Progress 03/11/17 to 03/10/22
Outputs
PROGRESS REPORT 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. 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. Researchers at El Reno, Oklahoma substantially completed all the objectives set forth in the research project. Specifically, scientists developed two software programs (or computer tools), which explicitly take into consideration the storm intensification under climate change during climate downscaling. One program is called SYNthetic weather generaTOR (SYNTOR in short), and another is referred as a Generator for Point Climate Change (GPCC). Both programs were compared and evaluated against the other seven existing climate downscaling methods at four Oklahoma weather recording stations (Weatherford, Idabel, Hooker, Kingfisher) using daily precipitation data from 1949 to 2016 and were found to have slight advantages over the other methods for simulating precipitation frequency, sequency, and extremes. Both programs were used to downscale climate projections of 25 Global Climate Models (GCM)/ Regional Climate Models (RCM) for the two Green House Gases emission scenarios (RCP 8.5 and 2.6) for the period of 2021-2080 for the Weatherford site. The downscaled climate change scenarios were used to drive the Water Erosion Prediction Project (WEPP) model for simulating the impacts of climate change on soil loss, surface runoff, grain yield, available soil moisture, and above-ground biomass in four common tillage systems and 11 cropping systems. Tillage systems included conventional till, conservation till, no-till, and delayed till. Crops included winter wheat, soybean, sorghum, canola, and cotton, which were planted in continuous monoculture or in rotation with alfalfa. Before using the WEPP model, the model was calibrated using average grain yields measured in Oklahoma as well as the soil loss rates measured with H-flumes and Cs-137 tracer in several small research watersheds. The simulated model outputs were presented in the forms of tables and graphs and analyzed statistically for the ranges of variations and the 95% confidence intervals. Compared with the present climate, future precipitation at the study region would decrease by about 3%, and crop yields decline by 10 to 20% except cotton due to decreased precipitation and rising temperature. Despite a decrease in precipitation, surface runoff and soil loss would increase slightly by <7% and <30%, respectively, due to increased occurrence of extreme rainfall events. Results showed that no-till systems are most effective in reducing soil erosion, followed by crop rotations with alfalfa. ACCOMPLISHMENTS 01 Effects of climate change and crop management systems on soil erosion and crop production analyzed.. Proper simulation of storm intensification under climate change is critical in projecting crop yield, surface runoff, and soil loss. Such impact assessments are imperative for developing strategic plans for food security and resources conservation. ARS researchers at El Reno, Oklahoma, developed 100 climate scenarios coupled with storm intensification, which were based on 25 downscaled General Circulation Model (GCM) projections under the Representative Concentration Pathways (RCP) 4.5 and 8.5 for the 2021⿿2080 period. The climate files were used to drive the Water Erosion Prediction Project (WEPP) model to simulate crop yield, runoff, and soil loss under 29 combinations of 11 cropping systems and 4 tillage systems. Results showed that annual precipitation would decline by ~3% in the study region during 2021⿿2080; but the future extreme storm events would increase, leading to an increase in soil loss. The amounts of surface runoff and soil loss from extreme storms would account for 46% and 64% of the annual totals, respectively. Average runoff and soil losses in crop-alfalfa rotation or double cropping systems would be much less than those in continuous monoculture cropping systems. No-till and crop-alfalfa rotations are the best agricultural management alternatives to mitigate soil erosion. The analysis of variance indicated that the GCM projections and cropping and tillage systems were the major contributors of uncertainty for projecting surface runoff and soil loss. Results also showed that crop yields would generally decrease in the region, simply due to the decline in annual precipitation. This work improved the simulation of surface runoff and soil loss under climate change by integrating storm intensification from multiple GCM ensembles. The results would be useful to NRCS engineers and producers in Oklahoma to develop soil and water conservation plans that mitigate soil loss and surface runoff while enhancing crop productivity under future climate change.
