effectively regionalizing (a) high‐flow magnitude and interannual variability, (b) intra‐annual flashiness of high‐flow and normal flow conditions, and (c) interannual variability in pothole water storage dynamics. Moreover, the model identifies vulnerable catchments with large high‐flow magnitude and variability—even in the absence of streamflow data—and delineates catchments with varying temporal variability in pothole water storage without requiring detailed pothole inventories. Our findings highlight the value of combining conceptual hydrology with data‐driven deep learning models in pothole‐dominated regions. This integrated approach enables the regionalization of high‐flow and pothole storage characteristics to ungauged catchments, providing critical insights for vulnerability assessment and the design of sustainable water and ecological management strategies in pothole‐dominated landscapes.