Post-wildfire Flood Inundation Modelling in Southern California: Implications for Dominant Processes and Parameter Identification

Authors

Francisca Olmos de Aguilera, Florida International UniversityFollow

Document Type

Thesis

Degree

Master of Science (MS)

Major/Program

Environmental Engineering

First Advisor's Name

Hector R. Fuentes

First Advisor's Committee Title

Committee chair

Second Advisor's Name

Ali Ebrahhimian

Second Advisor's Committee Title

Committee Member

Third Advisor's Name

Arturo Leon

Third Advisor's Committee Title

Committee Member

Fourth Advisor's Name

Nawa Raj Pradhan

Fourth Advisor's Committee Title

Committee Member

Keywords

Hydrology, post-fire hydrology, GSSHA, auto-calibration, runoff generation

Date of Defense

11-10-2022

Abstract

Wildfires produce large runoff volumes, sometimes in the form of debris flows (water-laden slurries of soil and rock that move rapidly through channels in steep landscapes) in response to moderate to severe precipitation events. For instance, in Santa Barbara and Ventura Counties following the 2017 Thomas Fire, on January 9th, 2018, an intense atmospheric river flood resulted in a series of destructive water and debris flows causing major damage to life and property. Inundation models that accurately parameterize and simulate hydrologic processes are in demand for post-wildfire flood susceptible regions like Southern California. This study utilizes the physics-based Gridded Surface Subsurface Hydrological Analysis, GSSHA, a fully coupled surface water/groundwater simulator with sediment transport capability, to simulate the flood event in the Santa Barbara watershed. The purpose of this study was to implement GSSHA to model pre- and post-fire conditions that allowed to locate dominant processes in relation to the model structure development for post-wildfire hydrologic modeling. A sensitivity analysis was performed using the SCE optimization algorithm.

Limitations in data due to the fire burning instrumentation equipment led to alternative techniques of parameterization. To reduce uncertainty, two methods of parameterization were applied: parameter transfer and optimization. It was found feasible to establish a transfer of parameters from a nearby, comparable watershed based on a previous study conducted by Pradhan and Floyd (2021). Subsequently, a sensitivity analysis was performed using the “Shuffled Complex Evolution” SCE optimization algorithm. The key parameters that were identified in the sensitivity analysis were manning’s roughness and the hydraulic conductivity reduction factor. Although sensitive to both parameters, the model was found to be significantly more sensitive to the change in hydraulic conductivity reduction factor. Both types of parameterization found that post-fire simulations compared well to the observed data for the 09 January 2018 rainfall event. The post-wildfire numerical modeling approach provided an improvement to the existing state-of-practice for predicting post-wildfire inundation risks.

Identifier

FIDC010969

ORCID

https://orcid.org/0000-0002-8683-0720

Recommended Citation

Olmos de Aguilera, Francisca, "Post-wildfire Flood Inundation Modelling in Southern California: Implications for Dominant Processes and Parameter Identification" (2022). FIU Electronic Theses and Dissertations. 5140.
https://digitalcommons.fiu.edu/etd/5140