Location: Agroclimate and Hydraulics Research Unit
2025 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 in Stillwater, Oklahoma, in collaboration with scientists from University of Missouri (MU), in Columbia, Missouri, Oklahoma State University (OSU), in Stillwater, Oklahoma, and other ARS locations, and engineers with the USDA-Natural Resources Conservation Service (NRCS) have continued research on monitoring dams and reservoirs with unmanned aerial systems (UAS), sensors, satellites, and use of artificial intelligence (AI) and machine learning (ML). Research this year focused on advancing mitigating impacts of earthen dam overtopping and erosion and forecasting reservoir water levels for small watershed dams typical of those impounded by USDA constructed dams. Research results are being integrated into national level working group analyses in collaboration with NRCS partners. For dam overtopping, artificial intelligence algorithms were utilized to forecast reservoir levels from meteorological observation networks (e.g., from National Oceanic and Atmospheric Administration (NOAA) and Mesoscale network (Mesonet) and low-cost water levels sensors. Real-time, twenty-four-hour ahead forecasts were developed for lakes with forecast accuracy of +/- 3 inches. Results from this work will inform ongoing efforts to install low-cost water level sensors and provide real-time dam overtopping forecasts. In addition, reservoir water level data collected and streaming from deployed instrumentation from the Lake Carl Blackwell site to the ARS cloud will be utilized by OSU scientists in collaboration with the National Aeronautic Space Administration (NASA) to evaluate in comparison to similar data collected from the Surface Water Ocean Topography (SWOT) satellite. This data will also be integrated with other reservoir water levels, groundwater/aquifer, and soil moisture data through the OSU Hydronet project, aimed to create a world-class water monitoring network for Oklahoma to complement the State’s world-class meteorological network, the Oklahoma Mesonet. In addition, for Objective 1, ARS scientists in Stillwater, Oklahoma, have continued to work on the development of a cost-effective metrological sensor network, so more spatial dense data may be captured to better understand the impacts of varying weather conditions on dam performance. ARS scientists have teamed up with scientists with Texas A&M University in College Station, Texas, to further the advancements of these technologies, so improvements can be made in the development of flood and drought forecast models.
For Objective 2, ARS scientists in Stillwater, Oklahoma, initiated testing of a labyrinth weir in combination of a stepped chute physical model. Data was collected to evaluate the discharge rating curve of this large-scale model in comparison to readily available data from smaller scale models to determine any evidence of scale effects created at smaller scales. ARS scientists in Stillwater, Oklahoma, completed a Sugar Creek Watershed site visit with USDA-NRCS Oklahoma engineers and technicians to observe the performance of riffle-pool rock chutes and other grade stabilization structures (e.g., drop inlets). ARS scientists in Stillwater, Oklahoma, in collaboration with OSU scientists in Stillwater, Oklahoma, completed additional edits of USDA-NRCS National Engineering Handbook (NEH) chapters on stepped spillways and plunge basins and resubmitted the chapters for NRCS to finalize. ARS scientists resubmitted the two-way covered risers at USDA-NRCS requests, so they could determine what additional information they wish to be included in the chapter. ARS scientists in Stillwater, Oklahoma, in collaboration with OSU scientists and ARS Partnerships for Data Innovations updated the dam inspection tool and provided USDA-NRCS engineers with a demonstration. In addition, the rock chute web-based design application programming language was transformed from C# to Python, so that it could be hosted on the web. Steps are being completed to finalize the program, so it will be available to practicing engineers.
For Objective 3, ARS scientists in Stillwater, Oklahoma, developed a novel physical model for soil erosion and tested from readily obtained field measurements. Erodibility of soils tested varied by four-orders of magnitude with changes induced by variability in soil moisture alone. Results of this work are under peer review and highlight the importance of changes in soil moisture when determining probability of failure of earthen dams. Prior to this research, earthen embankment erodibility was considered a static quantity post dam construction; when in fact, soil erodibility varies by orders of magnitude with variability induced by changes in soil moisture alone. Ignoring seasonal variability in soil moisture and its impact on soil erodibility may result in underestimations in probability of dam failure. In addition, ARS scientists in Stillwater, Oklahoma, constructed a moderate-scale earthen embankment to evaluate erosion processes. An UAV, topographic survey equipment, and water level monitoring sensors were used to gather data. Additionally, soil data (e.g., erodibility, soil moisture, particle size distribution, plasticity, etc.) were collected during the construction phase. These data will be used to compare to future data collected from a lime-treated embankment to determine if lime is a suitable treatment for stabilizing earthen embankment slopes. ARS scientists in Stillwater, Oklahoma, also conducted an embankment overtopping test to evaluate the performance of vegetation when a stability berm is constructed as part of the dam. UAV, topographic survey, and water level monitoring data were collected throughout the test, and data is under analysis.
