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Sagar Gautam
Bioscience Division, Sandia National Laboratory, Livermore, California, USA

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Research article
Published: 26 April 2021 in Hydrological Sciences Journal
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Predicting the impacts of projected change in precipitation (P) and temperature (T) on occurrence of drought and extreme events are essential for managing natural resources and setting policy. This study compares future occurrence of excessively dry and wet periods based on P, T, stream flow, soil moisture, and extreme P and T events. The comparisons are based on coupled future climate projections from multiple Earth system models downscaled using site-specific weather data and hydrologic model outputs for the Goodwater Creek Experimental Watershed, Missouri, USA. The use of multiple drought indices, downscaled climate data, and process model output facilitated drought prediction comparison for different land surface processes and its comparison. The P and T extremes and droughts were calculated using standardized indices. The results based on drought and extreme indices indicate increased frequency and duration of drought in the future, primarily due to a projected decline in summer precipitation resulting in summer droughts. The streamflow and soil water-based drought indices indicated increased spring drought risks in the future despite a precipitation increase indicating the importance of process representation with hydrologic models for drought computation.

ACS Style

Sagar Gautam; Christine Costello; Claire Baffaut; Allen Thompson; E. John Sadler. Projection of future drought and extreme events occurrence in Goodwater Creek Experimental Watershed, Midwestern US. Hydrological Sciences Journal 2021, 66, 1045 -1058.

AMA Style

Sagar Gautam, Christine Costello, Claire Baffaut, Allen Thompson, E. John Sadler. Projection of future drought and extreme events occurrence in Goodwater Creek Experimental Watershed, Midwestern US. Hydrological Sciences Journal. 2021; 66 (6):1045-1058.

Chicago/Turabian Style

Sagar Gautam; Christine Costello; Claire Baffaut; Allen Thompson; E. John Sadler. 2021. "Projection of future drought and extreme events occurrence in Goodwater Creek Experimental Watershed, Midwestern US." Hydrological Sciences Journal 66, no. 6: 1045-1058.

Journal article
Published: 19 March 2021 in Scientific Reports
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Understanding the influence of environmental factors on soil organic carbon (SOC) is critical for quantifying and reducing the uncertainty in carbon climate feedback projections under changing environmental conditions. We explored the effect of climatic variables, land cover types, topographic attributes, soil types and bedrock geology on SOC stocks of top 1 m depth across conterminous United States (US) ecoregions. Using 4559 soil profile observations and high-resolution data of environmental factors, we identified dominant environmental controllers of SOC stocks in 21 US ecoregions using geographically weighted regression. We used projected climatic data of SSP126 and SSP585 scenarios from GFDL-ESM 4 Earth System Model of Coupled Model Intercomparison Project phase 6 to predict SOC stock changes across continental US between 2030 and 2100. Both baseline and predicted changes in SOC stocks were compared with SOC stocks represented in GFDL-ESM4 projections. Among 56 environmental predictors, we found 12 as dominant controllers across all ecoregions. The adjusted geospatial model with the 12 environmental controllers showed an R2 of 0.48 in testing dataset. Higher precipitation and lower temperatures were associated with higher levels of SOC stocks in majority of ecoregions. Changes in land cover types (vegetation properties) was important in drier ecosystem as North American deserts, whereas soil types and topography were more important in American prairies. Wetlands of the Everglades was highly sensitive to projected temperature changes. The SOC stocks did not change under SSP126 until 2100, however SOC stocks decreased up to 21% under SSP585. Our results, based on environmental controllers of SOC stocks, help to predict impacts of changing environmental conditions on SOC stocks more reliably and may reduce uncertainties found in both, geospatial and Earth System Models. In addition, the description of different environmental controllers for US ecoregions can help to describe the scope and importance of global and local models.

ACS Style

Daniel Ruiz Potma Gonçalves; Umakant Mishra; Skye Wills; Sagar Gautam. Regional environmental controllers influence continental scale soil carbon stocks and future carbon dynamics. Scientific Reports 2021, 11, 1 -10.

AMA Style

Daniel Ruiz Potma Gonçalves, Umakant Mishra, Skye Wills, Sagar Gautam. Regional environmental controllers influence continental scale soil carbon stocks and future carbon dynamics. Scientific Reports. 2021; 11 (1):1-10.

Chicago/Turabian Style

Daniel Ruiz Potma Gonçalves; Umakant Mishra; Skye Wills; Sagar Gautam. 2021. "Regional environmental controllers influence continental scale soil carbon stocks and future carbon dynamics." Scientific Reports 11, no. 1: 1-10.

