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2024 South à£à£Ö±²¥Ðã Student Water Conference

2024 Eastern South à£à£Ö±²¥Ðã Water Conference

Eastern South à£à£Ö±²¥Ðã Water Conferences

South à£à£Ö±²¥Ðã Student Water Conferences
 

The mission of the South à£à£Ö±²¥Ðã Water Resources Institute is to coordinate research and training at South à£à£Ö±²¥Ðã State University and other affiliated educational institutions and agencies across the state in the broad area of water resources and reach out to stakeholders and citizens of South à£à£Ö±²¥Ðã to address water-related topics and problems. It administers the funds received from the U.S. Department of Interior, as made available through the Water Resources Research Act of 1984 as amended and from the state of South à£à£Ö±²¥Ðã. Funds received through these sources targeted for research are directed toward solving state, regional and national water problems. Graduate research training, technology transfer and information transfer are services provided through the Institute.

The South à£à£Ö±²¥Ðã Water Resources Institute is a part of the State Water Resources Research Institute Program (SWRRIP) as authorized by section 104 of the Water Resources Research Act of 1984 as amended. The state Water Resources Research Institutes authorized by the Act are organized as the National Institutes for Water Resources (NIWR). The are presently under the jurisdiction of the Department of Interior, USGS and are the research arm of the USGS and the link to the nation's land–grant universities.

The South à£à£Ö±²¥Ðã Water Resources Institute is accountable to the US Congress via its annual appropriation, a required annual report and a thorough Congressionally mandated peer review conducted every five years under the auspices of the U.S. Geological Survey.

Integrated Remote Sensing and Water Quality Analysis for Spatiotemporal Assessment of Surface Water Quality in Eastern South à£à£Ö±²¥Ðã
Principal Investigator: Sushant Mehan, South à£à£Ö±²¥Ðã State University

Current discrete noncontinuous time series data on water quality, sourced from monitoring agencies, are ineffective for in-depth, long-term trend analysis. This proposal outlines an innovative method to enhance water quality datasets using remote sensing paired with machine learning techniques, focusing on the water resources of South à£à£Ö±²¥Ðã. The goal is to achieve a comprehensive understanding of both surface and subsurface water trends by reconstructing historical water quality data for a water body (rivers, lakes, and streams). Additionally, the proposal introduces a hybrid modeling approach that integrates physical and statistical methodologies to assess water quality across different depths in the water profile. This novel approach has the potential to revolutionize water quality assessment and management, providing more accurate and timely data for decision-making. Key resources include satellite imagery, in-situ monitoring data, and databases from regional and state monitoring agencies, including the South à£à£Ö±²¥Ðã Department of Environment and Natural Resources (SD DENR). Rigorous quality checks and supplementary sampling will be implemented to ensure model accuracy and reliability. The proposed model will incorporate spectral indices from remote sensing alongside climate variables to predict continuous water quality data. We will validate our model at Lake Mitchell; however, validation efforts may be expanded to other water basins in the state, enhancing biannual water quality evaluations conducted by SD DANR and supporting EPA initiatives under 319 CWA to mitigate water quality degradation. 

Risk Assessment of Groundwater Contamination in the Madison Aquifer
Principal Investigator: Liangping Li, SD School of Mines and Technology

The Madison aquifer, composed of limestone and dolomite, has a complex hydrogeologic system characterized by preferred flow paths and conduits. Understanding the spatial distribution of these conduits is crucial for predicting flow and transport responses. The goal of this research is to develop a model-data integration methodology that optimally utilizes observations to estimate hydraulic conductivities (i.e., conduits), and therefore to provide uncertainty assessment of contaminant migration. This research aims to provide a fundamental understanding of groundwater flow and contaminant transport in complex geological formations, particularly in karst aquifers. The results will inform sustainable groundwater resource management and ensure the long-term availability of this vital resource.