For Objective 4, scientists from MU, in Columbia, Missouri, provided oversight of two students completing their Biological Engineering capstone project, three students completed research and training opportunities to design a micro irrigation system to be implemented at the University Experimental Station, and one student developed a prototype for secure and wireless transmission of sensor data from the field instrumentation to the cloud server. Three postdoctoral researchers, eight graduate students, three undergraduate students, and three postgraduate researchers are being trained in hydroclimatic data analytics including applications of machine learning, geospatial data visualization, hydrological model development, testing and validation, water quality assessment and policy aspects related to water and natural resources management. In addition, MU scientists in Columbia, Missouri, organized a week-long Geospatial Science camp for secondary middle and high school students. Thirty-four students participated in the summer camp, and three graduate students and two postdoctoral researchers were provided with outreach training opportunities. In addition, scientists completed a manuscript in collaboration with ARS scientists in Stillwater, Oklahoma, that is currently under review. For Objective 5, scientists with MU, in Columbia, Missouri, in collaboration with ARS scientists in Stillwater, Oklahoma, completed the development of hydrologic and biogeochemical transport models for the State of Missouri and the Mississippi River Basin with seven regional catchments covering an area of 3.2 M km2. The scientists also incorporated the operational details of reservoirs, and agricultural operations including row crops, which are significant in rainfall-runoff processes, were added. In addition, scientists compiled and incorporated details related to adoption and use of extreme weather adaptation and mitigation strategies among producers and consumers in Missouri. These datasets have the potential to develop decision support tools for complex water management and adaptation to severe weather. Scientists have also compiled a climate database for future precipitation and air temperature projections by 10 global climate models that provided data to the Sixth Phase of the Intergovernmental Panel on Climate Change – Coupled Model Intercomparison Project (CMIP6). This database was used to 1) develop multiple climate change indices that capture the extreme events in the Missouri River Basin, and 2) prepare weather input database for the hydrological models to evaluate changes in runoff patterns through 2050. Three postdoctoral researchers, eight graduate students, three undergraduate students, and three postgraduate researchers are being trained in hydroclimatic data analytics including applications of machine learning, geospatial data visualization, hydrological model development, testing and validation, water quality assessment and policy aspects related to water and natural resources management. Three manuscripts are currently being developed for peer review.
Accomplishments
1. Highlighting the impacts of extreme precipitation on agricultural landscapes and infrastructure. According to the National Oceanic and Atmospheric Administration, flooding in the United States resulted in nearly $183 billion in damages with more than $20 billion associated with crop and rangeland damages. To improve flood forecasting to mitigate agricultural losses, scientists with the University of Missouri, in Columbia, Missouri, in collaboration with ARS scientists in Stillwater, Oklahoma, investigated extreme precipitation variability and trends in the Mississippi River Basin daily using data from approximately 800 locations. Data demonstrated an average increasing trend and statistically significant increases in the Northern basin. Several indices that highlight the impacts of extreme precipitation on agriculture and water infrastructure were developed. This improved knowledge on the impacts of extreme precipitation on the stability of infrastructures such as levees and dams is being used to develop detailed hydrological models and analytical decision support tools.
2. Improved drought predictions in the Midwestern United States. The occurrence of drought is mainly due to abnormal and prolonged dry weather conditions that cause severe vegetation water stress, with it becoming more of an issue during summer months. According to the USDA-National Agricultural Statistics Service (NASS), the drought significantly affected corn production, with corn grain production down approximately 10 percent. The area harvested for corn grain was down an estimated seven percent in 2022 over the previous year. Additionally, the average United States corn yield decreased approximately 3.5 bushels between 2022 and 2021. Drivers of drought like regional atmospheric conditions and local landscape-level factors that affect crops like corn are poorly understood. Scientists with the University of Missouri, in Columbia, Missouri, in collaboration with ARS scientists in Stillwater, Oklahoma, examined the summer 2022 drought in the Midwestern United States using data available at the National Centers for Atmospheric Research and data collected by the scientists. Findings revealed that several atmospheric conditions including atmospheric pressure and wind patterns in the east Pacific Region may have triggered the onset of the drought. Stronger potential evaporative demand on the land surface and concurrent lack in rainfall extended the duration and spatial extent. Findings on the remote teleconnections and regional land surface conditions that led to the summer 2022 drought provide additional new knowledge to develop and improve drought predictions and subsequent early drought warnings in the Midwestern United States.
3. Refinement of drought monitoring informs the public of severe weather adaptation strategies. Drivers of drought like regional atmospheric conditions and local landscape-level factors that affect crop health and productivity are poorly understood; thus, affecting the profitability of today’s farms. Scientists with the University of Missouri, in Columbia, Missouri, in collaboration with ARS scientists in Stillwater, Oklahoma, evaluated an enhanced methodology for drought assessment in Missouri by integrating the Condition Monitoring Observer Reports, a drought impact classification framework, and the new drought index (NDI). The framework utilizes text processing techniques and analyzes atmospheric blocking patterns to provide a holistic understanding of drought severity and dynamics, ultimately refining drought monitoring and informing severe weather adaptation strategies.
Review Publications
Weaver, S.M., Lupo, A.R., Hunt, S., Aloysius, N. 2025. Refining drought assessment: A multi-dimensional analysis of condition monitoring observer reports in Missouri (2018–2024). Atmosphere. 16(4). Article 389. https://doi.org/10.3390/atmos16040389.
Dommo, A., Aloysius, N., Lupo, A., Hunt, S. 2024. Spatial and temporal analysis and trends of extreme precipitation over the Mississippi River Basin, USA during 1988-2017. Journal of Hydrology: Regional Studies. 56. Article 101954. https://doi.org/10.1016/j.ejrh.2024.101954.
Qiao, L., Livsey, D., Wise, J.L., Kadavy, K.C., Hunt, S., Wagner, K. 2024. Predicting flood stages in watersheds with different scales using hourly rainfall dataset: A high-volume rainfall features empowered machine learning approach. Science of the Total Environment. 950. Article 175231. https://doi.org/10.1016/j.scitotenv.2024.175231.