Methods for Estimating Surface Water Storage Changes and Their Evaluations
Bibliographic record
Abstract
Abstract Developing effective methods for estimating regional-scale surface water storage change (ΔSW) has become increasingly important for water resources studies and environmental impact assessment. Three methods for estimating monthly ΔSW are proposed in this study, of which one is based on land surface runoff and two that use water body water budgets. Water areas observed by Landsat satellites for Canada’s entire landmass are used for evaluation of the results. The surface runoff method achieved the least satisfactory results, with large errors in the cold season or dry regions. The two water-budget methods demonstrated significant improvements, particularly when water area dynamics is considered in the estimation of the water body water budget. The three methods performed consistently across different climate regions in the country and showed better correlations with observations over wet climate regions than over dry regions with poorly connected hydrological system. The results also showed the impact of glacier and permanent snow melts over the Rocky Mountains on basin-scale surface water dynamics. The methods and outputs from this study can be used for calibrating and validating hydrological and climate models, assessing climate change and human disturbance impacts on regional water resources, and filling the ΔSW data gaps in GRACE-based total water storage decompositions studies. Significance Statement The purpose of this study is to develop and evaluate methods for estimating regional-scale surface water storage change. This is important because information on surface water dynamics is limited for water resources studies and environmental impact assessment. Our study makes available two new methods which significantly improve on surface water storage estimation from the traditional runoff model. A guide on controls of surface water dynamics is provided for regions under various hydroclimate and physiographic–hydraulic conditions and reveals the influence of glacier melt on surface water variations.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".