One of the biggest challenges facing resource managers today is not knowing exactly when, where, or how climate change effects will unfold. In order to plan for this uncertain future, managers have begun to use a tool known as scenario planning, in which climate models are used to identify different plausible climate conditions, known as “scenarios”, for a particular area.   In a previous project, researchers with the North Central Climate Science Center worked with natural resource managers at Badlands National Park and on surrounding federal lands to model how different climate scenarios and management activities would impact the area’s resources. The model that was developed answers critical “what if” questions regarding how management actions might affect focal resources, such as grazing lands, under different future climate conditions. Building on this work, researchers will produce management-relevant publications that translate the previous project’s results into a format that can support management planning.   Using insights gained from the previous project, researchers will also design a process for integrating scenario planning and climate science into National Park Service (NPS) Resource Stewardship Strategies. These strategies are part of NPS’s streamlined approach for guiding prioritization of a park’s investments in resource stewardship. Researchers will work with Devils Tower National Monument in Wyoming as a case study for this integration effort.

Article for outlet: Plant Ecology. Abstract: Big sagebrush (Artemisia tridentata Nutt.) plant communities are widespread non-forested drylands in western North American and similar to all shrub steppe ecosystems world-wide are composed of a shrub overstory layer and a forb and graminoid understory layer. Forbs account for the majority of plant species diversity in big sagebrush plant communities and are important for ecosystem function. Few studies have explored the geographic patterns of forb species richness and composition and their relationships with environmental variables in these communities. Our objectives were to examine the small and large-scale spatial patterns in forb species richness and composition and the influence of environmental variables. We sampled forb species richness and composition along transects at 15 field sites in Colorado, Idaho, Montana, Nevada, Oregon, Utah, and Wyoming, built species-area relationships to quantify differences in forb species richness at sites, and used Principal Components Analysis and nonmetric multidimensional scaling to identify relationships among environmental variables and forb species richness and composition. We found that species richness was most strongly correlated with soil texture, while species composition was most related to climate. The combination of climate and soil texture influences water availability, with important consequences for forb species richness and composition, which suggests climate-change induced modification of soil water availability may have important implications for plant species diversity in the future. Our paper is the first to our knowledge to examine forb biodiversity patterns in big sagebrush ecosystems in relation to environmental factors across the big sagebrush region.

Scenario planning helps managers incorporate climate change into their natural resource decision making through a structured “what-if” process of identifying key uncertainties and potential impacts and responses. Although qualitative scenarios, in which ecosystem responses to climate change are derived via expert opinion, often suffice for managers to begin addressing climate change in their planning, this approach may face limits in resolving the responses of complex systems to altered climate conditions. In addition, this approach may fall short of the scientific credibility managers often require to take actions that differ from current practice. Quantitative simulation modeling of ecosystem response to climate conditions and management actions can provide this credibility, but its utility is limited unless the modeling addresses the most impactful and management-relevant uncertainties and incorporates realistic management actions. We use a case study to compare and contrast management implications derived from qualitative scenario narratives and from scenarios supported by quantitative simulations. We then describe an analytical framework that refines the case study’s integrated approach in order to improve applicability of results to management decisions. The case study illustrates the value of an integrated approach for identifying counterintuitive system dynamics, refining understanding of complex relationships, clarifying the magnitude and timing of changes, identifying and checking the validity of assumptions about resource responses to climate, and refining management directions. Our proposed analytical framework retains qualitative scenario planning as a core element because its participatory approach builds understanding for both managers and scientists, lays the groundwork to focus quantitative simulations on key system dynamics, and clarifies the challenges that subsequent decision making must address.