Impacts
(N/A)
Publications
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Progress 10/01/20 to 09/30/21
Outputs
PROGRESS REPORT 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. 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. Sub-objective 2A: Research continued on calibrating and evaluating the Water Erosion Prediction Project (WEPP) model for simulating spatial distribution of soil erosion in small watersheds. Five input files including weather, soil, topography, watershed configuration and structure, and crop management including tillage operations were compiled for three small watersheds in Coshoction, Ohio for the period of 1950 to 2015 (65 years total). Runoff volumes and sediment yields from each rainfall event during 1950-2015 were also collected and compiled for each watershed. Spatial erosion distribution at a 10-m grid during 1950-2015 was estimated using measured activity (similar to elemental concentration) of the radionuclide cesium 137 (Cs-137), and the proportional sediment contributions between overland erosion and ephemeral gully erosion were also estimated with Cs-137 for each watershed. The WEPP model was calibrated sequentially as follows. First, a key soil water conductivity parameter was varied or calibrated to match WEPP-simulated runoff volumes with the measured volumes. Second, soil erodibility parameters (reflecting soil⿿s susceptibility to erosion) were calibrated to match WEPP-simulated sediment yields with the measured yields. Third, soil erodibility for ephemeral gully erosion was checked and calibrated if needed to make sure the proportional sediment contribution from the gully was similar to the proportion estimated using Cs-137 for each watershed. The second and third steps were repeated as needed. The calibrated WEPP model were used to simulate the spatial distribution of soil erosion. The WEPP-simulated spatial erosion patterns are being compared with those estimated by Cs-137 for each watershed to assess the WEPP model⿿s ability to simulate spatial variation of soil erosion along hillslopes and ephemeral gully in each watershed. The results should be useful for improving the physically based WEPP model in simulating spatial variation of soil erosion. (Objective 2, sub-objective 2A) Record of Any Impact of Maximized Teleworking Requirement: The laboratory⿿s maximized telework posture due to COVID-19 had both positive and negative impacts on the research project. For the computer modeling objectives, it had positive effect as telework saved travel time and enhanced morale and productivity. However, for the objective of the Southern Plains Climate Hub, it adversely hindered face-to-face interactions with producers. ACCOMPLISHMENTS 01 Simulated effects of climate change and crop management systems on soil erosion. Increased climate variability and extremity under climate change will have adverse impacts on agricultural production and environmental protection. Proper simulation of such impacts is imperative for policy makers and producers to develop strategic plans for soil and water conservation and for mitigation of the adverse impacts of climate changes. ARS scientists in El Reno, Oklahoma downscaled 100 climate scenarios, projected by 25 Global Climate Models (GCM) under two greenhouse gas emission levels, to the Weatherford station in Oklahoma using two statistically downscaling tools that specifically consider the frequency and magnitude of extreme precipitation events. Both tools were parameterized for the present climate, and then modified according to the GCM-projected climate change signals for generating future climate. Four common tillage systems and 11 cropping systems were simulated for crop management. Tillage systems included conventional till, conservation till, no-till, and delayed till. Crops included winter wheat, soybean, sorghum, canola, and cotton, which were planted in continuous monoculture or in rotation with alfalfa. Compared with the present climate, future precipitation at the station would decrease by about 5%, and crop yields decline by 10 to 20% except cotton due to decreased precipitation and rising temperature. Despite a decrease in precipitation, surface runoff and soil loss would increase slightly by <7% and <30%, respectively, due to increased occurrence of extreme rainfall events. Results showed that no-till systems are most effective in reducing soil erosion, followed by crop rotations with alfalfa. These results would be useful to policy makers, soil and water conservationists, and producers regarding how to adapt to climate change by adjusting cropping and tillage systems in central Oklahoma to keep soil loss below the tolerable levels.
Impacts
(N/A)
Publications
- Chen, J., Zhang, X.J. 2021. Challenges and potential solutions in statistical downscaling of precipitation. Climatic Change. 165(63):1-19. https://doi.org/10.1007/s10584-021-03083-3.
- Chen, J., Brissette, F.P., Martel, J., Zhang, X.J., Frei, A. 2021. Relative importance of internal climate variability versus anthropogenic climate change in global climate change. Journal of Climate. 34(2):465-478. https://doi.org/10.1175/JCLI-D-20-0424.1.
