Location: Agroclimate and Hydraulics Research Unit2022 Annual Report
1. Fingerprint sediment sources and develop spatially distributed soil erosion data using fallout radionuclides for calibrating the Water Erosion Prediction Project (WEPP) model. 2. Enhance existing tools to improve simulation of storm intensification and assessment of climatic impact for long-term strategic conservation planning, and develop new seasonal analogue forecast tool for climate-smart decision support. Sub-objective 2.A: Improve methods of simulating storm intensification based on GCM/RCM-projected changes in high percentiles (tail) of daily precipitation for better downscaling of GCM/RCM climate projections to a target station for site specific impact assessment and conservation planning. Sub-objective 2.B. Develop a seasonal analogue forecast tool based on big data mining using an Artificial Intelligence (AI)-driven KNN algorithm for climate-smart decision support in managing winter wheat-livestock production in the Southern Great Plains (SGP). 3. Data collection, model calibration, and model simulation for long-term strategic planning and short-term tactical decision support for crop production and soil and water conservation. Sub-objective 3.A. Compile weather, wheat yield, cattle grazing data, and crop management information to calibrate and fine tune an existing wheat grazing model. Sub-Objective 3.B. Simulate wheat and beef production with the wheat-grazing model using monthly updated, seasonal analogue climate forecast data obtained in Sub-Objective 2.B for tactical within-season decision making in managing the wheat-livestock enterprise for select stations in the SGP. Sub-Objective 3.C. Simulate runoff, soil water balance, soil loss, and crop yield with the WEPP model to assess the impacts of storm intensification due to climate change on erosion and crop production under various cropping and tillage systems using downscaled GCM/RCM projections in Objective 2.A for strategic conservation planning at decadal scales.
The Food and Agriculture Organization has projected that food production needs to increase by 70% to feed the world population of 9.3 billion by 2050. However, agricultural production is being adversely impacted by global warming due to increasing extreme weather events and climate variability. Thus, adapting agriculture production to climate change and variation or developing climate-smart decision support information is imperative to feed the world by taking advantage of favorable changes while mitigating adverse impacts. This research seeks to refine climate downscaling tools to improve modeling of extreme precipitation events and their impacts on soil and water conservation measures, develop seasonal analogue climate forecasts and dual-purpose wheat decision support tools, and derive spatially distributed soil erosion data. The two weather generator-based downscaling tools will be further refined to simulate extreme precipitation events by explicitly manipulating the top percentiles of daily precipitation based on projected climate change signals or historical trends. A seasonal climate analogue tool will be developed using a K Nearest Neighbor approach driven by an Artificial Intelligence (AI)-based data mining algorithm. A wheat grazing model will be used along with seasonal forecasts to develop a tactical within season decision support tool for managing the wheat-livestock enterprise in central Oklahoma. In addition, improved simulation of extreme precipitation will afford great opportunities for more accurate assessments of climatic impacts on soil erosion and crop production and for development of better strategic conservation planning at decadal scales. The seasonal climate forecast and decision support tools are expected to have great impacts on the wheat-livestock enterprise, a major economic pillar, in the Southern Great Plains (SGP). Distributed erosion data, derived using the Cs-137 tracking technique, will be used to validate and improve process-based soil erosion models, which in turn will better assist in strategic planning of long-term soil and water conservation.
