The Evaporative Demand Drought Index (EDDI) is an experimental drought monitoring and early warning guidance tool. It examines how anomalous the atmospheric evaporative demand (E0; also known as "the thirst of the atmosphere") is for a given location and across a time period of interest. EDDI is multi-scalar, meaning that this period—or "timescale"—can vary to capture drying dynamics that themselves operate at different timescales; we generate EDDI at 1-week through 12-month timescales. This webpage offers a frequently updated assessment of current conditions across CONUS, southern parts of Canada, and northern parts of Mexico; a tool to generate historical time series of EDDI for a user-selected region; introductions to the EDDI team; and a list of resources for users to explore EDDI and its applications further.

This dataset represents the area in the Greater Yellowstone Ecosystem prioritized for different whitebark pine(Pinus albicaulis) management activities, summarized by climate suitability zones. This data was developed for use in a landscape simulation modeling study aimed at evaluating how well alternative management strategies maintain whitebark pine populations under historical climate and future climate conditions. For the study, we developed three spatial management alternatives for whitebark pine in the Greater Yellowstone Ecosystem representing no active management, current management, and climate-informed management. These management alternatives were implemented in the simulaton model FireBGCv2 under historical climate and three future climate change scenarios - the HadGEM-ES, CESM1-CAM5, and CNRM-CM5 Global Circulation Models under the RCP 8.5 emissions scenario. We worked with the Greater Yellowstone Coordinating Committee's (GYCC) Whitebark Pine Subcommittee to develop this spatial representation of their current management strategy. The treatments mapped represent a set of the treatments recommended in the GYCC Whitebark Pine 2011 Strategy document and include planting blister-rust resistant whitebark pine seedlings, competition removal thinning, wildland fire use and prescribed fire, and protection from mountain pine beetles using verbenone and carbaryl. We used historical and future projections of climate suitability based on species distribution models for whitebark pine (Chang et al. 2014) to map zones of core, deteriorating, and future whitebark pine habitat. Core zones were those areas that are currently suitable for whitebark and remain suitable in the future. Deteriorating zones were where the climatic conditions for whitebark pine are expected to decline. Future zones were areas that are projected to become newly suitable for whitebark pine. We then overlaid our climate zones for whitebark pine with similar projections of future climate suitability for all of whitebark pine’s competitors - Engelmann spruce, subalpine fir, lodgepole pine, and Douglas-fir (Piekielek et al. 2015. We discussed the different combinations of climate suitability zones (core, deteriorating, future) and potential future level of competition (low or high) from other species with the GYCC Whitebark Pine Subcommittee to determine which management activities should be prioritized within each management zone. The result is a map of management zones where different activities are prioritized to meet the goal of maintaining whitebark pine populations. This was used to determine which treatments would be implemented spatially during the simulation modeling, dependent upon additional criteria related to simulated stand-level conditions. In this dataset, we used the resulting map of spatially prioritized management activities to summarize the area prioritized for each management activity that fell within Core, Deteriorating, and Future climate suitability zones

This dataset represents the area in the Greater Yellowstone Ecosystem prioritized for different whitebark pine(Pinus albicaulis) management activities, summarized by land classes. This data was developed for use in a landscape simulation modeling study aimed at evaluating how well alternative management strategies maintain whitebark pine populations under historical climate and future climate conditions. For the study, we developed three spatial management alternatives for whitebark pine in the Greater Yellowstone Ecosystem representing no active management, current management, and climate-informed management. These management alternatives were implemented in the simulaton model FireBGCv2 under historical climate and three future climate change scenarios - the HadGEM-ES, CESM1-CAM5, and CNRM-CM5 Global Circulation Models under the RCP 8.5 emissions scenario. We worked with the Greater Yellowstone Coordinating Committee's (GYCC) Whitebark Pine Subcommittee to develop this spatial representation of their current management strategy. The treatments mapped represent a set of the treatments recommended in the GYCC Whitebark Pine 2011 Strategy document and include planting blister-rust resistant whitebark pine seedlings, competition removal thinning, wildland fire use and prescribed fire, and protection from mountain pine beetles using verbenone and carbaryl. We used historical and future projections of climate suitability based on species distribution models for whitebark pine (Chang et al. 2014) to map zones of core, deteriorating, and future whitebark pine habitat. Core zones were those areas that are currently suitable for whitebark and remain suitable in the future. Deteriorating zones were where the climatic conditions for whitebark pine are expected to decline. Future zones were areas that are projected to become newly suitable for whitebark pine. We then overlaid our climate zones for whitebark pine with similar projections of future climate suitability for all of whitebark pine’s competitors - Engelmann spruce, subalpine fir, lodgepole pine, and Douglas-fir (Piekielek et al. 2015. We discussed the different combinations of climate suitability zones (core, deteriorating, future) and potential future level of competition (low or high) from other species with the GYCC Whitebark Pine Subcommittee to determine which management activities should be prioritized within each management zone. The result is a map of management zones where different activities are prioritized to meet the goal of maintaining whitebark pine populations. This was used to determine which treatments would be implemented spatially during the simulation modeling, dependent upon additional criteria related to simulated stand-level conditions. In this dataset, we used the resulting map of spatially prioritized management activities to summarize the area prioritized for each management activity that fell within different land classifications (mutliple use forests, National Park Service lands, Wilderness lands, and non-federal lands).

