Machine learning estimation of fire arrival time from level-2 active fires satellite data

Producing high-resolution near-real-time forecasts of fire behavior and smoke impact that are useful for fire and air quality management requires accurate initialization of the fire location. One common representation of the fire progression is through the fire arrival time, which defines the time that the fire arrives at a given location. Estimating the fire arrival time is critical for initializing the fire location within coupled fire-atmosphere models. We present a new method that utilizes machine learning to estimate the fire arrival time from satellite data in the form of burning/not burning/no data rasters. The proposed method, based on a support vector machine (SVM), is tested on the 10 largest California wildfires of the 2020 fire season, and evaluated using independent observed data from airborne infrared (IR) fire perimeters. The SVM method results indicate a good agreement with airborne fire observations in terms of the fire growth and a spatial representation of the fire extent. A 12% burned area absolute percentage error, a 5% total burned area mean percentage error, a 0.21 False Alarm Ratio average, a 0.86 Probability of Detection average, and a 0.82 Sørensen’s coefficient average suggest that this method can be used to monitor wildfires in near-real-time and provide accurate fire arrival times for improving fire modeling even in the absence of IR fire perimeters

Incorporating a canopy parameterization within a coupled fire-atmosphere model to improve a smoke simulation for a prescribed burn

Forecasting fire growth, plume rise and smoke impacts on air quality remains a challenging task. Wildland fires dynamically interact with the atmosphere, which can impact fire behavior, plume rises, and smoke dispersion. For understory fires, the fire propagation is driven by winds attenuated by the forest canopy. However, most numerical weather prediction models providing meteorological forcing for fire models are unable to resolve canopy winds. In this study, an improved canopy model parameterization was implemented within a coupled fire-atmosphere model (WRF-SFIRE) to simulate a prescribed burn within a forested plot. Simulations with and without a canopy wind model were generated to determine the sensitivity of fire growth, plume rise, and smoke dispersion to canopy effects on near-surface wind flow. Results presented here found strong linkages between the simulated fire rate of spread, heat release and smoke plume evolution. The standard WRF-SFIRE configuration, which uses a logarithmic interpolation to estimate sub-canopy winds, overestimated wind speeds (by a factor 2), fire growth rates and plume rise heights. WRF-SFIRE simulations that implemented a canopy model based on a non-dimensional wind profile, saw significant improvements in sub-canopy winds, fire growth rates and smoke dispersion when evaluated with observations

A live fuel moisture climatology in California

In this study, observations of live fuel moisture content (LFMC) for predominantly sampled fuels in six distinct regions of California were examined from 2000 to 2021. To gather the necessary data, an open-access database called the Fuel Moisture Repository (FMR), was developed. By harnessing the extensive data aggregation and query capabilities of the FMR, which draws upon the National Fuel Moisture Database, valuable insights into the live fuel moisture seasonality were obtained. Specifically, our analysis revealed a distinct downtrend in LFMC across all regions, with the exception of the two Northernmost regions. The uptrends of LFMC seen in those regions are insignificant to the general downtrend seen across all of the regions. Although the regions do not share the same trends over the temporal span of the study, from 2017 to 2021, all the regions experienced a downtrend two times more severe than the general 22-year downtrend. Further analysis of the fuel types in each of the six regions, revealed significant variability in LFMC across different fuel types and regions. To understand potential drivers of this variability, the relationship between LFMC and drought conditions was investigated. This analysis found that LFMC fluctuations were closely linked to water deficits. However, the drought conditions varied across the examined regions, contributing to extreme LFMC variability. Notably, during prolonged drought periods of 2 or more years, fuels adapted to their environment by stabilizing or even increasing their maximum and minimum moisture values, contrary to the expected continual decrease. These LFMC trends have been found to correlate to wildfire activity and the specific LFMC threshold of 79% has been proposed as trigger of an increased likelihood of large fires. By analyzing the LFMC and fire activity data in each region, we found that more optimal local thresholds can be defined, highlighting the spatial variability of the fire response to the LFMC. This work expands on existing literature regarding the connections between drought and LFMC, as well as fire activity and LFMC. The study presents a 22-year dataset of LFMC spanning the entirety of California and analyses the LFMC trends in California that haven’t been rigorously studied before

Generative Algorithms for Fusion of Physics-Based Wildfire Spread Models with Satellite Data for Initializing Wildfire Forecasts

