Location: Agroclimate and Hydraulics Research Unit
2023 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, University of Missouri (MU) scientists in collaboration with ARS scientists in Stillwater, Oklahoma, have set up field instrumentation and unmanned aerial systems (UAS) monitoring experiment. Data collection including near real-time soil moisture, vegetation health, and local weather for season one was completed 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, were monitored. The second season of data collection has commenced at the micro-irrigation field experiment in 2023. The sites were instrumented with soil moisture sensors and weather stations. Weekly unmanned aerial flights were conducted to collect multispectral (five spectral bands) and thermal (two spectral bands) imagery at all the sites. The research team has developed UAS image processing protocols and completed the 2022 imagery processing. The MU and ARS team has developed standard operating procedures for UAS mission planning, image acquisition, processing, analysis, and interpretation.
In addition, MU scientists in collaboration with ARS scientists in Stillwater, Oklahoma, have set up instrumentation to gather local weather, soil moisture, and soil temperature, and developed programs to wirelessly transmit sensor data to a cloud database for processing and visualization. The sensor development will continue in 2023-2024 with the focus on cybersecurity issues during remote data transmission to cloud services.
ARS scientists in Stillwater, Oklahoma, deployed a U.S. manufactured prototype UAS to gather baseline data from the ARS Field Station Dam at the Woodward, Oklahoma, ARS location and the Lake Carl Blackwell Dam in Stillwater, Oklahoma. In addition, ARS scientists in collaboration with Virginia Tech University scientists redesigned a low-cost meteorological station to meet the National Defense Authorization Act and the Build America, Buy America Act. Alpha testing was conducted on approximately twenty sensor boards. An additional 130 sensor boards have been delivered to commence beta testing.
An Oklahoma State University (OSU) scientist in collaboration with an ARS scientist in Stillwater, Oklahoma, and ARS Partnerships for Data Innovations (PDI) scientists acted as the PDI cloud administrator for both the Azure cloud environment and the ArcGIS Enterprise production and development environments. This OSU scientist manages the day-to-day operations including management and support for user accounts and logins, ArcGIS license, website certificates, Azure resources and architecture. In addition, the scientist oversees data migrations and website development and works closely with the USDA cybersecurity team to ensure steps are taken to protect resources and data.
An OSU scientist in collaboration with ARS PDI scientific team members increased the number of scientists and researchers utilizing the PDI Cloud Environment to serve their research and data needs. The Enterprise Geospatial Management Office (EGMO) project within the USDA Office of Safety, Security, and Protection under the Homeland Security Presidential Directive was added to the PDI cloud to be hosted from the ArcGIS Enterprise Development environment. New websites, including Crop Wild Relatives, Farm Management, Beetbase, and BridgeCereal, were stood up within the PDI Cloud.
ArcGIS Knowledge was made available to our PDI members. The implementation of ArcGIS Knowledge in the production environment and creation of the ArcGIS enterprise development environment provides new methods for data processing, visualization, and presentation. The development environment created a space for PDI members to build, test, and break new applications and software without negatively impacting services currently used by the public in the production environment. ARS PDI team members are working to integrate Environmental Systems Research Institute’s (ESRI) Knowledge Graph database system with the National Agricultural Library Thesaurus (NALT) concept space. This project lays the foundation for data upload, analysis, and retrieval unlike any other within ARS. ARS PDI staff received training on the Knowledge platform. Integration of domain-specific terms from ARS scientists into NALT was initiated in early 2023. This work helped NAL refine their algorithm for reconciliation of submitted terms with existing NALT concepts and resulted in the creation of an online PowerApps tool to facilitate the review of reconciliation terms by domain experts. Collaboration of project personnel with a panel of data shapes experts assembled by NAL helped identify the needs for initial data review with end users prior to system input. A survey instrument, based on these findings, will be developed to streamline this process. Once established, this infrastructure will serve as the repository for all data associated with real-time and historic dam monitoring and inspection.
For Objective 2, an ARS scientist from Stillwater, Oklahoma, is completing data analysis of the Boil Springs Watershed Site #1 project. ARS scientists met with Utah State University (USU) scientists to discuss additional data analysis of the stepped spillway with labyrinth weirs project and about USU scientists leading the development of the USDA-NRCS National Engineering Handbook (NEH) chapter on labyrinth weirs. ARS scientists along with Oklahoma State University scientists and technical writers completed edits for a final review of the USDA-NRCS NEH Stepped Spillway Design Chapter. A new NEH Plunge Basins Chapter has been initiated. In addition, foundational work has been established for the development of modernized application tools based on these chapters.
