Modeling regional climate change impacts on available water for agriculture
Bibliographic record
Abstract
There is mounting evidence that increasing amounts of atmospheric carbon dioxide may lead to significant changes in global climate during this century. Global warming may have tremendous consequences for irrigated agriculture around the world; and the welfare of the communities in regions that depend on irrigation may be critically affected by climate change. The possible effects of such climatic changes on water resources for agriculture in the Arkansas River basin in Colorado, U.S., have been investigated. My aim is to improve the estimates of the potential impacts of climate change on the availability of irrigation water by using higher resolution climate scenarios, smaller temporal and spatial analysis scale and provide results that will be useful for the water planning and management decision-making processes. Results from general circulation models indicate that the potential impacts of climate change on this region include changes in winter snowfall and snow melt, seasonal rainfall amounts and intensities and winter and summer time average temperatures. Therefore, a framework was developed to quantify the effects of these seasonal impacts on the availability of irrigation water. Monthly surface water supplies, consumptive use, and water balance are estimated using neural networks, consumptive use, and water balance models respectively. As part of this study I used two transient climate scenarios extracted at high resolution from two General Circulation Models (GCM's); the HAD (Hadely center) and the CCC (Canadian Center). The high resolution was obtained by downscaling the output of the two GCM's to half-degree spatial resolution. Each GCM transient climate scenario was generated assuming 1% annual increase in CO2 concentrations. The methodology and results described in this study are contributing to the national analysis of impacts of climate change on the water sector. The frame work developed as part of this research will help a region plan for changes in water supply and demand and will give decision-makers a tool for evaluating the impacts of climate change. The data driven nature of the frame work makes it flexible so that it can be applied to different areas.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".