This dataset provides downscaled climate projections at 800m spatial resolution for nine ecologically-relevant climate variables for the north central US region between 35.5N-49N latitude and 88W-118W longitude from 12 Coupled Model Intercomparison Project - Phase 5 (CMIP5) climate model simulations (6GCMs x 2RCPs) which are downscaled using the Multivariate Adaptive Constructed Analog (MACA) method. These projections are available as five different (approximately) 30-year climate normals between 1950 and 2099 as monthly values, except for Aridity Index which are annual values. The five periods for which these climate normals are provided are 1950-1979 and 1980-2005 in the historic, and 2011-2040, 2041-2070 and 2071-2099 in the future. Six GCMs were selected for developing this dataset. This selection was done to facilitate availability of several divergent future scenarios for the north central US region, and are selected based on (a) divergence found for future changes in annual temperature and precipitation for the region based on the projections from 34 CMIP5 GCMs for both RCP 4.5 and RCP 8.5 emissions scenarios, as well as (b) their relatively higher accuracy in representing the historic climate for the western and central US region. The six GCMs include CanESM2, CCSM4, CNRM-CM5, GFDL-ESM2M, HadGEM2-ES and IPSL-CM5A-MR. The nine climate variables include aridity index (unitless), potential evapotranspiration (mm), precipitation (mm), relative humidity (%), downward solar radiation (W.m-2), maximum daily temperature (C), minimum daily temperature (C), average temperature (C), vapor pressure deficit (Pa). Most of these variables were directly available from the 4km MACAv2-METDATA archive at the monthly time frequency, while others such as aridity index, relative humidity, average temperature and vapor pressure deficits were calculated additionally. The climate normals for the different periods (mentioned above) were estimated at 4km spatial resolution and then spatially disaggregated to 800m spatial resolution using bilinear interpolation. A datafile on the elevation of a grid cell at 800m is also made available in this archive.

Water management planners and researchers throughout the world rely on hydrological models to forecast and simulate streamflow hydrology and hydrological events. These simulations are used to inform water management, municipal planning, and ecosystem conservation decisions, as well as to investigate potential effects of climate and land-use change on hydrology. 

Locating meadow study sitesMeadow centers as recorded in the ‘Copy of sitecords_areaelev from Caruthers thesis.xls’ file delivered by Debinski in November 2012 were matched to polygons as recorded in files ‘teton97map_area.shp’ and ‘gallatin97map_area.shp’ both also delivered by Debinski in November 2012.In cases where the meadow center did not fall within a meadow polygon, if there was a meadow polygon of the same meadow TYPE nearby (judgment was used here), the meadow center was matched with the meadow polygon of same meadow TYPE. In total, 29 of 30 Gallatin meadow sites and 21 of 25 Teton meadow sites were positively located.Identifying meadow pixels for analysisThe native MODIS 250-meter grid was reprojected to match meadow data and added to the GIS project window along with the meadow polygons. For context, aerial photography from ESRI’s basemap streaming services were also added to the ArcMap project. MODIS pixels that were at least half-covered by meadow polygon area were used in further ndvi analysis. Meadows that did not cover at least half of one MODIS pixel were eliminated from the analysis. In total, 17 Gallatin meadow sites (M1= 0; M2= 0; M3= 4; M4= 4; M5= 4; M6=5), covering at least half of 39 MODIS pixels (M1= 0; M2= 0; M3= 12; M4= 4; M5= 6; M6= 17), were used in further analysis and 16 Teton meadow sites (M1=3; M2=1; M3=4; M5=5; M6=3) covering at least half of 1252 MODIS pixels (M1= 105; M2= 1; M3= 25; M4=0 ; M5= 19; M6=1102), were used in further analysis.List of site names that were located, but not used in the NDVI analysis b/c they were too small: Gallatin – Porcupine Exclosure; Twin Cabin Willows; Figure 8; Taylor Fork; Teepee Sage; Daly North; Wapiti (Taylor Fork); Specimen Creek; Bacon Rind M1; Bacon Rind M4, Teepee wet; Daly SouthTeton – Cygnet Pond; Christian Pond; Willow Flats North; Willow Flats South; Sound of MusicMODIS preprocessing methods: MODIS MOD13Q1 representing observations of normalized difference vegetation index (NDVI) from March 2000 through December 2012 were downloaded from the USGS Land Processes Distributed Area Archive Center (LPDAAC) during the spring of 2013. Also downloaded at the same time were grids that described the estimated reliability of NDVI observations and the actual day of the year for each NDVI observation used in maximum compositing routines by the MODIS program. All MODIS data layers were reprojected to match meadow data layers.