Natural resource managers face the need to develop strategies to adapt to projected future climates. Few existing climate adaptation frameworks prescribe where to place management actions to be most effective under anticipated future climate conditions.  We developed an approach to spatially allocate climate adaptation actions and applied the method to whitebark pine (WBP; Pinus albicaulis) in the Greater Yellowstone Ecosystem (GYE).  WBP is expected to be vulnerable to climate-mediated shifts in suitable habitat, pests, pathogens, and fire. We worked with a team of biologists and managers to identify management actions aimed at mitigating climate impacts to WBP. Identified actions were spatially allocated across the GYE under two management strategies: (1) current management and (2) climate-informed management which used projected climate suitability for WBP and competing tree species to place management actions.  The current management strategy reflected current legal, policy and access contraints, such as restricting active management in Wilderness and remote locations, while the climate-informed management strategy was designed to maximize preservation of WBP forests regardless of such constraints. Thus, the climate-informed strategy highlighted how the spatial location of management actions might need to shift to most effectively maintain WBP forests under future climate conditions. The spatial distribution of actions and area treated differed among the current and climate-informed management strategies, with 33-60% more wilderness area prioritized for action under climate-informed management. High priority areas for implementing management actions include the 1-8% of the GYE where current and climate-informed management agreed, since this is where actions are most likely to be successful in the long-term and where current management permits implementation. Areas where climate-informed strategies agreed with one another but not with current management (6-22% of the GYE) are potential locations for experimental testing and monitoring of management actions. Our method for prioritizing locations for climate-adaptation actions is applicable to any species for which information regarding climate vulnerability and climate-mediated risk factors is available.

Rates of climate and land use change vary across the Great Plains and Rocky Mountains as do the responses of ecosystems to these changes. Knowledge of locations of rapid land use and climate change and changes in ecosystem services such as water runoff and ecological productivity are important for vulnerability assessment and crafting locally relevant adaptation strategies to cope with these changes. This project assessed the loss of public, private, and tribal lands due to ongoing land use intensifications and fragmentation extents across the NC CSC domain. In addition, the project evaluated how the climate, ecosystem processes, and vegetation have shifted over the past half century and how they are projected to change in the coming century under various future scenarios. These analyses were carried out in GWEs and EPA III level ecoregions centered at public, tribal, and private lands. These areas of natural vegetation provide ecosystem services important to local people and knowledge of patterns of climate and ecological change are important to resource managers. The results of the project can be used by the NC CSC Adaptation team to work with local stakeholders to develop strategies for coping with and adapting to the ongoing land use change and projected changes in climate.

Historical and projected climate data and water balance data under three GCMs (CNRM-CM5, CCSM4, and IPSL-CM5A-MR) from 1980 to 2099 was used to assess projected climate change impacts in North Central U.S. We obtained required data from MACA data (https://climate.northwestknowledge.net/MACA/). Historical time period ranges from 1980 to 2005, and projected time period ranges from 2071 to 2099. The climate data includes temperature and precipitation whereas water balance data includes Potential Evapotranspiration (PET) and Moisture Index (MI) estimated using Penman-Monteith and Thornthwaite methods defining as Penman PET, Penman MI, Thornthwaite PET and Thornthwaite MI.  Both types of MI was estimated as a ratio of Precipitation and Evapotranspiration. The MACA data includes Penman PET which was estimated using Penman-Monteith methods. However, Thornthwaite PET was estimated using Thornthwaite methods for this project. 

Historical and projected climate data and water balance data under three GCMs (CNRM-CM5, CCSM4, and IPSL-CM5A-MR) from 1980 to 2099 was used to assess projected climate change impacts in North Central U.S. We obtained required data from MACA data (https://climate.northwestknowledge.net/MACA/). Historical time period ranges from 1980 to 2005, and projected time period ranges from 2071 to 2099. The climate data includes temperature and precipitation whereas water balance data includes Potential Evapotranspiration (PET) and Moisture Index (MI) estimated using Penman-Monteith and Thornthwaite methods defining as Penman PET, Penman MI, Thornthwaite PET and Thornthwaite MI.  Both types of MI was estimated as a ratio of Precipitation and Evapotranspiration. The MACA data includes Penman PET which was estimated using Penman-Monteith methods. However, Thornthwaite PET was estimated using Thornthwaite methods for this project. For further details please see summary sheet below.