Developping a physically based groundwater vulnerability concept in a DPSIR framework
Bibliographic record
Abstract
A general physically based method is presented to assess vulnerability of groundwater to external pressures with respect to quality and/or quantity issues. In the vulnerability assessments, many scientific authors agree nowadays that ‘physically based’ methods must be preferred to traditional approaches based on empirical overlay and index methods where physical attributes are often mixed with implicitly embedded conventional priorities. Results from one or another of these last methods can consequently be very dissimilar for a given case study and decision makers are losing confidence in these tools. A methodology is proposed to reframe the groundwater vulnerability assessment in a Pressure-State-Impact causal chain that is familiar to decision makers. The DPSIR framework, for describing interactions between society and the environment, defines a chain of Drivers that exert Pressures on the State of a given resource, such as water, which then generates an Impact that will require an appropriate Response (Kristensen, 2004). The concept of groundwater vulnerability assessment considered here is based on the calculation of sensitivity coefficients for a user-defined groundwater state for which several physically-based indicators are proposed. These sensitivity coefficients reflect the easiness with which the groundwater state transmits pressures into impacts. They are grouped into a vulnerability matrix of pressures and impacts that quantify vulnerability for every combination of causal links identified in the DPSIR chain. For that reason, the sensitivity coefficients are converted to vulnerability, using the concept of ‘falling below a given threshold’, which is commonly used in socioeconomic sciences (Luers et al. 2003). Outside the careful selection of the sensitivity analysis method that can significantly influence the computational effort (Beaujean et al., 2013), emphasis will be given to the illustration of the general methodology on a simple case (of an alluvial aquifer with concerns related to water supply) demonstrating the potential use of this general and physically based vulnerability assessment method. While the methodology is general, the choice of causal chains has to be made prior to the calculation. The vulnerability is also related to a damaged state and is related to the ‘distance’ between the current state and a given threshold. This choice is arbitrary such that the vulnerability is sensitive to the choice of the threshold. The framework is general and, when applied to water, can include states that are not limited to quality such as, for example, water quantity and availability.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".