Location: Northwest Watershed Research Center
Project Number: 2052-21500-001-000-D
Project Type: In-House Appropriated
Start Date: Feb 26, 2024
End Date: Feb 25, 2029
Objective:
Objective 1: Conduct long-term, big-data oriented, network scale research (e.g., LTAR, NWERN, PhenoCam, and SCINet) and data synthesis to innovate applications and solutions for adaptive land and watershed management.
Sub-objective 1A: Develop and evaluate unoccupied aerial systems (UAS) remote sensing and other big data methods, analysis procedures, and applications for solving rangeland resource management problems.
Sub-objective 1B: Develop databases and web-tools to extract and optimize North American Multi-Model Ensemble (NMME) historical seasonal forecasts for multi-site and regional forecasting applications across the LTAR site network.
Sub-objective 1C: Evaluate Great Basin community and individual human dimension responses to cheatgrass, wildfire, and other challenges through expansion of spatially explicit formats integrated between ecological landscape layers with primary sociological data pertinent to management decision-making, perceived risk of annual grass invasion scales, and ranch-scale adaptation capacity behaviors.
Sub-objective 1D: Continue to collaborate in LTAR and other network cross-site research projects contributing leadership, expertise, and data to address the regional and national scale problems concerning environmental health, agricultural productivity, and human dimensions of U.S. agroecosystems.
Objective 2: Develop methods and tools to facilitate successful restoration outcomes in sagebrush-steppe ecosystems in the Great Basin.
Sub-objective 2A: Assess the efficacy of prescriptive cattle grazing for restoring degraded sagebrush-steppe rangelands currently dominated by invasive annual grasses.
Sub-objective 2B: Utilize seedlot and seedbed models to identify restoration opportunities in the highly variable and changing weather environment of the Intermountain western U.S.
Objective 3: Assess weather and climate impacts on soil health, seedling establishment and rangeland productivity given the complex soil, vegetation, and topography typical of the western U.S.
Sub-objective 3A: Utilize the Bureau of Land Management Land Treatment Digital Library (LTDL) and gridMET historical climate database to characterize what type of weather year is required to yield a positive restoration outcome after wildfire.
Sub-objective 3B: Determine whether seasonal climate forecasting has sufficient skill for predictions of positive and negative restoration outcomes for post-fire seeding projects in the Great Basin.
Approach:
Goal 1A: Develop UAS remote sensing for monitoring rangeland fuel load, height, and continuity. We will use a combination ongoing field data and UAS imagery collections (initiated in 2015) to develop tools and workflows for characterizing fuels in 3 vegetations types which dominate much of the Great Basin region. Hypoth. 1B: The North American Multi-Model Ensemble (NMME) can be used to develop forecasting applications across a wide sector of U.S. agricultural. We will conduct hindcast assessments of all current NMME models for the period 1982-2022. Hindcast skill will be evaluated by comparing predictions to a gridded historical weather database, gridMET spanning the contiguous U.S. Goal 1C: Develop a socio-ecological adaptation capacity index for the northern Great Basin region. About 50-60 interviews will be collected from rural communities of the region. Focus Groups will conduct participatory analyses of adaptation drivers and challenges identified from the interviews, and then weight these factors across geographic/social contexts to develop an applicable index. Goal 1D: Develop long-term vegetation datasets in support of the LTAR network. We will continue ongoing collections (begun in 2015) of foliar cover, biomass, species richness/abundance, and other vegetation field data as well as phenology camera imagery along an elevational and precipitation gradient with the Great Basin LTAR site. Hypoth. 2A: High Intensity Low Frequency (HILF) beef cattle grazing will more effectively promote restoration of cheatgrass-invaded rangelands than lower intensity, BLM-permitted cattle grazing. We will continue analysis and publication of vegetation response data from the previous 9 years of this experiment. This experiment will then be replicated at a new study area. Hypoth. 2B: Seedbed microclimatic indices are correlated with native species distributions and the persistence and spread of invasive species over space. Time-series estimates of seedbed temperature and water potential will be developed for multiple plant materials, locations and temporal scenarios at selected field sites in the western U.S. using the Simultaneous Heat and Water (SHAW) model. Hypoth. 3A: Postfire seedling establishment success is correlated with winter/spring precipitation and winter temperature conditions in the year after planting. We will screen the USGS Land Treatment Digital Library (LTDL) records of post-fire rehabilitation treatments in the Great Basin to identify those with sufficient post-treatment data to indicate the level of both seed-mix and individual seedlot success in the first one to three years after seeding. We will then identify seedbed-microclimatic profiles that are correlated with relative seeding success or non-success at all selected LTDL field sites. Hypoth. 3B: Postfire seeding success in the Great Basin can be predicted using climate forecasts and historical weather and restoration data. We will identify and optimize seasonal forecasting models for all sites from Sub-objective 3A parametric/nonparametric analyses will be used to determine whether climate metrics identified in Sub-objective 3A are associated with establishment success.
