Comparison of Downscaled RCM and GCM data for Hydrologic Impact Assessment
Notice bibliographique
Résumé
From observations of increases in global average air and oceanic temperatures, melting of polar ice and significant increases in net anthropogenic radiative forcing, it is clear our global climate system is undergoing substantial warming (IPCC, 2007). A key area of concern for hydrologists and engineers alike is to determine how this warming will affect various hydrologic processes. To date, climate change impact studies have generally involved the downscaling of large-scale atmospheric predictors with the result then being input into a hydrological model to see how flow in a river/basin will change under various future climate change scenarios. Although many studies have been completed using large scale global climate model (GCM) data, few studies have shown the strength of regional climate models (RCM). In this work, a comparison between the effectiveness of using CRCM4.2 vs. CGCM3.1 data in a climate change impact study (climate forcing under the SRES A2 climate scenario) is considered. The study area is the Chute-du-Diable sub-basin located within the Saguenay-Lac-Saint-Jean Watershed in Quebec, Canada. Downscaled results are compared with observed meteorological data for the years 1961-1990 at the Chute-des-Passes (CDP) and Chute-du-Diable (CD D) weather stations; and flow is simulated in the Mistassibi River and the Chute-du-Diable reservoir. A regression technique (SDSM) and a dynamic artificial neural network model (Time lagged feed-forward neural network (TLFN)) are used for downscaling the CRCM4.2 and CGCM3.1 data, and the HBV2005 hydrological modeling system is used for simulating flows in the watershed. For the current period (1961-1990), downscaling results reveal that downscaled CRCM4.2 is closer to observed meteorological data at both CDD and CDP stations than downscaled CGCM3.1 is. The Wilcoxon Rank-Sum test and Levene test reveal that regardless of the climate model, both TLFN and SDSM are capable of capturing the monthly means and variance of precipitation and temperature. Statistical results reveal that TLFN is best for downscaling temperature and SDSM is best for downscaling precipitation. With respect to the future climate scenario, regardless of the climate model or the downscaling method, a 1 to 3 ° C increase in annual mean maximum temperature and a 1 to 4°C increase in annual mean minimum temperature are predicted for the 2050s future period. In the case for precipitation, the CRCM4.2 model shows increases in annual precipitation will vary from 1 to 7% in the 2050s regardless of the downscaling method used. The CGCM3.1 model on the other hand, shows increases in annual precipitation ranging from 15 to 23% regardless of the downscaling method employed. Additionally, simulations of river flows and reservoir inflows reveals significant changes in mean flow will occur as a result of the warming trend. Simulations show that for both SDSM and TLFN, CRCM4.2 and CGCM3.1 show an increase in river flow and reservoir flows throughout all seasons except for the summer where reduction of flow is observed. Annually, at the Chute-du-Diable reservoir mean flow changes vary from a 16-28% increase in the 2050s and at the Mistassibi River annual mean flow changes vary from a 12-62% increase. In all cases CGCM3.1 model shows a larger increasing trend than the CRCM4.2 model.
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Comment cette classification a été obtenuedéplier
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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,012 | 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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, 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 ».