Location: Great Plains Agroclimate and Natural Resources Research
2019 Annual Report
Objectives
Objective 1: Develop new and enhance existing model components and methodologies to better estimate long term trends, variations, and uncertainty in future water availability due to climate change.
Objective 2: Determine the impacts of future variation or change in water availability on soil erosion, crop productivity, and resilience and sustainability of managed agricultural lands.
Objective 3: Develop long-range planning information for policy makers, environmental organizations, and conservation planners on potential future water availability, cropland productivity, and water and soil conservation options that would maintain or increase the resilience and sustainability of agricultural lands.
Objective 4: Develop science-based, region-specific information and technologies for agricultural and natural resource managers that enable climate-smart decision-making and transfer the information and technologies to users.
Approach
The Earth’s climate is warming and will likely continue to warm for the rest of this century. In the Southern Great Plains of the U.S., droughts are expected to increase in frequency, duration, and severity, and storm events to become more intense. Climate change poses a new set of challenges affecting future water availability, agricultural soil resources, and long term sustainability of rainfed crop production systems in the Southern Great Plains. The extent of climate change impacts on agriculture at the end of the century is unclear, and information on management strategies and conservation options to effectively adapt to and mitigate the detrimental effects of climate change is limited. This applied, goal-driven investigation uses available projections of precipitation, air temperature, and carbon dioxide levels through year 2100, and relies on agricultural system models to simulate impacts of climate change scenarios on rainfall-runoff, soil erosion, and sustainability of crop production systems. Long term land management strategies, agronomic options, and conservation measures that enhance future water availability, reduce soil erosion, and improve the sustainability of cropping systems are explored, and uncertainties in projected impacts are estimated. Effectiveness and risk of various strategies and options to reduce or offset climate change impacts are determined by evaluation of probability distributions of climate change impacts. Findings are expected to support national and regional strategic planning of alternative long term adaptive conservation measures that maintain effective, competitive, sustainable, and environmentally responsible agricultural cropping systems under changing and uncertain future climatic conditions.
Progress Report
Research continued on comparing the performances of ten different methods for downscaling precipitation spatially from a large area to a station, and temporally from monthly to daily precipitation values. Daily observed precipitation data from 1949 to 2016 at four Oklahoma weather recording stations (Weatherford, Idabel, Hooker, Kingfisher) were divided into two equal time periods for model calibration and validation. One set of analysis data from the National Centers for Environmental Prediction (NCEP) that overlies the four weather stations were downloaded from the web. The ten downscaling methods fall into one of three categories: 1) the machine learning category which included Artificial Neutral Networks, Support Vector Machine, and the K-Nearest Neighbors method; 2) the weather generator-based category which included a synthetic daily weather generator and the Generator for Point Climate Change (GPCC); and 3) the model output statistics category which included the mean- and distribution-based bias correction and delta change methods. All methods were trained for the calibration period by developing relationships between the large-scale NCEP predictors and the local-scale precipitation predictions. The calibrated methods were then applied to the validation time period and the NCEP data were downscaled to each weather station. The downscaled data were then compared to the observed data for each of the four weather stations to evaluate the performance of each method. Preliminary results indicated that machine learning methods performed the worst in downscaling climate data, while the performances of the weather generator-based methods and the model output statistics methods were acceptable, with the former being slightly better in simulating precipitation sequence and large storms. Comparison results will be used to select the most suitable downscaling methods for use in downscaling climate change projections and for assessing the impacts of climate change on crop production and soil erosion. (Objective 1; Sub-Objective 1.C)
Research continued on developing, testing, and implementing an empirical-conceptual method to incorporate the intensification of extreme storm events predicted by the atmospheric and climate change science community into an existing synthetic daily weather generator. A weather generator with such capabilities is highly desirable to evaluate environmental impacts of potential climate variation and change scenarios. It was established that storm intensification is best treated in a post weather generation step. First, the baseline daily weather is generated, then subsequently the generated daily weather is modified to meet the specifications of the extreme storm intensification. Second, the adjusted daily precipitation record for storm intensification is used to simulate the crop production potential, the soil erosion, and other impacts resulting from storm intensification. Two alternative implementation options were tested. The first option increased all storm events above a user specified daily precipitation threshold value (for example upper 95 percentile of the storm distribution) by a fixed percentage (for example 20% increase). The second option identified three intense storm categories (heavy, very heavy, and extreme events) and increased the precipitation in each category separately by a fixed but different percentage for each category. Both methods were tested and implemented as a post-processing adjustment of the generated baseline daily weather. Both methods required a net addition of precipitation to produce the intensification portion of extreme storm events. Research is currently underway to reduce or remove the bias of the additional precipitation produced by storm intensification and also to address the increased number of extreme storm events to reflect the increased return frequency of storm events of given size. (Objective 1; Sub-Objective 1.A)
The Southern Plains Climate Hub contributed to the overall project via applied research and science syntheses, dissemination of research results to technical and non-technical regional audiences, demonstration of climate-smart agricultural practices, and identification and leveraging of regional partner networks, including through cooperative agreements. These efforts advanced the Agency’s mission in the Southern Plains by enhancing the links between and among ARS science outcomes, applications of ARS programs to agricultural management contexts, and communication and promotion of ARS accomplishments to regional stakeholders. The Climate Hub developed and maintained an annual work plan consistent with fiscal year priorities as determined by national USDA Climate Hub Executive Committee and regional Southern Plains Climate Hub-Department of Interior South Central Climate Adaptation Science Center joint stakeholder committee. (Objective 4; Sub-Objective N/A)
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.
