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ARS Home » Plains Area » El Reno, Oklahoma » Grazinglands Research Laboratory » Agroclimate and Natural Resources Research » Research » Research Project #432214

Research Project: Uncertainty of Future Water Availability Due to Climate Change and Impacts on the Long Term Sustainability and Resilience of Agricultural Lands in the Southern Great Plains

Location: Agroclimate and Natural Resources Research

2018 Annual Report


1a. Objectives (from AD-416):
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.


1b. Approach (from AD-416):
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.


3. Progress Report:
The Southern Plains Climate Hub contributed to the overall project via applied research, dissemination of research results to technical and non-technical regional audiences, and identification and leveraging of regional partner networks, including through cooperative agreements and establishment of demonstration activities. 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 the national USDA Climate Hub Executive Committee. A synthetic daily weather generation model (SYNTOR) was enhanced by the additional capability to simulate daily weather with extreme storm intensification due to climate change. The model is empirical and non-parametric, and consists of adjusting the top five percentile range of the daily precipitation distribution by a pre-determined percentage increase based on the trend of the historical daily precipitation. This methodology is flexible and can accommodate a large range of storm sizes, and was incorporated into the SYNTOR model. The methodology is at the experimental stage. The generation of synthetic weather information using the SYNTOR model was illustrated in a proof-of-concept example. Selected synthetic daily weather generated by SYNTOR were compared to observed weather at the town of Weatherford in west central Oklahoma in the Great Plains region of the United States. Generated daily weather by the SYNTOR model compared well with observed weather values. Further evaluation of the extreme storm intensification and associated uncertainties and spatial variability is recommended. Generator for Point Climate Change (GPCC) is a computer program for downscaling climate change scenarios to a particular location for impact assessment of climate change. GPCC was modified and extended to incorporate a storm intensification factor to accommodate potential increases in extreme storm events in the future under climate change. More than 100 years of historical data from 8 Oklahoma stations and 10 stations around the world were used to develop and validate the storm intensity factor. Daily precipitation time-series at each station was divided into a calibration and a validation period in a way that the mean annual precipitation difference between the two periods was maximized to represent non-stationary precipitation changes in both mean precipitation and extreme events. GPCC model parameters were calibrated for the calibration period, and then the calibrated parameter values were adjusted according to the precipitation changes for the validation period. The adjusted parameters were used in GPCC to generate 100 years of daily precipitation for each station for the validation period, and the generated daily data were compared with the observed data in basic statistical terms, including number of wet days, maximum annual precipitation, and selected percentiles of daily precipitation distribution. It was found that the ratio of the 99.9th percentile of daily precipitation amount to the mean monthly precipitation was linearly related to the skewness coefficient that determines the frequency and magnitude of extreme events at a station. A general linear relationship was developed using all stations. The validation results showed that using the dimensionless ratio is promising. The methods need to be further validated for more stations under various climate scenarios. We are currently investigating how to extract the dimensionless ratio factor from daily data simulated by Regional Climate Models. The performance of two synthetic weather generators (SYNTOR and GPCC) was assessed under multi-year persistent dry and wet climatic conditions in Oklahoma. The study included calibration, validation, and application to changing precipitation amounts assumed to represent future climate changes. The difference in model calibration and validation procedure under unsteady climatic conditions were determined for each of the two weather generators. Generator SYNTOR calibrated the weather generation parameters (rainfall distribution, transition probabilities, regression coefficients) internally based on provided observed daily rainfall. For climate change conditions, the generation parameters are re-adjusted internally based on the provided mean calendar-month precipitation of the projected climate. GPCC calculates basic distribution parameters using daily precipitation of the calibration period, and then adjusts the calibrated baseline parameters according to monthly precipitation amounts of the future climate projections. The main difference between the two is that the former uses mean calendar-month precipitation (12 values), whereas GPCC requires time series of monthly precipitation for the entire duration of simulation. The ability to reproduce observed weather characteristics was based on comparing total rainfall depth, average number of rainy days per year, average annual maximum daily rainfall, precipitation occurrence probabilities, and cumulative frequency distribution of rainy days. The sensitivity of the synthetic weather generators to storm intensification was estimated by the change in the size of the upper range of the daily precipitation frequency curve. Both generators produced daily precipitation that displayed similar statistical characteristics. The largest differences were found to be in precipitation occurrence probabilities. Four Oklahoma stations and 17 stations across in the eastern U.S. were selected based on geographic locations and climate types. Daily precipitation and temperature, mostly ranging from 1911 to 2016, were downloaded from the National Weather Service database. The downloaded data were checked and missing data filled. Two sets of reanalysis data were downloaded from a data archive for North America. Nineteen commonly used atmospheric variables were extracted from the two reanalysis datasets to serve as predictors. More than 40 years of daily precipitation and temperature data from two datasets simulated by the Canadian Regional Climate Model were downloaded for comparisons between various downscaling methods, which will be conducted in year 2 of the project as listed in the milestones. In support of Southern Plains Climate Hub focus on recovery from and resilience to extreme events, conducted research on ecological recovery from large grassland wildfires using remote sensing approaches. The project is focusing on three multi-state fires in 2016 and 2017. Analysis is completed and manuscripts are being developed analysis of long-term high resolution precipitation data from the Oklahoma Micronet was completed with a collaborator from University of Illinois and a paper was published.


