Location: Agroclimate and Hydraulics Research Unit
2022 Annual Report
Objectives
1. Develop new and/or improve cloud-based technologies and engineering tools for data acquisition for dam, hydraulic structure, and channel monitoring and reservoir management that allow data to be used in off-site decision support systems and integrated into watershed modeling tools.
2. Develop new and/or enhance design guidance, engineering tools, software, and best management practice standards to monitor and assess the performance of dams and hydraulic structures as erosion control measures.
3. Enhance dam and/or spillway erosion prediction models through real-time monitoring and/or physical modeling of embankment dam and/or spillway erosion processes and breach.
4. Engage Missouri River Basin stakeholders through our University of Missouri Research and Extension partners to characterize water resource managers’ and producers’ behavior, attitudes, and economic considerations with respect to irrigation water use, conservation, and flood mitigation; and to introduce them to analytical based decision aides for evaluating new technologies, best management practices, and cost-benefit assessment.
5. Develop holistic stochastic optimization models, risk assessment, and decision support tools to improve sustainable agriculture production water management practices, while enhancing long-term landscape health in temperate environments. These models will focus on water availability, water storage, and flood mitigation with dynamic economic assessments. This objective will be met through a collaborative effort between HERU and University of Missouri partners.
Approach
Global environmental change and human activities are threatening water and land resources and economic growth. Further, water resources infrastructure is experiencing structural deterioration due to anthropogenic changes, which subsequently affect the water cycle and sediment and pollutant delivery to downstream waterbodies. Increasing occurrences of extreme weather exacerbate vulnerability of water infrastructure and threaten public health and safety. These challenges are acknowledged by bipartisan declarations to modernize water resources management and water infrastructure for a sustainable economy, advancements in agriculture and conservation, protection of public health, and support of healthy ecosystems. USDA plays an integral role in water resources management and availability across America and is equipped to meet the challenges through scientific discovery and engineering know-how. This project will focus a holistic approach to 1) develop new and/or improved cloud-based technologies and engineering tools for data acquisition for water resources infrastructure monitoring and reservoir management, which will allow data to be used in off-site decision support systems and integrated into watershed modeling tools, 2) expand hydrologic and hydraulic prediction models through real-time monitoring and/or physical modeling of water resources infrastructure through the implementation of a dam monitoring and inspection network, 3) develop new and/or enhanced designs, engineering tools, models, and best management practice standards to monitor and assess the performance of water resources infrastructure, 4) engage stakeholders through extension and outreach activities to assess the economic benefits of new and/or rehabilitated water resources infrastructure and conservation practices, and 5) develop stochastic optimization models and risk assessments to improve sustainable agriculture production and water management practices, while enhancing long-term landscape health in temperate environments. Federal and state agencies, agricultural producers and farmers, tribal organizations, emergency and floodplain managers, lending institutions, insurance agents, policy makers, and the international scientific community will reap the benefits of these advancements.
Progress Report
For Objective 1, ARS scientists at Stillwater, Oklahoma developed a proof of concept with scientists at Virginia Tech University in the development of low-cost sensor technology for a meteorological station that measures precipitation, air temperature, relative humidity, barometric pressure, wind speed, wind direction, solar radiation, soil moisture and more. In addition, a low-cost sensor for measuring reservoir water level monitoring was also developed. The components of these sensors are manufactured in the U.S. Data from the sensors have been successfully transmitted by radio or cellular modem to the ARS Geoevents server for scientists to store and analyze data. A team of collaborators including ARS scientists from multiple locations including Stillwater, Oklahoma, ARS Partnerships for Data Innovations, university, and private partners successfully integrated Survey123 with radio frequency identification (RFID) tags that streamline tasks and improve information and data flow. This technology will have a much broader application than originally developed. Specifically, this technology will be used for monitoring meteorological sensor network. Additionally, ARS scientists hosted a training opportunity for ARS scientists across the agency and stakeholders (e.g., USDA Forest Service). Private partners conducted the training that included hands-on learning of a new U.S. manufacturer unmanned arial vehicle (UAV) and image processing software. Five dam sites within the Stillwater Creek Watershed were identified as locations to conduct a pilot program for monitoring dams and reservoirs. These sites provide a broad range of dam and reservoir sizes and varying land uses (e.g., agricultural, and urban) in the vicinity of the sites selected. In addition, for Objectives 1 and 3, significant progress was made in collaboration with ARS Partnerships for Data Innovations, the USDA Office of Chief Information Officer, North Carolina State University, and private partners in developing the USDA level geospatial enterprise architecture that is building out the data pipelines from publicly available sources (e.g., U.S. Geological Survey, National Weather Service, National Oceanic and Atmospheric Administration, U.S. Army Corps of Engineers). In addition, through this partnership, a survey was completed at a USDA level seeking information about the technological resources and data that is used and created in USDA research and administrative efforts.
