Location: Great Plains Agroclimate and Natural Resources Research
2019 Annual Report
Accomplishments
1. Spatial distribution of soil erosion is estimated using a Cs-137 tracer. Soil erosion is a worldwide problem causing severe land degradation. Soil erosion information is critical for developing effective soil conservation plans. The lack of spatial soil erosion data has been a major constraint on verifying and improving soil erosion computer models by agricultural engineers and soil erosion scientists. Spatial erosion distribution information is also useful to soil conservationists to layout precision conservation plans. However, spatial erosion data cannot be easily obtained by the conventional erosion measurement techniques using runoff plots and watershed monitoring. Researchers at El Reno in Oklahoma evaluated and improved a soil tracking technique for deriving spatial erosion data using Cs-137 (Cesium) tracer. Spatial characteristics of Cs-137 tracer distribution were characterized under three major land uses including grasslands, forestlands, and croplands. The best sampling schemes for more accurate estimation of soil erosion rates using Cs-137 were recommended based on the spatial characteristics of Cs-137 distribution. The recommended sampling design was used to sample soil cores in several research watersheds of Agricultural Research Service (ARS). The validated Cs-137 erosion models were used to convert the measured Cs-137 inventories to soil erosion rates for the study watersheds. The derived spatial soil erosion data will be used to calibrate the process-based soil erosion model such as Water Erosion Prediction Project (WEPP). The calibrated WEPP model will be used to simulate the impact of climate changes including future storm intensification on soil erosion and to select best soil conservation practices that keep soil erosion rates below a tolerable level. This technique will be of interest and useful to hydrologists, agricultural engineers, soil scientists, and soil conservationists that need to estimate soil erosion rates and to deploy precision soil conservation measures.
2. Synthetic weather generation is enhanced with extreme storm intensification capabilities. The climate change literature points to an unambiguous upward trend in the frequency of heavy to extreme storms in selected regions of the U.S. This intensification of extreme storms was expected to continue to increase and disrupt the environment by creating more frequent and severe flooding episodes. Related examples of potential long-term impacts include uncertainty of available water resource, sustainability of competing land management alternatives, profitability of agricultural cropping systems, and increasing water demand by a growing society. Investigations of such weather dependent problems generally require long-term weather records that are rarely available for the locale of interest. Computer based generation of synthetic daily weather data can provide help in many situations. Researchers at El Reno in Oklahoma enhanced an existing stochastic weather generator, called SYNTOR. Initially SYNTOR was design to generate alternative daily precipitation and air temperature realizations that had statistical properties similar to those of the parent historical weather it was intended to simulate. New capabilities were added to SYNTOR to facilitate simulation of daily weather records for anticipated climate change and storm intensification scenarios. Climate change was simulated by adjusting the weather generation parameters to reflect the change in mean monthly precipitation and air temperature values. Storm intensification was approximated by increasing the top percentiles of storm distributions by a user pre-specified precipitation amount. Applications of SYNTOR produced daily precipitation statistics that were consistent with those of related observations. The custom SYNTOR weather generator software and a User Manual are available upon request.
3. Research translation and science synthesis. An ongoing challenge for ARS is ensuring that its science outcomes are relevant to agricultural decision-makers. A primary role of the Southern Plains Climate Hub, in addressing this challenge, is to synthesize research outcomes, translate scientific accomplishments, and identify priority research questions appropriate to Southern Plains agriculture. Key accomplishments over the past year in this area include: analyses of regional variations and trends in intense precipitation, frost indicators, and drought (e.g., International Journal of Climatology, Journal of the American Water Resources Association, Environmental Research Letters, and Climate manuscripts); an evaluation of regional climate services activities across the Americas (e.g., Climate Services manuscript); a climate vulnerability assessment of a large urban forest in the region (e.g., June 2019 Austin Texas stakeholder workshop); successful acquisition of extramural funding (e.g., USDA National Institute of Food and Agriculture Sustainable Agricultural Systems grazing management project award), and establishing new research partnerships (e.g., USDA Forest Service Rocky Mountain Research Station interagency agreement). These efforts promote the scientific achievements and capacity of ARS and partner organizations while making climate-smart agricultural science available and relevant to a broader regional audience
4. Tool development and technology transfer. An ongoing challenge for ARS is ensuring that its science outcomes are informing improved agricultural management practices. A primary role of the Southern Plains Climate Hub, in addressing this challenge, is to cultivate the development of new management tools and technologies appropriate to Southern Plains agriculture, and promote a climate-literate USDA workforce that can better apply these tools and technologies. Key accomplishments over the past year in this area include: organization and facilitation of prescribed wildfire training schools for producers (e.g., January 2019 Concho Oklahoma, May 2019 Woodward Oklahoma); reporting on lessons learned for wildfire preparedness and recovery in the region (e.g., 2016-2018 Southern Plains Wildfire Assessment Report); training of USDA field staff on drought impacts reporting and monitoring (e.g., July 2019 High Plains Drought Monitoring Technical Workshop); continued expansion of precipitation observations in Oklahoma (e.g., Community Collaborative Rain, Hail, and Snow (CoCoRaHS) network); and continued demonstration of best practices for soil health management, including through new agreements (e.g., Quapaw Nation demonstration farm). These efforts promote more robust links between ARS science and agricultural production through the incorporation of climate-smart information and perspectives into management practices.