For Objective 3, an ARS scientist in Stillwater, Oklahoma, completed testing using new and existing technologies for gathering changes in water and bed surfaces by measuring resonant vibrations of the bed surface using accelerometers and capturing significant erosion events with time-lapse cameras. Data analysis has commenced. In addition, ARS scientists in Stillwater, Oklahoma, completed the first series of testing of the complex geometry dam overtopping facility. Two embankment sections were tested to evaluate their performance under concentrated flow conditions along the intersection of the embankment and the natural landscape, and the other two embankment sections were tested to evaluate their performance due to flow disruption caused by a stability berm. Data was gathered using a sonar sensos, scanning survey equipment, and a UAS. Data analysis has commenced, and test embankments have been repaired and sodded for additional testing.
In addition, ARS scientists in collaboration with Kansas State University scientists have initiated sensitivity analysis of the Window Dam Analysis Modules (WinDAM) computational model. ARS scientists in collaboration with USDA-NRCS engineers and Kansas State University scientists conducted a WinDAM and Sites training for USDA-ARS scientists, USDA-NRCS engineers, and Oklahoma State University collaborating scientists. The training was filmed to develop a virtual training to be made available for stakeholders.
For Objective 4, University of Missouri (MU) scientists in collaboration with ARS scientists in Stillwater, Oklahoma analyzed initial survey data collected from Missouri farmers, producers, and consumers relative to the adoption and use of extreme weather adaptation and mitigation strategies. In addition, additional follow-up surveys of Missouri farmers, producers, and consumers have been completed.
For Objective 5, University of Missouri (MU) scientists in collaboration with ARS scientists in Stillwater, Oklahoma, have compiled geospatial, weather, and other biophysical data to develop hydrologic and hydrodynamic models for the Missouri and Mississippi River Basins. A database with all the small reservoirs in the project area has also been developed. Hydrologic models have been developed for the Missouri-Mississippi River Basin with seven regional catchments covering an area of 3.2 M km2 and for the catchment areas covering the States of Missouri and Oklahoma. Major reservoir storage details have been added, and reservoir operational details are being gathered to update the model. Methods to incorporate the operational details of small reservoirs at sub-catchment level are being developed. Agricultural operations including row crops which are significant in rainfall-runoff processes, have been added using the USDA-National Agricultural Statistics Service (NASS) annual crop data layer. A computer program has been developed and is being tested that will assist in automating the crop-rotations annually at sub-catchment level and updating daily weather data near real-time from national databases.
MU scientists in partnership with ARS scientists in Stillwater, Oklahoma, have compiled a climate database with future precipitation and air temperature projections by ten global climate models that provided data to the sixth phase of the Intergovernmental Panel on Climate Change (IPCC) Coupled Model Intercomparison Project (CMIP6). This database is currently being used to (i) develop multiple climate change indices that capture the changes in extreme events in the Missouri River Basin, and (ii) prepare weather input database for the hydrological models to evaluate changes in runoff patterns through 2050. A study evaluating and comparing the 2022 summer drought in Missouri with other past extreme dry and wet events in the region has been completed. An assessment evaluating six potential evapotranspiration methods in the midwestern region has been completed using daily weather database for the period 1980-2019. This work examined the effectiveness of different methods of assessing evapotranspiration, with a focus on limitations of available meteorological observations.
Accomplishments
1. Internet of Things (IoT) hub and Campbell CR1000x data transmission. A working proof of concept (POC) for incorporating the Azure IoT Hub for ingesting data from remote data sensors has been successfully developed by ARS researchers at Stillwater, Oklahoma, in collaboration with scientists from Oklahoma State University and ARS Partnerships for Data Innovations. This POC safely and securely transmits data to the ARS Partnerships for Data Innovations IoT cloud environment hub from the Campbell Scientific CR1000x data logger. In addition, data is streamlined from the IoT Hub to the GeoEvent Server. This data transmission process provides the scientific community including USDA-ARS a cost-savings by reducing the time it takes to manually collect the data from each data logger in the field. With data streaming in real-time, scientists will be able to identify data errors caused by instrumentation malfunction sooner. Real-time data streaming reduces human data touches and the introduction of human errors. Additionally, scientific staff can start data analysis sooner with time save manually downloading data from the dataloggers. End-users like scientists; federal, state, and university partners; farmers; producers; dam owners and managers; irrigation district managers; and policy makers will have access to data more quickly.
