Managing the Cascading Risks of Droughts: Institutional Adaptation in Transboundary River Basins
Bibliographic record
Abstract
Abstract Transboundary river basins experience complex coordination challenges during droughts. The multiscale nature of drought creates potential for spillovers when upstream adaptation decisions have cascading impacts on downstream regions. This paper advances the institutional analysis and development (IAD) framework to examine drought adaptation decision‐making in a multijurisdictional context. We integrate concepts of risk management into the IAD framework to characterize drought across its natural and human dimensions. A global analysis identifies regions where severe droughts combine with institutional fragmentation to require coordinated adaptation. We apply the risk‐based IAD framework to examine drought adaptation in the Rio Bravo/Grande—an archetypical transboundary river shared by the United States and Mexico and by multiple states within each country. The analysis draws on primary data and a questionnaire with 50 water managers in four distinct, yet interlinked, “institutional catchments,” which vary in terms of their drought characteristics, socioeconomic attributes, and governance arrangements. The results highlight the heterogeneity of droughts and uneven distribution of their impacts due to the interplay of drought hazards and institutional fragmentation. Transboundary water sharing agreements influence the types and sequence of interactions between upstream and downstream jurisdictions, which we describe as spillovers that involve both conflict and cooperation. Interdependent jurisdictions often draw on informal decision‐making venues (e.g., data sharing, operational decisions) due to the higher transaction costs and uncertainty associated with courts and planning processes, yet existing coordination and conflict resolution venues have proven insufficient for severe, sustained droughts. Observatories will be needed to measure and manage the cascading risks of drought.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".