Objective 1: Research continued on compiling and analyzing existing soil loss data estimated using the Cs-137 techniques in four small ARS research watersheds. Four Cs-137 erosion estimation models were compared and evaluated. The best method was used to estimate spatial patterns of soil erosion for the selected small watersheds. There were more than ten downslope transect profiles in each watershed. In addition, Cs-137 data measured along eight longitudinal hillslope profiles in a native rangeland area in the Fort Cobb Reservoir Experimental watershed were collected and analyzed to estimate soil erosion patterns along each hillslope profile. Those spatial soil erosion patterns along each slope transect will be used to evaluate the Water Erosion Prediction Project (WEPP) model’s ability to simulate spatial redistribution of soil erosion along different downslope profiles. Two field surveys were conducted in the Fort Cobb Reservoir Experimental watershed and its vicinity area to locate different slope shapes and forms. More slope transects would be sampled if needed. Sub-objective 2A: Research continued on acquiring Global Circulation Model (GCM) projections. Two sets of bias corrected and statistically downscaled (GCM) projections were downloaded from the Coupled Model Intercomparison Project Phase 5 (CMIP5). These datasets provide bias corrected daily precipitation and temperature at a 1/16th degree grid. One dataset was from the Bias Correction with Constructed Analogs (BCCA) and another set from the Localized Constructed Analogs (LOCA). Both the hindcast (1950-2005) and future projections (2006-2100) were retrieved from CMIP5 for the Weatherford station in central Oklahoma. Two future projections, one using medium Representative Concentration Pathway (RCP) 4.5 and another using high RCP 8.5, were selected. In total, 32 LOCA-downscaled GCMs and 20 BCCA-downscaled GCMs were assessed. Both downscaling methods tended to project more wet days than the observed frequency on the study site. Compared with the BCCA method, the LOCA method is better at preserving downscaled extreme events. For better simulating storm intensification, researchers at El Reno, Oklahoma, compared the observed trends of extreme daily precipitation events between two time periods of 1950–1979 and 1980–2005 with the simulated (hindcast) corresponding trends between the same periods at the Weatherford station. The extreme daily precipitation was defined as daily precipitation of = 95th percentile. The models were ranked based on errors in the percent change of the upper 95th percentiles from 1950-1979 to 1980-2005 between observed and downscaled daily extreme precipitation, and the top 25 projections were selected, which are simulated by 14 GCMs. Given that these 25 projections had extreme precipitation trends that mostly resembled the observed trends in the historical period, it was therefore assumed that these 25 projections would afford a reasonable simulation of future extreme precipitation trends and variability. The two future periods of 2021-2050 and 2051-2080 were selected to reflect temporal trends of future extreme precipitation under climate change. To further test the model screening procedure, 40 realizations of daily precipitation for the period of 1950-2100 were downloaded for the Weatherford study site from the Coupled Model Intercomparison Project Phase 6 (CMIP6). The 40 ensemble members, provided by the Community Earth System Model 2-Large Ensemble (CESM2-LE) project was simulated under the emission scenario of Shared Socioeconomic Pathway 3-7.0 (SSP370) but with different atmospheric and oceanic initial conditions. Top 20 ensemble members were selected based on errors between observed and simulated percent changes of the 95th and 99.9th percentiles between the two periods of 1950–1979 and 1980–2005. Simulated percent changes (relative to 1950-2019) of the selected top 20 members were compared to those of all 40 members for the two future periods using box plots to show that the selection procedure can reduce overall uncertainty of the projections. Sub-objective 2B: Research continued on developing a prototype of the analog forecast tool using big data mining for a discrete number of stations across Oklahoma. Collaborations between researchers at El Reno, Oklahoma, and researchers from the University of Texas, Arlington are ongoing in support of this project. A working prototype of the seasonal forecast tool has been developed using 5 weather observing stations across the state of Oklahoma. Primary testing and development of the tool was conducted using data from a western Oklahoma weather station before being implemented across the other four weather stations, which represent the diverse precipitation climatology in the state. Only 2 variables have been used to build the historical analogs (temperature and precipitation amount), however recent work is developing a dataset using a variety of additional variables such as incoming solar radiation and dewpoint temperature using Oklahoma Mesonet