In the coming century, climate change is projected to impact precipitation and temperature regimes worldwide, with especially large effects in drylands. We use big sagebrush ecosystems as a model dryland ecosystem to explore the impacts of altered climate on ecohydrology and the implications of those changes for big sagebrush plant communities using output from 10 Global Circulation Models (GCMs) for two representative concentration pathways (RCPs). We ask: 1) What is the magnitude of variability in future temperature and precipitation regimes among GCMs and RCPs for big sagebrush ecosystems and 2) How will altered climate and uncertainty in climate forecasts influence key aspects of big sagebrush water balance? We explored these questions across 1980-2010, 2030-2060, and 2070-2100 to determine how changes in water balance might develop through the 21st century. We assessed ecohydrological variables at 898 sagebrush sites across the western US using a process-based soil water model, SOILWAT to model all components of daily water balance using site-specific vegetation parameters and site-specific soil properties for multiple soil layers. Our modeling approach allowed for changes in vegetation based on climate. Temperature increased across all GCMs and RCPs, while changes in precipitation were more variable across GCMs. Winter and spring precipitation was predicted to increase in the future (7% by 2030-2060, 12% by 2070-2100), resulting in slight increases in soil water potential (SWP) in winter. Despite wetter winter soil conditions, SWP decreased in late spring and summer due to increased evapotranspiration (6% by 2030-2060, 10% by 2070-2100) and groundwater recharge (26% and 30% increase by 2030-2060 and 2070-2100). Thus, despite increased precipitation in the cold season, soils may dry out earlier in the year, resulting in potentially longer drier summer conditions. If winter precipitation cannot offset drier summer conditions in the future, we expect big sagebrush regeneration and survival will be negatively impacted, potentially resulting in shifts in the relative abundance of big sagebrush plant functional groups. Our results also highlight the importance of assessing multiple GCMs to understand the range of climate change outcomes on ecohydrology, which was contingent on the GCM chosen.

NOTICE: Given the large size of the MACAv2METDATA dataset, and a known issue with the data server being used to host it, initial load times may take a very long time and / or time out. Subsequent requests should be faster due to caching, but the cache clears periodically and the dataset must be rescanned prior to access. We are working on a fix for this issue. In the mean time, please use the dataset with care and make sureyou've reviewed the GDP scalability guidelines. https://my.usgs.gov/confluence/display/GeoDataPortal/Geo+Data+Portal+Scalability+Guidelines This archive contains daily downscaled meteorological and hydrological projections for the Conterminous United States at 1/24-deg resolution utilizing the Multivariate Adaptive Constructed Analogs (MACA, Abatzoglou, 2012) statistical downscaling method with the METDATA (Abatzoglou,2013) training dataset. The downscaled meteorological variables are maximum/minimum temperature(tasmax/tasmin), maximum/minimum relative humidity (rhsmax/rhsmin)precipitation amount(pr), downward shortwave solar radiation(rsds), eastward wind(uas), northward wind(vas), and specific humidity(huss). The downscaling is based on the 365-day model outputs from different global climate models (GCMs) from Phase 5 of the Coupled Model Inter-comparison Project (CMIP3) utlizing the historical (1950-2005) and future RCP4.5/8.5(2006-2099) scenarios. Leap days have been added to the dataset from the average values between Feb 28 and Mar 1 in order to aid modellers. See: http://maca.northwestknowledge.net/ for more information.

This landcover raster was generated through a Random Forest predictive model developed in R using a combination of image-derived and ancillary variables, and field-derived training points grouped into 18 classes. Overall accuracy, generated internally through bootstrapping, was 75.5%. A series of post-modeling steps brought the final number of land cover classes to 28.

This landcover raster was generated through a Random Forest predictive model developed in R using a combination of image-derived and ancillary variables, and field-derived training points grouped into 18 classes. Overall accuracy, generated internally through bootstrapping, was 72.7%. A series of post-modeling steps brought the final number of land cover classes to 28.

This study had two objectives: first, to generate a landcover map for the Charles M. Russell Wildlife Refuge (CMR) emphasizing the distribution of land cover types in relation to greater sage grouse ( Centrocercus urophasianus) habitat needs, and second, to provide data that would allow a determination of whether results were better with SPOT imagery or Landsat 8 imagery. SPOT imagery is provided at a 10m pixel resolution, while Landsat 8 is at 30m. Results from this classification will allow managers to determine which resolution provides the accuracy needed for habitat planning and management.

This code was used in a simulated decision analysis project designed to evaluate the value of different kinds of information with regard to making optimal investments in invasive plant control programs. The code was developed in the R programming environment. The file "sim_code.R" contains the initialization of the parameters and analysis; the file "pop_sim.ccp" is a C++ program that executes the actual simulation and returns the results to R. We developed a hypothetical scenario in which a manager is tasked with control of invasive plants on 100 management units each 100 ha in size. 90 of these units were assumed to be under private management and 10 were assumed to be conservation units (i.e. under public management). For this problem we assumed that there were only two target species to control: leafy spurge (Euphorbia esula) and yellow toadflax (Linaria vulgaris). These two species were identified of examples of species that are controlled within the LCC geography. They are also species for which there is some literature to help parameterize a model. We should note that this model is gross generalization and was not intended to provide insight into invasive species biology. Because this model is stochastic, we measured this objective as the number of years out of 50 that at least 50% of the units were uninfested. We arbitrarily chose the target of 50%, which could have been any value. However, we felt that because these species are so difficult to control, target of 50% was fairly attainable. The dynamics of the model assume that management units can either be infested with one of the species, both of them or none at all. We further assumed that once a unit was infested, the species immediately achieved some average density and began producing propagules. These propagules had to disperse some arbitrary distance before they could infest another unit. Once those seeds landed in an uninfested unit, there was some probability they would lead to an infestation. To describe infestation dynamics, we developed a model that was composed of eight states to describe the management units. The model simulates the dynamics of infestation and control and then produces an Expected Value of Information analysis that shows the improvement in performance given resolution of each uncertain parameter in the model.

