Changing climate conditions can make water management planning and drought preparedness decisions more complicated than ever before. Resource managers can no longer rely solely on historical data and trends to base their actions, and are in need of science that is relevant to their specific needs and can directly inform important planning decisions. Questions remain, however, regarding the most effective and efficient methods for extending scientific knowledge and products into management and decision-making. This study analyzed two unique cases of water management to better understand how science can be translated into resource management actions and decision-making.  In particular, this project sought to understand 1) the characteristics that make science actionable and useful for water resource management and drought preparedness, and 2) the ideal types of scientific knowledge or science products that facilitate the use of science in management and decision-making. The first case study focused on beaver mimicry, an emerging nature-based solution that increases the presence of wood and woody debris in rivers and streams to mimic the actions of beavers. This technique has been rapidly adopted by natural resource managers as a way to restore riparian areas, increase groundwater infiltration, and slow surface water flow so that more water is available later in the year during hotter and dryer months. The second case study focused on an established research program, Colorado Dust on Snow, that provides water managers with scientific information explaining how the movement of dust particles from the Colorado Plateau influences hydrology and the timing and intensity of snow melt and water runoff into critical water sources. This program has support from and is being used by several water conservation districts in the state. Understanding how scientific knowledge translates into action and decision-making in these cases is expected to strengthen our knowledge of actionable science in the context of drought and its impacts on ecosystems. The project team gathered qualitative data through stakeholder interviews and will conduct an extensive literature review. Findings from these efforts will also be incorporated into a broader Intermountain West synthesis effort to determine and assess commonalities and differences among socio-ecological aspects of drought adaptation and planning.

The Wind River Indian Reservation (WRIR) in west-central Wyoming is home to the Eastern Shoshone and Northern Arapaho tribes, who reside near and depend on water from the streams that feed into Wind River. In recent years, however, the region has experienced frequent severe droughts, which have affected tribal livelihoods and cultural activities. Scientists with the North Central Climate Adaptation Science Center (NCCASC) at Colorado State University, the National Drought Mitigation Center (NDMC) at the University of Nebraska-Lincoln, and several other university and agency partners in the region worked in close partnership with tribal water managers to assess how drought affects the reservation, which included the integration of social, ecological, and hydro-climatological sciences with local knowledge. The study revealed a long history with drought in the region, as well as issues that limit the tribe’s ability to manage their water resources. In addition, changing hydroclimate conditions were identified that can result in changing drought characteristics, which increases the need for adaptive management strategies. The findings are helping to inform the creation of a climate monitoring system and drought management plan, which have been supported with additional technical and financial support from the High Plains Regional Climate Center (HPRCC) and NOAA’s National Integrated Drought Information System (NIDIS). The drought plan will integrate climate science with hydrologic, social and ecological vulnerabilities and risks, and identify response capacities and strategies to support the Tribal Water Code and related resources management. Ultimately, the plan will help the tribes ensure that agricultural and other societal needs are met during times of drought. As part of the project, tribal water managers and the public were also engaged in educational activities related to water resources and drought preparedness through joint activities with Wyoming Experimental Program to Stimulate Competitive Research (EPSCoR) to build the tribe’s ability to response to future drought.

The purpose of this study was to understand how the U.S. Department of Interior’s federal land and resource managers and their stakeholders (i.e., NPS, BLM, FWS, BOR, BIA and tribes, among others) are experiencing and dealing with drought in their landscapes. The database is part of the Drought Risk and Adaptation in the Interior project. We conducted in-depth interviews (n=41) with DOI and tribal land managers in three case sites across the north central United States (northwest Colorado, southwest South Dakota, and Wind River Reservation), the goal of which was to develop a better understanding of drought vulnerabilities, risks, and responses in high-risk, multi-jurisdictional landscapes across the Missouri River Basin. DRAI posed the following research questions: 1. How do different resource managers from the Department of Interior (DOI), other federal agencies, and tribal communities perceive and characterize drought risk for the lands they manage? 2. How are their respective grassland/rangeland, fish and wildlife, and forest management decisions affected by those drought risk perceptions? 3. What indicators (e.g., climate science, local knowledge) are used to document and understand drought conditions and progression? 4. What are the impacts of drought to key management targets and livelihoods? 5. What are their differential capacities (and barriers) for responding to and preparing for drought risks? Data was analyzed using a grounded-theory approach, where risk perceptions, responses, and capacities to respond are derived from the stakeholders themselves. The database includes 41 in-depth interview transcripts with DOI (USFWS, BLM, NPS, BIA) land/resource managers, state and district water administrators, and tribal land and resource managers from 3 case sites. A range of expertise was represented in these interviews and included water resource managers/engineers, ecologists, wildlife biologists, fire coordinators, rangeland management specialists, among others. Each transcript has been coded, analyzed, and compared across cases and management situations in the context of the 5 overarching questions, as well as in the context of the inter-related climate drivers, ecological impacts, and adaptation/responses in the context of drought and climate change. The database includes 300+ social, climate, and ecological codes that describe the social-ecological context of drought and drought management in each respective case. See cross-listed publications and reports for major findings.