For Objective 1, scientists at the University of Missouri (MU) in collaboration with ARS scientists at Stillwater, Oklahoma, set-up field instrumentation and UAV monitoring experiments to track near real-time soil moisture, vegetation health and local weather at the University of Missouri Experimental Stations. Three sites, (i) soybean precision agriculture and cover crop testbed, (ii) micro-irrigation and (iii) no-till corn-soybean conservation agriculture testbed, are being monitored. The sites are instrumented with soil moisture sensors and weather stations. Site (iii) has three H-flumes installed at the drainage outlets and has been in operational for nearly 20 years. Each of these outlets drain agricultural land implemented with agroforestry, contour grass-legume strip and no-practice fields. Runoff, sediment and nutrient fluxes are measured at each location. In addition, weekly UAV flights are conducted to collect multispectral (five spectral bands) and thermal (two spectral bands) imagery at all the sites. The research team is developing image processing protocols that will streamline weekly tasks with a goal to deliver processed images within 24 hours after acquisition. In addition, instrumentation is being tested to transmit the ground-based sensor data to a computer server on MU campus for processing and visualization. These outputs, data processing protocols and visualization tools will be transferred to USDA data portals. In addition, a collaborator provided training opportunities for two electrical engineering undergraduates to assemble a sensor to measure water depth, turbidity and water temperature in small reservoirs and streams. The students presented their work at the university research forum. The sensor development will be continued in year 2.
Real-time soil moisture monitoring sensors are set up at multiple Agriculture Experimental Stations at collaborators institutions. These sensors are being set up to transmit data to a server at the MU Data Center which can be used to develop water management decisions including drought prediction in crop production systems. Weekly multispectral and thermal imageries are being acquired using UAVs and processed with a 24-hour turnaround time. Procedures are being developed to process the sensor data that can be used for real-time predictions and shot-term forecasts of water demand by agricultural crops. Graduate students at the collaborator’s institution are being trained on image acquisition and processing.
For Objective 2, an ARS scientist completed the construction of a physical model of a stepped spillway with a broad-crested weir entrance with eleven bridge wall piers across the weir. Design of model was based on USDA-Natural Resources Conservation Service (NRCS) constructed Boil Springs Watershed Dam Site #1 in Oklahoma. Testing was completed, and data analysis is underway. The ARS scientist along with cooperators at Oklahoma State University completed a draft of the Stepped Spillway Design Chapter for the USDA-NRCS National Engineering Handbook. Field site selection in the Oklahoma Sugar Creek Watershed has been initiated to monitor the performance of riffle-pool rock chutes grade stabilization structures, and literature on historic studies from the watershed is being gathered and reviewed.
For Objective 3, an ARS scientist conducted literature review of vegetated embankment erosion. Testing has commenced to explore new and existing technologies (e.g., 3-dimensional scanning survey equipment, sonar sensors, traditional analog sensors, and manually operated point gauges) for gathering water and bed surfaces and erosion by measuring resonant vibrations of the bed surface using accelerometers and capturing significant erosion events with time-lapse cameras. These tests were conducted as a proof of concept before large-scale testing is performed. Using new technologies like scanning survey equipment and sensors will provide for a safer manner for which data can be collected.
For Objective 4, scientists at MU in collaboration with ARS scientists at Stillwater, Oklahoma have completed surveys related to adoption and use of extreme weather adaptation and mitigation strategies among producers and consumers in Missouri. These datasets have the potential to help develop decision support tools for complex water management and climate change adaptation.
For Objective 5, ARS scientists at Stillwater, Oklahoma in collaboration of the ARS Office of National Programs, the National Agricultural Library, ARS Partnerships for Data Innovations, and a private partner have released a pilot platform, protocols.io. Membership for this platform is made available to all ARS personnel and includes flexible workspace options, access to a private ARS workspace, unlimited private protocols, free protocol import service, plus dedicated onboarding support. This platform has the potential to fundamentally change how ARS creates, develops, and shares agency methodologies and standard operating procedures.
Additionally for Objective 5, scientists with MU in collaboration with ARS scientists at Stillwater, Oklahoma compiled necessary geospatial and weather data to develop hydrologic and hydrodynamic models for the Missouri and Mississippi River catchment areas. Hydrologic models have been developed for the Missouri River Basin, and for the catchment areas covering the States of Missouri and Oklahoma. The current versions of the models are set up to run at daily time steps using the North American Land Data Assimilation System weather dataset for the period 1979 to present. Currently, the models are being updated to incorporate land management decisions and reservoirs and reservoir operations. Documentation and metadata for the datasets and associated computer programs to process for model development are being prepared by the MU scientists.