Original research
Published: 05 August 2020 in GCB Bioenergy
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National scale projections of bioenergy crop yields and their environmental impacts are essential to identify appropriate locations to place bioenergy crops and ensure sustainable land use strategies. In this study, we used the process‐based Daily Century (DAYCENT) model with site‐specific environmental data to simulate sorghum (Sorghum bicolor L. Moench) biomass yield, soil organic carbon (SOC) change, and nitrous oxide emissions across cultivated lands in the continental United States (US). The simulated rainfed dry biomass productivity ranged from 0.8 to 19.2 Mg ha‐1 yr‐1, with a spatiotemporal average of Mg ha‐1 yr‐1, and a coefficient of variation of 35%. The average SOC sequestration and direct nitrous oxide emission rates were simulated as Mg CO2e ha‐1 yr‐1 and Mg CO2e ha‐1 yr‐1, respectively. Compared to field‐observed biomass yield data at multiple locations, model predictions of biomass productivity showed a root mean square error (RMSE) of 5.6 Mg ha‐1 yr‐1. In comparison to the multi state (n=21) NASS database, our results showed RMSE of 5.5 Mg ha‐1 yr‐1. Model projections of baseline SOC showed RMSE of 1.9 kg m‐2 in comparison to a recently available continental SOC stock dataset. The model‐predicted N2O emissions are close to 1.25% of N input. Our results suggest 10.2 million ha of cultivated lands in the Southern and Lower Midwestern US will produce >10 Mg ha‐1 yr‐1 with net carbon sequestration under rainfed conditions. Cultivated lands in Upper Midwestern states including Iowa, Minnesota, Montana, Michigan and North Dakota showed lower sorghum biomass productivity (average: 6.9 Mg ha‐1 yr‐1) with net sequestration (average: 0.13 Mg CO2e ha‐1 yr‐1). Our national‐scale spatially explicit results are critical inputs for robust life‐cycle assessment of bioenergy production systems and land use‐based climate change mitigation strategies.

ACS Style

Sagar Gautam; Umakant Mishra; Corinne D. Scown; Yao Zhang. Sorghum biomass production in the continental United States and its potential impacts on soil organic carbon and nitrous oxide emissions. GCB Bioenergy 2020, 12, 878 -890.

AMA Style

Sagar Gautam, Umakant Mishra, Corinne D. Scown, Yao Zhang. Sorghum biomass production in the continental United States and its potential impacts on soil organic carbon and nitrous oxide emissions. GCB Bioenergy. 2020; 12 (10):878-890.

Chicago/Turabian Style

Sagar Gautam; Umakant Mishra; Corinne D. Scown; Yao Zhang. 2020. "Sorghum biomass production in the continental United States and its potential impacts on soil organic carbon and nitrous oxide emissions." GCB Bioenergy 12, no. 10: 878-890.

Emerging technologies
Published: 04 September 2019 in Ecosphere
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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.

ACS Style

Rachel K. Owen; Elisabeth B. Webb; Keith W. Goyne; Bohumil M. Svoma; Sagar Gautam. Framework for using downscaled climate model projections in ecological experiments to quantify plant and soil responses. Ecosphere 2019, 10, 1 .

AMA Style

Rachel K. Owen, Elisabeth B. Webb, Keith W. Goyne, Bohumil M. Svoma, Sagar Gautam. Framework for using downscaled climate model projections in ecological experiments to quantify plant and soil responses. Ecosphere. 2019; 10 (9):1.

Chicago/Turabian Style

Rachel K. Owen; Elisabeth B. Webb; Keith W. Goyne; Bohumil M. Svoma; Sagar Gautam. 2019. "Framework for using downscaled climate model projections in ecological experiments to quantify plant and soil responses." Ecosphere 10, no. 9: 1.

Technical paper
Published: 02 July 2019 in JAWRA Journal of the American Water Resources Association
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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.

ACS Style

Quang A. Phung; Allen L. Thompson; Claire Baffaut; Christine Costello; E. John Sadler; Bohumil M. Svoma; Anthony Lupo; Sagar Gautam. Climate and Land Use Effects on Hydrologic Processes in a Primarily Rain‐Fed, Agricultural Watershed. JAWRA Journal of the American Water Resources Association 2019, 55, 1196 -1215.

AMA Style

Quang A. Phung, Allen L. Thompson, Claire Baffaut, Christine Costello, E. John Sadler, Bohumil M. Svoma, Anthony Lupo, Sagar Gautam. Climate and Land Use Effects on Hydrologic Processes in a Primarily Rain‐Fed, Agricultural Watershed. JAWRA Journal of the American Water Resources Association. 2019; 55 (5):1196-1215.

Chicago/Turabian Style

Quang A. Phung; Allen L. Thompson; Claire Baffaut; Christine Costello; E. John Sadler; Bohumil M. Svoma; Anthony Lupo; Sagar Gautam. 2019. "Climate and Land Use Effects on Hydrologic Processes in a Primarily Rain‐Fed, Agricultural Watershed." JAWRA Journal of the American Water Resources Association 55, no. 5: 1196-1215.

