Tools and Trainings
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Wildland–urban interface fires (WUI fires) can pose a significant threat to water resources, including drinking water supplies, water treatment infrastructure, ecosystem function, and agricultural irrigation. Wildfires, especially WUI fires, are expected to increase in frequency and severity. Despite the need for effective mitigation and response strategies for wildfires, rapid research co-production to support decision-making for water incident response and water management is generally limited. This manuscript draws on five U.S. wildfire case studies to highlight how research co-production between scientists, water agencies, and managers supports more effective decision-making for water resilience and recovery. The case studies demonstrate the importance of rapid response activities, coordinating collaborative response, pre-wildfire preparation, and knowledge co-production among agencies, researchers, and managers in addressing the impacts of wildfires on water supply and quality. The lessons learned emphasize opportunities to pivot wildfire-water research and operations from reactive to proactive, focusing on mutually beneficial activities such as understanding watershed health, fostering collaboration, embracing new discoveries and tools, and enabling pre-wildfire research through table-top activities, workshops, pre-fire data collection and analysis, and appointing a central water response lead. These outcomes inform the development of a research-to-operations and operations-to-research (R2O2R) co-production framework and future opportunities to guide proactive response and management efforts before, during, and after wildfire.
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Reliable estimates of debris-flow volume can be used to help predict the magnitude of debris-flow hazards following wildfire in the western United States. In this study, we compiled and used a database of 227 postfire debris-flow volumes that were collected across the western United States to develop a multiple linear regression model for predicting postfire debris-flow volume. We explored 36 predictor variables related to rainfall, terrain, and fire characteristics, and selected the model with the combination of variables that yielded the most accurate predictions of debris-flow volume. We evaluated model performance against the entire volume database, as well as against four subsets of volume data from southern California, the Intermountain West, the Southwest, and regions with limited volume data, such as northern California and Washington. We also compared model performance against 3 existing postfire debris-flow volume models that were developed for use in southern California, the Intermountain West, and the Southwest. We demonstrate that the new volume model performs as well as the regional models in the regions for which they were developed and outperforms existing models when applied to volumes from data-limited regions in the western United States. These results indicate that the debris-flow volume model introduced in this study can be used to improve postfire hazard assessments across the western United States, especially outside of southern California.
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This study uses data from the US Geological Survey (USGS) Wildfire Hazard and Risk Assessment Clearinghouse to characterize consistency and variation across categories and contexts. We applied descriptive statistics to summarize RFs, using tables, box-and-whisker plots and heat maps stratified by highly valued resource or asset (HVRA) category and spatial scale. RFs and value definitions vary, especially for ecosystem-related resources. Some functions, such as for buildings in the wildland–urban interface (WUI), translate well across contexts, while others require more input. Some functions are broadly transferable, while others need customization. This analysis provides references and starting points for improvement to RFs in QWRAs. Expanding the clearinghouse and dataset and building more transparency in expert elicitation can build trust among communities, agencies and end-users, and can support efficient use of limited resources to mitigate wildfire risk.
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We tested models for 18 common overstory plant species in different functional groups including shrubs, low sagebrush and rubber rabbitbrush, and the perennial bunchgrass bluebunch wheatgrass. Using Structure-from-Motion photogrammetry and a stacked ensemble learning approach, we achieved a mean classification accuracy of 92.1% (95% CI, 90.9–93.2%) and a weighted F1 score of 91.5% (95% CI, 90.2–92.8%), indicating strong performance despite a highly imbalanced dataset dominated by a few common species. Models trained with low-cost red, green, and blue imagery performed nearly as well as those using multispectral data, with only about a 1% difference in F1 score. However, model transferability was limited: classification accuracy declined sharply at sites where species composition differed from training data, with F1 scores ranging from <0.09 to >0.90 across test sites. These results suggest that although low-cost UAVs can produce accurate and scalable species maps, reliable application across diverse rangelands will require strategic field sampling or shared training datasets. Our findings provide practical guidance for researchers and land managers seeking to incorporate UAV technology into biodiversity monitoring, restoration planning, and invasive species management.
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Researchers from the University of Montana and Working Lands for Wildlife have released a new open-access dataset that maps vegetation structure at sub-meter resolution across the contiguous United States.
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The Wildfire Resilience Index (WRI) is an interactive tool designed to support communities and landscapes living with wildfire in 12 Western US states, British Columbia, and the Yukon Territory (see image on the right). Wildfire is natural and inevitable across the western United States and Canada. Living with it requires a shared understanding of how systems-both ecological and human-resist harm and recover after fire. Together, these abilities define resilience.
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Invasive annual grasses (IAG) pose one of the most significant and rapidly expanding threats to rangeland health across the western United States. These exotic grasses include cheatgrass, medusahead, and ventenata, and when they overtake rangelands, they alter fire regimes, reduce habitat quality, and diminish long-term productivity. Developing effective management strategies and treatment prescriptions requires an understanding of the degree of invasion in an ecological context, including site potential, competitive balance with perennial grasses and forbs, and overall productivity. The Invasion Severity Index (ISI) maps and web app provide a simple, interactive platform to help conservation planners and land managers prioritize and plan invasive annual grass treatments across the sagebrush biome. Using cutting-edge Rangeland Analysis Platform (RAP) 10-meter resolution data, ISI maps depict five invasion levels linked to specific management strategies and actions. The ISI assesses the severity of annual grass invasion relative to perennial forb and grass cover and bare ground, providing an ecologically grounded framework for prioritizing management and aligning treatment techniques with site resilience and recovery potential. Other reference layers and features allow users to understand landscape context, consider trends through time, visualize specific vegetative thresholds, and generate time series charts.
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To effectively manage fire, land and fire managers need detailed, current local information – for example, the amount of burnable material present, fuel moisture levels, winds, temperatures, and terrain changes across time and space. Managers also need these data to decide where, when, and how to treat a landscape while balancing costs and benefits, projected wildfire risk, and potential impacts.
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The frequency and severity of wildfires are changing around the country. To understand their impact on the landscape, federal agencies and states are conducting fire needs assessments. Fire needs assessments help fire stakeholders understand where, what type of, and how much fire needs to occur to reduce the destructive effects of wildfire and restore or maintain ecosystem health and resiliency.
Fire is a natural and necessary process in many ecosystems, but its role can vary widely depending on landscape conditions, ecological goals, and management history. In some places, fire is missing where it’s needed. In others, it’s occurring too frequently or with damaging severity. Understanding where and how fire should be applied—or avoided—is essential for effective landscape management.
A Fire Needs Assessment (FNA) helps land managers and ecologists evaluate the ecological role of fire across their landscapes. It provides a spatial framework for identifying where fire can support ecological health, and where it may be causing harm.
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Article describes development and use of the database.