Soil temperature and moisture (soil-climate) affect plant growth and microbial metabolism, providing a mechanistic link between climate and growing conditions. However, spatially explicit soil-climate estimates that can inform management and research are lacking. We developed a framework to estimate spatiotemporal-varying soil moisture (monthly, annual, and seasonal) and temperature-moisture regimes as gridded surfaces by enhancing the Newhall simulation model. Importantly, our approach allows for the substitution of data and parameters, such as climate, snowmelt, soil properties, alternative potential evapotranspiration equations and air-soil temperature offsets. We applied the model across the western United States using monthly climate averages (1981–2010). The resulting data are intended to help improve conservation and habitat management, including but not limited to increasing the understanding of vegetation patterns (restoration effectiveness), the spread of invasive species and wildfire risk. The demonstrated modeled results had significant correlations with vegetation patterns—for example, soil moisture variables predicted sagebrush (R2 = 0.51), annual herbaceous plant cover (R2 = 0.687), exposed soil (R2 = 0.656) and fire occurrence (R2 = 0.343). Using our framework, we have the flexibility to assess dynamic climate conditions (historical, contemporary or projected) that could improve the knowledge of changing spatiotemporal biotic patterns and be applied to other geographic regions.
It is a critical time to reflect on the National Ecological Observatory Network (NEON) science to date as well as envision what research can be done right now with NEON (and other) data and what training is needed to enable a diverse user community. NEON became fully operational in May 2019 and has pivoted from planning and construction to operation and maintenance. In this overview, the history of and foundational thinking around NEON are discussed. A framework of open science is described with a discussion of how NEON can be situated as part of a larger data constellation—across existing networks and different suites of ecological measurements and sensors. Next, a synthesis of early NEON science, based on >100 existing publications, funded proposal efforts, and emergent science at the very first NEON Science Summit (hosted by Earth Lab at the University of Colorado Boulder in October 2019) is provided. Key questions that the ecology community will address with NEON data in the next 10 yr are outlined, from understanding drivers of biodiversity across spatial and temporal scales to defining complex feedback mechanisms in human–environmental systems. Last, the essential elements needed to engage and support a diverse and inclusive NEON user community are highlighted: training resources and tools that are openly available, funding for broad community engagement initiatives, and a mechanism to share and advertise those opportunities. NEON users require both the skills to work with NEON data and the ecological or environmental science domain knowledge to understand and interpret them. This paper synthesizes early directions in the community’s use of NEON data, and opportunities for the next 10 yr of NEON operations in emergent science themes, open science best practices, education and training, and community building.
Macrosystem-scale research is supported by many ecological networks of people, infrastructure, and data. However, no network is sufficient to address all macrosystems ecology research questions, and there is much to be gained by conducting research and sharing resources across multiple networks. Unfortunately, conducting macrosystem research across networks is challenging due to the diversity of expertise and skills required, as well as issues related to data discoverability, veracity, and interoperability. The ecological and environmental science community could substantially benefit from networking existing networks to leverage past research investments and spur new collaborations. Here, we describe the need for a “network of networks” (NoN) approach to macrosystems ecological research and articulate both the challenges and potential benefits associated with such an effort. We describe the challenges brought by rapid increases in the volume, velocity, and variety of “big data” ecology and highlight how a NoN could build on the successes and creativity within component networks, while also recognizing and improving upon past failures. We argue that a NoN approach requires careful planning to ensure that it is accessible and inclusive, incorporates multimodal communications and ways to interact, supports the creation, testing, and promulgation of community standards, and ensures individuals and groups receive appropriate credit for their contributions. Additionally, a NoN must recognize important trade-offs in network architecture, including how the degree of centralization of people, infrastructure, and data influence network scalability and creativity. If implemented carefully and thoughtfully, a NoN has the potential to substantially advance our understanding of ecological processes, characteristics, and trajectories across broad spatial and temporal scales in an efficient, inclusive, and equitable manner.
Understanding the paths by which water flows through the landscape is critical for providing fresh water for human use, maintaining ecosystem function, and better predicting how disturbances such as fire or drought may impact water quantity and water quality. Yet projected changes in climate, disturbances, and land use , are likely to alter hydrologic flow paths, and .natural resource managers increasingly require information about projected changes in water flow paths to plan for the future. To meet this need, researchers will conduct a synthesis of changing hydrologic processes in the North Central region, and communicate the identified management options and opportunities to natural resource managers in federal and state agencies. Through this project, a postdoctoral fellow will evaluate: 1) how water flow paths and water quality vary with land-use and disturbance regimes; 2)how shifts in the timing and magnitude of snow and rain inputs alter low flows and stream permanence; and 3) how forest management techniques, such as forest thinning, can mitigate the sensitivity of forests and streamflow to droughts The results of this project can help natural resource managers better understand the future of aquatic flows in the North Central region and will also contribute to a national-scale synthesis on the future of aquatic flows across the United States.
