Wildfire Simulation Research Articles

Variations in the sensitivity of the optically stimulated luminescence from quartz in the Xifeng section: A spatiotemporal comparison across the Chinese Loess Plateau since the last interglaciation

The sensitivity of the optically stimulated luminescence (OSL) from quartz is thought to be mainly influenced by source rock (crystallization temperature and quartz defect types) and depositional history (irradiation/bleaching cycles). In this study, the OSL sensitivity of quartz of different grain sizes from loess (L1) and paleosol (S1) layers at the Xifeng section of the Chinese Loess Plateau was analyzed. The OSL sensitivity of the samples from the Xifeng section shows a distinct difference between glacial and interglacial cycles, which is consistent with the patterns observed from magnetic susceptibility (MS) and grain sizes. The highest OSL sensitivity is observed in S1, which is twelve times the lowest sensitivity observed in L1. Moreover, the contribution of the fast component of quartz OSL from the paleosol accounts for more than 90% of the total OSL signal. In contrast, the middle and slow components contribute more in loess, and a significant variation among different aliquots was observed. Comparison of the data obtained from loess sections from different regions, including Xifeng, Luochuan, Jingyuan and Shimao, show significant differences in the magnitudes of OSL sensitivity but a similar pattern between glacial and interglacial periods. The results of this study suggest that, except for the wildfire model proposed to interpret the high OSL sensitivity in paleosol (Zhang, 2018), the shift of wind pattern of westerly wind (i.e., transporting “dim” source from North Tibetan Plateau and northwesterly wind (i.e., transporting “bright” source from Alxa Arid Lands and the subsequent mixture from the different sources may partially contribute to the different OSL sensitivities observed for Xifeng and Luochuan. In contrast, the OSL sensitivity of Shimao samples from L1 was affected by the input from the Mu Us Desert, and those of Jingyuan may affected by the contribution from the Yellow River. • The fast components of the “bright” paleosol with high OSL sensitivity accounted for more than 90%, and the differences among aliquots were slight. In contrast, the same components for “dim” loess occupied below 90%, and the difference among aliquots was significant. • The spatial changes of OSL sensitivity vary more significantly during interglacial periods than that during glacial periods. • Wind pattern likely have potential cause for the variation of quartz OSL sensitivity in CLP.

Electrical Responses of Pinus halepensis Mill. as an Indicator of Wildfire Risk in Mediterranean Forests by Complementing Live Fuel Moisture

Pinus halepensis forests, as Mediterranean-type ecosystems, are subject to high levels of wildfire risk in times of drought, with meteorological conditions of water stress and very high temperatures, mainly in summer. Considering the difficulty of knowing the phenological state of this species, the objective of this research was to evaluate the possibility of implementing the electrical responses (voltage and short-circuit current) as a variable in fire risk management models, compared to live fuel moisture. On the one hand, the obtained results demonstrate non-significant differences between the moisture content of the different fractions of the living branches (base and half of the branch and live fuel), even in times of drought with hydric stress and very high temperatures. Live fuel moisture of Pinus halepensis does not show significant seasonal variations under the influence of extreme fire risk factors. For this reason, it should be complemented with other variables for fire risk management models. On the other hand, the differences registered in the electrical signal show oscillations with significant variations, which are strongly correlated with the periods of extremely favourable meteorological conditions for wildfires. So, the voltages measured show ranges that correspond with great accuracy to the FWI. Voltage variation is dependent on the hydraulic dynamic plant behaviour and a result of the physiological response of pine trees to abiotic stress of drought. It is an easy-to-measure electrical parameter as well as a very reliable indicator with a high correlation with wildfire risk. Thus, electrical responses could add more knowledge about the phenological state of the trees in dependence on stress climatic conditions, allowing integration of these variables in the preventive wildfire modelling and management.

