Location: Southwest Watershed Research
Project Number: 2022-13610-011-00
Start Date: Jan 30, 2012
End Date: Jan 29, 2017
Methods of investigation include field and laboratory experimentation, as well as the development and use of state-of-the-science watershed models and the use of remote sensing for watershed characterization. Multiple methods and techniques will be employed to improve the prediction of plant-water soil dynamics under objective one. They include data assimilation techniques to incorporate both in-situ and remotely sensed measurements into simulation models. In addition, algorithms for enhanced retrieval of watershed characteristics and state variables will be developed. Results from this research are critical for extension of results to large-areas using remote sensing and critical for improved inputs and parameter estimates for the models addressed under objective three. Research undertaken to address objective two more closely focuses on ecohydrology and determining changes in the cycling of energy, water and carbon as well as changes in the composition of plant communities across a wide range of time scales. This includes global change impacts on ecohydrologic processes (including water, nutrient and energy cycles) that underpin ecosystem structure and function. Thus, objective two also focuses on the relationship between global change, ecohydrology and watershed response, which will allow the evaluation of the combined impacts of climate change, intensive land use and species invasions on ecohydrological processes that are critical to maintaining ecosystems. It includes three Multi-Location Projects (MLPs) led or co-led by scientists in this research unit, which will examine observations across decadal and continental scales using observations from USDA’s national network of experimental watersheds, ranges and forests. To address objective three we will develop tools and methods to enhance watershed and rangeland management through wider accessibility of databases from our long-term experimental watersheds, and by development and testing of watershed and decision support models which can assimilate remotely sensed data and incorporate economic and ecosystem service information.