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Forests across the Western U.S. face unprecedented risk due to historic fire exclusion, environmental degradation, and climate change. Forest management activities like ecological thinning, prescribed burning, and meadow restoration can improve landscape resilience. Resilient forests are at a lower risk of high-intensity wildfires, drought, insects, and other disturbances and provide a wide range of benefits to ecosystems and communities. However, insufficient funding limits implementation of critically needed management. To address this challenge, we propose a multi-benefit framework that leverages the diverse benefits of forest management to engage a suite of stakeholders in sharing project costs. We take a three-pronged approach to develop our conceptual model: examining existing frameworks for environmental project implementation, conducting a literature review of forest management benefits, and evaluating case studies. Through our framework, we describe the steps to engage partners, starting by identifying benefits that could accrue to potential public and private beneficiaries, and moving through an iterative and collaborative process of valuing benefits, which can accrue over different spatial and temporal scales, in close consultation with potential beneficiaries themselves. The aim of this approach is to stack funding streams associated with each valued benefit to fully fund a given forest management project. The multi-benefit framework has the potential to unlock new sources of funding to meet the exceptional challenges of climate and wildfire disturbances. We apply the framework to dry forests of the Western U.S., but opportunities exist for expanding and modifying this approach to any geography or ecosystem where management provides multiple benefits.

Wildfire modifies the short- and long-term exchange of carbon between terrestrial ecosystems and the atmosphere, with impacts on ecosystem services such as carbon uptake. Dry western US forests historically experienced low-intensity, frequent fires, with patches across the landscape occupying different points in the fire-recovery trajectory. Contemporary perturbations, such as recent severe fires in California, could shift the historic stand-age distribution and impact the legacy of carbon uptake on the landscape. Here, we combine flux measurements of gross primary production (GPP) and chronosequence analysis using satellite remote sensing to investigate how the last century of fires in California impacted the dynamics of ecosystem carbon uptake on the fire-affected landscape. A GPP recovery trajectory curve of more than five thousand fires in forest ecosystems since 1919 indicated that fire reduced GPP by [Formula: see text] g C m[Formula: see text] y[Formula: see text]([Formula: see text]) in the first year after fire, with average recovery to prefire conditions after [Formula: see text] y. The largest fires in forested ecosystems reduced GPP by [Formula: see text] g C m[Formula: see text] y[Formula: see text] (n = 401) and took more than two decades to recover. Recent increases in fire severity and recovery time have led to nearly [Formula: see text] MMT CO[Formula: see text] (3-y rolling mean) in cumulative forgone carbon uptake due to the legacy of fires on the landscape, complicating the challenge of maintaining California's natural and working lands as a net carbon sink. Understanding these changes is paramount to weighing the costs and benefits associated with fuels management and ecosystem management for climate change mitigation.

Nature-based Climate Solutions (NbCS) are managed alterations to ecosystems designed to increase carbon sequestration or reduce greenhouse gas emissions. While they have growing public and private support, the realizable benefits and unintended consequences of NbCS are not well understood. At regional scales where policy decisions are often made, NbCS benefits are estimated from soil and tree survey data that can miss important carbon sources and sinks within an ecosystem, and do not reveal the biophysical impacts of NbCS for local water and energy cycles. The only direct observations of ecosystem-scale carbon fluxes, for example, by eddy covariance flux towers, have not yet been systematically assessed for what they can tell us about NbCS potentials, and state-of-the-art remote sensing products and land-surface models are not yet being widely used to inform NbCS policymaking or implementation. As a result, there is a critical mismatch between the point- and tree-scale data most often used to assess NbCS benefits and impacts, the ecosystem and landscape scales where NbCS projects are implemented, and the regional to continental scales most relevant to policymaking. Here, we propose a research agenda to confront these gaps using data and tools that have long been used to understand the mechanisms driving ecosystem carbon and energy cycling, but have not yet been widely applied to NbCS. We outline steps for creating robust NbCS assessments at both local to regional scales that are informed by ecosystem-scale observations, and which consider concurrent biophysical impacts, future climate feedbacks, and the need for equitable and inclusive NbCS implementation strategies. We contend that these research goals can largely be accomplished by shifting the scales at which pre-existing tools are applied and blended together, although we also highlight some opportunities for more radical shifts in approach.