Sentimental wildfire: a social-physics machine learning model for wildfire nowcasting

The intensity of wildfires and wildfire season length is increasing due to climate change, causing a greater threat to the local population. Much of this population are increasingly adopting social media, and sites like Twitter are increasingly being used as a real-time human-sensor network during natural disasters; detecting, tracking and documenting events. The human-sensor concept is currently largely omitted by wildfire models, representing a potential loss of information. By including Twitter data as a source in our models, we aim to help disaster managers make more informed, socially driven decisions, by detecting and monitoring online social media sentiment over the course of a wildfire event. This paper implements machine learning in a wildfire prediction model, using social media and geophysical data sources with Sentiment Analysis to predict wildfire characteristics with high accuracy. We also use wildfire-specific attributes to predict online social dynamics, as this has been shown to be indicative of localised disaster severity. This may be useful for disaster management teams in identifying areas of immediate danger. We combine geophysical satellite data from the Global Fire Atlas with social data provided by Twitter. We perform data collection and subsequent analysis & visualisation, and compare regional differences in online social sentiment expression. Following this, we compare and contrast different machine learning models for predicting wildfire attributes. We demonstrate social media is a predictor of wildfire activity, and present models which accurately model wildfire attributes. This work develops the concept of the human sensor in the context of wildfires, using users’ Tweets as noisy subjective sentimental accounts of current localised conditions. This work contributes to the development of more socially conscious wildfire models, by incorporating social media data into wildfire prediction and modelling.

Stability and Optimal Control of Tree-Insect Model under Forest Fire Disturbance

In this article, we propose a mathematical model for insect outbreaks coupled with wildfire disturbances and an optimization model for finding suitable wildfire frequencies. We use a refined Holling II function as a model for the nonlinear response of fire frequency against trees and insects. The results show that for the tree–insect–wildfire model, there is a coexistence equilibrium in the system. Sensitivity analysis is performed to determine the effect of wildfire on trees in the optimization model. The results show that forest fires have a significant impact on the equilibrium mechanism of tree–insect coexistence. Numerical simulations suggest that in some areas of high fire intensity, there may be positive feedback between disturbances from wildfires and insect outbreaks. The result is consistent with the present theory in this field.

A high-resolution fuel type mapping procedure based on satellite imagery and neural networks: Updating fuel maps for wildfire simulators

A major limitation in the simulation of forest fires involves the proper characterization of the surface vegetation over the study area, based on land cover maps. Unfortunately, these maps may be outdated, with areas where vegetation is either not documented or inaccurately portrayed. These limitations may impair the predictions of wildfire simulators or the design of risk maps and prevention plans. This study proposes a complete procedure for fuel type classification using satellite imagery and fully-connected neural networks. Specifically, our work is based on pixel-based processing cells, generating high-resolution maps. The field study is located in the Northeast of Castilla y León, a central Spanish region, and the Rothermel criteria was followed for the fuel classification. The results record an accuracy of close to 78% on the test sets for the two studied settings, improving on the results reported in previous studies and ratifying the robustness of our approach. Additionally, the confusion matrix analysis and the per-class statistics computed confirm good reliability for all fuel types in a cross-validation framework. The predicted maps can be used on wildfire simulators through GIS tools.

Priorities and Effectiveness in Wildfire Management: Evidence from Fire Spread in the Western United States

Costs of fighting wildfires have increased substantially over the past several decades. Yet surprisingly little is known about the effectiveness of wildfire suppression or how wildfire incident managers prioritize resources threatened within a wildfire incident. We investigate the determinants of wildfire suppression effort using a novel empirical strategy comparing over 1,400 historical fire perimeters to the spatial distribution of assets at risk. We find that fires are more likely to stop spreading as they approach homes, particularly when homes are of greater value. This effect persists after controlling for physical factors (fuels, landscape, and weather) using a state-of-the-art wildfire simulation tool. As well, the probability that spread will be halted is affected by characteristics of homes 1–2 kilometers beyond a fire’s edge. Overall, we find that suppression efforts can substantively affect wildfire outcomes but that some groups may benefit more from wildfire management than others.

RADAR-Vegetation Structural Perpendicular Index (R-VSPI) for the Quantification of Wildfire Impact and Post-Fire Vegetation Recovery

