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ARS Home » Plains Area » El Reno, Oklahoma » Grazinglands Research Laboratory » Agroclimate and Natural Resources Research » Research » Publications at this Location » Publication #361622

Research Project: Towards Resilient Agricultural Systems to Enhance Water Availability, Quality, and Other Ecosystem Services under Changing Climate and Land Use

Location: Agroclimate and Natural Resources Research

Title: Generation of synthetic daily weather for climate change scenarios and extreme storm intensification

Author
item Garbrecht, Jurgen
item Zhang, Xunchang
item Brown, David
item Busteed, Phillip

Submitted to: Environment and Natural Resources Research
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/15/2019
Publication Date: 3/20/2019
Citation: Garbrecht, J.D., Zhang, X.J., Brown, D.P., Busteed, P.R. 2019. Generation of synthetic daily weather for climate change scenarios and extreme storm intensification. Environment and Natural Resources Research. 9(2). https://doi.org/10.5539/enrr.v9n2p1.
DOI: https://doi.org/10.5539/enrr.v9n2p1

Interpretive Summary: Long-term simulations in watershed hydrology, soil and nutrient transport, and sustainability of agricultural production systems require long-term weather records that are often not available. Here a synthetic daily weather generation model is used generate alternative historical weather realizations as well as daily weather records for climate change scenarios. An application was presented for semi-arid climate conditions in west-central Oklahoma. Storm intensification was approximated by increasing the top 90 percentiles of storm distribution by a predefined amount based on previous studies of trends in United States precipitation. In order to best assess potential changes in weather type, frequencies, and intensities in a changing climate, alternative synthetic weather realizations are needed to enable the analysis of a range of potential responses and impacts, thereby capturing the uncertainty associated with applications that depend on the stochastic nature of weather. The merits of this proof-of-concept study extend beyond the specific example presented for west-central Oklahoma. In many instances, agricultural producers require reliable weather information at daily time scales to make critical decisions about crop planting and rotations, livestock grazing, and prescribed burning, among other management tools. The ability of a model to generate consistent daily weather conditions, under varying and user-defined climate change projection scenarios, provides both land managers and extension professionals with a tool to better ascertain risks and opportunities to production systems. Further exploration of the value of generated weather alternatives is recommended to establish the magnitude and increased risk of agricultural production targets and soil erosion thresholds due to climate change and extreme storm intensification.

Technical Abstract: Long-term simulations in watershed hydrology, soil and nutrient transport, and sustainability of agricultural production systems require long-term weather records that are often not available at the location of interest. Generation of synthetic daily weather data is a common approach to augment limited weather observations. Here a synthetic daily weather generation model (called SYNTOR) is described. SYNTOR fulfills the traditional role of generating alternative weather realizations that have statistical properties similar to those of the parent historical weather it is intended to simulate. In addition, it has the capability to simulate daily weather records for climate change scenarios and storm intensification due to climate change. The various model components are briefly summarized and an application is presented for semi-arid climate conditions in west-central Oklahoma. SYNTOR generated daily weather compared well with observed weather values. Climate change is simulated by adjusting weather generation parameters to reflect the changed mean monthly weather values of climate projections. Storm intensification is approximated by increasing the top 90 percentiles of storm distribution by a predefined amount based on previous studies of trends in United States precipitation. Further evaluation of published storm intensification values and associated uncertainties and spatial variability is recommended.