Accomplishments
1. Real-time weather and reservoir monitoring sensors developed at a cost-savings of 99%. ARS scientists at Stillwater, Oklahoma, along with scientists from Virginia Tech University developed weather and reservoir monitoring low-cost sensor stations. These stations have a cost of $250, a 99% cost-savings as compared to the commercially available scientific grade weather stations at a cost of approximately $30,000 per station. Scientists will be able to deploy sensor networks more densely should they need to do so for developing new and improved decision support tools, models, and applications for forecasting flooding and drought. These sensors provide data and information to a vast array of end-users like farmers, producers, emergency managers, dam owners, investors, and policy makers. This data and information can be accessible for irrigation scheduling, rural and municipal water supply allocations, emergency preparedness, dam operation and maintenance, and development of zoning regulations.
2. Real-time data transmitted in a seamless integrated system. ARS scientists at Stillwater, Oklahoma, in collaboration with scientists at Virginia Tech University developed a seamless integrated system that transmits real-time data from the field through radio or cellular connection to the ARS Geoevents server without the use of a data logger as a go-between. Transmission of the data in this manner allows for real-time delivery of data to a vast array of end-users through data dashboards. This integrated system for transmitting data will provide the scientific community millions of dollars in cost-savings, given that data loggers for data collection can be costly (e.g., $2000 per data logger). In addition, the transmission of data through this seamless system will reduce the number of data handlers, improve efficiency of data collection, and reduce data errors.
3. Integration of engineering tools and technologies to streamline data collection. Research field data collection can be labor intensive, especially when data is collected through hand-written field notes, and manually entered into a computer. Data handling can go through multiple touches before being electronically entered, and thus, data errors can propagate. ARS scientists at Stillwater, Oklahoma, Kerrville, Texas, and Puerto Rico, in collaboration with ARS Partnerships for Data Innovations, USDA-Animal and Plant Health Inspection Service (APHIS), and the Texas Animal Health Commission, teamed with a private partner to customize Survey123 tools that integrate with TruTest Radio-Frequency Identification (RFID) readers to record multiple data fields that transmit to a cloud-based database. The data was used to personalized Environmental Systems Research Institute (ESRI) FieldMaps mobile application for monitoring trends and GPS coordinates relative to the data. These integrated tools streamline data collection that will have broader application including use in monitoring dam and reservoir sensor networks.
4. U.S. manufactured UAV and image processing software developed for scientific use. Unmanned aerial vehicles (UAV) and image processing software for scientific use are vital for monitoring dam performance, crop yields, harmful algal blooms in reservoirs, and reservoir water levels for residential and commercial water use. ARS scientists at Stillwater, Oklahoma, in collaboration with ARS Partnerships for Data Innovations, and private partners, developed and trained Federal partners (e.g., ARS scientists from across the agency and USDA Forest Service engineers) on the operation of a new U.S. manufactured unmanned aerial vehicle and image processing software specifically developed for scientific research.
Review Publications
Hunt, S.L., Kadavy, K.C., Wahl, T.L., Moses, D.W. 2022. Physical modeling of beveled-face stepped chute. Water. 14(3). Article 365. https://doi.org/10.3390/w14030365.
Wise, J., Karami, H., Corina, A.N., Opedal, N., Vrålstad, T., Sangelsland, S., Nygaard, R. 2022. Prediction of methane leakage through primary cement barrier in the High Island, OPD, Gulf of Mexico. Journal of Natural Gas Science and Engineering. 101. Article 104511. https://doi.org/10.1016/j.jngse.2022.104511.
Hunt, S.L., Temple, D.M., Neilsen, M.L., Abdelfatah, A., Tejral, R.D. 2021. WinDAM C: Analysis tool for predicting breach erosion processes of embankment dams due to overtopping or internal erosion. Applied Engineering in Agriculture. 37(3):523-534. https://doi.org/10.13031/aea.14334.
Hunt, S.L., Kadavy, K.C. 2021. Lessons learned in stepped chute research instrumentation. Applied Engineering in Agriculture. 37(3):513-521. https://doi.org/10.13031/aea.14333.
Fox, G.A., Guertault, L., Bolinaga-Castro, C., Allen, P., Bigham, K.A., Bonelli, S., Hunt, S.L., Kassa, K., Langendoen, E.J., Porter, E., Shafii, I., Wahl, T., Thompson, T.W. 2022. Perspectives: Lessons learned, challenges and opportunities in quantifying cohesive soil erodibility with the jet erosion test (JET). Journal of the ASABE. 65(2):197-207. https://doi.org/10.13031/ja.14714.
