Comparing CMIP-3 and CMIP-5 climate projections on flooding estimation of Devils Lake of North Dakota, USA
Bibliographic record
Abstract
Background Water level fluctuations in endorheic lakes are highly susceptible to even slight changes in climate and land use. Devils Lake (DL) in North Dakota, USA is an endorheic system that has undergone multi-decade flooding driven by changes in regional climate. Flooding mitigation strategies have centered on the release of lake water to a nearby river system through artificial outlets, resulting in legal challenges and environmental concerns related to water quality, downstream flooding, species migration, stakeholder opposition, and transboundary water conflicts between the US and Canada. Despite these drawbacks, running outlets would result in low overspill risks in the next 30 years. Methods In this study we evaluated the efficacy of this outlet-based mitigation strategy under scenarios based on the latest IPCC future climate projections. We used the Coupled Model Intercomparison Project CMIP-5 weather patterns from 17 general circulation models (GCMs) obtained under four representative concentration pathways (RCP) scenarios and downscaled to the DL region. Then, we simulated the changes in lake water levels using the soil and water assessment tool based hydrological model of the watershed. We estimated the probability of future flood risks under those scenarios and compared those with previously estimated overspill risks under the CMIP-3 climate. Results The CMIP-5 ensemble projected a mean annual temperature of 5.78 °C and mean daily precipitation of 1.42 mm/day; both are higher than the existing CMIP-3 future estimates of 4.98 °C and 1.40 mm/day, respectively. The increased precipitation and higher temperature resulted in a significant increase of DL’s overspill risks: 24.4–47.1% without release from outlets and 3.5–14.4% even if the outlets are operated at their combined full 17 m 3 /s capacity. Discussion The modeled increases in overspill risks indicate a greater frequency of water releases through the artificial outlets. Future risk mitigation management should include providing a flood warning signal to local resource managers, and tasking policy makers to identify additional solution measures such as land use management in the upper watershed to mitigate DL’s flooding.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Simulation or modeling | high |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Simulation or modeling | high |
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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".