This project conducts an interdisciplinary, technical assessment of key social-ecological vulnerabilities, risks, and response capacities of the Wind River Indian Reservation (WRIR) to inform development of decision tools to support drought preparedness. It also provides opportunities for 1) development of tribal technical capacity for drought preparedness, and 2) educational programming guided by tribal needs, Traditional Ecological Knowledge (TEK), and indigenous observations of drought for tribal members, with a longer-term goal of transferring lessons learned to other tribes and non-tribal entities. This project has foundational partnerships between the Eastern Shoshone and Northern Arapaho tribes of the WRIR, the National Drought Mitigation Center (NDMC) at the University of Nebraska-Lincoln, the North Central Climate Science Center (NCCSC) at Colorado State University, University of Wyoming EPSCoR, and multiple government agencies and university partners to develop decision tools to support drought preparedness. Other partners include the USDA Northern Plains Regional Climate Hub and NRCS, the Western Water Assessment at CU Boulder, NOAA National Integrated Drought Information System (NIDIS), the High Plains Regional Climate Center, US Fish and Wildlife Service, USGS, BIA, Great Northern LCC, and other North Central University Consortium scientists. The project’s decision target is a WRIR Drought Management Plan that integrates state-of-the art climate science with hydrologic, social, and ecological vulnerabilities and risks, and identifies response capacities and strategies to support the Tribal Water Code and related resources management.

Locating meadow study sitesMeadow centers as recorded in the ‘Copy of sitecords_areaelev from Caruthers thesis.xls’ file delivered by Debinski in November 2012 were matched to polygons as recorded in files ‘teton97map_area.shp’ and ‘gallatin97map_area.shp’ both also delivered by Debinski in November 2012.In cases where the meadow center did not fall within a meadow polygon, if there was a meadow polygon of the same meadow TYPE nearby (judgment was used here), the meadow center was matched with the meadow polygon of same meadow TYPE. In total, 29 of 30 Gallatin meadow sites and 21 of 25 Teton meadow sites were positively located.Identifying meadow pixels for analysisThe native MODIS 250-meter grid was reprojected to match meadow data and added to the GIS project window along with the meadow polygons. For context, aerial photography from ESRI’s basemap streaming services were also added to the ArcMap project. MODIS pixels that were at least half-covered by meadow polygon area were used in further ndvi analysis. Meadows that did not cover at least half of one MODIS pixel were eliminated from the analysis. In total, 17 Gallatin meadow sites (M1= 0; M2= 0; M3= 4; M4= 4; M5= 4; M6=5), covering at least half of 39 MODIS pixels (M1= 0; M2= 0; M3= 12; M4= 4; M5= 6; M6= 17), were used in further analysis and 16 Teton meadow sites (M1=3; M2=1; M3=4; M5=5; M6=3) covering at least half of 1252 MODIS pixels (M1= 105; M2= 1; M3= 25; M4=0 ; M5= 19; M6=1102), were used in further analysis.List of site names that were located, but not used in the NDVI analysis b/c they were too small: Gallatin – Porcupine Exclosure; Twin Cabin Willows; Figure 8; Taylor Fork; Teepee Sage; Daly North; Wapiti (Taylor Fork); Specimen Creek; Bacon Rind M1; Bacon Rind M4, Teepee wet; Daly SouthTeton – Cygnet Pond; Christian Pond; Willow Flats North; Willow Flats South; Sound of MusicMODIS preprocessing methods: MODIS MOD13Q1 representing observations of normalized difference vegetation index (NDVI) from March 2000 through December 2012 were downloaded from the USGS Land Processes Distributed Area Archive Center (LPDAAC) during the spring of 2013. Also downloaded at the same time were grids that described the estimated reliability of NDVI observations and the actual day of the year for each NDVI observation used in maximum compositing routines by the MODIS program. All MODIS data layers were reprojected to match meadow data layers.All negative NDVI values which are thought to correspond to standing water, partial snow-cover or wet bare soil were set to ‘NA’values (Huete, Justice and van Leeuwen 1999)The following steps were used to remove any conifer/evergreen signal from NDVI data and are based on an understanding that each pixel has a different “background”(i.e. no-growth) greenness against which any seasonal change must be compared (Beever et al. 2013; Piekielek and Hansen 2013). These methods also help to eliminate long gaps in data that can allow smoothing algorithms to interpolate beyond the valid range of data (in the case of NDVI from 0 –1):Annual minimum NDVI values that were labeled as high-quality were identified in the 13 year time-series.The bottom first percentileof a distribution of minimum values was used as the “background”value to fill-in missing values when the target was identified as being under snow cover.All NDVI values identified by pixel-reliability grids as being of high or marginal quality werepreserved and snow-covered pixels and dates were filled in with each pixels “background”value.Composite day of year grids were used to identify the actual date from which the 16-day maximum composite NDVI value came.Each pixel’s entire time-series (2000 –2012) was smoothed in a weighted regression framework against time using smoothing splines (Chambers and Hastie 1992). NDVI data of marginal quality and snow-covered background values contributed half the weight to final smoothed values as did high-quality values. The final smoothed values were used to interpolate the time-series to a daily time-step and to record annual NDVI amplitudes. Land surface phenology metrics were calculated as follows:Start of season (SOS) –the first annual day of year when smoothed NDVI crosses half of its annual amplitude (White et al. 2009).End of Season (EOS) –the last day of year when smoothed NDVI crosses half of its annual amplitude.Maximum annual NDVI (MAX) –the highest annual smoothed NDVITiming of annual maximum (DOYMAX) –the smoothed day of year when NDVI reaches its maximum valueEstimated annual productivity (INDVI) –the integrated area under the growing season (SOS to EOS) NDVI curve (Goward et al 1985).

