The Hydrologic Processes Triggering Post-Wildfire Landslides from Watershed to Global Scales
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».