4. Accomplishments
1. 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, El Reno, Oklahoma, 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: better understanding the climate adaptation benefits of rangeland livestock production (e.g., The Rangeland Journal manuscript); evaluating the delivery of climate information through regional services programs (e.g., Climate Services manuscript); advancing the understanding of soil health management in the region through new on-farm research (e.g., Agriculture and Environmental Letters manuscript, Southern SARE Program grant); convening scientific and non-scientific audiences in dedicated settings to facilitate science translation (e.g., 2018 Great Plains Grassland Summit, 2018 North American Drought Monitor Forum), and establishing new research partnerships (e.g., Kansas State University cooperative 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.

2. 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, El Reno, Oklahoma, 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: testing and release of new climate Extension modules via Kansas State University; training of USDA staff on drought monitoring and information services (e.g., 2018 Amarillo drought workshop); training of USDA staff on climate prediction and information applications (e.g., 2018 NOAA Climate Prediction Center workshop); demonstration of soil health management technologies to regional producers (e.g., 2018 Redlands College field day), and expansion of precipitation observations in Oklahoma (e.g., Community Collaborative Rain, Hail, and Snow (CoCoRaHS) network). These efforts promote more robust links between ARS science and agricultural production through the incorporation of climate-smart information and perspectives into management practices.

3. 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, El Reno, Oklahoma, 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: responding to extreme weather and climate impacts through public information events (e.g., 2018 Southern Plains Wildfire Forum); promoting climate-smart agriculture through web-based social media platforms (e.g., Southern Plains Podcast); engaging traditionally underrepresented regional audiences (e.g., 2017 Cheyenne and Arapaho Nations soil health field day); establishing new program partnerships to reach larger agricultural audiences (e.g., Society for Range Management wildfire collaboration); utilizing educational mechanisms to release climate-smart information (e.g., Future Farmers of America (FFA) soil health curriculum); and formalizing 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.

4. Generation of synthetic weather alleviates weather data shortage in assessment of climate change impacts. A common problem in investigations of climate change impacts on hydrology, environment, and agricultural crop production is the shortage of observed weather data and more importantly daily weather for potential future climate change conditions. The effects of the shortage of daily weather data worsens as the number of climate change impact investigations and assessments needing daily weather data increases. ARS research scientists at El Reno, Oklahoma, have enhanced the capabilities of an existing synthetic weather generation model by incorporating seasonal climate outlooks, climate change, and storm intensification options. The enhancements enable exploration of a greater range of climate change alternatives, including associated causes and impacts. The storm intensification option modifies the upper 5 percentiles of the daily precipitation distribution to simulate the effects of increasing and more frequent weather extremes. Research scientists, water resources engineers, and land managers that investigate the sustainability of agricultural land productivity, hydrologic water availability, environmental quality, or the effectiveness of conservation programs are potential users of this synthetic weather generation technology.

5. Sub-hourly rainfall intensity has increased in long-term research watershed. Intensification of precipitation poses a great threat to soil and water resources and agricultural sustainability. Scientists at the USDA-ARS, El Reno, Oklahoma, and University of Illinois collaborators conducted a frequency analysis on sub-hourly precipitation events measured at the USDA-ARS Little Washita River Experimental Watershed from 1961-2015. During the 1962–2015 period, this region experienced successive regional long-term wet (1962–1995) and dry (1996–2015) dominant periods of more than 20 years each. During the wet period, precipitation intensity ranging from 1 to 12 mm in 5 min. were 20% less likely to occur while event daily average precipitation and number of events increased by 10% and 11%, respectively. In contrast, during the dry period, intensity ranging from 1 to 12 mm in 5 min. became more likely to occur (64% increase; 1996–2015) while event daily average precipitation and number of events decreased by 16% and 18%, respectively. Overall, comparisons with the baseline data (1962–2015) indicated that in the last 20 years, sub-hourly precipitation intensities have increased while daily event duration and extreme 5-min precipitation intensities larger than 24 mm (precipitation intensification below extremes) have remained unchanged. Better understanding of changes in precipitation intensities over time allows assessment of changes in erosion and runoff risks.


Review Publications
Zhang, X.J. 2017. Several key issues on using 137Cs method for soil erosion estimation. Bulletin of Soil and Water Conservation. 37(5):342-346.

Doughty, R., Xiao, X., Wu, X., Zhang, Y., Bajgain, R., Zhou, Y., Qin, Y., Zou, Z., Mccarthy, H., Friedman, J., Wagle, P., Basara, J., Steiner, J.L. 2018. Responses of gross primary production of grasslands and croplands under drought, pluvial, and irrigation conditions during 2010-2016, Oklahoma, USA. Agricultural Water Management. 204:47-59. https://doi.org/10.1016/j.agwat.2018.04.001.

Lin, X., Harrington Jr., J., Ciampitti, I., Gowda, P., Brown, D.P., Kisekka, I. 2018. Kansas trends and changes in temperature, precipitation, drought, and frost-free days from the 1890s to 2015. Journal of Contemporary Water Research and Education. 162: 18-30.

Guzman, J.A., Chu, M.L., Steiner, J.L., Starks, P.J. 2018. Assessing and quantifying changes in precipitation patterns using event-driven analysis. Journal of Hydrology. 15: 1-15. https://doi.org/10.1016/j.ejrh.2017.11.006.