Spatial dependence of floods and droughts: learning from differences in regional and seasonal patterns
Bibliographic record
Abstract
Regional flood and drought events often have more severe impacts than localized events in terms of damages and costs, the number of affected people, and habitat changes. Understanding which regions may be jointly affected by such extreme events can help us to derive reliable regional risk estimates, plan and manage resource flows, and develop suitable adaptation measures. However, the spatial dimension of droughts and floods is often neglected when deriving hazard estimates and we know little about the processes governing their spatial dependencies. Therefore, we investigate how and why spatial dependencies in droughts and floods vary seasonally and regionally over the United States. We aim to gain new insight into processes governing spatial dependencies of droughts and floods by contrasting their regional and seasonal patterns. To map regions with a similar seasonal flood and drought behavior, respectively, we introduce a measure of connectedness, which quantifies the number of catchments with which a specific catchment co-experiences flood or drought events. We then summarize the spatial dependencies by identifying regions with a similar flood behavior and regions with a similar drought behavior. To do so, we use a hierarchical clustering procedure on the F-madogram, which is a measure of spatial dependence for extremes. We look at regional and seasonal differences in spatial dependence both for floods and droughts and subsequently compare the two phenomena. We find that spatial dependence is over all seasons stronger for droughts than for floods. Both types of extremes, however, show regional and seasonal differences in spatial connectedness. Droughts show the strongest spatial dependence in fall. In contrast, the Rocky Mountains show the highest spatial dependence of droughts in winter because of snow accumulation. Very low spatial dependence is found in spring. The seasonal, spatial dependence patterns of floods are opposed to the one of droughts. Spatial flood dependence is highest in spring, especially in mountainous areas, high in winter at the Pacific coast and the Appalachian Mountains, and high in summer in the Rocky Mountains. In contrast, spatial connectedness is very weak in fall. We conclude that spatial dependence patterns are stronger for droughts than floods because of the slower processes and longer durations associated with the phenomenon. Furthermore, we conclude that both meteorological and land surface processes such as snowmelt and the availability of soil moisture shape the spatial dependence patterns of each extreme.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".