- Li, A., Zhang, X.J., Liu, B. 2021. Effects of DEM resolutions on soil erosion prediction using Chinese soil loss equation. Geomorphology. 384:107706. https://doi.org/10.1016/j.geomorph.2021.107706.
- Wang, L., Zheng, F., Liu, G., Zhang, X.J., Wilson, G.V., Shi, H., Liu, X. 2021. Seasonal changes of soil erosion and its spatial distribution on a long gentle hillslope in the Chinese Mollisol region. International Soil and Water Conservation Research. https://doi.org/10.1016/j.iswcr.2021.02. 001.
- Zhang, X.J., Shen, M., Chen, J., Homan, J.W., Busteed, P.R. 2021. Evaluation of statistical downscaling methods for simulating daily precipitation distribution, frequency, and temporal sequence. Transactions of the ASABE. 64(3). https://doi.org/10.13031/trans.14097.
- Chen, H., Liu, G., Zhang, X.J., Shi, H. 2021. Quantifying sediment source contributions in an agricultural catchment with ephemeral and classic gullies using 137Cs technique. Geoderma. 398:115112. https://doi.org/10. 1016/j.geoderma.2021.115112.
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Progress 10/01/19 to 09/30/20
Outputs
Progress Report 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. 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. 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 01 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.
Impacts
(N/A)
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.
- 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.
- 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.
- 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.
- 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.
- Zhang, X.J. 2020. Dynamic depth distribution of cesium-133 near soil surfaces in packed soils under multiple simulated rains. Catena. 194:104710.
- 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.
- Zhang, X.J., Zheng, F., Chen, J., Garbrecht, J.D. 2020. Characterizing detachment and transport processes of interrill soil erosion. Geoderma. 376:114549.
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Progress 10/01/18 to 09/30/19
Outputs
Progress Report 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. 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. Research continued on comparing the performances of ten different methods for downscaling precipitation spatially from a large area to a station, and temporally from monthly to daily precipitation values. Daily observed precipitation data from 1949 to 2016 at four Oklahoma weather recording stations (Weatherford, Idabel, Hooker, Kingfisher) were divided into two equal time periods for model calibration and validation. One set of analysis data from the National Centers for Environmental Prediction (NCEP) that overlies the four weather stations were downloaded from the web. The ten downscaling methods fall into one of three categories: 1) the machine learning category which included Artificial Neutral Networks, Support Vector Machine, and the K-Nearest Neighbors method; 2) the weather generator-based category which included a synthetic daily weather generator and the Generator for Point Climate Change (GPCC); and 3) the model output statistics category which included the mean- and distribution-based bias correction and delta change methods. All methods were trained for the calibration period by developing relationships between the large-scale NCEP predictors and the local-scale precipitation predictions. The calibrated methods were then applied to the validation time period and the NCEP data were downscaled to each weather station. The downscaled data were then compared to the observed data for each of the four weather stations to evaluate the performance of each method. Preliminary results indicated that machine learning methods performed the worst in downscaling climate data, while the performances of the weather generator-based methods and the model output statistics methods were acceptable, with the former being slightly better in simulating precipitation sequence and large storms. Comparison results will be used to select the most suitable downscaling methods for use in downscaling climate change projections and for assessing the impacts of climate change on crop production and soil erosion. (Objective 1; Sub-Objective 1.C) Research continued on developing, testing, and implementing an empirical- conceptual method to incorporate the intensification of extreme storm events predicted by the atmospheric and climate change science community into an existing synthetic daily weather generator. A weather generator with such capabilities is highly desirable to evaluate environmental impacts of potential climate variation and change scenarios. It was established that storm intensification is best treated in a post weather generation step. First, the baseline daily weather is generated, then subsequently the generated daily weather is modified to meet the specifications of the extreme storm intensification. Second, the adjusted daily precipitation record for storm intensification is used to simulate the crop production potential, the soil erosion, and other impacts resulting from storm intensification. Two alternative implementation options were tested. The first option increased all storm events above a user specified daily precipitation threshold value (for example upper 95 percentile of the storm