Training points collected in the field between 2012 and 2013 were grouped into 18 classes: Forested Burn (66), Foothill Woodland Steppe Transition (73), Greasewood Flat (73), Greasewood Steppe (239), Greasewood Sage Steppe (277), Great Plains Badlands (166), Great Plains Riparian (255), Low Density Sage Steppe (776), Medium Density Sage Steppe (783), Mixed Grass Prairie (555), Mixed Grass Prairie Burned (278), Ponderosa Pine Woodland and Shrubland (512), Riparian Floodplain (223), Semi-Desert Grassland (103), Sparsely Vegetated Mixed Shrub (252), Silver Sage Flat (70) , Silver Sage Steppe (64), and Water (246). When insufficient field data were available for a class, we augmented it through photointerpretation of 15 cm aerial imagery, using expert knowledge and field experience to guide us. The final dataset had 5,011 training points.

The habitats and food resources required to support breeding and migrant birds dependent on North American prairie wetlands are threatened by impending climate change. The North American Prairie Pothole Region (PPR) hosts nearly 120 species of wetland-dependent birds representing 21 families. Strategic management requires knowledge of avian habitat requirements and assessment of species most vulnerable to future threats. We applied bioclimatic species distribution models (SDMs) to project range changes of 29 wetland-dependent bird species using ensemble modeling techniques, a large number of General Circulation Models (GCMs), and hydrological climate covariates. For the U.S. PPR, mean projected range change, expressed as a proportion of currently occupied range, was −0.31 (± 0.22 SD; range − 0.75 to 0.16), and all but two species were projected to lose habitat. Species associated with deeper water were expected to experience smaller negative impacts of climate change. The magnitude of climate change impacts was somewhat lower in this study than earlier efforts most likely due to use of different focal species, varying methodologies, different modeling decisions, or alternative GCMs. Quantification of the projected species-specific impacts of climate change using species distribution modeling offers valuable information for vulnerability assessments within the conservation planning process.

Climate change poses major challenges for conservation and management because it alters the area, quality, and spatial distribution of habitat for natural populations. To assess species’ vulnerability to climate change and target ongoing conservation investments, researchers and managers often consider the effects of projected changes in climate and land use on future habitat availability and quality and the uncertainty associated with these projections. Here, we draw on tools from hydrology and climate science to project the impact of climate change on the density of wetlands in the Prairie Pothole Region of the USA, a critical area for breeding waterfowl and other wetland-dependent species. We evaluate the potential for a trade-off in the value of conservation investments under current and future climatic conditions and consider the joint effects of climate and land use. We use an integrated set of hydrological and climatological projections that provide physically based measures of water balance under historical and projected future climatic conditions. In addition, we use historical projections derived from ten general circulation models (GCMs) as a baseline from which to assess climate change impacts, rather than historical climate data. This method isolates the impact of greenhouse gas emissions and ensures that modeling errors are incorporated into the baseline rather than attributed to climate change. Our work shows that, on average, densities of wetlands (here defined as wetland basins holding water) are projected to decline across the U.S. Prairie Pothole Region, but that GCMs differ in both the magnitude and the direction of projected impacts. However, we found little evidence for a shift in the locations expected to provide the highest wetland densities under current vs. projected climatic conditions. This result was robust to the inclusion of projected changes in land use under climate change. We suggest that targeting conservation towards wetland complexes containing both small and relatively large wetland basins, which is an ongoing conservation strategy, may also act to hedge against uncertainty in the effects of climate change.

The Scaling Climate Change Adaptation in the Northern Great Plains through Regional Climate Summaries and Local Qualitative-Quantitative Scenario Planning Workshops project synthesizes climate data into 3-5 distinct but plausible climate summaries for the northern Great Plains region; crafts quantitative summaries of these climate futures for two focal areas; and applies these local summaries by developing climate-resource-management scenarios through participatory workshops and, where possible, simulation models. The two focal areas are central North Dakota and southwest South Dakota (Figure 1). The primary objective of this project is to help resource managers and scientists in a focal area use scenario planning to make management and planning decisions based on assessments of critical future uncertainties.This report summarizes project work for public and tribal lands in the central North Dakota focal area, with an emphasis on Knife River Indian Villages National Historic Site. The report explainsscenario planning as an adaptation tool in general, then describes how it was applied to the central North Dakota focal area in three phases. Priority resource management and climate uncertainties were identified in the orientation phase. Local climate summaries for relevant, divergent, and challenging climate scenarios were developed in the second phase. In the final phase, a two-day scenario planning workshop held November 12-13, 2015 in Bismarck, ND, featured scenario development and implications, testing management decisions, and methods for operationalizing scenario planning outcomes.