A Model-Agnostic Representation of Prairie Pothole Hydrology: Enhancing Generality and Implementation Across Hydrological Models
Bibliographic record
Abstract
This repository contains the HDS standalone software source code and the source codes of the following modified hydrological models, which were modified to accommodate HDS, HYPE, MESH, and SUMMA. This repository also includes the model inputs and results for the three hydrological models at the Smith Creek Research Basin (SCRB). The software and data are part of the following paper "A Model-Agnostic Representation of Prairie Pothole Hydrology: Enhancing Generality and Implementation Across Hydrological Models" submitted to Water Resources Research for publication. The source codes are also available at the following github repositories: HDS: https://github.com/CH-Earth/HDS HYPE: https://sourceforge.net/projects/hype/files/ MESH: https://github.com/MESH-Model/MESH-Dev SUMMA: https://github.com/CH-Earth/summa/tree/develop The following folders are included: HDS_standalone_code: contains the HDS standalone source code along with a hypothetical test case. HYPE: This folder contains the modified HYPE model source code (located under source_code subfolder) and model setup files and results for the comparison of HDSv1 and HDSv2 with uncalibrated model setup (located under runs/HDS_v1_v2_comparison subfolder), HYPE-ilake model (located under runs/HYPE-ilake subfolder), and HYPE-HDS model (located under runs/HYPE-HDS subfolder). MESH: This folder contains the modified MESH model source code (located under source_code subfolder) and model setup files and results for MESH-PDMROF model (located under runs/MESH-PDMROF subfolder) and MESH-HDS model (located under runs/MESH-HDS subfolder) SUMMA: This folder contains the modified SUMMA model source code (located under source_code subfolder) and model setup files and results for SUMMA-noPothole model (located under runs/SUMMA-noPothole subfolder) and SUMMA-HDS model (located under runs/SUMMA-HDS subfolder) Abstract Modelling 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 modelling approaches have been developed to represent these dynamics, but many 1) lump depressions and permit spill only after a fixed capacity is reached, 2) rely heavily on calibration, 3) are unsuitable for large basins, 4) do not account for non-pothole contributions, and/or 5) 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 Modelling 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.051 | 0.022 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".