5. Communication, education, and stakeholder outreach. An ongoing challenge for ARS is ensuring that its science outcomes are being communicated to a broad range of audiences, including the next generation of agricultural scientists and producers. A primary role for the Southern Plains Climate Hub, in addressing this challenge, is to communicate with and educate regional audiences on climate science and climate-smart agricultural management practices, and to develop, sustain, and leverage science and services partners through regional outreach and extension activities. Key accomplishments over the past year in this area include: demonstrating the value of climate-smart agriculture to USDA agency and program leaders (e.g., October 2018 USDA Farm Production and Conservation Technology Showcase); engaging conservation managers and practitioners on climate-smart tools, technologies, and information resources (e.g., special session at February 2019 National Association of Conservation Districts annual meeting); continuing to promote climate-smart agriculture through web- and social media-based platforms (e.g., Southern Plains Podcast); utilizing educational mechanisms to share climate-smart information with the next generation of agriculturalists (e.g., BlueSTEM AgriLearning Center teacher training events); and sustaining pathways for ARS, USDA, and partner organizations to inform the Climate Hub’s priorities and activities (e.g., Southern Plains Climate Hub-South Central Climate Adaptation Science Center joint steering committee). These efforts enhance the use and applicability of ARS science by broadening its reach and communicating its value within and beyond the Southern Plains region.
Review Publications
Kloesel, K., Bartush, B., Banner, J., Brown, D.P., Lemery, J., Lin, X., Loeffler, C., McManus, G., Mullens, E., Nielsen-Gammon, J., Shafer, M., Sorensen, C., Sperry, S., Wildcat, D., Ziolkowska, J. 2018. Southern Great Plains. In: Reidmiller, D.R., C.W. Avery, D.R. Easterling, K.E. Kunkel, K.L.M. Lewis, T.K. Maycock, and B.C. Stewart. Impacts, Risks, and Adaptation in the United States: Fourth National Climate Assessment, Volume II. Washington, DC, USA: U.S. Global Change Research Program. p. 978-1026.
Chen, J., Brissette, F.P., Zhang, X.J., Chen, H., Guo, S., Zhang, Y. 2019. Bias correcting climate model multi-member ensembles to access climate change impacts on hydrology. Climatic Change. 153(3):361-377. https://doi.org/10.1007/s10584-019-02393-x.
Guo, Q., Chen, J., Zhang, X.J., Shen, M., Chen, H., Guo, S. 2019. A new two-stage multivariate quantile mapping method for bias correcting climate model outputs. Climate Dynamics. https://doi.org/10.1007/s00382-019-04729-w.
Zhang, X.J. 2018. Determining and modeling dominant processes of interrill soil erosion. Water Resources Research. 55(1):4-20. https://doi.org/10.1029/2018WR023217.
Niu, B., Qu, J., Zhang, X.J., Liu, B., Tan, L., An, Z. 2019. Quantifying provenance of reservoir sediment using multiple composite fingerprints in an arid region experiencing both wind and water erosion. Geomorphology. 332:112-121. https://doi.org/10.1016/j.geomorph.2019.02.011.
Liu, J., Zhou, Z., Zhang, X.J. 2019. Impacts of sediment load and size on rill detachment under low flow discharges. Journal of Hydrology. 570:719-725. https://doi.org/10.1016/j.jhydrol.2019.01.033.
Avila-Carrasco, R., Junez-Ferreira, H.E., Gowda, P., Steiner, J.L., Moriasi, D.N., Starks, P.J., Gonzalez, T.J., Villalobos, A.A., Bautista-Capetillo, C. 2018. Evaluation of satellite-derived rainfall data for multiple physio-climatic regions in the Santiago River Basin, Mexico. Journal of the American Water Resources Association. 54(5):1-19. https://doi.org/10.1111/1752-1688.12672.
