Economic valuation of subsurface water contributions to watershed ecosystem services using a fully integrated groundwater–surface-water model
Bibliographic record
Abstract
Water is essential for all ecosystem services, yet a comprehensive assessment and economic valuation of total (overall) water contributions to ecosystem services' production using a fully integrated groundwater–surface-water model has never been attempted. Quantification of the many ecosystem services impacted by water demands an analytical approach that implicitly characterizes both subsurface and surface water resources. However, incorporating subsurface water into ecosystem services' evaluation is a recognized scientific challenge. In this study, a fully integrated groundwater–surface-water model – HydroGeoSphere (HGS) – is used to capture changes in subsurface water, surface water, and transpiration (green water use), and along with an economic valuation approach, it forms the basis of an ecosystem services' assessment for an 18-year period (2000–2017) in the 3830 km 2 South Nation watershed (SNW), a mixed-use but predominantly agricultural watershed in eastern Ontario, Canada. Using green water volumes generated by HGS and ecosystem services' values as inputs, the marginal productivity of water is calculated to be CAD 0.26 m −3 (in 2022 Canadian dollars). Results show maximum green water values during the driest years, with the extreme drought of 2012 being the highest at CAD 424.7 million. In contrast, the green water value in wetter years was as low as CAD 245.9 million, while the 18-year average was CAD 338.83 million. Because subsurface water is the sole contributor to the green water supply, it plays a critical role in sustaining ecosystem services during drought conditions. This study provides new insight into the economic contributions of subsurface water and its role in sustaining ecosystem services during droughts, and it puts forth an improved methodology for watershed-based management and valuation of ecosystem services.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".