On the benefit of the Canadian Small Lake Model to better represent the impact of small natural lakes on GEM-Hydro streamflow simulations.
Notice bibliographique
Résumé
This study aims at assessing the impact of a new lake model on streamflow simulations performed with the GEM-Hydro hydrologic model developed at ECCC. GEM-Hydro is at the heart of the National Surface and River Prediction System (NSRPS) which ECCC uses to forecast river flows over most of Canada. The GEM-Hydro model mainly consists of the GEM-Surf component to represent surface processes, and of the Watroute model to represent river and lake routing, in order to perform streamflow simulations and forecasts. The surface component of GEM-Hydro can simulate 5 different types of surfaces. Currently, the water tile consists of a very simple algorithm which, in terms of water balance, consists of producing runoff fluxes simply equal to precipitation minus evaporation. This runoff over water surfaces is then provided as input, along with runoff and drainage generated over other surface tiles, to the Watroute model. The Watroute version used in GEM-Hydro currently only represents major lakes (area greater than 100km2) along the river networks, and does not represent the impact that small lakes can have on streamflow, which mainly consists in slowing down runoff before it reaches the main streams of the network. Recently, the Canadian Small Lake Model (CSLM) was implemented in the surface component of GEM-Hydro to represent the energy and water balance over water tiles more accurately. So far, CSLM simulations have been shown promising in terms of evaporation, ice cover, absolute and dew point temperature simulations, compared with the former algorithm used over water. However, the impact of CSLM on the resulting streamflow simulations performed with GEM-Hydro has not been evaluated yet. This study aims first at evaluating the impact of CSLM on streamflow simulations, and secondly at testing different CSLM configurations as well as different coupling strategies with Watroute, with the objective of finding the best set up for the prediction of streamflow in Canada. For example, overland runoff generated by the land tile can be provided to the water tile of the same grid point in different ways, and the outflow computed at the outlet of the water tile can be computed with different parameters. Moreover, different outflow computations have to be taken into account depending on if the water tile of a grid point represents subgrid-scale lakes, or if on the contrary it belongs to a lake spanning over multiple model grid points. To do so, different GEM-Hydro open-loop simulations have been performed on the Lake of the Woods watershed, located in Canada, with and without CSLM to represent water tiles. The CSLM configurations leading to the best results are presented here. CSLM simulations are also evaluated in terms of surface fluxes, to ensure that the main purpose of the model, which is to improve surface fluxes to ultimately improve atmospheric forecasts, is preserved, compared to the default configuration of the model. Ideas for further improving the coupling between the GEM-Hydro surface and routing components, in terms of lake processes, are also presented and will be tested in future work.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 0,001 |
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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».