A benchmark test problem for reservoir simulation models and comparison of their results
Bibliographic record
Abstract
This paper tests the ability of different reservoir operation models to represent a relatively simple water rationing policy through a multi-year simulation of the Upper Krishna River Basin in India. The tested policies involve reductions in water demand as a function of storage levels. The models that were tested include Mike Hydro Basin, the Water Evaluation and Planning (WEAP) model, HEC-ResSIM model, and the WEB.BM model. Two more simulation models were tested by their vendors, who subsequently declined joint participation in this publication. The models utilized single-timestep progression and assumed steady-state flows, implying an expectation of similar solutions among the models. The results show that Mike Hydro Basin and HEC-ResSIM fail to allocate water according to the prescribed rules. The paper provides a problem description and downloadable input and output data. Modelling practitioners can test the performance of their models by using the benchmark solution presented in this paper.
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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 | 0.001 | 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".