distribution) by a fixed percentage (for example 20% increase). The second option identified three intense storm categories (heavy, very heavy, and extreme events) and increased the precipitation in each category separately by a fixed but different percentage for each category. Both methods were tested and implemented as a post-processing adjustment of the generated baseline daily weather. Both methods required a net addition of precipitation to produce the intensification portion of extreme storm events. Research is currently underway to reduce or remove the bias of the additional precipitation produced by storm intensification and also to address the increased number of extreme storm events to reflect the increased return frequency of storm events of given size. (Objective 1; Sub-Objective 1.A) The Southern Plains Climate Hub contributed to the overall project via applied research and science syntheses, dissemination of research results to technical and non-technical regional audiences, demonstration of climate-smart agricultural practices, and identification and leveraging of regional partner networks, including through cooperative agreements. 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 national USDA Climate Hub Executive Committee and regional Southern Plains Climate Hub-Department of Interior South Central Climate Adaptation Science Center joint stakeholder committee. (Objective 4; Sub-Objective N/A) Accomplishments 01 Spatial distribution of soil erosion is estimated using a Cs-137 tracer. Soil erosion is a worldwide problem causing severe land degradation. Soil erosion information is critical for developing effective soil conservation plans. The lack of spatial soil erosion data has been a major constraint on verifying and improving soil erosion computer models by agricultural engineers and soil erosion scientists. Spatial erosion distribution information is also useful to soil conservationists to layout precision conservation plans. However, spatial erosion data cannot be easily obtained by the conventional erosion measurement techniques using runoff plots and watershed monitoring. Researchers at El Reno in Oklahoma evaluated and improved a soil tracking technique for deriving spatial erosion data using Cs-137 (Cesium) tracer. Spatial characteristics of Cs-137 tracer distribution were characterized under three major land uses including grasslands, forestlands, and croplands. The best sampling schemes for more accurate estimation of soil erosion rates using Cs-137 were recommended based on the spatial characteristics of Cs-137 distribution. The recommended sampling design was used to sample soil cores in several research watersheds of Agricultural Research Service (ARS). The validated Cs-137 erosion models were used to convert the measured Cs-137 inventories to soil erosion rates for the study watersheds. The derived spatial soil erosion data will be used to calibrate the process-based soil erosion model such as Water Erosion Prediction Project (WEPP). The calibrated WEPP model will be used to simulate the impact of climate changes including future storm intensification on soil erosion and to select best soil conservation practices that keep soil erosion rates below a tolerable level. This technique will be of interest and useful to hydrologists, agricultural engineers, soil scientists, and soil conservationists that need to estimate soil erosion rates and to deploy precision soil conservation measures. 02 Synthetic weather generation is enhanced with extreme storm intensification capabilities. The climate change literature points to an unambiguous upward trend in the frequency of heavy to extreme storms in selected regions of the U.S. This intensification of extreme storms was expected to continue to increase and disrupt the environment by creating more frequent and severe flooding episodes. Related examples of potential long-term impacts include uncertainty of available water resource, sustainability of competing land management alternatives, profitability of agricultural cropping systems, and increasing water demand by a growing society. Investigations of such weather dependent problems generally require long-term weather records that are rarely available for the locale of interest. Computer based generation of synthetic daily weather data can provide help in many situations. Researchers at El Reno in Oklahoma enhanced an existing stochastic weather generator, called SYNTOR. Initially SYNTOR was design to generate alternative daily precipitation and air temperature realizations that had statistical properties similar to those of the parent historical weather it was intended to simulate. New capabilities were added to SYNTOR to facilitate simulation of daily weather records for anticipated climate change and storm intensification scenarios. Climate change was simulated by adjusting the weather generation parameters to reflect the change in mean monthly precipitation and air temperature values. Storm intensification was approximated by increasing the top percentiles of storm distributions by a user pre-specified precipitation amount. Applications of SYNTOR produced daily precipitation statistics that were consistent with those of related observations. The custom SYNTOR weather generator software and a User Manual are available upon request. 