Assessing Long-Term Hydrological Impact of Climate Change Using an Ensemble Approach and Comparison with Global Gridded Model-A Case Study on Goodwater Creek Experimental Watershed

Gautam, Sagar; Costello, Christine; Baffaut, Claire; Thompson, Allen L.; Svoma, Bohumil M.; Phung, Quang A.; Sadler, E. J.

doi:10.3390/w10050564

Cited by 21 publications

(25 citation statements)

References 79 publications

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“…On the other hand, Kang et al [8] and others indicated increased crop production with a modest rise in average temperature of 1-3 • C, but decreasing yields above this range. From the hydrological modeling perspective, the Soil and Water Assessment Tool (SWAT) [11] has been used to assess quality and quantity issues [12,13] to identify critical source areas [14] and impacts on crop-yield [15,16] due to changes in climate and land uses in order to suggest improved management practices [17].…”

Section: Introductionmentioning

confidence: 99%

Projected Climate Could Increase Water Yield and Cotton Yield but Decrease Winter Wheat and Sorghum Yield in an Agricultural Watershed in Oklahoma

Gharibdousti

Kharel

Miller

et al. 2019

Water

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Climate change impacts on agricultural watersheds are highly variable and uncertain across regions. This study estimated the potential impacts of the projected precipitation and temperature based on the downscaled Coupled Model Intercomparison Project 5 (CMIP-5) on hydrology and crop yield of a rural watershed in Oklahoma, USA. The Soil and Water Assessment Tool was used to model the watershed with 43 sub-basins and 15,217 combinations of land use, land cover, soil, and slope. The model was driven by the observed climate in the watershed and was first calibrated and validated against the monthly observed streamflow. Three statistical matrices, coefficient of determination (R 2 ), Nash-Sutcliffe efficiency (NSE), and percentage bias (PB), were used to gauge the model performance with satisfactory values of R 2 = 0.64, NS = 0.61, and PB = +5% in the calibration period, and R 2 = 0.79, NSE = 0.62, and PB = −15% in the validation period for streamflow. The model parameterization for the yields of cotton (PB = −4.5%), grain sorghum (PB = −27.3%), and winter wheat (PB = −6.0%) resulted in an acceptable model performance. The CMIP-5 ensemble of three General Circulation Models under three Representative Concentration Pathways for the 2016-2040 period indicated an increase in both precipitation (+1.5%) and temperature (+1.8 • C) in the study area. This changed climate resulted in decreased evapotranspiration (−3.7%), increased water yield (23.9%), decreased wheat yield (−5.2%), decreased grain sorghum yield (−9.9%), and increased cotton yield (+54.2%) compared to the historical climate. The projected increase in water yield might provide opportunities for groundwater recharge and additional water to meet future water demand in the region. The projected decrease in winter wheat yield-the major crop in the state-due to climate change, may require attention for ways to mitigate these effects.

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Section: Introductionmentioning

confidence: 99%

Projected Climate Could Increase Water Yield and Cotton Yield but Decrease Winter Wheat and Sorghum Yield in an Agricultural Watershed in Oklahoma

Gharibdousti

Kharel

Miller

et al. 2019

Water

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“…Gautam et al. () also found future spring and mean annual water yield will increase in the Goodwater Creek, Missouri due to projected changes in climate conditions.…”

Section: Resultsmentioning

confidence: 98%

“…Previous studies in the Midwest region had simulated an increase in spring precipitation and a decrease in summer precipitation under RCP 4.5 and RCP 8.5 (Gautam et al. ). Neupane and Kumar () also found precipitation during the spring months will increase, but will decrease in the summer by the end of the 21st Century in the Big Sioux River watershed, which is located in the North Central region and drains into the Missouri River.…”

Section: Resultsmentioning

confidence: 99%

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Climate and Land Use Effects on Hydrologic Processes in a Primarily Rain‐Fed, Agricultural Watershed

Phung

Thompson

Baffaut

et al. 2019

J American Water Resour Assoc

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Anticipating changes in hydrologic variables is essential for making socioeconomic water resource decisions. This study aims to assess the potential impact of land use and climate change on the hydrologic processes of a primarily rain‐fed, agriculturally based watershed in Missouri. A detailed evaluation was performed using the Soil and Water Assessment Tool for the near future (2020–2039) and mid‐century (2040–2059). Land use scenarios were mapped using the Conversion of Land Use and its Effects model. Ensemble results, based on 19 climate models, indicated a temperature increase of about 1.0°C in near future and 2.0°C in mid‐century. Combined climate and land use change scenarios showed distinct annual and seasonal hydrologic variations. Annual precipitation was projected to increase from 6% to 7%, which resulted in 14% more spring days with soil water content equal to or exceeding field capacity in mid‐century. However, summer precipitation was projected to decrease, a critical factor for crop growth. Higher temperatures led to increased potential evapotranspiration during the growing season. Combined with changes in precipitation patterns, this resulted in an increased need for irrigation by 38 mm representing a 10% increase in total irrigation water use. Analysis from multiple land use scenarios indicated converting agriculture to forest land can potentially mitigate the effects of climate change on streamflow, thus ensuring future water availability.

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“…We selected gamma-based quantile mapping as a bias correction technique as it has been reported as less sensitive to differences in climate projection time frame and emissions scenarios, when compared to empirical quantile mapping (Lafon et al 2013). Comprehensive bias correction details for precipitation adjustments can be found in Gautam et al (2018). After adjusting projections, we selected three models based on the following criteria for years 2070-2099: (1) an average model, representing the most typical precipitation conditions projected in RCP4.5 scenarios;…”

Section: Future Climate Projections and Historical Data: Approach Utimentioning

confidence: 99%

Framework for using downscaled climate model projections in ecological experiments to quantify plant and soil responses

et al. 2019

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Soil and plant responses to climate change can be quantified in controlled settings. However, the complexity of climate projections often leads researchers to evaluate ecosystem response based on general trends, rather than specific climate model outputs. Climate projections capture spatial and temporal climate extremes and variability that are lost when using mean climate trends. In addition, application of climate projections in experimental settings remains limited. Our objective was to develop a framework to incorporate statistically downscaled climate model projections into the design of temperature and precipitation treatments for ecological experiments. To demonstrate the utility of experimental treatments derived from climate projections, we used wetlands in the Great Plains as a model ecosystem for evaluating plant and soil responses. Spatial and temporal projections were selected to capture variability and intensity of projected future conditions for exemplary purposes. To illustrate climate projection application for ecological experiments, we developed temperature and precipitation treatments based on moderate‐emissions scenario climate outputs (i.e., RCP4.5–650 ppm CO2 equivalent). Our temperature treatments captured weekly trends that represented cool, average, and warm temperature predictions, and our daily precipitation treatments mimicked various seasonal precipitation trends and extreme events projected for the late 21st century. Treatments were applied to two short‐term controlled experiments evaluating (1) plant germination (temperature treatment applied in growth chamber) and (2) soil nitrogen cycling (precipitation treatment applied in greenhouse) responses to projected future conditions in the Great Plains. Our approach provides flexibility for selecting appropriate and precise climate model outputs to design experimental treatments. Using these techniques, ecologists can better incorporate variation in climate model projections for experimentally evaluating ecosystem responses to future climate conditions, reduce uncertainty in predictive ecological models, and apply predicted outcomes when making management and policy decisions.

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Assessing Long-Term Hydrological Impact of Climate Change Using an Ensemble Approach and Comparison with Global Gridded Model-A Case Study on Goodwater Creek Experimental Watershed

Cited by 21 publications

References 79 publications

Projected Climate Could Increase Water Yield and Cotton Yield but Decrease Winter Wheat and Sorghum Yield in an Agricultural Watershed in Oklahoma

Projected Climate Could Increase Water Yield and Cotton Yield but Decrease Winter Wheat and Sorghum Yield in an Agricultural Watershed in Oklahoma

Climate and Land Use Effects on Hydrologic Processes in a Primarily Rain‐Fed, Agricultural Watershed

Framework for using downscaled climate model projections in ecological experiments to quantify plant and soil responses

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