An analytical solution for rapidly predicting post‐fire peak streamflow for small watersheds in southern California
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 estima...
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Published in | Hydrological processes Vol. 35; no. 1 |
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Main Authors | , , , , , , , |
Format | Journal Article |
Language | English |
Published |
Hoboken, USA
John Wiley & Sons, Inc
01.01.2021
Wiley Subscription Services, Inc |
Subjects | |
Online Access | Get full text |
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Summary: | 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.
Rowe et al. (1949), a common method for predicting post‐fire peak streamflow, is critically reviewed and analyzed for 33 small watersheds in southern California. This method is found to be insensitive to a variety of spatially and temporally variant morphological and climactic characteristics. An analytical solution is proposed using machine learning to characterize response using a collection of derived parameters. |
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Bibliography: | Funding information Joint Fire Science Program Graduate Research Innovation Award, Grant/Award Number: #19‐1‐01‐55; San Diego State University, Grant/Award Number: Master's Research Scholarship; Department of Conservation ‐ California Geological Survey |
ISSN: | 0885-6087 1099-1085 |
DOI: | 10.1002/hyp.13976 |