Location: Agroclimate and Hydraulics Research Unit
Project Number: 3070-11130-007-000-D
Project Type: In-House Appropriated
Start Date: Feb 15, 2022
End Date: Feb 14, 2027
Objective:
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.
Approach:
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.
