Comparison of Downscaled RCM and GCM data for Hydrologic Impact Assessment
Why this work is in the frame
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Bibliographic record
Abstract
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.000 |
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 it