Development of a non-contact, AI-driven method for rapid assessment of surface water quality based on imagery and smells
Principal Investigator: Xufei Yang, South à£à£Ö±²¥Ðã State University

Surface water quality management is becoming more data-driven, calling for cost-effective and innovative methods for rapid assessment of water samples. UAV-based hyperspectral imagery, an optical remote sensing method, has emerged as a promising tool for water quality monitoring. This technology leverages deep learning and other AI algorithms for advanced image analysis [1,2]. However, this method can be compromised by sediments, aquatic plants, and other factors causing turbidity or color in water. We hypothesize that the limitation can be addressed by complementing hyperspectral imagery (i.e., vision) with scent data derived from gas sensor arrays. This project aims to test this hypothesis by scanning grab samples taken from various surface water bodies in the lab using an integrated system combining hyperspectral imaging and olfactory measurement. A multimodal deep convolutional neural network (DCNN) algorithm will be utilized to establish and train the prediction model capable of retrieving key water quality parameters from both imagery and scent data. The specific objectives are to (1) integrate a hyperspectral camera and an E-nose into a usable measurement system; (2) analyze field-collected surface water samples using the developed system and conventional analytical methods; (3) develop an AI deep learning model to establish correlations between the imagery and scent data obtained with water quality parameters; and (4) validate the developed AI model using an independent sample set. If proven effective, this novel approach will supplement existing surface water quality assessment methods, including both onsite surveys and lab analyses, potentially reducing labor and costs associated with water quality monitoring.

Pilot scale (Year 2) study for utilizing nanobubble technology for dairy processing effluent management

Maneesha S Mohan, Associate Professor and Alfred Chair in Dairy Manufacturing, South à£à£Ö±²¥Ðã State University

The use of nanobubble technology is a novel process for improving waste treatment efficiency. The discharge of high organic load effluent into waterways has shown to affect water quality, induce algal blooms and affect aquatic life. However, limited research has been done on utilizing nanobubble technology in high load wastewater treatment from dairy processing industries. South à£à£Ö±²¥Ðã is a large dairy producer and processor, increasing the relevance of utilizing novel technologies and research for reducing dairy processing effluent load on our waterways. The proposed research will use nanobubble technology for reducing the load of the effluent produced by inducing rapid and effective interactions, oxidation, and degradation of different components in the effluent. We will scope the stages of the dairy processing effluent system where the utilization of nanobubbles will be most effective.

 Improving Crop Resilience to Climate Variability through Refinement of Crop Water Use in the Skunk Creek Watershed.
Lead PI: Dr. R. Behnke, South à£à£Ö±²¥Ðã State University.

Large variations in year-to-year precipitation occur frequently in South à£à£Ö±²¥Ðã, so that producers often deal with dry or drought conditions one year, and wet or flooding conditions the next.  The amount of water available for crop, livestock, and land management practices changes drastically as these conditions change, with a critical component of this value being a function of evapotranspiration, which is how soil or surface water is lost through evaporation and plant transpiration.  To properly determine how much water is available, actual evapotranspiration (ETa) needs to be calculated, as compared to reference evapotranspiration (ETr).  This is normally accomplished by multiplying widely used, generalized crop coefficients by ETr.  However, more accurate measurements of ETa can be determined using eddy covariance observations.  This project will measure actual evapotranspiration rates from a site in the Skunk Creek Watershed and compare them against 1) ETa estimated from crop coefficients and growth stage, and 2) spatially modeled estimates of ETa (Abatzoglou, J.T., 2013; Senay, G.B. and Kagone, S., 2019).  Biases in water availability from these different sources will be analyzed, and regional scaling of ETa (and water availability) from these data sources will be validated against measured soil moisture from the South à£à£Ö±²¥Ðã Mesonet.  Our objectives are to use these results to look at potential impacts on yield estimation, drainage tile management, and land and water use management practices, as well as apply for further funding for additional eddy covariance towers across the state.  

