Current Water Quality Research
Harnessing machine learning and satellite remote sensing to assess the impacts of best management practices in water quality
PI: Greg Silsbe, Research Assistant Professor, Horn Point Laboratory, University of Maryland Center for Environmental Science (UMCES)
Co-PIs:
- Lorena Silva, Research Assistant Scientist, Horn Point Laboratory, UMCES
- Matthew Houser, Senior Social Scientist, The Nature Conservancy Chesapeake Bay Agriculture Program and Research Assistant Professor, Horn Point Laboratory, UMCES
- Xiaoxu Guo, Associate Research Scientist, Horn Point Laboratory, UMCES
Duration: Two years. Funded in 2025.
Grant: $106,882
Description: In 2025, nitrogen and phosphorus pollution in the Chesapeake Bay is expected to exceed EPA reduction targets, with agricultural runoff being a major contributor. Recently convened scientific and technical advisory committees have emphasized that future efforts to reduce nutrient pollution should focus heavily on non-point sources, with a strong emphasis on agricultural lands where current levels of best management practice (BMP) adoption are insufficient to meet nutrient reduction targets. However, tracking BMP effectiveness is challenging due to gaps in traditional water quality monitoring.
A new research project aims to use machine learning (ML) and satellite imagery to provide more accurate and detailed assessments of nutrient pollution in the Choptank River, a key tributary of the Chesapeake Bay heavily impacted by agricultural runoff. By improving water quality predictions, the project will help refine strategies for pollution reduction.
The study also collaborates with The Nature Conservancy’s farmer incentive program, testing whether real-time pollution data, such as spikes in nutrient runoff after storms, encourages farmers to adopt BMPs. Partnering with Choptank Riverkeeper for ShoreRivers Matt Pluta on programs for water sampling, the research will enhance understanding of human impacts on water quality and support more effective restoration efforts. “Research projects like these are critical for keeping up with the necessary understanding of how human activities on land are impacting water quality conditions and how we can better approach restoration efforts to achieve maximum results and co-benefits,” Pluta said.
Riparian Buffers, Water Quality and Carbon Sequestration
PI: David Newburn (University of Maryland Department of Agricultural and Resource Economics)
Co-PIs: Erik Lichtenberg (UMD AGNR)
Duration: May 2023 to July 2024
Grant: $65,514
Description: The study, written by Dr. David Newburn, an environmental and resource economist at the University of Maryland (UMD) College of Agriculture and Natural Resources, takes an experimental approach to evaluating methods and potential incentives to increase buffer adoption and enhance their environmental effectiveness.
Maryland has existing state and federal programs, such as the Conservation Buffer Initiative and Conservation Reserve Enhancement Program, that incentivize landowners who sign up to install buffers. Newburn’s study uses a survey of the owners of farmland throughout Maryland and embedded an experiment to elicit landowners willing to enroll in alternative buffer incentive programs varying in payment amounts and contract length. The study then combines this survey data with modeling to determine the likelihood that farmers will participate and what environmental benefits for water quality and carbon sequestration are achieved under each scenario.
(Photo Courtesy of the Chesapeake Bay Program)
Quantifying actual nutrient load reduction in drainage structures
PI: Dr. Hemendra Kumar (UMD AGNR)
Co-PIs: Dr. Ritesh Karki (UMD AGNR), Timothy Rosen (ShoreRivers), Ariana Muñoz (ShoreRivers), and Dr. Steve Lyon (The Ohio State University)
Duration: April 2024 to March 2026
Grant: $95,560
Description: Research in the Delmarva region focuses on implementing advanced technologies to address the critical issue of balancing food production and water quality in agriculture. The Chesapeake Bay clean-up effort underscores the urgency to restore the Bay and its contributing waters, highlighting the region's need for sustainable agricultural practices. Nutrient transport from agricultural fields poses a challenge to water quality. This research centers on implementing drainage water management (DWM), specifically exploring its potential to treat nutrient loss hot spots and hot moments (a particular moment in time when nutrient loss occurs) while empowering farmers as environmental stewards.
DWM involves installing water control structures at edge-of-field outlets to manage the water and nutrient flow, and reduce subsurface drainage flow and retain nutrient-rich water within the landscape, preventing its harmful effects on water bodies. These structures act as a tool to improve crops’ nutrient use efficiency, use of fertilizers, and manage in-field water levels sustainably. This research aims to quantify the impact of automated and manual DWM systems on water and nutrient flux from agricultural fields. Automated DWM utilizes sensor technologies to dynamically control water levels in the field’s drainage system based on real-time data. Manual DWM structures allow farmers to manually adjust the water levels in drainage pipes and control the flow of water and nutrients from the field. The researchers aim to assess the effectiveness of automated and manual DWM structures in reducing nutrient loads from fields in the Delmarva region.
“Our research on drainage water management not only benefits States’ agricultural community but also plays a crucial role in the larger effort to restore and preserve the health of Chesapeake Bay. By empowering farmers with smart agricultural technologies, we aim to reduce nutrient loading, safeguard water quality and contribute to a healthier environment for everyone in the Delmarva region.”