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SDSU Template, Version 11.1 RUNOFF PREDICTION AND ECOHYDROLOGICAL RECOVERY FOR SMALL CATCHMENTS AFTER FIRE SOUTHERN CALIFORNIA _______________ A Thesis Presented to the Faculty of San Diego State University _______________ In Partial Fulfillment of the Requirements for the Degree Master of Science in Civil Engineering _______________ by Brenton A. Wilder Spring 2021 iii Copyright © 2021 by Brenton A. Wilder All Rights Reserved iv DEDICATION For my parents, Ian and Melissa, and my sister, Emma. v Purpose is an essential element of you. It is the reason you are on the planet at this particular time in history. Your very existence is wrapped up in the things you are here to fulfill … remember, the struggles along the way are only meant to shape you for your purpose. -- Chadwick Boseman vi ABSTRACT OF THE THESIS Runoff Prediction and Ecohydrological Recovery for Small Catchments after Fire in Southern California by Brenton A. Wilder Master of Science in Civil Engineering San Diego State University, 2021 Over the past few decades, increasing fire frequency and severity in southern California - and across the western United States - has posed a concern to the safety and well-being of communities and ecosystems. Increased aridity coupled with water stressed vegetation from prolonged droughts are leading to a higher propensity for larger, more intense fires that directly impact ecohydrological processes such as streamflow and evapotranspiration (ET). Accurate characterization of these processes are required to improve rapid response efforts and resource management to promote resilient communities along the wildland-urban interface. This thesis presents methods to improve emergency rapid predictions of post-fire streamflow and characterization of ecohydrological recovery after fire. A random forest machine learning algorithm with 45 catchment parameters was created to predict post-fire peak streamflow during the period 1920 to 2019. By incorporating additional characteristics about meteorological and catchment properties, the random forest, flood forecasting technique provided more realistic predictions of peak streamflow in relation to Rowe et al. (1949), a commonly used flood frequency method. The time elapsed after fire, peak hourly rainfall intensity, and drainage area were important factors that increased accuracy of the random forest predictions. To improve vegetation assessments and resource management, two satellite-based ET products, ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) Priestley-Taylor (PT-JPL) algorithm and Operational Simplified Surface Energy Balance Model (SSEBop), were used to evaluate conditions in relation to the 2018 Holy Fire. There was high uncertainty in post-fire ET between ECOSTRESS PT-JPL and SSEBop daily scaled ET due to the coarse spatial resolution of SSEBop and high spatial heterogeneity of the burn severity. To link recovery and hydrology, hydrologic signatures were quantified at the annual timescale for burned and unburned catchments. Post-fire water balance calculation for WY 2020 showed high uncertainty between ECOSTRESS PT-JPL and SSEBop, where differences in storage between catchments varied by over 500-mm depending on the model. Finally, ECOSTRESS PT-JPL was used to differentiate the landscape recovery by soil burn severity, vegetation species, slope aspect, and riparian area. The findings of this thesis improve upon our current methods in hydrologic modeling associated with fire in southern California. vii TABLE OF CONTENTS PAGE ABSTRACT ............................................................................................................................. vi LIST OF TABLES .....................................................................................................................x LIST OF FIGURES ................................................................................................................. xi ACKNOWLEDGEMENTS ................................................................................................... xiii CHAPTER 1 INTRODUCTION .........................................................................................................1 1.1 Motivation ..........................................................................................................1 1.2 Research Objectives ...........................................................................................2 2 IMPROVING RAPID PREDICTIONS OF POST-FIRE PEAK STREAMFLOW FOR SMALL CATCHMENTS IN SOUTHERN CALIFORNIA ...............................................................................................................4 2.1 Introduction to Post-Fire Hazards in Southern California .................................4 2.2 Study Area .........................................................................................................7 2.2.1 Regional Geology .....................................................................................9 2.2.2 Climate and Wildfire...............................................................................10 2.2.3 Post-fire Processes ..................................................................................10 2.3 Methods............................................................................................................12 2.3.1 Data .........................................................................................................12 2.3.2 Performance Measurements ....................................................................12 2.3.3 Peak Streamflow Modeling Methods......................................................13 2.3.3.1 Flood Frequency of Historical Flows ............................................13 2.3.3.2 Rowe, Countryman, and Storey .....................................................13 2.3.3.3 Random Forest Post-Fire Models ..................................................15 2.4 Results ..............................................................................................................16 2.4.1 Rowe, Countryman, and Storey Model...................................................16 2.4.1.1 RCS Performance Before Fire .......................................................16 viii 2.4.1.2 RCS Performance After Fire ..........................................................18 2.4.2 Random Forest Post-fire Model Performance ........................................19 2.4.3 Towards an Analytical Solution for Assessing Post-fire Peak Flows ................................................................................................................21 2.5 Discussion ........................................................................................................22 2.5.1 The Future Role of Rowe, Countryman, and Storey Model ...................22 2.5.2 Role of Machine Learning as a Tool for Post-fire Risk Assessment .......................................................................................................24 2.5.3 Factors Influencing Post-fire Peak Streamflow ......................................25 2.5.4 Analytical Solution .................................................................................27 2.6 Conclusion .......................................................................................................28 3 CHARACTERIZING FIRE SEVERITY AND ECOHYDROLOGICAL RECOVERY FOR THE 2018 HOLY FIRE IN SOUTHERN CALIFORNIA ...........29 3.1 Introduction ......................................................................................................29 3.2 Materials and Methods .....................................................................................31 3.2.1 Study Area and Hydrologic Data ............................................................31 3.2.2 Remote Sensing and Spatial Products.....................................................33 3.2.3 Climatology.............................................................................................35 3.2.4 Ecohydrological Analysis .......................................................................36 3.2.4.1 Correlation of SSEBop and ECOSTRESS PT-JPL .......................36 3.2.4.2 Pre-fire Above-ground Biomass and Post-fire Vegetation Recovery ....................................................................................................36 3.2.4.3 Hydrologic Signatures ...................................................................37 3.3 Results ..............................................................................................................38 3.3.1 Climate and ET Processes .......................................................................38 3.3.2 Pre-Fire Above-Ground Biomass ...........................................................40 3.3.3 Correlation of SSEBop and ECOSTRESS PT-JPL with Respect to the Holy Fire ................................................................................................41 3.3.4 Post-fire Ecohydrology and Hydrologic Signatures ...............................42 3.3.5 Spatial and Temporal Recovery of Post-Fire ET ....................................45 3.4 Discussion ........................................................................................................46 3.4.1 Role of Climate Extremes .......................................................................46 3.4.2 High Uncertainty in Post-Fire ET ...........................................................47 ix 3.4.3 Linking Ecohydrological Recovery After Fire to Observed Data using Hydrologic Signatures ............................................................................48
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