Journal article
Published: 26 April 2018 in Water
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Potential impacts of climate change on the hydrological components of the Goodwater Creek Experimental Watershed were assessed using climate datasets from the Coupled Model Intercomparison Project Phase 5 and Soil and Water Assessment Tool (SWAT). Historical and future ensembles of downscaled precipitation and temperature, and modeled water yield, surface runoff, and evapotranspiration, were compared. Ensemble SWAT results indicate increased springtime precipitation, water yield, surface runoff and a shift in evapotranspiration peak one month earlier in the future. To evaluate the performance of model spatial resolution, gridded surface runoff estimated by Lund–Potsdam–Jena managed Land (LPJmL) and Jena Diversity-Dynamic Global Vegetation model (JeDi-DGVM) were compared to SWAT. Long-term comparison shows a 6–8% higher average annual runoff prediction for LPJmL, and a 5–30% lower prediction for JeDi-DGVM, compared to SWAT. Although annual runoff showed little change for LPJmL, monthly runoff projection under-predicted peak runoff and over-predicted low runoff for LPJmL compared to SWAT. The reasons for these differences include differences in spatial resolution of model inputs and mathematical representation of the physical processes. Results indicate benefits of impact assessments at local scales with heterogeneous sets of parameters to adequately represent extreme conditions that are muted in global gridded model studies by spatial averaging over large study domains.

ACS Style

Sagar Gautam; Christine Costello; Claire Baffaut; Allen Thompson; Bohumil M. Svoma; Quang A. Phung; Edward J. Sadler. 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. Water 2018, 10, 564 .

AMA Style

Sagar Gautam, Christine Costello, Claire Baffaut, Allen Thompson, Bohumil M. Svoma, Quang A. Phung, Edward J. Sadler. 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. Water. 2018; 10 (5):564.

Chicago/Turabian Style

Sagar Gautam; Christine Costello; Claire Baffaut; Allen Thompson; Bohumil M. Svoma; Quang A. Phung; Edward J. Sadler. 2018. "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." Water 10, no. 5: 564.

Journal article
Published: 09 February 2018 in Rangeland Ecology & Management
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Runoff from grazing pasture lands can impact water quality in receiving streams if not well managed. Management consists of conservation practices to reduce runoff and pollutants transport. Simulation models have been effectively used to design and implement these conservation practices. The Agricultural Policy Environmental Extender (APEX), a process-based hydrologic model, was used in this study to simulate the management impacts on surface runoff from three small grazed pasture watersheds located at the North Appalachian Experimental Watersheds near Coshocton, Ohio. Specific objectives of this study were to 1) calibrate the APEX model and test runoff predictions against measured runoff and 2) simulate the long-term impacts of different management scenarios on surface runoff. Results show that the APEX model simulated surface runoff reasonably well with the coefficient of determination (R2) and Nash-Sutcliffe efficiency values varying from 0.49 to 0.72 and from 0.25 to 0.60 for calibration and validation, respectively. After validation, the APEX model was run for 37 yr (1975 − 2011) for long-term scenarios to analyze the impacts of soil properties and management on surface runoff. Data from this study indicated that keeping the watershed land use as a hay meadow instead of grazing significantly reduced cumulative runoff by 58 − 67%. Buffer strips of perennial grasses resulted in decreased simulated runoff. To simulate the impacts of soils on runoff, the surface (0 − 5 cm) soil properties of the toe position were applied to the entire grazed watershed. Subsequently, the increase in soil richness resulted in reduction (≤ 5%) in surface runoff. The simulation results from the present study demonstrate the benefits of hayed meadow over grazed pasture and further predict the decreased trend of runoff due to soil properties change and buffer strips.

ACS Style

Sagar Gautam; Eric Mbonimpa; Sandeep Kumar; James Bonta. Simulating Runoff from Small Grazed Pasture Watersheds Located at North Appalachian Experimental Watershed in Ohio. Rangeland Ecology & Management 2018, 71, 363 -369.

AMA Style

Sagar Gautam, Eric Mbonimpa, Sandeep Kumar, James Bonta. Simulating Runoff from Small Grazed Pasture Watersheds Located at North Appalachian Experimental Watershed in Ohio. Rangeland Ecology & Management. 2018; 71 (3):363-369.

Chicago/Turabian Style

Sagar Gautam; Eric Mbonimpa; Sandeep Kumar; James Bonta. 2018. "Simulating Runoff from Small Grazed Pasture Watersheds Located at North Appalachian Experimental Watershed in Ohio." Rangeland Ecology & Management 71, no. 3: 363-369.

