Global groundwater archetypes: a new typology of groundwater interactions with social and ecological systems and an outlook for sustainable development
Bibliographic record
Abstract
Groundwater resources do not exist in isolation but are deeply connected with social and ecological systems. As humans continue to modify the land surface, drive climate change, and place greater pressures on global freshwater resources, it is increasingly necessary to assess global groundwater resources through their relationships to these coupled systems. While several global classifications of physical groundwater systems exist, there is no data-driven global typology based on groundwater interactions with connected social and ecological systems. Though physical attributes remain hydrogeologically important, a more expansive systems-oriented classification is needed for policy development, applied research, and to develop the next generation of global hydrological models.We fill this gap by producing a spatially-explicit, moderate resolution (5 arcminute) global map of groundwater system archetypes based on groundwater interactions with social and ecological systems. These include interactions with streamflow, ecosystems, climate, agriculture, the economy, and water governance and management, all underpinned by existing global data. Archetypes, each with a unique set of interaction strengths and combinations, form a finite set of characteristic “fingerprints” that represent the dominant modes of interactions between groundwater and connected social and ecological systems. We find all WHYMAP large aquifer systems of the world are characterized by multiple social-ecological archetypes, suggesting that differentiated, context-appropriate approaches are necessary within large aquifers that are often assumed as uniform in global assessments and initiatives.We derive archetypes using multiple clustering algorithms and assign archetype membership based on majority agreement across clustering methods after cluster reclassification to create comparable maps. This multiple-method approach renders the archetypes more robust and less contingent on a single clustering algorithm while simultaneously enabling greater representation of archetype uncertainty.We additionally provide an outlook on sustainable development opportunities and challenges for each archetype. We summarize data sets that represent notable social-ecological outcomes related to the UN Sustainable Development Goals (SDGs), including: crop yield gaps (SDG 2), remotely sensed groundwater storage trends (SDG 6), economic inequality (SDG 10), human modification of terrestrial systems (SDG 15), and likelihood for hydropolitical interaction (SDG 16), among others. This work provides a number of useful contributions. First, the combination of archetyping (i.e., system characterization) and archetype-specific SDG outlook analysis provides a robust, data-driven overview of the role of groundwater in the global sustainability discourse. Secondly, the archetypes identify social-ecological system similarities across the globe, which may support interregional cooperation and networking, coordinated investment and interventions. Thirdly, as we harness the rapid growth in global data that document groundwater system interactions as the basis for our analysis, we simultaneously provide a synthesis and snapshot of the pertinent global data space. This snapshot can be used to identify the need for further data collection, especially on socio-economic interactions that remain underrepresented in global data. And finally, the archetypes raise awareness, build capacity, and shift mental models about the emerging perspective that it is necessary to conceptualize groundwater as a socially and ecologically connected resource.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".