Hydrologic Model Evaluation and Assessment of Projected Climate Change Impacts Using Bias-Corrected Stream Flows
Bibliographic record
Abstract
Hydrologic models driven by downscaled meteorologic data from general circulation models (GCM) should be evaluated using long-term simulations over a historical period. However, simulations driven by GCM data cannot be directly evaluated using observed flows, and the confidence in the results can be relatively low. The objectives of this paper were to bias correct simulated stream flows from calibrated hydrologic models for two basins in New Jersey, USA, and evaluate model performance in comparison to uncorrected simulations. Then, we used stream flow bias correction and flow duration curves (FDCs) to evaluate and assess simulations driven by statistically downscaled GCMs for the historical period and the future time slices 2041–2070 and 2071–2099. Bias correction of stream flow from simulations increased confidence in the performance of two previously calibrated hydrologic models. Results indicated there was no difference in projected FDCs for uncorrected and bias-corrected flows in one basin, while this was not the case in the second basin. This result provided greater confidence in projected stream flow changes in the former basin and implied more uncertainty in projected stream flows in the latter. Applications in water resources can use the methods described to evaluate the performance of GCM-driven simulations and assess the potential impacts of climate change with an appropriate level of confidence in the model results.
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How this classification was reachedexpand
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, unvalidatedMachine predicted; a candidate call from one teacher head, 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".