On inclusion of water resource management in Earth System models – Part 2: Representation of water supply and allocation and opportunities for improved modeling
Bibliographic record
Abstract
Abstract. Human water use has significantly increased during the recent past. Water allocation from surface and groundwater sources has altered terrestrial discharge and storage, with large variability in time and space. Water supply and allocation, therefore, should be considered with water demand and appropriately included in large-scale models to address various online and offline implications, with or without considering possible climate interactions. Here, we review the algorithms developed to represent the elements of water supply and allocation in large-scale models, in particular Land Surface Schemes and Global Hydrologic Models. We noted that some potentially-important online implications, such as the effects of large reservoirs on land-atmospheric feedbacks, have not yet been addressed. Regarding offline implications, we find that there are important elements, such as groundwater availability and withdrawals, and the representation of large reservoirs, which should be improved. Major sources of uncertainty in offline simulations include data support, water allocation algorithms and host large-scale models. Considering these findings with those highlighted in our companion paper, we note that advancements in computation, host models, system identification algorithms as well as remote sensing and data assimilation products can facilitate improved representations of water resource management at larger scales. We further propose a modular development framework to consider and test multiple datasets, algorithms and host models in a unified model diagnosis and uncertainty assessment framework. We suggest that such a framework is required to systematically improve current representations of water resource management in Earth System models. A key to this development is the availability of regional scale data. We argue that the time is right for a global initiative, based on regional case studies, to move this agenda forward.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".