Research
Advancing the frontiers of wildfire science, our lab pioneers physics-based models, data-driven tools, and AI-powered solutions to transform wildfire modeling, risk assessment, and resilience—bridging breakthrough research with real-world challenges.
Ongoing Projects
Transport and deposition patterns of wildfire-produced ash, firebrand, and smoke and their ecological significance
The objective of this work is to develop a mechanistic and unifying framework that captures the full lifecycle of airborne pyrogenic particles generated during wildfires, including their generation, lofting, atmospheric transport, and eventual deposition. By integrating physical modeling with collected field data, we aim to bridge the gap between theoretical predictions and real-world outcomes. Specifically, we compare model outputs with the mass and chemical composition of particles collected in the field at various distances from the 2021 Caldor Fire in California to assess the accuracy and applicability of the proposed framework. Furthermore, we examine the ecological implications of the deposition footprint, highlighting how these particles may influence ecosystem recovery in post-fire landscapes.
Team Members: Facundo Scordo, Majid Bavandpour, Dani Or, Hamed Ebrahimian, Sudeep Chandra
Probabilistic Wildfire Risk Assessment (PWRA): An efficient uncertainty propagation framework
Wildfires are becoming increasingly destructive, turning into a national crisis and highlighting the urgent need for more advanced risk assessment tools to aid in planning and preparedness. This work presents a new framework that redefines wildfire risk assessment by shifting from traditional single-point loss estimates to a probabilistic, uncertainty-aware approach. Unlike conventional “red-zone” maps, which offer limited insight, our method quantifies and propagates uncertainties throughout the modeling process. This enables decision-makers and communities to evaluate outcomes based on specific quantities of interest. By incorporating advanced analytical techniques such as the unscented transform, the framework also improves computational efficiency—reducing computational costs without compromising accuracy, making it highly scalable for large and complex regions.
Team Members: Majid Bavandpour, Hamed Ebrahimian, Dani Or
Deep learning for near real-time wildfire monitoring
The objective of this work is to enhance a previously-developed deep learning approach for wildfire monitoring by incorporating additional preprocessing steps aimed at improving data quality. These improvements focus on refining the input satellite data to reduce noise, correct artifacts, and better align spatial and temporal features between different satellite sources such as GOES-17 and VIIRS. By improving the quality of the input data, the deep learning models can more accurately estimate active fire locations and brightness temperatures. The goal is to increase both the spatial resolution and reliability of the outputs, ultimately leading to more accurate and timely wildfire detection, monitoring, and response capabilities using satellite data and deep learning methods.
Team Members: Ryota Yagi, Majid Bavandpour, Hamed Ebrahimian, Dani Or
Firebrand ignition model
The objective of this work is to develop a reduced physics-based model for firebrand ignition upon landing. In this work, we have divided the firebrand ignition problem into two steps. In the first step, the radiative heat generated from different ember configurations under various environmental conditions is characterized using FDS simulation and validated with experimental data. In the second step, the resulting heat time history is reflected on various fuel beds. Ignition is modeled through a simplified process and validated with experimental data. At the end a machine learning classifier is trained on more than one million of simulated combinations firebrand, fuel bed, and environmental condition to develop a ignition criteria for application in wildland fire simulation platforms.
Team Members: Kasra Shamsaei, Majid Bavandpour, Hamed Ebrahimian, Dani Or
Recent Completed Projects
Review of wildfire modeling considering effects on land surfaces
This work introduces hydrologists, soil scientists, and ecologists to principles and advances in modeling of wildfire dynamics and rates of spread to improve understanding of capabilities and limitations offered by modern wildfire models. It highlights the common omission of wildfire effects on soil processes and emphasizes how wildfire models can help fill this gap by providing boundary conditions necessary for quantifying thermal impacts on soils. By enhancing cross-disciplinary collaboration, the review seeks to support more accurate representation of wildfire impacts on soil and hydrologic systems, as well as the recovery of fire-affected landscapes. It outlines the fundamental characteristics, processes, and metrics central to wildfire science and offers an overview of modeling approaches used for research and prescribed fire planning.
Team Members: Dani Or and Hamed Ebrahimian
Smoke from regional wildfires alters lake ecology
We analyzed a 2018 event in which wildfire smoke covered Castle Lake, a low-productivity lake in California, for 55 days. This study compared the lake's conditions during this smoke event with data from the previous four years (2014-2017). The results showed that the smoke reduced incident ultraviolet-B (UV-B) radiation by 31% and photosynthetically active radiation (PAR) by 11%, significantly altering the light regime. Underwater, UV-B and PAR decreased by 65% and 44%, respectively, while the lake's heat content dropped by 7%. Despite these changes, primary production in shallow waters increased by 109%, likely due to a release from photoinhibition. In contrast, deep-water primary production decreased, and the chlorophyll-a peak in deep waters failed to develop, probably due to the reduced PAR. The study also found that while zooplankton biomass and migration patterns showed little change, trout were absent from littoral-benthic habitats during the smoke period. These findings highlight the varied effects of smoke on lake ecosystems.
