Program Areas
The Department of Soil, Water, and Ecosystem Sciences (SWES) research, teaching, and extension programs integrate knowledge and expertise from many disciplines. These include soil and water sciences, chemistry, physics, mathematics, microbiology, ecology, biology, and remote sensing technologies. Our efforts are integrative across disciplines, spatial scales (field, landscape, global), and ecosystems (agricultural, urban, and natural systems). We leverage artificial intelligence (AI), other modeling approaches, and novel technologies (nanotechnologies) to link soil, water, and plant applications. Specific program areas include:
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Water Quality and Watershed Management
Our efforts characterize the impacts of soil, water, and watershed management practices on surface water (i.e., canals, streams, lakes, rivers, wetlands, estuaries, coastal zone) and groundwater quantity and quality (relative to nutrients, pesticides, emerging contaminants, pathogens) across agricultural, urban, and natural ecosystems. Additionally, we aim to identify and/or develop methods and practices for protecting water quality and ecosystem services/functions as informed by ecological risk assessments.
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Soil Health and Nutrient/Agrochemical Management
We assess methods for measuring and improving soil health - using biological, physical, and chemical indicators - and soil carbon cycling. We also develop and optimize management recommendations (best management practices, BMPs) that balance crop production and environmental quality needs. This work includes emergent technologies that will help address food safety and security concerns in a changing world while protecting environmental quality.
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Ecosystem and Community Ecology
Working across a range of managed and unmanaged ecosystems - including agricultural, urban, forests, wetlands, estuaries, and coasts - we study ecological processes at multiple temporal, spatial, and biological scales. Our work emphasizes the understanding of valuable ecosystem services and functions, including how they are affected by management practices. This work couples ecological principles with field observations, experimental manipulations, and modeling approaches to develop innovative solutions that inform effective ecosystem conservation, management, and restoration.
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Hydrology and Critical Zone Science
By using a variety of experimental, observational, and computational methods at multiple scales, we seek to better understand and predict the transport of mass (water, nutrient, solutes, gases) and energy within the soil-plant-atmosphere continuum across natural and managed ecosystems. These efforts also emphasize hydrological processes, such as infiltration, runoff, and groundwater recharge, and they predict how variability in land use, climate, vegetation, and management strategies impact ecosystems in addition to soil and water resources.
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Biogeochemistry
We study coupled biogeochemical cycling of macro-elements (carbon, nitrogen, phosphorus, and sulfur), metals, and other elements in the context of ecosystem productivity, energy flow and balance, and earth-system feedbacks. Our efforts support ecosystem services and functions, water and soil quality, and inform management strategies for sea level rise and ecosystem restoration and conservation. We aim to use fundamental knowledge of biogeochemistry to mitigate ecological and environmental risks.
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Integrative Transdisciplinary Modeling Across Ecosystems
Using novel technologies, like environmental informatics, and earth system science, our efforts combine proximal soil sensing, drone, and satellite remote sensing data to facilitate GeoAI modeling (machine learning, deep learning), pedometrics, mechanistic and process-based modeling, and hybrid modeling. These approaches are used to study soil health, water quality and quantity, land use change, and food security across terrestrial and aquatic ecosystems. This happens at the local-, regional-, and global-scale as well as the responses of the land-surface to global-scale changes in thermal and hydrological conditions.