The USGS National Climate Adaptation Science Center (NCASC) is currently engaged in an Ecological Drought initiative, focused on understanding the impacts of drought on natural ecosystems across the country. This project supported the Ecological Drought initiative by creating an Intermountain West Drought Social Science Synthesis Working Group. The goal of this working group was to investigate human dimensions of ecological drought across the intermountain west from a comparative, regional perspective. Throughout the Intermountain West, there has been significant investment in understanding how social factors influence manager and citizen experiences of drought in particular locations. Yet there is still a gap in knowledge of how human dimensions of drought impacts, planning, and resilience are similar and different across cases and regions. The working group engaged social scientists from federal agencies and universities to identify common trends in drought management across the Intermountain West to inform more effective drought preparedness and response across the region. Project outputs included two conference sessions, a typology manuscript to be submitted by the end of FY19, and the conceptual framing of a rapid assessment methodology that was subsequently developed into a standalone project.
Drought is an inescapable reality in many regions, including much of the western United States. With climate change, droughts are predicted to intensify and occur more frequently, making the imperative for drought management even greater. Many diverse actors – including private landowners, business owners, scientists, non-governmental organizations (NGOs), and managers and policymakers within tribal, local, state, and federal government agencies – play multiple, often overlapping roles in preparing for and responding to drought. Managing water is, of course, one of the most important roles that humans play in both mitigating and responding to droughts; but, focusing only on “water managers” or “water management” fails to capture key elements related to the broader category of drought management. The respective roles played by those managing drought (as distinct from water managers), the interactions among them, and the consequences in particular contexts, are not well understood. Our team synthesized insights from 10 in-depth case studies to understand key facets of decision making about drought preparedness and response. We present a typology with four elements that collectively describe how decisions about drought preparedness and response are made (context and objective for a decision; actors responsible; choice being made or action taken; and how decisions interact with and influence other decisions). The typology provides a framework for system-level understanding of how and by whom complex decisions about drought management are made. Greater system-level understanding helps decision makers, program and research funders, and scientists to identify constraints to and opportunities for action, to learn from the past, and to integrate ecological impacts, thereby facilitating social learning among diverse participants in drought preparedness and response.
In 1969, researchers developed the first global circulation model (Ruttiman 2006); however, it was not until 2014 that modelers first attempted a global ecosystem and biodiversity model that included human pressures (i.e., the Madingley Model) (Harfoot et al. 2014). Other large-scale models of biodiversity exist, such as GLOBIO (Alkemade et al. 2009), but to date there are no well accepted global biodiversity models similar to global circulation models that can help guide global biodiversity policy development and targets. The lack of global biodiversity models compared to the extensive array of general circulation models provides a unique opportunity for climate, ecosystem, and biodiversity modeling experts to determine similarities and differences in modeling approaches to inform development of integrated global biodiversity modeling approaches. More accurate and comprehensive biodiversity models are needed to understand how countries individually and as a whole are progressing towards the internationally defined targets (e.g., Aichi Biodiversity Targets and Sustainable Development Goals) to inform global biodiversity conservation, monitoring, and sustainable use (Tittensor 2014). In addition, the scenarios and modeling summary from the Intergovernmental Platform on Biodiversity and Ecosystem Services (IPBES) identified a need for better assessment of biodiversity models and progress towards more global models. Collaborative approaches between the biodiversity and global climate modeling communities can provide information to advance biodiversity models and improve each community’s approaches to forecasting change. Collaboration can also help tighten the linkages between biodiversity and climate and land-use models as climate change and other anthropogenic stressors continue to threaten biodiversity and its ecosystem services. To address the need for improved large-scale biodiversity models, experts in biodiversity and climate modeling and remote sensing fields came together via a series of in-person workshops and virtual discussions. Our goals were to 1) identify strategies (both qualitative and quantitative) from climate models to be applied to large-scale biodiversity models, 2) to explore NASA and other remote sensing products to assist in global biodiversity modeling efforts and 3) to address and build on gaps and data needs to inform development of GEOBON Essential Biodiversity Variables (EBV) and tracking and development of the next generation of Aichi Biodiversity Targets and Sustainable Development Goals. The first in-person meeting was held in June 2017 with 20 in-person and remote participants in Reston, VA and a second in-person meeting in February 2018 with 18 in-person and remote participants in Tucson, AZ to address these objectives. Participants came from national and international academic institutions, government agencies, and non-governmental organizations and were from various stages in their careers. The workshop series resulted in three main outcomes, including a list of lessons learned and recommendations from those with expertise in climate modeling to address goal 1 above, a framework for assessment and refinement of diverse biodiversity models using remote sensing tools to address goal 1, 2, and 3 above, and lastly the development of a meta-conceptual biodiversity model to inform future model development and needs. Below is a detailed overview highlighting recommendations and outcomes of the workshop series.