station data. Thus far results have shown that the prototype has promise in forecasting seasonal precipitation amounts across Oklahoma. The current system produces monthly forecasts that outperform climatology (using the climatological amount of precipitation as the forecast amount) 60% of the time at worst. Further, errors produced by the system are on average at or less than 1 inch of precipitation for the monthly forecasts. While the results for the weather stations with more precipitation and more variable precipitation are not as good as those for the drier stations, results still show that the current system is able to, on average, outperform climatology. However, the current forecast system is hindered by the expected issue of over forecasting at lower precipitation values and under forecasting at higher precipitation values that exists within historical analog forecast systems. While this issue is inherent in this type of forecasting system, results using the observed value of precipitation to identify the “best” forecast show that it is possible to produce a monthly forecast with a reduced over/under forecasting impact. Thus, current research tasks are directed at tuning the current system to attempt to objectively identify or select the historical analog that produces a forecast close to this “best” forecast. Overall, results from the work in the last year show promise for using this system to produce seasonal forecasts of precipitation for use in managing the winter wheat livestock production system within the Southern Great Plains. Objective 3: Research continued on compiling wheat growth and wheat grazing data from Oklahoma State University field experiments. Four experiment stations (Chickasha, Stillwater, Lahoma, and Marshall) were selected. Daily weather data including precipitation, maximum and minimum temperature, and solar radiation from 1995 to 2021 were downloaded for each site from the Oklahoma Mesonet. Basic soil properties including soil texture and organic matter contents were obtained from published scientific paper as well as extension publications. Seven wheat varieties (Doublestop CL+, Gallagher, Green Hammer, Smith Gold, Jaggar, Endurance, and Duster) which are popular in Oklahoma were selected. The wheat management information including standard and intensive managements, grain-only wheat, and dual-purpose wheat were compiled for the selected varieties and locations. The compiled variables include: soil fertility test results, planting dates, seeding rates, fertilization dates and rates, tillage systems, wheat fall biomass, wheat yields, and wheat phenology such as dates of the first hollow stem, heading and harvest (maturity date if indicated). For wheat grazing trials with steers at Marshall, start and end grazing dates, initial steer body weight, stocking rates, and average daily weight gains during the grazing period were obtained from our collaborators at the Oklahoma State University (OSU) Animal Science Department. All those data will be used to calibrate the wheat growth model and wheat grazing model for simulating grain-only wheat and dual-purpose wheat, respectively. Research also continued on compiling the experimental data of carbon dioxide (CO2) impact on crop growth and yields. Crop yield responses to elevated 2xCO2 were compiled from review papers and other scientific publications in the literature for the selected species. The major crops grown in Oklahoma including wheat, soybean, sorghum, cotton, canola, and alfalfa were selected. The average yield changes for the doubled CO2 concentration obtained from the literature were used to calibrate plant growth component in the Water Erosion Prediction Project (WEPP) model by varying two key growth parameters pertinent to energy and water use efficiencies as affected by elevated CO2. The average grain yields in Oklahoma were used to calibrate the WEPP model’s plant growth parameters. The calibrated WEPP model was used to simulate the effects of climate change on soil erosion and surface runoff under four tillage systems and 11 cropping systems including monocrop and crop-alfalfa rotations.
Yuan, L., Zhang, X.J., Busteed, P.R., Flanagan, D.C., Srivastava, A. 2022. Modeling surface runoff and soil loss response to climate change under GCM ensembles and multiple cropping and tillage systems in Oklahoma. Soil and Tillage Research. 218. Article 105926. https://doi.org/10.1016/j.still.2021.105296.
Yuan, L., Zhang, X.J., Busteed, P.R., Flanagan, D.C. 2022. Simulating the potential effects of elevated CO2 concentration and temperature coupled with storm intensification on crop yield, surface runoff, and soil loss based on 25 GCMs ensemble: A site-specific case study in Oklahoma. Catena. 214:106251. https://doi.org/10.1016/j.catena.2022.106251.
Flanagan, P.X., Mahmood, R., Sohl, T., Svoboda, M., Wardlow, B., Hayes, M., Rappin, E. 2021. Simulated atmospheric response to four projected land use land cover change scenarios for 2050 in the north central United States. Earth Interactions. 152(1):177-194.