Projected suitable habitat models were constructed in randomForest (R package, version 4.6-10) using a set of presence points for the species derived from element occurrence and herbarium records, together with temperature, precipitation, and soil variables. The current distribution used modeled historic period (1970-2000) climate variables from the appropriate matching GCM model run. These model parameters were then used with projected climate data to get future (2020-2050) modeled suitable habitat for each scenario. Modeled past suitable habitat and modeled future suitable habitat are combined to show areas of change, using various thresholds to distinguish change categories, as well as current mapped J. osteosperma habitats from LANDFIRE existing vegetation (version 1.3.0). Current JUOS habitat is represented as areas with probability greater than the all-scenario average model-reported threshold (sensitivity = specificity) AND currently mapped as JUOS. These probability threshold levels were also applied to projected future habitat (since we have no “future” mapping), with the final model was classified as: Value Habt Class Current 2035 1 Lost >= 0.90 < 0.55 2 Threatened >= 0.90 >= 0.55 and < 0.90 3 Persistent >= 0.90 >= 0.90 4 Emergent < 0.90 >= 0.90 0 none of the above where: 0.90 is the average probability of occurrence value from the 3 scenarios, current timeframe, where JUOS is known to occur (using LANDFIRE vegetation). 0.55 is the average probability of occurrence value from the 3 scenarios, current timeframe, where the model specificity = the model sensitivity.

Projected suitable habitat models were constructed in randomForest (R package, version 4.6-10) using a set of presence points for the species derived from element occurrence and herbarium records, together with temperature, precipitation, and soil variables. The current distribution used modeled historic period (1970-2000) climate variables from the appropriate matching GCM model run. These model parameters were then used with projected climate data to get future (2020-2050) modeled suitable habitat for each scenario. Modeled past suitable habitat and modeled future suitable habitat are combined to show areas of change, using various thresholds to distinguish change categories, as well as current mapped J. osteosperma habitats from LANDFIRE existing vegetation (version 1.3.0). Current JUOS habitat is represented as areas with probability greater than the all-scenario average model-reported threshold (sensitivity = specificity) AND currently mapped as JUOS. These probability threshold levels were also applied to projected future habitat (since we have no “future” mapping), with the final model was classified as: Value Habt Class Current 2035 1 Lost >= 0.90 < 0.55 2 Threatened >= 0.90 >= 0.55 and < 0.90 3 Persistent >= 0.90 >= 0.90 4 Emergent < 0.90 >= 0.90 0 none of the above where: 0.90 is the average probability of occurrence value from the 3 scenarios, current timeframe, where JUOS is known to occur (using LANDFIRE vegetation). 0.55 is the average probability of occurrence value from the 3 scenarios, current timeframe, where the model specificity = the model sensitivity.

Projected suitable habitat models were constructed in randomForest (R package, version 4.6-10) using a set of presence points for the species derived from element occurrence and herbarium records, together with temperature, precipitation, and soil variables. The current distribution used modeled historic period (1970-2000) climate variables from the appropriate matching GCM model run. These model parameters were then used with projected climate data to get future (2020-2050) modeled suitable habitat for each scenario. Modeled past suitable habitat and modeled future suitable habitat are combined to show areas of change, using various thresholds to distinguish change categories, as well as current mapped J. osteosperma habitats from LANDFIRE existing vegetation (version 1.3.0). Current JUOS habitat is represented as areas with probability greater than the all-scenario average model-reported threshold (sensitivity = specificity) AND currently mapped as JUOS. These probability threshold levels were also applied to projected future habitat (since we have no “future” mapping), with the final model was classified as: Value Habt Class Current 2035 1 Lost >= 0.90 < 0.55 2 Threatened >= 0.90 >= 0.55 and < 0.90 3 Persistent >= 0.90 >= 0.90 4 Emergent < 0.90 >= 0.90 0 none of the above where: 0.90 is the average probability of occurrence value from the 3 scenarios, current timeframe, where JUOS is known to occur (using LANDFIRE vegetation). 0.55 is the average probability of occurrence value from the 3 scenarios, current timeframe, where the model specificity = the model sensitivity.

Projected suitable habitat models were constructed in Maxent (version 3.3; Phillips et al. 2004, 2006) using a set of presence points for the species derived from element occurrence and herbarium records, together with temperature, precipitation, and soil variables. The current distribution used modeled historic period (1970-2000) climate variables from the appropriate matching GCM model run. These model parameters were then used with projected climate data to get future (2020-2050) modeled suitable habitat for each scenario. Modeled past suitable habitat and modeled future suitable habitat are combined to show areas of change, using various thresholds to distinguish change categories, as well as current mapped sagebrush-occupied habitats from SWReGAP landcover (USGS 2004). Current occupied habitat is represented as areas with probability greater than the all-scenario average model-reported threshold (sensitivity = specificity) AND currently mapped as the appropriate sagebrush type. These probability threshold levels were also applied to projected future habitat (since we have no “future” mapping), with the final model was classified as: Value Habt Class Current 2035 1 Lost >= 0.56 < 0.34 2 Threatened >= 0.56 >= 0.34 and < 0.56 3 Persistent >= 0.56 >= 0.56 4 Emergent < 0.56 >= 0.56 0 none of the above where: 0.56 is the average probability of occurrence value from the 3 scenarios, current timeframe, where vaseyana is known to occur (using SWReGAP landcover). 0.34 is the average probability of occurrence value from the 3 scenarios, current timeframe, where the model specificity = the model sensitivity.