03 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, 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: analyses of regional variations and trends in intense precipitation, frost indicators, and drought (e.g., International Journal of Climatology, Journal of the American Water Resources Association, Environmental Research Letters, and Climate manuscripts); an evaluation of regional climate services activities across the Americas (e.g., Climate Services manuscript); a climate vulnerability assessment of a large urban forest in the region (e.g., June 2019 Austin Texas stakeholder workshop); successful acquisition of extramural funding (e.g., USDA National Institute of Food and Agriculture Sustainable Agricultural Systems grazing management project award), and establishing new research partnerships (e.g., USDA Forest Service Rocky Mountain Research Station interagency 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 04 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, 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: organization and facilitation of prescribed wildfire training schools for producers (e.g., January 2019 Concho Oklahoma, May 2019 Woodward Oklahoma); reporting on lessons learned for wildfire preparedness and recovery in the region (e.g., 2016-2018 Southern Plains Wildfire Assessment Report); training of USDA field staff on drought impacts reporting and monitoring (e.g., July 2019 High Plains Drought Monitoring Technical Workshop); continued expansion of precipitation observations in Oklahoma (e.g., Community Collaborative Rain, Hail, and Snow (CoCoRaHS) network); and continued demonstration of best practices for soil health management, including through new agreements (e.g., Quapaw Nation demonstration farm). These efforts promote more robust links between ARS science and agricultural production through the incorporation of climate-smart information and perspectives into management practices. 05 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, 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: demonstrating the value of climate-smart agriculture to USDA agency and program leaders (e.g., October 2018 USDA Farm Production and Conservation Technology Showcase); engaging conservation managers and practitioners on climate-smart tools, technologies, and information resources (e.g., special session at February 2019 National Association of Conservation Districts annual meeting); continuing to promote climate-smart agriculture through web- and social media-based platforms (e.g., Southern Plains Podcast); utilizing educational mechanisms to share climate-smart information with the next generation of agriculturalists (e.g., BlueSTEM AgriLearning Center teacher training events); and sustaining 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.
Impacts
(N/A)
Publications
- Kloesel, K., Bartush, B., Banner, J., Brown, D.P., Lemery, J., Lin, X., Loeffler, C., McManus, G., Mullens, E., Nielsen-Gammon, J., Shafer, M., Sorensen, C., Sperry, S., Wildcat, D., Ziolkowska, J. 2018. Southern Great Plains. In: Reidmiller, D.R., C.W. Avery, D.R. Easterling, K.E. Kunkel, K. L.M. Lewis, T.K. Maycock, and B.C. Stewart. Impacts, Risks, and Adaptation in the United States: Fourth National Climate Assessment, Volume II. Washington, DC, USA: U.S. Global Change Research Program. p. 978-1026.
- Chen, J., Brissette, F.P., Zhang, X.J., Chen, H., Guo, S., Zhang, Y. 2019. Bias correcting climate model multi-member ensembles to access climate change impacts on hydrology. Climatic Change. 153(3):361-377.
- Guo, Q., Chen, J., Zhang, X.J., Shen, M., Chen, H., Guo, S. 2019. A new two-stage multivariate quantile mapping method for bias correcting climate model outputs. Climate Dynamics.
- Zhang, X.J. 2018. Determining and modeling dominant processes of interrill soil erosion. Water Resources Research. 55(1):4-20.
- Niu, B., Qu, J., Zhang, X.J., Liu, B., Tan, L., An, Z. 2019. Quantifying provenance of reservoir sediment using multiple composite fingerprints in an arid region experiencing both wind and water erosion. Geomorphology. 332:112-121.
- Liu, J., Zhou, Z., Zhang, X.J. 2019. Impacts of sediment load and size on rill detachment under low flow discharges. Journal of Hydrology. 570:719- 725.
- Avila-Carrasco, R., Junez-Ferreira, H.E., Gowda, P., Steiner, J.L., Moriasi, D.N., Starks, P.J., Gonzalez, T.J., Villalobos, A.A., Bautista- Capetillo, C. 2018. Evaluation of satellite-derived rainfall data for multiple physio-climatic regions in the Santiago River Basin, Mexico. Journal of the American Water Resources Association. 54(5):1-19.
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Progress 10/01/17 to 09/30/18
Outputs
Progress Report 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. 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. 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. Accomplishments 01 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. 02 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. 03 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. 04 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. 05 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.
Impacts
(N/A)
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.
- 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.
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