The precise information on fuel characteristics is essential for wildfire modelling and management. Satellite remote sensing can provide accurate and timely measurements of fuel characteristics. However, current estimates of fuel load changes from optical remote sensing are obstructed by seasonal cloud cover that limits their continuous assessments. This study utilises remotely sensed Synthetic-Aperture Radar (SAR) (Sentinel-1 backscatter) data as an alternative to optical-based imaging (Sentinel-2 scaled surface reflectance). SAR can penetrate clouds and offers high-spatial and medium-temporal resolution datasets and can hence complement the optical dataset. Inspired by the optical-based Vegetation Structural Perpendicular Index (VSPI), an SAR-based index termed RADAR-VSPI (R-VSPI) is introduced in this study. R-VSPI characterises the spatio-temporal changes in fuel load due to wildfire and the subsequent vegetation recovery thereof. The R-VSPI utilises SAR backscatter (σ°) from the co-polarized (VV) and cross-polarized (VH) channels at a centre frequency of 5.4 GHz. The newly developed index is applied over major wildfire events that occurred during the “Black Summer” wildfire season (2019–2020) in southern Australia. The condition of the fuel load was mapped every 5 (any orbit) to 12 (same orbit) days at an aggregated spatial resolution of 110 m. The results show that R-VSPI was able to quantify fuel depletion by wildfire (relative to healthy vegetation) and monitor its subsequent post-fire recovery. The information on fuel condition and heterogeneity improved at high-resolution by adapting the VSPI on a dual-polarization SAR dataset (R-VSPI) compared to the historic forest fuel characterisation methods (that used visible and infrared bands only for fuel estimations). The R-VSPI thus provides a complementary source of information on fuel load changes in a forest landscape compared to the optical-based VSPI, in particular when optical observations are not available due to cloud cover.

Improving WRF-Fire Wildfire Simulation Accuracy Using SAR and Time Series of Satellite-Based Vegetation Indices

Wildfire simulations depend on fuel representation. Present fuel models are mainly based on the density and properties of different vegetation types. This study aims to improve the accuracy of WRF-Fire wildfire simulations, by using synthetic-aperture radar (SAR) data to estimate the fuel load and the trend of vegetation index to estimate the dryness of woody vegetation. We updated the chaparral and timber standard woody fuel classes in the WRF-Fire fuel settings. We used the ESA global above-ground biomass (AGB) based on SAR data to estimate the fuel load, and the Landsat normalized difference vegetation index (NDVI) trends of woody vegetation to estimate the fuel moisture content. These fuel sub-parameters represent the dynamic changes and spatial variability of woody fuel. We simulated two wildfires in Israel while using three different fuel models: the original 13 Anderson Fire Behavior fuel model, and two modified fuel models introducing AGB alone, and AGB and dryness. The updated fuel model (the basic fuel model plus the AGB and dryness) improved the simulation results significantly, i.e., the Jaccard similarity coefficient increased by 283% on average. Our results demonstrate the potential of combining satellite SAR data and Landsat NDVI trends to improve WRF-Fire wildfire simulations.

Short- and long-term wildfire threat when adapting infrastructure for wildlife conservation in the boreal forest.

Managers designing infrastructure in fire‐prone wildland areas require assessments of wildfire threat to quantify uncertainty due to future vegetation and climatic conditions. In this study, we combine wildfire simulation and forest landscape composition modeling to identify areas that would be highly susceptible to wildfire around a proposed conservation corridor in Québec, Canada. In this measure, managers have proposed raising the conductors of a new 735‐kV hydroelectric powerline above the forest canopy within a wildlife connectivity corridor to mitigate the impacts to threatened boreal woodland caribou (Rangifer tarandus). Retention of coniferous vegetation, however, can increase the likelihood of an intense wildfire damaging powerline infrastructure. To assess the likelihood of high‐intensity wildfires for the next 100 years, we evaluated three time periods (2020, 2070, 2120), three climate scenarios (observed, RCP 4.5, RCP 8.5), and four vegetation projections (static, no harvest, extensive harvesting, harvesting excluded in protected areas). Under present‐day conditions, we found a lower probability of high‐intensity wildfire within the corridor than in other parts of the study area, due to the protective influence of a nearby, poorly regenerated burned area. Wildfire probability will increase into the future, with strong, weather‐induced inflation in the number of annual ignitions and wildfire spread potential. However, a conversion to less‐flammable vegetation triggered by interactions between climate change and disturbance may attenuate this trend. By addressing the range of uncertainty of future conditions, we present a robust strategy to assist in decision‐making about long‐term risk management for both the proposed conservation measure and the powerline.

System-Level Performance Analysis of Cooperative Multiple Unmanned Aerial Vehicles for Wildfire Surveillance Using Agent-Based Modeling