2. Stepped chute design criteria adopted for the Whittier Narrows Modification Project in Los Angeles County, California. The stepped chute design criteria developed by ARS researchers at Stillwater, Oklahoma, was adopted by the U.S. Army Corps of Engineers (USACE) for the congressionally funded Whittier Narrows Modification Project in Los Angeles County, California. Originally constructed by the USACE in 1957, the Whittier Narrows Dam was a flood-risk management project and became central to the Los Angeles County Drainage Area flood control system. Located 11 miles east of downtown Los Angeles, California, the dam provides flood-risk reduction for more than 1.2 million people and provides on average $169 million in annual benefits. In addition, the recreational areas (e.g., picnic, sporting and BMX (bicycle moto cross) facilities, fishing, equestrian center, golf course, trail system, and arena for rodeos, professional wrestling, boxing, and Latin entertainment) provided by the dam and reservoir have an estimated visitation of 2.1 million people annually. The adoption of the ARS stepped chute design criteria for the dam modification will allow these benefits to continue.
3. Conservation Reserve Program data collection tool. A Conservation Reserve Program (CRP) is a land conservation program administered by the USDA-Farm Service Agency (FSA). Farmers enroll in the program and agree to remove environmentally sensitive land from agricultural production and plant species that will improve environmental health and quality in exchange for an annual rental payment. A CRP Survey123 tool was created by ARS researchers at Stillwater, Oklahoma, in partnership with Oklahoma State University scientists and ARS Partnerships for Data Innovations to improve field work efficiency and data analysis for FSA employees during later phases of the project by ARS researchers at Stillwater, Oklahoma. The tool provides for a data management plan that includes field/lab data collection (Login, Initiate, and Field Entry), data submission, data transfer between cloud platforms, and data storage (Esri servers and Microsoft Azure). Prior to the development of the tool, CRP data was collected on paper data forms that then required manual entry into the computer, which was time-consuming and tedious. The dam inspection tool previously developed to improve efficiency in data collection provided a roadmap for developing the CRP tool and future tools like them. The development of this tool will benefit the USDA-FSA in administering the program more efficiently for American farmers enrolled in the program by reducing data touches and errors, and it will benefit ARS scientists in their field and lab data collection, data analysis, and data sharing with collaborators.
4. Cloud-based PostgreSQL database. ARS scientists at Stillwater, Oklahoma, in collaboration with Oklahoma State University and ARS Partnerships for Data Innovations developed a cloud-based PostgreSQL database was developed to host various data and information (e.g., vegetation, soil, and gas exchange for wetlands) from field and lab measurements. Digesting different sources of data in different forms and types is challenging. A hybrid strategy that stores data either in individual columns or JavaScript Object Notation (JSON) (e.g., multiple entries in a single column) depending on the complexity of certain data inputs was used. JSON structure saves storage space and improves data retrieval and efficiency. This proof-of-concept database will transform how data is stored and utilized by Agricultural Research Service scientists and their targeted end-user (e.g., farmers, producers, emergency managers, irrigation districts, policy makers, federal and state agencies, municipalities, and university collaborators).
5. Modernization of COTton MANagement (COTMAN) data collection tool. With more than 85,000 lines of programming code, ARS scientist at Stillwater, Oklahoma, in collaboration with Oklahoma State University and ARS Partnerships for Data Innovations modernized the COTton MANagement (COTMAN) data collection tool to a ESRI (Environment Systems Research Institute) Survey123 data collection tool. Not updated since the mid-2000’s, the original tool was experiencing browser compatibility issues and functioning on specialized handheld field data collection devices that were far beyond their end-of-life. Modernization of this tool increases data collection efficiency including transmittal speed for data to a cloud environment. Farmers, producers, the cotton industry, and scientists use this tool to monitor crop development status, to detect stress, and to assist with in-season and out-of-season management decisions. The programming framework for this tool provides proof of concept that will be utilized on similar data collection tools including those related to vegetation performances on dams, levees, and spillways.
Review Publications
Wise, J.L., Al Dushaishi, M., Bocanegra-Yanez, J., Lee, H., Hunt, S. 2023. Experimental analysis of Wellbore cement-steel bond mechanics and characterization. Geoenergy Science and Engineering. 225. Article 211709. https://doi.org/10.1016/j.geoen.2023.211709.
Wise, J.L., Hunt, S., Al Dushaishi, M. 2023. Prediction of earth dam seepage using a transient thermal finite element model. Water. 15(7). Article 1423. https://doi.org/10.3390/w15071423.
Skevas, T., Grashuis, J., Massey, R., Hunt, S. 2023. Farm impacts of the 2019 Missouri River floods and economic valuation of flood risk reduction. Journal of Environmental Management. 344. Article 118483. https://doi.org/10.1016/j.jenvman.2023.118483.