This project combined tree-ring based paleo and modern climate and hydrologic research aimed at understanding the primary influences on drought risk and water reliability in basins critical for western U.S. water resources. New paleohydrologic datasets and analyses were developed and applied to contextualize future streamflow projections and address specific water management questions. These questions centered around optimizing future water management protocols for numerous objectives ranging from improving agricultural water allocation during drought while maintaining instream flows for aquatic ecosystem health to the testing of operations across large river systems with complex infrastructure critical for downstream flood control, navigation, and hydropower generation. USGS scientists worked closely with the Bureau of Reclamation (Reclamation) to estimate both past and future drought risk at key management locations throughout the Missouri River basin, the Milk and Saint Mary Rivers system, and across the major managed river systems in the western United States. These efforts provided a roadmap for future water management strategies under changing climate and water supply conditions, which are detailed in Reclamation’s newly completed Missouri Headwaters Basin Study, the 2021 SECURE Water Act Report, and the forthcoming update of the Saint Mary and Milk Rivers Basin Study. Among the major scientific findings to emerge was a new understanding of the long-term (1200-year) history of drought variability for the Missouri River, which highlighted the unusual severity of the early 2000s drought across the Rocky Mountain headwaters and adjacent high plains. By combining the extended drought record with extensive modern and paleoclimate records, we document how warming exacerbates severities of naturally occurring droughts, with recent decades defined by “hot” droughts and the 2000s (2001-2010) drought ranking as the most severe event in 1,200 years. Increasingly severe drought events such as this strain already over-allocated water resources that multiple sectors of society depend heavily upon.
Wildfire occurrence varies among regions and through time due to the long-term impacts of climate on fuel structure and short-term impacts on fuel flammability. Identifying the climatic conditions that trigger extensive fire years at regional scales can enable development of area burned models that are both spatially and temporally robust, which is crucial for understanding the impacts of past and future climate change. We identified region-specific thresholds in fire-season aridity that distinguish years with limited, moderate, and extensive area burned for 11 extensively forested ecoregions in the western United States. We developed a new area burned model using these relationships and demonstrate its application in the Southern Rocky Mountains using climate projections from five global climate models (GCMs) that bracket the range of projected changes in aridity. We used the aridity thresholds to classify each simulation year as having limited, moderate, or extensive area burned and defined fire-size distributions from historical fire records for these categories. We simulated individual fires from a regression relating fire season aridity to the annual number of fires and drew fire sizes from the corresponding fire-size distributions. We project dramatic increases in area burned after 2020 under most GCMs and after 2060 with all GCMs as the frequency of extensive fire years increases. Our adaptable model can readily incorporate new observations (e.g., extreme fire years) to directly address the non-stationarity of fire-climate relationships as climatic conditions diverge from past observations. Our aridity threshold fire model provides a simple yet spatially robust approach to project regional changes in area burned with broad applicability to ecosystem and vegetation simulation models.
Wildfires and housing development have increased since the 1990s, presenting unique challenges for wildfire management. However, it is unclear how the relative influences of housing growth and changing wildfire occurrence have altered risk to homes, or the potential for wildfire to threaten homes. We used a random forests model to predict burn probability in relation to weather variables at 1-km resolution and monthly intervals from 1990 through 2019 in the Southern Rocky Mountains ecoregion. We quantified risk by combining the predicted burn probabilities with decadal housing density. We then compared the predicted burn probabilities and risk across the study area with observed values and quantified trends. Finally, we evaluated how housing growth and changes in burn probability influenced risk individually and combined. Fires burned 9055 km2 and exposed more than 8500 homes from 1990 to 2019. Observed burned area increased 632% from the 1990s to the 2000s, which combined with housing growth, resulted in a 1342% increase in homes exposed. Increases continued in the 2010s but at lower rates; burned area by 65% and exposure by 32%. The random forests model had excellent fit and high correlation with observations (AUC = 0.88 and r = 0.9). Observed values were within the 95% uncertainty interval for all years except 2016 (burned area) and 2000 (exposure). However, our model overpredicted in years with low observed burned area and underpredicted in years with high observed burned area. Overpredictions in risk resulted in lower rates of change in predicted risk compared with change in observed exposure. Increases in risk between the 1990s and 2000s were primarily due to warmer and drier weather conditions and secondarily because of housing growth. However, increases between the 2000s and 2010s were primarily due to housing growth. Our modeling approach identifies spatial and temporal patterns of wildfire potential and risk, which is critical information to guide decision-making. Because the drivers behind risk shift over time, strategies to mitigate risk may need to account for multiple drivers simultaneously.