In this paper, we propose an agent-based approach for the evaluation of Multiple Unmanned Autonomous Vehicle (MUAV) wildfire monitoring systems for remote and hard-to-reach areas. Emerging environmental factors are causing a higher number of wildfires and keeping these fires in check is becoming a global challenge. MUAV deployment for the monitoring and surveillance of potential fires has already been established. However, most of the scholarly work is still focused on MUAV operations details. In wildfire surveillance and monitoring, evaluations of the system-level performance in terms of the analysis of the effects of individual behavior on system surveillance has yet to be established. Especially in an MUAV system, the individual and cooperative behaviors of the team affect the overall performance of the system. Such systems are dynamic and stochastic because of an ever-changing environment. Quantifying the emergent system behavior and general performance measures of such a system by analytical methods is challenging. In our work, we present an agent-based model for MUAV surveillance missions. This paper focuses on the overall system performance of cooperative UAVs performing forest fire surveillance. The principal theme is to present the effects of three behaviors on overall performance: (1) the area allocation and (2) dynamic coverage, and (3) the effects of forest density on team allocation. For area allocation, three behaviors are simulated: (1) randomized, (2) two-layer barrier sweep coverage, and (3) full sweep coverage. For dynamic coverage, the effects of communication and resource unavailability during the mission are studied by analyzing the agent’s downtime spent on refueling. Last, an extensive simulation is carried out on wildfire models with varying forest density. It is found that cooperative complete sweep coverage strategies perform better than the rest and the performance of the team is greatly affected by the forest density.

Data‐driven spatio‐temporal analysis of wildfire risk to power systems operation

Wildfires are natural or man-made disasters that continuously threaten portions of the transmission and distribution grid, and thus the stability of the electric grid. This paper presents a two-stage framework for assessing power system-wildfire risk using a data-driven wildfire prediction model. The first stage of the framework estimates the spatio-temporal probability of potential wildfire ignition and propagation using a deep neural network in combination with the wildfire physical spread model. Analysis reveals similar spatial and temporal patterns between the model-predicted wildfire ignition potential and actual wildfire ignition. Motivated by these observations, the second stage assesses the wildfire risk in the power grid operation in terms of potential loss of load by de-energisation, through combining geospatial information system data of the power grid topology and the stochastic spatio-temporal wildfire model developed in the first stage. The electric power utility applications introduced by the proposed framework are twofold: 1) a spatio-temporal risk model for proactive de-energisation against potential power system failure-induced wildfire, and 2) a spatio-temporal spreading model for optimal grid operations against exogenous wildfire. The proposed model, based on real-world dataset, is demonstrated on the IEEE 24-bus test system mapped to a study area in Northern California, while the results illustrate the proposed model can achieve the best performance in potential wildfire ignition detection (AUC of 0.995) compared to other baselines, as well as demonstrates the risk-aware operation of the power system enabled by the proposed framework.

Modelling Fire Behavior to Assess Community Exposure in Europe: Combining Open Data and Geospatial Analysis

Predicting where the next large-scale wildfire event will occur can help fire management agencies better prepare for taking preventive actions and improving suppression efficiency. Wildfire simulations can be useful in estimating the spread and behavior of potential future fires by several available algorithms. The uncertainty of ignition location and weather data influencing fire propagation requires a stochastic approach integrated with fire simulations. In addition, scarcity of required spatial data in different fire-prone European regions limits the creation of fire simulation outputs. In this study we provide a framework for processing and creating spatial layers and descriptive data from open-access international and national databases for use in Monte Carlo fire simulations with the Minimum Travel Time fire spread algorithm, targeted to assess cross-boundary wildfire propagation and community exposure for a large-scale case study area (Macedonia, Greece). We simulated over 300,000 fires, each independently modelled with constant weather conditions from a randomly chosen simulation scenario derived from historical weather data. Simulations generated fire perimeters and raster estimates of annual burn probability and conditional flame length. Results were used to estimate community exposure by intersecting simulated fire perimeters with community polygons. We found potential ignitions can grow large enough to reach communities across 27% of the study area and identified the top-50 most exposed communities and the sources of their exposure. The proposed framework can guide efforts in European regions to prioritize fuel management activities in order to reduce wildfire risk.

Building a machine learning surrogate model for wildfire activities within a global Earth system model

Abstract. Wildfire is an important ecosystem process, influencing land biogeophysical and biogeochemical dynamics and atmospheric composition. Fire-driven loss of vegetation cover, for example, directly modifies the surface energy budget as a consequence of changing albedo, surface roughness, and partitioning of sensible and latent heat fluxes. Carbon dioxide and methane emitted by fires contribute to a positive atmospheric forcing, whereas emissions of carbonaceous aerosols may contribute to surface cooling. Process-based modeling of wildfires in Earth system land models is challenging due to limited understanding of human, climate, and ecosystem controls on fire counts, fire size, and burned area. Integration of mechanistic wildfire models within Earth system models requires careful parameter calibration, which is computationally expensive and subject to equifinality. To explore alternative approaches, we present a deep neural network (DNN) scheme that surrogates the process-based wildfire model with the Energy Exascale Earth System Model (E3SM) interface. The DNN wildfire model accurately simulates observed burned area with over 90 % higher accuracy with a large reduction in parameterization time compared with the current process-based wildfire model. The surrogate wildfire model successfully captured the observed monthly regional burned area during validation period 2011 to 2015 (coefficient of determination, R2=0.93). Since the DNN wildfire model has the same input and output requirements as the E3SM process-based wildfire model, our results demonstrate the applicability of machine learning for high accuracy and efficient large-scale land model development and predictions.

