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Justin Pflug

Publications and source records attributed to Justin Pflug.

5 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2025

An Observation-Driven Framework for Modeling Post-Fire Hydrologic Response: Evaluation for Two Central California Case Studies

In a warming climate, wildfires are becoming increasingly common, especially in semi-arid environments. Wildfires can disrupt forest ecosystems and induce changes to the land surface. Collectively, these impacts can alter the hydrologic response of a catchment following a fire, resulting in increased potential for surface runoff, reduced evapotranspiration, and, ultimately, a higher risk for flash flooding and mass wasting. The timescale of post-fire recovery of hydrological processes to return to pre-fire conditions is not well established due to the lack of ground measurements. Accurate characterization of the impacts of fire on hydrologic response is also challenging to simulate, given the complex interplay of various processes. Here, we present a generalized framework to quantify the impacts of wildfire on runoff generation. We consider the disturbances in the vegetation and soil as the two main factors contributing to post-fire floods. Using an ensemble modeling structure to account for parameter uncertainty, remotely sensed leaf area index (LAI) is assimilated into a land surface model (LSM) to simulate vegetation disturbance, and the maximum land surface saturation LSM parameter is decreased to parameterize the soil disturbance following observed fires. We consider the impacts of fire-induced changes to LAI and soil saturation on hydrologic states like runoff and evapotranspiration for two case studies. These case studies demonstrate the general applicability of hydrophobicity formulation to serve as a guideline for exploring the range of hydrologic responses post-fire.

Fire Hydrology↗
NASA NTRS2024

Extending the Utility of Space-Borne Snow Water Equivalent Observations Over Vegetated Areas With Data Assimilation

Snow is a vital component of the earth system, yet no snow-focused satellite remote sensing platform currently exists. In this study, we investigate how synthetic observations of snow water equivalent (SWE) representative of a synthetic aperture radar remote sensing platform could improve spatiotemporal estimates of snowpack. We use a fraternal twin observing system simulation experiment, specifically investigating how much snow simulated using widely used models and forcing data could be improved by assimilating synthetic observations of SWE. We focus this study across a 24° x 37° domain in the western USA and Canada, simulating snow at 250 m resolution and hourly time steps in water year 2019. We perform two data assimilation experiments, including (1) a simulation excluding synthetic observations in forests where canopies obstruct remote sensing retrievals and (2) a simulation inferring snow distribution in forested grid cells using synthetic observations from nearby canopy-free grid cells. Results found that, relative to a nature run, or assumed true simulation of snow evolution, assimilating synthetic SWE observations improved average SWE biases at maximum snowpack timing in shrub, grass, crop, bare-ground, and wetland land cover types from 14 %, to within 1 %. However, forested grid cells contained a disproportionate amount of SWE volume. In forests, SWE mean absolute errors at the time of maximum snow volume were 111 mm and average SWE biases were on the order of 150 %. Here the data assimilation approach that estimated forest SWE using observations from the nearest canopy-free grid cells substantially improved these SWE biases (18 %) and the SWE mean absolute error (27 mm). Simulations employing data assimilation also improved estimates of the temporal evolution of both SWE and runoff, even in spring snowmelt periods when melting snow and high snow liquid water content prevented synthetic SWE retrievals. In fact, in the Upper Colorado River region, melt-season SWE biases were improved from 63 % to within 1 %, and the Nash–Sutcliffe efficiency of runoff improved from −2.59 to 0.22. These results demonstrate the value of data assimilation and a snow-focused globally relevant remote sensing platform for improving the characterization of SWE and associated water availability.

Justin Pflug↗
NASA NTRSDate not supplied

An Observation-Driven Framework for Modeling Post-Fire Hydrologic Response: Evaluation for Two Central California Case Studies

In a warming climate, wildfires are becoming increasingly common, especially in semi-arid environments. Wildfires can disrupt forest ecosystems and induce changes to the land surface. Collectively, these impacts can alter the hydrologic response of a catchment following a fire, resulting in increased potential for surface runoff, reduced evapotranspiration, and, ultimately, a higher risk for flash flooding and mass wasting. The timescale for post-fire recovery of hydrological processes to return to pre-fire conditions is not well established due to the lack of ground measurements. Accurate characterization of fire impacts on hydrologic response is also challenging to simulate, given the complex interplay of various processes. Here, we present a generalized framework to quantify wildfire impacts on runoff generation. We consider the vegetation and soil disturbances as the primary contributors to post-fire floods. Using an ensemble modeling structure to account for parameter uncertainty and consider model sensitivity, remotely sensed leaf area index (LAI) is assimilated into a land surface model (LSM) to simulate vegetation disturbance, and the maximum land surface saturation LSM parameter is decreased to parameterize the soil disturbance following observed fires. We evaluated the impacts of fire-induced changes to LAI and soil saturation on hydrologic states like runoff and evapotranspiration. Analysis of two events in central California shows that 1) the LAI assimilation has a greater impact on water balance; and 2) the soil scheme captures a range of outcomes, with substantial impacts on surface runoff for some ensemble members, which could affect flood potential.

Timothy M Lahmers↗