Abstract Modeling streamflow in low‐lying, flat, and pothole‐dominated prairie or Arctic regions is challenging due to variable non‐contributing areas that influence how runoff translates to streamflow. Several modeling approaches have been developed to represent these dynamics, but many (a) lump depressions and permit spill only after a fixed capacity is reached, (b) rely heavily on calibration, (c) are unsuitable for large basins, (d) do not account for non‐pothole contributions, and/or (e) are not model‐agnostic. Here we present HDSv2, a second‐generation Hysteretic Depressional Storage (HDS) module that is open‐source, model‐agnostic, numerically robust, and grounded in long‐established physical understanding of prairie potholes. HDSv2 represents dynamic contributing area and storage–discharge hysteresis, enabling realistic simulation of fill‐and‐spill behavior and cold‐region processes. We couple HDSv2 with three hydrological and land‐surface models of differing architectures: HYPE (Hydrological Predictions for the Environment), MESH (Modélisation Environnementale communautaire—Surface and Hydrology), and SUMMA (Structure for Unifying Multiple Modeling Alternatives), applied in the Smith Creek River Basin, Canada. Results show that HDSv2 improves numerical stability and process fidelity relative to the original HDS model, which exhibited instabilities affecting contributing‐area simulation within HYPE. Across all host models, integrating HDSv2 produces more robust hydrographs than the original configurations and better reproduces observed relationships between depressional storage and contributing area. Although hydrograph improvements vary by host, additional performance metrics show consistent gains in both high and low flow conditions. These findings demonstrate that HDSv2 provides a transferable and scalable pathway for incorporating depressional‐storage dynamics into diverse hydrological models and regions.
Moghairib et al. (Tue,) studied this question.