Socio-Hydrogeology: uncovering the hidden connections within the Human-Groundwater Cycle
Bibliographic record
Abstract
Socio-hydrogeology has been recently proposed as a new approach in the field of human-water research, focusing on the assessment of the reciprocity between people and groundwater. Notwithstanding some obvious similarities with socio-hydrology, there are notable, and indeed important differences; while socio-hydrology aims to investigate and understand the dynamic interactions and feedbacks between (surface)water and people, due to the more private and local nature of groundwater in many instances, socio-hydrogeology seeks to understand individuals and communities as a primary source, pathway and receptor for potable groundwater supplies, including the role of (local) knowledge, beliefs, risk perception, tradition/history, and consumption. In essence, the “socio” in socio-hydrology might be said to represent society, while its counterpart within socio-hydrogeology embodies sociology, including social, cognitive, behavioural and socio-epidemiological science. Moreover, while socio-hydrology tends towards examination of human-water interactions at relatively larges scales via coupled modelling, socio-hydrogeology is often focused at a significantly smaller scale (e.g. individual household or community supplies), and as such, employs a wide range of mixed methods, including modelling, albeit to a lesser degree. Being at its early development stage, the discipline is still being defined and formalized. Nevertheless, several researchers are currently implementing this approach worldwide. By presenting a comparative analysis of the approaches and outcomes from several socio-hydrogeological studies undertaken across a range of socio-demographic and climatic regions including Canada, Italy, India, Ireland, Myanmar and Tunisia, this presentation will highlight the benefits and shortcomings of going beyond classical hydrogeological and hydrogeochemical investigations targeted to assess the impact of human activities on groundwater quality and quantity, and indeed, the effects of these impacts on associated individuals and communities (i.e. humans frequently represent the issue, the receptor and the solution). By shedding light on the added value of understanding the cause-effect relations between people and the hidden component of the water cycle (e.g. to jointly assess how scarce and polluted groundwater affect human/social wellbeing), socio-hydrogeology can provide evidence-based solutions to regionally bespoke problems. Similarly, otherwise neglected local or regional information can add value to scientific outcomes and contribute to foster new groundwater management actions tailored on the needs of local populations as well as on the overall achievement of long-term sustainability. Socio-hydrogeology can therefore provide new insights useful for socio-hydrological modelling, and, together, they can effectively underpin successful Integrated Water Resources Management plans at local and regional scale. Perhaps most importantly, it is hoped that by initiating discussion between practitioners of both sub-disciplines, experiences, expertise and perspectives can be shared and employed (e.g. more “technical” modelling within socio-hydrogeology, increased integration of “non-expert” knowledge within socio-hydrology) in order to bolster both areas of study, with an overarching objective of protecting the entire hydrological cycle, and the people supplied and impacted by it.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".