Climate change is amplifying both wildfire burned area and severity, as well as incidents of drought-induced tree mortality (dieback). Direct effects from climate change amplify wildfires and episodes of drought-induced dieback have well-known impacts on forest's ability to regulate climate, provide water, and store carbon. Less understood are how past disturbances produce interaction effects that can change subsequent disturbance occurrence and intensity, with implications for management decisions that can promote forest resistance and resilience. We constructed two parallel forest chrono-sequences by combining a geospatial database of historical fire with satellite and airborne observations of forests in the Sierra Nevada of California to assess the impact of fire history on vegetation recovery, water use (evapotranspiration), and drought-induced forest dieback. We used these data sets to assess two research questions: (1.) Does fire history amplify or reduce drought-dieback intensity? (2.) What mechanisms explain how fire-induced changes to forest structure and ET alter subsequent forest dieback intensity? We show that recent fire history decreased drought-induced forest dieback intensity, compared to unburned controls. These fire-affected forests were characterized by reduced tree cover and decreased evapotranspiration, which combined to increase drought resistance more than would be expected by either effect individually. Two decades post-fire, evapotranspiration returned to pre-fire conditions. Tree and shrub cover started to approach pre-fire conditions, except for high severity fires where decreased tree cover and increased shrub cover persisted. Field based research on fuels treatments suggests that fire history may also increase longer term forest resilience. In fire-prone conifer forests, interaction effects from recent low and moderate severity fires will increase drought resistance and perhaps longer-term forest stability. • Fire history reduced tree cover and water use and increased shrub cover. • Fire history decreased forest dieback intensity compared to unburned controls. • Reduced dieback intensity suggests an antagonistic interaction effect from fire history. • Prescribed fires and low and moderate severity wildfires increased forest resistance and perhaps resilience to drought. [ABSTRACT FROM AUTHOR]

Reliable partitioning of micrometeorologically measured evapotranspiration (ET) into evaporation (E) and transpiration (T) would greatly enhance our understanding of the water cycle and its response to climate change related shifts in local‐to‐regional climate conditions and rising global levels of vapor pressure deficit (VPD). While some methods on ET partitioning have been developed, their underlying assumptions make them difficult to apply more generally, especially in sites with large contributions of E. Here, we report a novel ET partitioning method using artificial neural networks (ANNs) in combination with a range of environmental input variables to predict daytime E from nighttime ET measurements. The study uses eddy covariance data from four restored wetlands in the Sacramento‐San Joaquin Delta, California, USA, as well as leaf‐level T data for validation. The four wetlands vary in their vegetation make‐up and structure, representing a range of ET conditions. The ANNs were built with increasing complexity by adding the input variable that resulted in the next highest average value of model testing R2 across all sites. The order of variable inclusion (and importance) was: VPD > gap‐filled sensible heat flux (H_gf) > air temperature (Tair) > friction velocity (u∗) > other variables. The model using VPD, H_gf, Tair, and u∗ showed the best performance during validation with independent data and had a mean testing R2 value of 0.853 (averaged across all sites, range from 0.728 to 0.910). In comparison to other methods, our ANN method generated T/ET partitioning results which were more consistent with CO2 exchange data especially for more heterogeneous sites with large E contributions. Our method improves the understanding of T/ET partitioning. While it may be particularly suited to flooded ecosystems, it can also improve T/ET partitioning in other systems, increasing our knowledge of the global water cycle and ecosystem functioning. [ABSTRACT FROM AUTHOR]

Forests across the Western U.S. face unprecedented risk due to historic fire exclusion, environmental degradation, and climate change. Forest management activities like ecological thinning, prescribed burning, and meadow restoration can improve landscape resilience. Resilient forests are at a lower risk of high-intensity wildfires, drought, insects, and other disturbances and provide a wide range of benefits to ecosystems and communities. However, insufficient funding limits implementation of critically needed management. To address this challenge, we propose a multi-benefit framework that leverages the diverse benefits of forest management to engage a suite of stakeholders in sharing project costs. We take a three-pronged approach to develop our conceptual model: examining existing frameworks for environmental project implementation, conducting a literature review of forest management benefits, and evaluating case studies. Through our framework, we describe the steps to engage partners, starting by identifying benefits that could accrue to potential public and private beneficiaries, and moving through an iterative and collaborative process of valuing benefits, which can accrue over different spatial and temporal scales, in close consultation with potential beneficiaries themselves. The aim of this approach is to stack funding streams associated with each valued benefit to fully fund a given forest management project. The multi-benefit framework has the potential to unlock new sources of funding to meet the exceptional challenges of climate and wildfire disturbances. We apply the framework to dry forests of the Western U.S., but opportunities exist for expanding and modifying this approach to any geography or ecosystem where management provides multiple benefits., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)