Abstract (from Bioscience): Biodiversity projections with uncertainty estimates under different climate, land-use, and policy scenarios are essential to setting and achieving international targets to mitigate biodiversity loss. Evaluating and improving biodiversity predictions to better inform policy decisions remains a central conservation goal and challenge. A comprehensive strategy to evaluate and reduce uncertainty of model outputs against observed measurements and multiple models would help to produce more robust biodiversity predictions. We propose an approach that integrates biodiversity models and emerging remote sensing and in-situ data streams to evaluate and reduce uncertainty with the goal of improving policy-relevant biodiversity predictions. In this article, we describe a multivariate approach to directly and indirectly evaluate and constrain model uncertainty, demonstrate a proof of concept of this approach, embed the concept within the broader context of model evaluation and scenario analysis for conservation policy, and highlight lessons from other modeling communities
The USGS National Climate Change and Wildlife Science Center (NCCWSC) is currently engaged in an Ecological Drought initiative, focused on understanding the impacts of drought on natural ecosystems across the country. This project was designed to support the Ecological Drought initiative by creating a USGS EcoDrought Actionable Science Working Group. The goal of this working group was to identify science needs for drought-related decisions and to provide natural resource managers with practical strategies for adapting to and planning for drought. The working group engaged social scientists to garner advice on relevant social science research questions and data needs, as well as to identify any regulatory, institutional, or cultural barriers that may impede adaptation efforts by managers. This approach was taken to help ensure that the science being produced on ecological drought is actionable – that is, it addressed critical stakeholder questions, took into account the complex social dynamics of drought adaptation, and was created to be easily used by decision-makers. The ultimate goal of the working group was to use integrated social-ecological analysis to forecast the potential implications of drought, to provide better access to climate and drought-related data, and to develop tools that enable managers to visualize the potential impacts of management decisions before they are implemented.
As our world changes and communities are faced with uncertain future climate conditions, decision making and resource planning efforts can often no longer rely on historic scientific data alone. Scientific projections of what might be expected in the future are increasingly needed across the country and around the world. Scientists and researchers can develop these projections by using computer models to simulate complex elements of our climate and their interactions with ecosystems, wildlife, and biodiversity. While an extensive array of general circulation models (GCMs, climate models of the general circulation of the atmosphere and ocean) exist, there is currently a lack of global biodiversity models. This project aims to bring together climate, ecosystem, and biodiversity modeling experts through a series of in-person workshops and virtual discussions to promote development of integrated approaches in modeling global biodiversity. The main goals of these workshops and discussions are to 1) identify lessons learned (both qualitative and quantitative) from climate models to then be applied to large-scale terrestrial biodiversity models, 2) to explore NASA and other remote sensing products to assist in global biodiversity and ecosystem models, and 3) to address and build on gaps and data needs (e.g., finer scale ecological and evolutionary processes) previously identified by the Intergovernmental Platform on Biodiversity and Ecosystem Services (IPBES) as necessary to inform the IPBES global biodiversity assessment.
Time and money for conservation are limited, so there is a need for responsible investments that embrace the realities of climate change. Droughts, floods, wildfires, hotter temperatures, declining snowpack, and changing streamflow are already significantly affecting wildlife and their habitats. In some cases, managers may decide to make strategic adjustments in how their actions are designed, where those actions are located, and when actions are needed most, in order to achieve management goals. A key part of making these forward-looking decisions is having access to climate information that can be integrated into an agency’s decision-making process. When science is conducted without an understanding of how that research might be incorporated into a management decision, the information produced may not be useful to decision makers. We addressed these concerns by creating an opportunity for wildlife and habitat managers and climate experts to work hand-in-hand to discuss how changing landscapes might affect management decisions, identify available climate science that can inform those decisions, and identify gaps in available knowledge that need to be filled in order to make better, climate-informed decisions. Our multi-year project had three parts: 1) Asking state wildlife managers in the North Central region what species, habitats, or issues are high priorities for their agencies and constituencies, and vulnerable to the effects of a changing climate, 2) Working with one of those state wildlife agencies—the Wyoming Game and Fish Department--to develop and apply a process for integrating best-available climate science and expert opinion into the Wyoming Statewide Habitat Plan, and 3) Identifying management-relevant information gaps that could drive climate research investments by the North Central CASC and others, to better inform future management decisions. The climate-informed Wyoming Statewide Habitat Plan and other project products offer useful models for making climate science actionable and relevant for managers’ decisions.
