Hydrology and society: Phenomena emerging from the interactions and feedbacks between human and water systems
Bibliographic record
Abstract
Water resources management and hydrological risk reduction require anticipation of emergent (unexpected or unintended) phenomena as fundamental dynamics of complex human-water systems. Explaining and characterizing these sociohydrological phenomena is a central focus of Panta Rhei–Everything Flows, the Scientific Decade of the International Association of Hydrological Sciences (2013-2022). Here we use System Dynamics’ archetypes to describe and classify socio-hydrological phenomena emerging from nonlinear interactions between human and water systems. Archetypes illustrate dynamic behaviours that are frequently observed across (seemingly) different systems, contexts or problem settings. They include intended or unintended actions and reactions with explicit recognition of delays in reaction times. Reinforcing (or positive) and balancing (or negative) feedback loops and system boundaries are key elements for describing archetypes. In the study of human-water systems, the most common archetypes are Limits to Growth, Fixes that Backfire, Success to the Successful, and Tragedy of the Commons. Using system archetypes to generalize the phenomena in coupled human-water systems allows for the comparison among case studies along with the integration of multiple dimensions, thereby contributing to the development of new theories which provide common causes and explanations. We first present several classes of sociohydrological phenomena that have been explored over the past decade, and then discuss their role in explaining the dynamics of human-water systems. Finally, we engage with the implications for integrated water resources management, hydrological risk reduction, and water governance.
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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 | 0.002 |
| 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 | 0.010 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".