Modeling Post‐Wildfire Hydrologic Response: Review and Future Directions for Applications of Physically Based Distributed Simulation. Issue 2 (22nd February 2023)

• Record Type: Journal Article
• Authors: Ebel, Brian A.; Shephard, Zachary M.; Walvoord, Michelle A.; Murphy, Sheila F.; Partridge, Trevor F.; Perkins, Kim S.
• Is Part Of: Earth's future. Volume 11:Issue 2(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2023-02-22
• Subjects: wildfire -- wildland fire -- hydrologic model -- streamflow -- groundwater -- water supply; Environmental sciences -- Periodicals; Environmental sciences; Periodicals; 550
• DOI: 10.1029/2022EF003038
• Languages: English
• ISSNs: 2328-4277

Abstract

Abstract: Wildfire is a growing concern as climate shifts. The hydrologic effects of wildfire, which include elevated hazards and changes in water quantity and quality, are increasingly assessed using numerical models. Post‐wildfire application of physically based distributed models provides unique insight into the underlying processes that affect water resources after wildfire. This work reviews and synthesizes post‐wildfire applications of physically based distributed models by examining the scales and geographic/ecohydrologic distribution of model applications, hydrologic response process representation, model parameterization, and model performance metrics. Highlighted gaps and opportunities for advancing physically based distributed hydrologic response modeling after wildfire include the following: (a) applying models in under‐represented geographic (S. America, Africa, Asia) and ecohydrologic regions (arid or dry subhumid climates), (b) incorporating all four major streamflow generation mechanisms (infiltration excess, saturation excess, subsurface storm flow, and groundwater flow), (c) representing integrated vadose zone and saturated zone processes to better capture subsurface streamflow generation, (d) building new remotely sensed model parameterization methods for precipitation interception, infiltration, and overland flow that account for burn severity and recovery, (e) incorporating distributed state variables (e.g., soil moisture, groundwater levels) in model performance assessment, (f) designing model intercomparison studies, including field datasets specifically for post‐wildfire model development and validation, (g) linking mechanistic vegetation regrowth models with hydrologic models to improve simulation of process shifts as ecosystems recover, and (h) creating a new community modeling framework to integrate modeling advances across the wildfire science community. Plain Language Summary: Wildfire can lead to flooding, debris flows, and water supply issues. Computer models of the post‐fire landscape response to rainfall are often used to predict the magnitude and timing of hydrologic concerns. In this work, we review and synthesize post‐fire applications of a type of computer model, termed physically based distributed models that use equations based on physical principles to predict where and when post‐fire problems may be most substantial. This work examines where models have been applied, how the models are set up, and how closely the models match measured responses to identify gaps and opportunities to improve hydrology modeling after fire. Key Points: Including subsurface processes and distributed hydrologic states in model performance metrics could improve post wildfire hydrologic models Remotely sensed model parameterization methods incorporating burn severity and temporal recovery are opportunities for model progress Wildfire‐specific model intercomparisons and supporting data collection will advance predictive capability