Location: Southwest Watershed Research Center
Project Number: 2022-13610-013-000-D
Project Type: In-House Appropriated
Start Date: Jan 30, 2022
End Date: Jan 29, 2027
Objective:
Objective 1. Quantify the magnitude and variability of the water balance components in semiarid landscapes and identify their controlling processes. 1.A: As an LTAR observatory, continue to collect and curate WGEW datasets including precipitation, runoff, sediment, pond runoff and sediment, meteorology, soil moisture, fluxes, vegetation, spatial datasets, and make datasets available under FAIR principles. 1.B: Quantify intra-storm variation in stable isotope values of precipitation over WGEW and identify relative influence of moisture source, season, local weather and sub-cloud processes. 1.C: Track daily watershed water balance components for rangeland ecosystems in the WGEW and SRER for improved assessment of water status and associated productivity. 1.D: Incorporate a variety of enhancements into watershed and erosion models maintained by the SWRC to add additional sub-processes, reduce predictive uncertainty, make them easier to use, enhance integration with land management agency workflows, and expand their use geographically.
Objective 2: As part of the Long-Term Agroecosystem Research (LTAR) network, characterize and quantify impacts of water and agriculture/water management on semiarid watershed and agroecosystem processes. 2.A: Assess how novel remote sensing tools and low-cost, automated optical imagery can be used to quantify evapotranspiration and vegetation carbon uptake in water-limited regions. 2.B: Improve large-scale mapping of rangeland vegetation cover, lifeform, and biomass to classify rangeland ecological sites and states. 2.C: Quantify the long-term variability of riparian woodland evapotranspiration and CO2 exchange and their controls. 2.D: Assess impacts of altered temporal rainfall regime on semiarid grassland water and carbon cycling processes.
Objective 3: Quantify and predict effects of climatic change, plant community transitions, and conservation practices on ecological, hydrological, and erosion processes. 3.A: Develop new conceptual and quantitative frameworks to assess the impacts of brush management on ecosystem structure and function and enhanced delivery of ecosystem services. 3.B: Assess impacts of climate change, wildfire, and vegetation management on hydrology and erosion processes across spatial scales within the rangeland-dry forest continuum. Two Goals are included for this Sub-objective. 3.C: Conduct field-based experiments on southwestern U.S. rangelands to assess the impact of woodland encroachment/infilling and tree removal conservation practices on vegetation, surface soils, and hydrology and erosion processes. 3.D: Evaluate the hydrologic, geomorphic, and ecologic impacts of failed soil and water conservation structures in Southwest rangelands. 3.E: Quantify how weather variability and potential changes in climate impact ecosystem net and gross carbon uptake in the water-limited Southwest. 3.F: Quantify how snowmelt amount and timing are impacted by vegetation structure under changing climate, wildfire, and vegetation management in the semiarid interior western U.S. 3.G: Estimate runoff and erosion risks over western U.S. rangelands.
Approach:
Objective 1. A. Collect and make available Walnut Gulch Experimental Watershed (WGEW) datasets including precipitation, runoff, sediment, pond runoff and sediment, meteorology, soil moisture, fluxes, vegetation, spatial datasets. B. Quality-control and collate precipitation samples during summer rainfall events using a custom autosampler. C. Make measurements of precipitation, soil water content, runoff and evapotranspiration in the headwater watersheds of the WGEW and Santa Rita Experimental Range from the SECA network to track daily water balance components. D. Add functionality to existing runoff and erosion models to improve the applicability and ease of use for watershed management and assessments.
Objective 2. A. Evaluate novel remote sensing spectral tools across the gradients of spatial and temporal dryland measurements. B. Use field measurements of cover, biomass and lifeform along with remotely sensed data to classify states on ecological sites. Structure from Motion will be used to estimate the distribution of cover and biomass by lifeform using machine learning (ML) and estimate erosion and runoff model parameters within the common site/state combinations. C. Use eddy covariance flux data from a riparian woodland site to better understand what controls annual ET and productivity. D. Utilize the Rainfall Manipulation facility in the SRER to fully control precipitation (using rainout shelters and irrigations) over hydrologically isolated plots with equal mixtures of multiple semiarid grassland plants and initiate hydroclimate disturbance treatments.
Objective 3. A. Test for impacts on measured runoff after brush management treatments and demonstrate Rangeland Hydrology and Erosion Model (RHEM) capability to accurately simulate runoff and erosion processes for tree canopy and intercanopy areas on untreated and treated sites. B. Conduct a series of field studies quantifying impacts of fire on vegetation, ground cover, soil water repellency, infiltration, and runoff and erosion processes, and evaluate climatic and vegetation controls on surface water supplies using daily streamflow records in watersheds of the Colorado River Basin. C. Use artificial rainfall simulation and overland flow experiments to quantify infiltration, runoff, rainsplash, and erosion on tree-encroached sagebrush with tree-removal practices. D. Quantify the impacts of failed conservation structures using LiDAR data, aerial photographs, and satellite imagery. E. Use water and carbon flux data to better understand ecosystem responses to short and long term climate variability and improve models. F. Combine various datasets to quantify how snowmelt amount and timing are impacted by vegetation structure. G. Employ ML methods complemented by auxiliary data to develop relationships to field-collected variables from monitoring locations across the West and determine if ML techniques can predict RHEM parameters and runoff and erosion predictions directly.