Forests in the western US are increasingly impacted by climate change. Warm, dry conditions associated with climate change both increases fire activity in western forests and make it more difficult for forests to recover after wildfires. If forests fail to recover, they may shift to non-forest ecosystems like grasslands or shrublands. It is important to understand where fires may result in the loss of forests because forests provide a variety of ecosystem services, including carbon storage, water regulation and supply, and biodiversity. Western forests are also integral for the timber industry and valued for their recreation opportunities, which can also support local economies. The goal of this project is to identify which areas are most vulnerable to post-fire conversions from forest to non-forest ecosystems under current and future climate conditions. We will combine information about the climatic controls on post-fire tree regeneration with spatial predictions of high-severity fire to map areas that are likely to experience the combination of high-severity fire (if they burn), and limited post-fire tree regeneration due to post-fire climate conditions. Identifying which areas are most at-risk of post-fire vegetation shifts will help land managers to plan and prioritize management activities related to high-severity fire risks and to post-fire forest ecosystem change. For example, if a municipal watershed is identified as highly vulnerable to forest loss following fire, managers may choose to implement fuel treatments in that watershed to reduce the risk of high-severity fire. Alternatively, in areas that we identify as climatically unsuitable for tree seedlings, managers may save resources by not attempting to plant trees following fire in an area where the likelihood of success is low.
The Greater Yellowstone Area (GYA) is an iconic landscape with national parks, iconic species like grizzly bears and elk, and over 11,500 square miles of forest. While fires are a natural part of the GYA, climate change and land management legacies are increasing the frequency and size of severe fires. Climate change interacts with these fires to shift conifer forests to non-forested grassland and sagebrush ecosystems. These transformations impact species habitat, carbon storage, and other management goals on public lands. However, managing for “natural ecosystems” is not always possible in the face of climate change. The Resist-Accept-Direct framework (RAD) can help: under RAD, managers can resist change to maintain ecosystems, accept climate- and wildfire-driven ecological change, even if that means losing species habitat or ecosystem services, or direct ecosystem changes to maintain or gain key resources or services. For this project, researchers will work with managers in the GYA to implement RAD as a strategy to manage wildfires and subsequent ecosystem changes. With managers from each GYA agency researchers will identify 1) shared and unique management goals, 2) management options that can resist, accept, or direct wildfire-related ecosystem changes, and 3) ways to coordinate RAD implementation across agencies, since fires span management boundaries on the landscape This project will help managers identify and coordinate approaches to achieve their conservation goals in the context of climate change, ensuring the preservation of key species, ecosystems, and resources in the North Central CASC region’s public lands.
Prescribed burning – planned, controlled fires conducted under weather and fuel conditions designed for safety and effectiveness – is a common practice used to maintain and restore native prairies in the Northern Great Plains. However, climate change will affect the number of days in a year, and when, suitable conditions for prescribed fires occur. For instance, warmer temperatures may shift these “good prescribed-fire days” earlier in the spring and later in the fall, but uncertainty about future climate makes it hard to predict how large shifts will be and if the number of good fire days each year will generally increase or decrease. Further, it’s hard to know whether prescribed fires will continue to achieve their goals in new conditions. This project will measure how the number and timing of good fire days has changed over the last 30 years and predict how they will change over the next 50 years under four plausible future climate scenarios. Changes to longer-term weather patterns – in the seasons leading up to and following prescribed fires – may also change the effectiveness of the fires in achieving their goals, like reducing Kentucky bluegrass, cheatgrass, and other invasive grasses. To address this issue, the project will also use data from long-term plant monitoring programs to look for patterns in how prairie responds to prescribed fire in different seasonal and annual weather conditions. Land management agencies in the Northern Great Plains like the National Park Service, U.S. Fish and Wildlife Service, and U.S. Forest Service use prescribed fires often, so it is important for them to understand how climate change will affect the number and timing of good prescribed-fire days and fire’s effects. To that end, the ultimate goal of this project is to create a model that will help managers develop effective prescribed fire strategies for an uncertain future climate.