An analytical solution for rapidly predicting post-fire peak streamflows for small watersheds in southern California


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Created: Oct 25, 2020 at 5:44 p.m.
Last updated: Dec 01, 2020 at 4:33 a.m.
DOI: 10.4211/hs.9e38375a19cf4355aac466ccd78e8282
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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.

Subject Keywords

Resource Level Coverage

Spatial

Coordinate System/Geographic Projection:
WGS 84 EPSG:4326
Coordinate Units:
Decimal degrees
Place/Area Name:
southern California
North Latitude
34.8252°
East Longitude
-116.0180°
South Latitude
32.2059°
West Longitude
-120.5004°

Temporal

Start Date:
End Date:

Content

Additional Metadata

Name Value
Supplementary Information Additional details on each of the features of this dataset can be found in the "Supplementary Information " section of the published paper (https://onlinelibrary.wiley.com/doi/abs/10.1002/hyp.13976).

Credits

Funding Agencies

This resource was created using funding from the following sources:
Agency Name Award Title Award Number
Joint Fire Science Program Graduate Research Innoation Award 19-1-01-55
San Diego State University Master's Research Scholarship
Department of Conservation - California Geological Survey Graduate Assistant - Geology, Hydrology, Geomorphology (San Diego)

How to Cite

Wilder, B. A., A. M. Kinoshita, J. T. Lancaster, P. H. Cafferata, D. B. Coe, B. J. Swanson, W. R. Short (2020). An analytical solution for rapidly predicting post-fire peak streamflows for small watersheds in southern California, HydroShare, https://doi.org/10.4211/hs.9e38375a19cf4355aac466ccd78e8282

This resource is shared under the Creative Commons Attribution CC BY.

 http://creativecommons.org/licenses/by/4.0/
CC-BY

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