The Hydrologic Processes Triggering Post-Wildfire Landslides from Watershed to Global Scales
Bibliographic record
Abstract
Wildfire is often observed to increase landslide hazards. This phenomenon results in a cascade of natural hazards in which the wildfire not only causes risk to surrounding communities and infrastructure, but then is followed by a landslide resulting in further risk. Post-wildfire landslides are typically hydrologically triggered; heavy rain causes excessive runoff, resulting in erosion and ultimately a debris flow. This dissertation explores the hydrologic triggers of landslides, and particularly post-wildfire landslides through three studies at different scales and focusing on different aspects of the challenges of predicting and characterizing these events. First, a study of 5313 rainfall triggering landslides across the globe investigates regional differences in relative precipitation magnitude and seasonality between post-wildfire landslides and rainfall-drive landslides in unburned locations. This study confirms that, in most regions, wildfire increases susceptibility to landslides as evidenced by the relatively smaller amounts of precipitation that suffice to trigger post-wildfire landslides. However, each region shows unique patterns in the seasonality of post-wildfire landslides when compared to the seasonality of landslides in unburned locations. These patterns are suggestive of the different physical mechanisms that may be at play in triggering these landslides. The second analysis explores how the uncertainty in precipitation measurements may impact the ability to identify regional landslide hazards. Focusing on the western United States and Canada, this study compares four precipitation products, representing gauge-based, radar-based, and satellite-based methods of precipitation measurement. The storm characteristics and ability to predict landslides using 4 established Intensity-Duration Threshold models are compared across the four precipitation products and 177 rainfall-triggered landslide sites. The choice of precipitation product is found to introduce great uncertainty into the quantification of landslide triggering storm characteristics. It is recommended that multiple precipitation products are combined in order to account for this error. Finally, a third study assesses the utility of data assimilation with a physically-based model for identifying post-wildfire changes in hydrology and optimal model parameters. A Particle Batch Smoother algorithm is used with the Distributed Hydrology Soil Vegetation (DHSVM) model to capture streamflow at the Matilija Creek watershed before and after a wildfire. Comparisons are made of changes in parameters both pre- and post-wildfire as well as between wetter and drier years. Both of the comparisons result in statistically significant changes in parameters, and at least some of the post-wildfire changes are consistent with known physical changes caused by wildfire, such as decreases in leaf-area index and maximum infiltration. Overall, this dissertation highlights the variability in the hydrologic processes that can trigger post-wildfire landslides depending on the situation. The complexities of these cascading natural hazards, due to measurement uncertainty and the variable physical mechanisms at play, and the analysis tool applied to the problem are investigated in this dissertation.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".