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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".