Estimation of wildfire wind conditions via perimeter and surface area optimization

This paper shows that the prediction capability of wildfire progression can be improved by estimation of a single prevailing wind vector parametrized by a wind speed and a wind direction to drive a wildfire simulation created by FARSITE. Estimations of these wind vectors are achieved in this work by a gradient-free optimization via a grid search that compares wildfire model simulations with measured wildfire perimeters, where noisy observations are modeled as uncertainties on the locations of the vertices of the measured wildfire perimeters. Two optimizations are established to acquire the optimal wind speed and wind direction. To formulate a perimeter optimization, an uncertainty-weighted least-squares error is computed between the vertices of the simulated and measured wildfire perimeter. The challenge in this approach is to match the number of vertices on the simulated and measured wildfire perimeter via interpolation of perimeter points and their uncertainties. For a surface area optimization, an uncertainty-weighted surface area error is introduced to capture the surface of the union minus the intersection of the simulated and measured wildfire perimeter. The challenge in this approach is to formulate a surface area error, weighted by the uncertainties on the locations of the vertices of the measured wildfire perimeter. The optimization in this paper is based on an iterative refinement of a grid of the wind vector and provides robustness to intermittent erroneous results produced by FARSITE, while allowing parallel execution of wildfire model calculations. This paper is an extension of the work in Tan et al., (2021). Results on wind vector estimation are illustrated on two historical wildfire events: the 2019 Maria Fire that burned south of the community of Santa Paula in the area of Somis, CA, and the 2019 Cave Fire that started in the Santa Ynez Mountains of Santa Barbara County.

Wildfire probability models calibrated using past human and lightning ignition patterns can inform mitigation of post-fire hydrologic hazards

Most wildfires are started by humans, however, geographic variation of potential ignition sources is not often explicitly accounted for in wildfire simulation modelling or risk assessments. In this study, we investigated how patterns of human and lightning ignitions can influence modelled fire simulations and demonstrate how these data can be used to assess post-fire flooding and sediment transport. We used historical ignition data (1992–2015) to characterize ignition patterns for thirteen mountain ranges in southern Arizona, United States, and developed FlamMap burn probability (BP) models for three scenarios: human ignition, lightning ignition, and random ignition. We then developed a watershed-scale case study assessing the impacts of ignition scenarios on post-fire hydrology using the KINEROS2 model that simulates runoff and erosion. BP models illustrated considerable differences in landscape fire risk between the three ignition scenarios. Results from the watershed model indicate the greatest impacts from the post-fire human ignition scenario, with a 10-fold increase in sediment discharge and four-fold increase in peak flow compared to pre-fire conditions. Our results show that consideration of ignition source and location is important for assessing fire risk, and our modelling approach provides a planning mechanism to identify locations most at risk to fire-induced flood hazards, where prevention and mitigation activities can be focused.

Classification of Mediterranean Shrub Species from UAV Point Clouds

Modelling fire behaviour in forest fires is based on meteorological, topographical, and vegetation data, including species’ type. To accurately parameterise these models, an inventory of the area of analysis with the maximum spatial and temporal resolution is required. This study investigated the use of UAV-based digital aerial photogrammetry (UAV-DAP) point clouds to classify tree and shrub species in Mediterranean forests, and this information is key for the correct generation of wildfire models. In July 2020, two test sites located in the Natural Park of Sierra Calderona (eastern Spain) were analysed, registering 1036 vegetation individuals as reference data, corresponding to 11 shrub and one tree species. Meanwhile, photogrammetric flights were carried out over the test sites, using a UAV DJI Inspire 2 equipped with a Micasense RedEdge multispectral camera. Geometrical, spectral, and neighbour-based features were obtained from the resulting point cloud generated. Using these features, points belonging to tree and shrub species were classified using several machine learning methods, i.e., Decision Trees, Extra Trees, Gradient Boosting, Random Forest, and MultiLayer Perceptron. The best results were obtained using Gradient Boosting, with a mean cross-validation accuracy of 81.7% and 91.5% for test sites 1 and 2, respectively. Once the best classifier was selected, classified points were clustered based on their geometry and tested with evaluation data, and overall accuracies of 81.9% and 96.4% were obtained for test sites 1 and 2, respectively. Results showed that the use of UAV-DAP allows the classification of Mediterranean tree and shrub species. This technique opens a wide range of possibilities, including the identification of species as a first step for further extraction of structure and fuel variables as input for wildfire behaviour models.