This dataset provides downscaled climate projections at 800m spatial resolution for nine ecologically-relevant climate variables for the north central US region between 35.5N-49N latitude and 88W-118W longitude from the Canadian Centre for Climate Modeling and Analysis model, CanESM2, simulations (r1i1p1) from two emissions scenarios (RCP 4.5 and 8.5), which are downscaled using the Multivariate Adaptive Constructed Analog (MACA) method. These projections are available as five different (approximately) 30-year climate normals between 1950 and 2099 as monthly values, except for Aridity Index which are annual values. The five periods for which these climate normals are provided are 1950-1979 and 1980-2005 in the historic, and 2011-2040, 2041-2070 and 2071-2099 in the future. The nine climate variables include aridity index (unitless), potential evapotranspiration (mm), precipitation (mm), relative humidity (%), downward solar radiation (W.m-2), maximum daily temperature (C), minimum daily temperature (C), average temperature (C), vapor pressure deficit (Pa). Most of these variables were directly available from the 4km MACAv2-METDATA archive at the monthly time frequency, while others such as aridity index, relative humidity, average temperature and vapor pressure deficits were calculated additionally. The climate normals for the different periods (mentioned above) were estimated at 4km spatial resolution and then spatially disaggregated to 800m spatial resolution using bilinear interpolation. A datafile on the elevation of a grid cell at 800m is also made available in this archive.

This dataset provides downscaled climate projections at 800m spatial resolution for nine ecologically-relevant climate variables for the north central US region between 35.5N-49N latitude and 88W-118W longitude from the National Center of Atmospheric Research (USA) model, CCSM4, simulations (r6i1p1) from two emissions scenarios (RCP 4.5 and 8.5), which are downscaled using the Multivariate Adaptive Constructed Analog (MACA) method. These projections are available as five different (approximately) 30-year climate normals between 1950 and 2099 as monthly values, except for Aridity Index which are annual values. The five periods for which these climate normals are provided are 1950-1979 and 1980-2005 in the historic, and 2011-2040, 2041-2070 and 2071-2099 in the future. The nine climate variables include aridity index (unitless), potential evapotranspiration (mm), precipitation (mm), relative humidity (%), downward solar radiation (W.m-2), maximum daily temperature (C), minimum daily temperature (C), average temperature (C), vapor pressure deficit (Pa). Most of these variables were directly available from the 4km MACAv2-METDATA archive at the monthly time frequency, while others such as aridity index, relative humidity, average temperature and vapor pressure deficits were calculated additionally. The climate normals for the different periods (mentioned above) were estimated at 4km spatial resolution and then spatially disaggregated to 800m spatial resolution using bilinear interpolation. A datafile on the elevation of a grid cell at 800m is also made available in this archive.