As the National Climate Adaptation Science Center (CASC) develops a strategic effort around fire science, there is a critical need to develop a national-scale synthesis effort that identifies key regional CASC activities previously conducted, as well as major science gaps that may be addressed by a coordinated CASC network approach. The North Central CASC postdoctoral fellow will play a leadership role in the National CASC Climate Adaptation Postdoctoral (CAP) Fellows Future of Fire cohort to help identify the common efforts and leveraging points to shape the national-scale synthesis. Currently there is limited North Central CASC supported fire science available for the North Central region. To meet this need, the North Central CASC postdoctoral fellow will develop region-specific fire information relevant to resource managers that are challenged with making decisions to adapt to changing fire risk and ecosystem responses. This project aims to determine the future size and number of fires, total burn area, and rates of change among years and across space in the contiguous United States. The goal is to explain changes in these fire variables in relation to climate change and changing housing density, which drives human ignitions and fire suppression efforts. To predict the future size and number of fires, statiscal models that look at fire-climate relationships will be applied to climate data output from several global climate models under two future climate scenarios. The results will help improve future fire projections based on climate modeling and data at spatial- and temporal-scales relevant for resource managers, with a focus on: i) identifying regions where fire has historically been infrequent or absent; ii) changes to fire extremes and other important aspects of fire behavior that have an impact on fire operations/management (i.e., timing, intensity, seasonal length); and iii) changes that will exceed the capacity of current institutional management approaches. Additionally, the postdoctoral fellow will help coordinate a team of regional partners, scientists and managers to determine what information is most useful for decision-making. This engagement with practitioners will be beneficial in informing the national-scale synthesis and identification of key metrics.
The design of this survey protocol is based on the indicator framework presented in Wall et. al (2017 https://doi.org/10.1175/WCAS-D-16-0008.1) and is intended to evaluate projects funded by Climate Adaptation Science Centers. The intended respondents are stakeholders who were engaged in the creation of scientific knowledge and tools during these projects. The questions cover three topical areas: process (engagement in the process of knowledge production), outputs/outcomes (use of information), and impacts (building of relationships and trust).
These data were compiled for the study: Divergent climate change effects on widespread dryland plant communities driven by climatic and ecohydrological gradients. The objectives of our study were to (1) describe how climate change will alter the biomass and composition of key plant functional types; (2) quantify the impacts of climate change on future functional type biomass and composition along climatic gradients; (3) identify if and which geographic locations will be relatively unaffected by climate change while others experience large effects; and (4) determine if there is consistency in climate change impacts on plant communities among a representative set of climate scenarios. These data represent geographic patterns in simulated plant functional biomass of big sagebrush plant communities (cheatgrass, perennial forbs, C3 perennial grasses, C4 perennial grasses, perennial grasses, big sagebrush) as across-year averages of differences ("change") between projected future climates (years 2030-2060 and 2070-2100) derived from STEPWAT2 simulations run with each of 13 Global Climate Models (GCMs; CanESM2, CESM1-CAM5, CSIRO-Mk3-6-0, FGOALS-g2, FGOALS-s2, GISS-E2-R, HadGEM2-CC, HadGEM2-ES, inmcm4, IPSL-CM5A-MR, MIROC5, MIROC-ESM, and MRI-CGCM3; Maurer et al. 2007) that participated in CMIP5 for representative concentration pathways RCP4.5 and RCP8.5 and historical (years 1980-2010) values. Data of across-year averages of simulations under historical ("current"; years 1980-2010) climate and median differences ("change") between projected future climates (years 2030-2060 and 2070-2100) derived as medians across 13 Global Climate Models are available from the data release by Renne et al. (2021). These data were created in 2020 and 2021 for the area of the sagebrush region in the western U.S.A. These data were created by a collaborative research project between the U.S. Geological Survey, Marshall University, U.S. Fish and Wildlife Service, Yale University, and University of Wyoming, using a new multivariate matching algorithm (Renne et al., 202X.) which transfers simulated plant functional biomass of big sagebrush plant communities from 200 sites to a gridded product with 30-arcsecond spatial resolution. These data can be used with high resolution matching of projected decreases of big sagebrush, perennial C3 grass and perennial forb biomass in warm, dry sites; no projected change or increases in functional type biomass in cold, moist sites; and widespread projected increases in perennial C4 grasses across big sagebrush plant communities in the sagebrush region of the western U.S.A. (Palmquist et al. 2021) and within a scope as defined by the study. These data may also be used to evaluate the potential impact of changing climate conditions on geographic patterns in simulated plant functional biomass of big sagebrush plant communities within the scope defined by the study. In particular, these results will be useful for informing the design of long-term landscape conservation efforts to maintain and expand wildlife habitat across the sagebrush biome.