An analytical solution for rapidly predicting post‐fire peak streamflow for small watersheds in southern California

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.

Predicting Postfire Sediment Yields of Small Steep Catchments Using Airborne Lidar Differencing

“…The wildfire burned ∼94 km 2 through steep (∼15 to > 45°) terrain dominated by chaparral vegetation and underlain by two dominant lithologies: Jurassic metasedimentary units composed of highly fractured argillites and quartzites and Cretaceous granitic bedrock (Morton & Miller, 2006). Initial postfire assessments by state and federal agencies (Schwartz & Stempniewicz, 2018; USGS, 2018) and field observations (Guilinger et al., 2020; Wilder et al., 2021) noted landscape conditions known to increase PFDF susceptibility such as enhanced soil‐water repellency and loading of channel networks with loose material through dry ravel processes. We also used dry ravel data from the 2020 Apple Fire (Figure 2) as a validation data set.…”

A review of common natural disasters as analogs for asteroid impact effects and cascading hazards

“…All the models simulate an increase in runoff that is due to a combination of reduced soil water infiltration, a reduction in water storage due to burned surface litter/duff, and an overall reduction in roughness from vegetation incineration. Relatively simple non-distributed modeling methods are frequently applied for timeliness (e.g., USDA TR-55 (USDA 2009 ), Wildcat-5 (Hawkins and Munoz 2011 ), U.S. Geological Survey Linear Regression Equations (see Kinoshita et al 2014 ), Rowe, Countryman, and Storey (1949), and Wilder et al ( 2021 )). Distributed models such as HEC-HMS (USACE 2010 ) and Kineros2 (Goodrich et al 2012 ) are used more frequently to estimate distributed rainfall and runoff.…”

Predicting wildfire induced changes to runoff: A review and synthesis of modeling approaches

“…Kinoshita et al (2014) considered five different models for peak flow estimation, the Rowe, Countryman, and Storey (RCS) model, United States Geological Survey Linear Regression Equations, USDA Windows Technical Release 55, Wildcat5, and finally the U.S. Army Corps of Engineers Hydrologic Modeling System (the latter three of which were CN approaches). Wilder et al (2020) considered three different data‐driven models for estimating peak flow post‐wildfire, including the RCS methodology and two random forest models. They found that the first of these methods frequently under‐predicted peak flow, while the two machine learning models had superior performance, and highlighted a need for increased collection of high spatiotemporal resolution data of rainfall, streamflow, and sediment loading.…”