ABSTRACT:

Ecohydrological processes such as evapotranspiration (ET) and streamflow are highly variable after fire in Mediterranean systems and require accurate assessments to improve long-term risk mitigation of erosion and peak flows and revegetation strategies, especially at the small catchment scale. Using the case of the 2018 Holy Fire in southern California, we characterized 1) pre-fire rainfall and evapotranspiration conditions and 2) recovery of ecohydrological processes using a paired analysis between an unburned (Santiago) and burned (Coldwater) catchment. ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS), Operational Simplified Surface Energy Balance Model (SSEBop), vegetation indices, and local rainfall-runoff data were used to characterize the sites and investigate spatial and temporal patterns of post-fire ET. Consistent with the drought conditions in California, we observed low precipitation and ET prior to the fire. Additionally, compared to other vegetation types, montane hardwood species were more likely to be classified as high soil burn severity. We also found that the high spatial and temporal resolution of ECOSTRESS provided more information about the general ET patterns. After the fire, ECOSTRESS ET was sensitive to parameters such as slope aspect, soil burn severity, and vegetation species, which has implications for post-fire vegetation recovery and water storage. This work demonstrates opportunities to apply ECOSTRESS ET across globally diverse ecoregions and small catchment scales to identify potentially high-risk areas and improve fire risk and vegetation recovery assessments.

ABSTRACT:

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ABSTRACT:

Following wildfires, the probability of flooding and debris flows increase, posing risks to human lives, downstream communities, infrastructure, and ecosystems. In southern California (USA), the Rowe, Countryman, and Storey (RCS) 1949 methodology is an empirical method that is used to rapidly estimate post‐fire peak streamflow. We re‐evaluated the accuracy of RCS for 33 watersheds under current conditions. Pre‐fire peak streamflow prediction performance was low, where the average R2 was 0.29 and average RMSE was 1.10 cms/km2 for the 2‐ and 10‐year recurrence interval events, respectively. Post‐fire, RCS performance was also low, with an average R2 of 0.26 and RMSE of 15.77 cms/km2 for the 2‐ and 10‐year events. We demonstrated that RCS overgeneralizes watershed processes and does not adequately represent the spatial and temporal variability in systems affected by wildfire and extreme weather events and often underpredicted peak streamflow without sediment bulking factors. A novel application of machine learning was used to identify critical watershed characteristics including local physiography, land cover, geology, slope, aspect, rainfall intensity, and soil burn severity, resulting in two random forest models with 45 and five parameters (RF‐45 and RF‐5, respectively) to predict post‐fire peak streamflow. RF‐45 and RF‐5 performed better than the RCS method; however, they demonstrated the importance and reliance on data availability. The important parameters identified by the machine learning techniques were used to create a three‐dimensional polynomial function to calculate post‐fire peak streamflow in small catchments in southern California during the first year after fire (R2 = 0.82; RMSE = 6.59 cms/km2) which can be used as an interim tool by post‐fire risk assessment teams. We conclude that a significant increase in data collection of high temporal and spatial resolution rainfall intensity, streamflow, and sediment loading in channels will help to guide future model development to quantify post‐fire flood risk.