A Model‐Agnostic Representation of Prairie Pothole Hydrology: Enhancing Generality and Implementation Across Hydrological Models

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.

Authors

Institutions

Publication Details

Journal
Water Resources Research
Published
2026-03-31
DOI
https://doi.org/10.1029/2025wr043074
Primary Topic
Hydrology and Watershed Management Studies
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A Model‐Agnostic Representation of Prairie Pothole Hydrology: Enhancing Generality and Implementation Across Hydrological Models

Tricia Stadnyk, Alain Pietroniro, Mohamed Ismaiel Moghairib, Martyn P. Clark
Water Resources Research
Hydrology and Watershed Management Studies
article

A Model‐Agnostic Representation of Prairie Pothole Hydrology: Enhancing Generality and Implementation Across Hydrological Models

Tricia Stadnyk, Alain Pietroniro, Mohamed Ismaiel Moghairib, Martyn P. Clark
article en

Abstract

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.

Water Resources ResearchVol. 62(4)
University of Calgary (CA)
National Oceanic and Atmospheric Administration
Life in Land
Openalex Percentile: Top 37%
Hydrology and Watershed Management Studies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.