Team Members: Facundo Scordo and Sudeep Chandra
Wildfire smoke effects on lake-habitat specific metabolism: Toward a conceptual understanding
This study aimed to evaluate the metabolic response of lake habitats to wildfire smoke by comparing key ecological parameters during years with and without smoke exposure. We conducted our work on the offshore and nearshore habitats of a clear, low-productivity lake (Castle Lake, California), assessing the light regime, gross primary production (GPP), ecosystem respiration (ER), and net ecosystem production (NEP). During smoke cover, incident ultraviolet-B (UV-B) radiation and photosynthetically active radiation (PAR) decreased by. 53% and 28%, respectively. In comparison, water column extinction coefficients for UV-B and PAR increased by 20% and 18%, respectively. In the offshore zone, productivity increased during smoke periods, likely due to reduced solar radiation, with PAR levels still sufficient to saturate productivity. However, nearshore productivity remained unchanged, possibly due to the adaptation of these habitats to high-intensity UV-B radiation. The study highlights the significance of understanding how wildfire smoke affects the energy balance of specific lake habitats.
Team Members: Facundo Scordo and Sudeep Chandra
Wildfire smoke reduces lake ecosystem metabolic rates unequally across a trophic gradient
The objective of this study was to quantify the impact of wildfire smoke on lakes, with a particular focus on trends in smoke coverage and its effects on ecosystem functions such as gross primary production and ecosystem respiration. The research assessed these impacts across ten lakes in California, USA, which varied in water clarity and nutrient concentrations. During the three years with the heaviest smoke coverage (2018, 2020, and 2021), lakes experienced 23 to 45 days of medium to high-density smoke. This led to a 20% reduction in shortwave radiation flux and a five-fold increase in acceptable concentrations of particulate matter. The results indicated that ecosystem respiration generally declined during periods of cover of smoke, with the most significant declines observed in low-nutrient, cold lakes. The response of gross primary production to smoke was more variable, depending on the specific attributes of each lake. The study concluded that both lake characteristics and the timing of wildfires are crucial in mediating the effects of smoke on freshwater ecosystems.
Team Members: Facundo Scordo and Sudeep Chandra
Deep learning to Improve spatial resolution of GOES-17 wildfire boundaries
Wildfires in the U.S. are becoming more frequent and severe, highlighting the need for better emergency response systems with high temporal and spatial monitoring. Satellites like GOES-17 offer high temporal (1–5 min) but low spatial (≥2 km) resolution, while VIIRS provides the opposite: low temporal (~12 h) but high spatial (375 m) resolution. This study uses deep learning (DL), specifically an Autoencoder model, to enhance GOES-17’s spatial resolution using VIIRS data. Multiple DL architectures and loss functions were tested on wildfires from 2019–2021 in the western U.S. Results show DL can generate GOES-17 images with VIIRS-like resolution, enabling high-resolution, near-real-time wildfire monitoring and semi-continuous fire progression tracking.
Team Members: Mukul Badhan, Kasra Shamsaei, Hamed Ebrahimian
Data-driven firebrand generation model
This study presents a data-driven firebrand generation model designed for integration into wildland fire simulation platforms to enable fire spotting prediction. The model was developed by compiling and synthesizing experimental data from the literature and consists of three components that estimate firebrand yield, mass distribution, and the relationship between mass and projected area. Model inputs include fuel type, consumed fuel mass, fuel moisture content, and wind speed; outputs consist of firebrand realizations with assigned mass and projected area. Validation against independent experimental datasets shows good agreement between predicted and observed firebrand mass and projected area distributions. The model is computationally efficient, requires only readily available input parameters from existing wildland fire models, and delivers reliable predictions, making it well suited for incorporation into current simulation platforms.
Team Members: Kasra Shamsaei, Hamed Ebrahimian, Dani Or
Numerical simulation of historic wildfires
Through a series of studies, we evaluate the performance of Weather Research and Forecast-Fire (WRF-Fire), a coupled fire-atmosphere wildland fire simulation platform, in simulating a large historic fire, including the 2018 Camp Fire. The simulation results are compared to high-temporal-resolution fire perimeters derived from NEXRAD radar observations and show non-negligible discrepancies between simulated fire boundary and the observations with regard to rate of spread (ROS) and spread direction. Next, the sensitivity of the model to a series of modeling parameters and assumptions governing the simulated wind field are investigated. Finally in an attempt to improve the heat flux presentation in the model, a canopy fuel model is added to the modeling platform and the heat release scheme into the atmosphere is corrected and improved.
Team Members: Kasra Shamsaei, Hamed Ebrahimian