QES-Fire: a dynamically coupled fast-response wildfire model

A microscale wildfire model, QES-Fire, that dynamically couples the fire front to microscale winds was developed using a simplified physics rate of spread (ROS) model, a kinematic plume-rise model and a mass-consistent wind solver. The model is three-dimensional and couples fire heat fluxes to the wind field while being more computationally efficient than other coupled models. The plume-rise model calculates a potential velocity field scaled by the ROS model’s fire heat flux. Distinct plumes are merged using a multiscale plume-merging methodology that can efficiently represent complex fire fronts. The plume velocity is then superimposed on the ambient winds and the wind solver enforces conservation of mass on the combined field, which is then fed into the ROS model and iterated on until convergence. QES-Fire’s ability to represent plume rise is evaluated by comparing its results with those from an atmospheric large-eddy simulation (LES) model. Additionally, the model is compared with data from the FireFlux II field experiment. QES-Fire agrees well with both the LES and field experiment data, with domain-integrated buoyancy fluxes differing by less than 17% between LES and QES-Fire and less than a 10% difference in the ROS between QES-Fire and FireFlux II data.

Externalities in the Wildland - Urban Interface: Private Decisions, Collective Action, and Results from Wildfire Simulation Models for California

Externalities in the Wildland - Urban Interface: Private Decisions, Collective Action, and Results from Wildfire Simulation Models for California

Increasing radiant heat flux affects leaf flammability patterns in plant species of eastern Australian fire-prone woodlands.

Leaf flammability is a functional trait that can vary widely among plant species. At present, however, the effects that increasing radiant heat flux have on variation in leaf flammability among species are not well understood. Yet, such effects could have important implications for wildfire models that take into account species' differences in flammability. We examined how five leaf flammability attributes spanning ignitibility (times to incandescence and flaming), sustainability (incandescence and flame durations) and combustibility (proportion of leaves entering flaming combustion) responded to increasing radiant heat fluxes (29.6 to 96.6 kWm-2 ) in 10 species of fire-prone woodlands. As radiant heat flux increased, times to incandescence and flaming became significantly faster and proportions of leaves entering flaming combustion became significantly higher. In contrast, incandescence duration became significantly shorter at high radiant heat flux. Differences among species in these flammability attributes decreased with increasing radiant heat flux, with species becoming significantly more similar to each other. Differences among species in flame duration, however, were not significantly affected by increasing radiant heat flux, with leaf flaming durations in each species remaining relatively fixed across the radiant heat flux gradient. Our findings show that leaf flammability is significantly affected by increasing radiant heat flux. We suggest that of the flammability attributes assessed in our study, flame duration is the most informative to include in wildfire models which explicitly consider species' flammability, given that differences among species in flame duration are maintained across a radiant heat flux gradient.

Societal benefits from wildfire mitigation activities through payments for watershed services: Insights from Colorado

Payments for watershed services (PWS) programs are becoming a popular governance approach in the western United States (US) to fund forest management aimed at source water protection. In this paper we conduct a cost-benefit analysis (CBA) of one of the first collaboratively funded PWS programs in the US, located in the municipal watersheds servicing Denver, Colorado. We combine wildfire modeling, sediment modeling, and primary and secondary data on economic values to quantify the impact of the program on protecting multiple values at risk. Our results show that while the program has led to diverse societal benefits, it is only economically efficient (benefit-cost ratio greater than one) when all co-benefits beyond source water protection are considered, and fuels treatments are assumed to encounter wildfire. When the probability of wildfire is accounted for, economic benefits would need to be triple what was estimated in our analysis to achieve economic efficiency. Our findings suggest that improving spatial prioritization of interventions would increase economic benefits and better data on treatment placement and costs would help facilitate future CBA of PWS programs focused on wildfire mitigation.