Nanobubble technology for dairy processing effluent management
Lead PI: Maneesha S Mohan, South à£à£Ö±²¥Ðã State University

The use of nanobubble technology is a novel process for improving waste treatment efficiency. The discharge of high organic load effluent into waterways has shown to affect water quality, induce algal blooms and affect aquatic life.  However, limited research has been done on utilizing nanobubble technology in high load wastewater treatment from dairy processing industries. South à£à£Ö±²¥Ðã is a large dairy producer and processor, increasing the relevance of utilizing novel technologies and research for reducing dairy processing effluent load on our waterways. The proposed research will use nanobubble technology for reducing the load of the effluent produced by inducing rapid and effective interactions, oxidation, and degradation of different components in the effluent. We will scope the stages of the dairy processing effluent system where the utilization of nanobubbles will be most effective.

Transformation of Nitrate to Ammonia by Biochar Supported Ru Catalysts and Recovery of Ammonia as N-Fertilizer on Biochar
Lead PI: Dr. Tao Ye, South à£à£Ö±²¥Ðã School of Mines and Technology

South à£à£Ö±²¥Ðã is dependent on groundwater as its primary source of drinking water. The increasing use of synthetic N fertilizers to maximize crop yields and the rising production of manure N by livestock farms have resulted in growing nitrate (NO₃^−) contamination of groundwater in areas of intensive agriculture. Consumption of drinking water with high concentrations of NO₃^− can cause methemoglobinemia in infants (blue baby syndrome) and increase the risk of adverse health outcomes (e.g., colorectal cancer, thyroid disease, and birth defects). The U.S. Environmental Protection Agency (EPA) set a maximum contaminant level (MCL) of 10 mg-N L^–1 (45 mg L^–1 as NO₃^−) for NO₃^− in public drinking water. It was estimated that 22 % of private wells exceed the NO₃^− MCL in agriculture regions across the U.S. and they more likely lack enough funds for NO₃^− treatment technology. Therefore, cost-effective NO₃^− treatment methods are thus urgently needed. Current technologies for NO₃^− removal such as ion exchange and reverse osmosis are expensive and energy intensive. Moreover, these methods produce waste brine that contains high concentrations of salts and NO₃^−. The waste brine with accumulated NO₃^− is often disposed as waste without further reuse. The potential value of the N resource in the NO₃^− is thus lost and has been largely overlooked. Therefore, we aim to develop Ru catalysts supported on biochar for cost-effective transformation of NO₃^− to ammonia and develop biochar as adsorbents for the recovery of the NH3 produced from NO₃^−.

Algae pull nutrients from swine facility manure, air

Swine manure is a rich source of nutrients, but its high phosphorus content in comparison to the other nutrients the crop needs means only so much can be spread on a field.

PI's:

• Gary Anderson, Department of Agricultural and Biosystems Engineering
• Xufei Yang, Department of Agricultural and Biosystems Engineering
• Kyungnan Min, Department of Civil and Environmental Engineering

Students:

• Doctoral student Augustina Osabutey

Read the Algae pull nutrients from swine facility manure, air news story.

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SDSU study examines woodchip quality in bioreactors

Draining excess water from fields is good for agricultural production, but the nutrient-laden water flowing through the drainage tile can pollute nearby water bodies. Diverting the water through an underground chamber filled with wood chips, known as a woodchip bioreactor, can help remove nitrates and thereby reduce the environmental impact on creeks, streams and lakes.

PI's:

• Guanghui Hua, Department of Civil and Environmental Engineering
• Chris Schmit, Department of Civil and Environmental Engineering
• Kyungnan Min, Department of Civil and Environmental Engineering

Students:

• Doctoral student Abdoul Aziz Kouanda

Read the SDSU study examines woodchip quality in bioreactors news story.

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Nonprofit joins battle to mitigate Lake Mitchell algal blooms

Lake Mitchell has a long history of algal blooms. The reservoir, built in 1928, was once a source of drinking water for the city of Mitchell and recreation for the community. However, by the 1990s, algal blooms increased due to nutrients accumulating in the lake. By 2003, the city stopped using the lake as its sole source for drinking water.

PI's:

• John McMaine, Department of Agricultural and Biosystems Engineering
• Bruce Bleakley, Department of Biology and Microbiology

Student:

• Master’s student Sumit Kumar Ghosh

Read the Nonprofit joins battle to mitigate Lake Mitchell algal blooms news story.