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: GREAT PLAINS AGROCLIMATE AND NATURAL RESOURCES RESEARCH

2020 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
Sub-objective 2B: Downscaling climate projections of 25 Global Climate Models (GCM)/Regional Climate Models (RCM) for better simulating future storm intensification. To screen GCM/RCM models, we evaluated 52 GCM projections and 24 RCM projections for the period of 1950-2005 for the Weatherford, Oklahoma, site by comparing the trends of the simulated intense rainfall storms with those of the observed storms during 1950-2005. We selected the top 25 models based on their performance ranks, and downloaded the precipitation and temperature projections of the 25 models for the period of 2005 to 2080 for the two green house gases emission scenarios (RCP 8.5 and 2.6) for the Weatherford site. We extracted climate change signals, including the magnitudes and frequencies of heavy storms, from the downloaded 25 models. A SYNthetic weather generaTOR (called SYNTOR) and a Generator for Point Climate Change (GPCC) were used to downscale future projections of the 25 models to the Weatherford location for the two periods of 2021 to 2050 and 2051 to 2080. The downscaling processes included the calibration of each GCM/RCM model for the period of 2005 to 2020, development of parameter values of SYNTOR and GPCC to simulate future daily precipitation sequence and amounts, and adjustment of heavy storm sizes through post-processing. The final 100 downscaled projections for the two future periods will be used to drive the Water Erosion Prediction Project (WEPP) model for simulating the impacts of future climate changes on water resources, soil erosion and crop production (Objective 2, Sub-objective 2B). Sub-objective 2A: Calibration of the Water Erosion Prediction Project (WEPP) model. Weather, soil, topography, sediment, surface runoff, and crop history and management data were collected and compiled for the three small watersheds (#123, 109, and 118) at the former USDA-ARS Appalachian Experimental Watershed Station, Coshoction, Ohio, for the period of 1950 to 2015. Data were used to compile four input files of climate, soil, topography, and crop management to drive the WEPP model. Four Cs-137 models were used to convert the measured Cs-137 inventories in the three watersheds to soil erosion rates at the 10-m sampling grid. The derived spatial soil erosion data in the three watersheds were used to calibrate the WEPP model for runoff and sediment prediction. The estimated proportion of soil erosion between ephemeral gully and hillslopes was used to calibrate the WEPP model to ensure that sediment source contributions were simulated correctly by WEPP. The calibrated WEPP model will be used to simulate the impact of climate changes, including future storm intensification, on soil erosion and availability of surface and soil water. The simulated results will also be used to select best soil conservation practices that keep soil erosion rates below a tolerable level (Objective 2, Sub-objective 2A).

Accomplishments
1. Nine climate downscaling methods evaluated. Spatial mismatch between Global Climate Model projections and input data needs of hydrological models inhibits projection of climate change impacts on soil erosion and crop production at field scales. Statistical methods are widely used to bridge the spatial gap. However, different downscaling methods often produce different climate change data, affecting simulated crop and soil loss yields. ARS researchers at El Reno, Oklahoma, evaluated the accuracy of downscaling methods for simulating daily precipitation amounts, frequency, and sequence at four Oklahoma stations representing arid to humid climate regions. The National Center for Environmental Prediction (NCEP) reanalysis data at about 250 km grid were downscaled to four stations by nine methods and compared with the measured data to evaluate the performance of each method. The top four methods were Generator for Point Climate Change, Synthetic Weather Generator, Distribution-based Bias Correction, and Local Bias Correction. The first two were weather generator-based methods, and the last two were bias correction methods. Overall results indicated that weather generator-based methods had certain advantages in simulating daily precipitation time series of future climate changes. The selected methods are being used to downscale climate change scenarios for the current project. The findings will be useful to climatologists and hydrologists who need to simulate potential impacts of climate changes on natural resources.

Review Publications
Liu, J., Zhang, X.J., Zhou, Z. 2019. Quantifying effects of root systems of planted and natural vegetation on rill detachment and erodibility of a loessial soil. Soil & Tillage Research. 195:104420. https://doi.org/10.1016/j.still.2019.104420.
Zheng, F., Zhang, X.J., Wang, J., Flanagan, D.C. 2019. Assessing applicability of the WEPP hillslope model to steep landscapes in the northern Loess Plateau of China. Soil & Tillage Research. 197:104492. https://doi.org/10.1016/j.still.2019.104492.
Deines, J.M., Schipanski, M.E., Golden, B., Zipper, S.C., Nozari, S., Rottler, C.M., Guerrero, B., Sharda, V. 2020. Transitions from irrigated to dryland agriculture in the Ogallala Aquifer: Land use suitability and regional economic impacts. Agricultural Water Management. 233:106061. https://doi.org/10.1016/j.agwat.2020.106061.
Peng, P., Zhang, X.J., Chen, J. 2019. Modeling the contributions of oceanic moisture to summer precipitation in eastern China using 18O. Journal of Hydrology. 581:124304. https://doi.org/10.1016/j.jhydrol.2019.124304.
Guo, Q., Chen, J., Zhang, X.J., Xu, C., Chen, H. 2020. Impacts of using state-of-the-art multivariate bias correction methods on hydrological modeling over North America. Water Resources Research. 56(5):e2019WR026659. https://doi.org/10.1029/2019WR026659.
Zhang, X.J. 2020. Dynamic depth distribution of cesium-133 near soil surfaces in packed soils under multiple simulated rains. Catena. 194:104710. https://doi.org/10.1016/j.catena.2020.104710.
Niu, B., Zhang, X.J., Qu, J., Liu, B., Homan, J.W., Tan, L., An, Z. 2019. Using multiple composite fingerprints to quantify source contributions and uncertainties in an arid region. Journal of Soils and Sediments. 20:1097–1111. https://doi.org/10.1007/s11368-019-02424-1.
Zhang, X.J., Zheng, F., Chen, J., Garbrecht, J.D. 2020. Characterizing detachment and transport processes of interrill soil erosion. Geoderma. 376:114549. https://doi.org/10.1016/j.geoderma.2020.114549.