Increases in wildfire activity and the resulting impacts have prompted the development of high-resolution wildfire behavior models for forecasting fire spread. Recent progress in using satellites to detect fire locations further provides the opportunity to use measurements to improve fire spread forecasts from numerical models through data assimilation. This work develops a method for inferring the history of a wildfire from satellite measurements, providing the necessary information to initialize coupled atmosphere-wildfire models from a measured wildfire state in a physics-informed approach. The fire arrival time, which is the time the fire reaches a given spatial location, acts as a succinct representation of the history of a wildfire. In this work, a conditional Wasserstein Generative Adversarial Network (cWGAN), trained with WRF-SFIRE simulations, is used to infer the fire arrival time from satellite active fire data. The cWGAN is used to produce samples of likely fire arrival times from the conditional distribution of arrival times given satellite active fire detections. Samples produced by the cWGAN are further used to assess the uncertainty of predictions. The cWGAN is tested on four California wildfires occurring between 2020 and 2022, and predictions for fire extent are compared against high resolution airborne infrared measurements. Further, the predicted ignition times are compared with reported ignition times. An average Sorensen's coefficient of 0.81 for the fire perimeters and an average ignition time error of 32 minutes suggest that the method is highly accurate

Data_Sheet_1_A live fuel moisture climatology in California.pdf

In this study, observations of live fuel moisture content (LFMC) for predominantly sampled fuels in six distinct regions of California were examined from 2000 to 2021. To gather the necessary data, an open-access database called the Fuel Moisture Repository (FMR), was developed. By harnessing the extensive data aggregation and query capabilities of the FMR, which draws upon the National Fuel Moisture Database, valuable insights into the live fuel moisture seasonality were obtained. Specifically, our analysis revealed a distinct downtrend in LFMC across all regions, with the exception of the two Northernmost regions. The uptrends of LFMC seen in those regions are insignificant to the general downtrend seen across all of the regions. Although the regions do not share the same trends over the temporal span of the study, from 2017 to 2021, all the regions experienced a downtrend two times more severe than the general 22-year downtrend. Further analysis of the fuel types in each of the six regions, revealed significant variability in LFMC across different fuel types and regions. To understand potential drivers of this variability, the relationship between LFMC and drought conditions was investigated. This analysis found that LFMC fluctuations were closely linked to water deficits. However, the drought conditions varied across the examined regions, contributing to extreme LFMC variability. Notably, during prolonged drought periods of 2 or more years, fuels adapted to their environment by stabilizing or even increasing their maximum and minimum moisture values, contrary to the expected continual decrease. These LFMC trends have been found to correlate to wildfire activity and the specific LFMC threshold of 79% has been proposed as trigger of an increased likelihood of large fires. By analyzing the LFMC and fire activity data in each region, we found that more optimal local thresholds can be defined, highlighting the spatial variability of the fire response to the LFMC. This work expands on existing literature regarding the connections between drought and LFMC, as well as fire activity and LFMC. The study presents a 22-year dataset of LFMC spanning the entirety of California and analyses the LFMC trends in California that haven’t been rigorously studied before.</p

Relationship of weather types on the seasonal and spatial variability of rainfall, runoff, and sediment yield in the western Mediterranean basin

Summarization: Rainfall is the key factor to understand soil erosion processes, mechanisms, and rates. Most research was conducted to determine rainfall characteristics and their relationship with soil erosion (erosivity) but there is little information about how atmospheric patterns control soil losses, and this is important to enable sustainable environmental planning and risk prevention. We investigated the temporal and spatial variability of the relationships of rainfall, runoff, and sediment yield with atmospheric patterns (weather types, WTs) in the western Mediterranean basin. For this purpose, we analyzed a large database of rainfall events collected between 1985 and 2015 in 46 experimental plots and catchments with the aim to: (i) evaluate seasonal differences in the contribution of rainfall, runoff, and sediment yield produced by the WTs; and (ii) to analyze the seasonal efficiency of the different WTs (relation frequency and magnitude) related to rainfall, runoff, and sediment yield. The results indicate two different temporal patterns: the first weather type exhibits (during the cold period: autumn and winter) westerly flows that produce the highest rainfall, runoff, and sediment yield values throughout the territory; the second weather type exhibits easterly flows that predominate during the warm period (spring and summer) and it is located on the Mediterranean coast of the Iberian Peninsula. However, the cyclonic situations present high frequency throughout the whole year with a large influence extended around the western Mediterranean basin. Contrary, the anticyclonic situations, despite of its high frequency, do not contribute significantly to the total rainfall, runoff, and sediment (showing the lowest efficiency) because of atmospheric stability that currently characterize this atmospheric pattern. Our approach helps to better understand the relationship of WTs on the seasonal and spatial variability of rainfall, runoff and sediment yield with a regional scale based on the large dataset and number of soil erosion experimental stations.Presented on: Atmospher