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Hi! My name is Brent, I received my bachelors of science from Cal Poly Pomona in Civil Engineering and I am currently finishing up my master's thesis at San Diego State University in Civil Engineering with an emphasis in water resources. My work thus far has looked to investigate impacts of fire to ecohydrologic processes, and I am currently applying to PhD programs with the hope of continuing this work.
Accurate field data are required to predict elevated runoff and sediment transport to aid post-fire planning. This is especially significant at the small catchment scale, where these runoff processes
Brenton Wilder; Alicia Kinoshita. Post-fire Vegetation and Hydrologic Recovery in a Mediterranean Climate. 2021, 1 .
AMA StyleBrenton Wilder, Alicia Kinoshita. Post-fire Vegetation and Hydrologic Recovery in a Mediterranean Climate. . 2021; ():1.
Chicago/Turabian StyleBrenton Wilder; Alicia Kinoshita. 2021. "Post-fire Vegetation and Hydrologic Recovery in a Mediterranean Climate." , no. : 1.
The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) provides remotely-sensed estimates of evapotranspiration at 70 m spatial resolution every 1–5 days, sampling across the diurnal cycle. This study, in partnership with an operational water management organization, the Eastern Municipal Water District (EMWD) in Southern California, was conducted to evaluate estimates of evapotranspiration under ideal conditions where water is not limited. EMWD regularly uses a ground-based network of reference evapotranspiration (ETo) from the California Irrigation Management Information System (CIMIS); yet, there are gaps in spatial coverage and questions of spatial representativeness and consistency. Space-based potential evapotranspiration (PET) estimates, such as those from ECOSTRESS, provide consistent spatial coverage. We compared ECOSTRESS ETo (estimated from PET) to CIMIS ETo at five CIMIS sites in Riverside County, California from July 2018–June 2020. We found strong correlations between CIMIS ETo and ECOSTRESS ETo across all five sites (R2 = 0.89, root mean square error (RMSE) = 0.11 mm hr−1). Both CIMIS and ECOSTRESS ETo captured similar seasonal patterns through the study period as well as diurnal variability. There were site-specific differences in the relationship between ECOSTRESS AND CIMIS, in part due to spatial heterogeneity around the station site. Consequently, careful examination of landscapes surrounding CIMIS sites must be considered in future comparisons. These results indicate that ECOSTRESS successfully retrieves PET that is comparable to ground-based reference ET, highlighting the potential for providing observation-driven guidance for irrigation management across spatial scales.
Gurjot Kohli; Christine Lee; Joshua Fisher; Gregory Halverson; Evan Variano; Yufang Jin; Daniel Carney; Brenton Wilder; Alicia Kinoshita. ECOSTRESS and CIMIS: A Comparison of Potential and Reference Evapotranspiration in Riverside County, California. Remote Sensing 2020, 12, 4126 .
AMA StyleGurjot Kohli, Christine Lee, Joshua Fisher, Gregory Halverson, Evan Variano, Yufang Jin, Daniel Carney, Brenton Wilder, Alicia Kinoshita. ECOSTRESS and CIMIS: A Comparison of Potential and Reference Evapotranspiration in Riverside County, California. Remote Sensing. 2020; 12 (24):4126.
Chicago/Turabian StyleGurjot Kohli; Christine Lee; Joshua Fisher; Gregory Halverson; Evan Variano; Yufang Jin; Daniel Carney; Brenton Wilder; Alicia Kinoshita. 2020. "ECOSTRESS and CIMIS: A Comparison of Potential and Reference Evapotranspiration in Riverside County, California." Remote Sensing 12, no. 24: 4126.
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. This article is protected by copyright. All rights reserved.
Brenton A. Wilder; Jeremy T. Lancaster; Peter H. Cafferata; Drew B. R. Coe; Brian J. Swanson; Donald N. Lindsay; William R. Short; Alicia M. Kinoshita. An analytical solution for rapidly predicting post‐fire peak streamflow for small watersheds in southern California. Hydrological Processes 2020, 35, 1 .
AMA StyleBrenton A. Wilder, Jeremy T. Lancaster, Peter H. Cafferata, Drew B. R. Coe, Brian J. Swanson, Donald N. Lindsay, William R. Short, Alicia M. Kinoshita. An analytical solution for rapidly predicting post‐fire peak streamflow for small watersheds in southern California. Hydrological Processes. 2020; 35 (1):1.
Chicago/Turabian StyleBrenton A. Wilder; Jeremy T. Lancaster; Peter H. Cafferata; Drew B. R. Coe; Brian J. Swanson; Donald N. Lindsay; William R. Short; Alicia M. Kinoshita. 2020. "An analytical solution for rapidly predicting post‐fire peak streamflow for small watersheds in southern California." Hydrological Processes 35, no. 1: 1.