A Model-Agnostic Representation of Prairie Pothole Hydrology: Enhancing Generality and Implementation Across Hydrological Models
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Notice bibliographique
Résumé
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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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle