Location: Northwest Watershed Research Center
Project Number: 2052-13610-015-000-D
Project Type: In-House Appropriated
Start Date: Jan 14, 2022
End Date: Jan 13, 2027
Objective 1) Develop improved snowmelt and streamflow forecasting tools. Sub-objective 1A) Improve spatial representation of precipitation and solar radiation as snow model forcing data. Sub-objective 1B) Develop and improve model linkages between spatially distributed snowmelt and streamflow generation. Objective 2) Quantify and predict terrestrial ecosystem carbon dynamics, including rangeland productivity, soil respiration, carbon flux, and carbon sequestration in response to water availability and climate variability. Sub-objective 2A) Identify and model linkages between climate variability, water availability, and primary productivity. Sub-objective 2B) Improve understanding of soil carbon dynamics related to soil carbon sequestration. Objective 3) Develop long-term observational data sets for climate, hydrology, vegetation, soils, geophysics, and water quality to make inferences about function, long-term productivity and sustainability of rangeland ecosystems that can be widely used in local, regional, and national models and in collaboration with the LTAR network. Sub-objective 3A) Maintain and enhance long-term observational infrastructure for climate, hydrology, vegetation, soils, geophysics, and water quality in support of network wide LTAR collaborations and research community at large. Sub-objective 3B) Quantify climate change effects on hydrology and the past, present, and future sustainability of rangeland ecosystems using the long-term dataset from RCEW.
Objective 1 builds on the snowmelt and streamflow forecasting advancements made with the iSnobal model during the last five-year project cycle that enabled near real-time snowmelt forecasting in support of operational water supply forecasting and water management. We will take a four-pronged approach to further improve operational streamflow forecasting: 1) We will take advantage of recent advances in estimating precipitation patterns and snow depth over mountainous areas from airplane overflights; 2) We will use satellite obseravations of solar reflectance, snow cover, and cloud cover to better estimate the solar energy absorbed by the snow; 3) We will develop approaches to estimate streamflow from simuated snowmelt using historical relationships between measured streamflow and simulated snow melt; and 4) We will couple the iSnobal model with an existing model that routes snowmelt water to the stream. In Objective 2, we will combine field observations and modeling tools to better understand and predict water and carbon dynamics in semi-arid rangeland ecosystems. Tools for quantifying and modeling vegetation productivity and carbon storage of sagebrush ecosystems will be developed, providing a better understanding of vegetation productivity and soil carbon sequestration in water-limited ecosystems. Research will capitalize on the network of research sites along an elevation/climate gradient within the Reynolds Creek Experimental Watershed (RCEW). Measurements include CO2 uptake and emission from plants and soil, weather observations, soil temperature/water/CO2 profiles, chambers that measure soil CO2 emission, etc. Annual vegetation surveys and cameras that track plant growth/phenology are available at three of the sites. Using the natural gradient in climate and productivity across the research sites presents a unique opportunity to study factors regulating carbon fluxes and productivity and observe changes in ecosystem function as climate and ecohydrological properties shift. Data will be used to test and improve existing models that simulate management and climate on vegetation productivity and carbon storage within the soil. In Objective 3, we will expand the scientific infrastructure of the RCEW to: 1) quantify offsite transfer of water and carbon in streams and groundwater; 2) measure changes in productivity and carbon cycling as sagebrush ecosystems transition to invasive annual grasses; and 3) support collaborations both within USDA-ARS, especially with the Long-Term Agroecosystem Research (LTAR) network, and with our University collaborators. We also take advantage of our long-term record to document ecohydrological change thatr has occurred in the past 60 years on the RCEW. Approaches that will be pursued if initial methods are unsuccessful include; 1) using alternative satellite products if the data from the aging MODIS satellite proves problematic, 2) using existing inhouse computational infrastructure if the coupled snowmelt-streamflow model does not lend itself to a High-Performance Cluster, 3) using a different model (UNSATCHEM) if soil inorganic process are significant and cannot be easily implemented into the SHAW model.