Journal article
Published: 14 May 2015 in Computers and Electronics in Agriculture
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The Agricultural Policy Environmental eXtender (APEX), a comprehensive hydrologic model well-suited for small watersheds, requires understanding of the input parameters for improved calibration. The “trial and error” method for calibrating the APEX model has been used very commonly in previous studies. In this study, the automatic calibration software Parameter Estimation (PEST) was combined with the conventional trial-and-error method to improve APEX calibration. The proposed Combined PEST and Trial–Error (CPTE) approach can overcome: (i) weaknesses of “Trial–Error” method in terms of tediousness and subjectivity involved in the decision to end a calibration, and (ii) drawback of PEST in that it may lead to biased simulation due to ignoring local specific condition. A case study was developed to verify the CPTE approach. The results based on APEX runoff simulation indicate that the CPTE approach greatly improved the calibration of APEX model with respect to model performance criteria. Coupling inverse modeling and trial–error manual method can be an efficient and effective alternative in calibrating the APEX model.

ACS Style

E.G. Mbonimpa; Sagar Gautam; L. Lai; S. Kumar; J.V. Bonta; X. Wang; R. Rafique. Combined PEST and Trial–Error approach to improve APEX calibration. Computers and Electronics in Agriculture 2015, 114, 296 -303.

AMA Style

E.G. Mbonimpa, Sagar Gautam, L. Lai, S. Kumar, J.V. Bonta, X. Wang, R. Rafique. Combined PEST and Trial–Error approach to improve APEX calibration. Computers and Electronics in Agriculture. 2015; 114 ():296-303.

Chicago/Turabian Style

E.G. Mbonimpa; Sagar Gautam; L. Lai; S. Kumar; J.V. Bonta; X. Wang; R. Rafique. 2015. "Combined PEST and Trial–Error approach to improve APEX calibration." Computers and Electronics in Agriculture 114, no. : 296-303.

Journal article
Published: 01 March 2015 in Journal of Soil and Water Conservation
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Long-term hydrologic data sets are required to quantify the impacts of management and climate on runoff at the field scale where management practices are applied. This study was conducted to evaluate the impacts of long-term management and climate on runoff from a small watershed managed with no-till (NT) system. The Agricultural Policy Environmental eXtender (APEX), a field scale hydrologic model which is capable of simulating the management and climate impacts on runoff, was used in this study. The specific objectives of the study were to (1) simulate the impacts of cropping management and tillage system on runoff and (2) simulate climate change impacts on runoff using different temperature, precipitation, and carbon dioxide (CO2) scenarios generated from the APEX model. The study was conducted on a small watershed located on the North Appalachian Experimental Watershed (NAEW) near Coshocton, Ohio. This watershed (WS 118, ~0.79 ha [1.95 ac]) includes NT management with two periods of crop rotations: corn (Zea mays L.)–soybean (Glycine max L.)–rye (Secale cereale L.) (CSR; 2000 to 2005) and continuous corn (CC; 2006 to 2011). The results from this study indicate that the CSR rotation showed 37% lower simulated mean annual runoff compared with that of CC under NT system. The climate change scenarios indicated runoff was most sensitive to the precipitation, and interactions of precipitation, temperature, and CO2 concentrations. The highest increase of runoff (61%) was observed with 15% increase of precipitation, and the highest reduction in runoff (47%) with 15% decrease in precipitation, demonstrating the nonlinearity of hydrological systems. The results demonstrate the benefits of cover crops in the CSR over the CC rotation under NT system and show the significant impacts of climate change on runoff response from a small, upland, agricultural watershed. For future research, climate change impacts on runoff can be assessed using downscaled climate models that take into consideration interaction among weather parameters.

ACS Style

Sagar Gautam; E. G. Mbonimpa; S. Kumar; J. V. Bonta; R. Lal. Agricultural Policy Environmental eXtender model simulation of climate change impacts on runoff from a small no-till watershed. Journal of Soil and Water Conservation 2015, 70, 101 -109.

AMA Style

Sagar Gautam, E. G. Mbonimpa, S. Kumar, J. V. Bonta, R. Lal. Agricultural Policy Environmental eXtender model simulation of climate change impacts on runoff from a small no-till watershed. Journal of Soil and Water Conservation. 2015; 70 (2):101-109.

Chicago/Turabian Style

Sagar Gautam; E. G. Mbonimpa; S. Kumar; J. V. Bonta; R. Lal. 2015. "Agricultural Policy Environmental eXtender model simulation of climate change impacts on runoff from a small no-till watershed." Journal of Soil and Water Conservation 70, no. 2: 101-109.

Journal article
Published: 02 January 2014 in Soil Science and Plant Nutrition
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ACS Style

S. Kumar; T. Nakajima; E.G. Mbonimpa; Sagar Gautam; U.R. Somireddy; A. Kadono; R. Lal; R. Chintala; R. Rafique; N. Fausey. Long-term tillage and drainage influences on soil organic carbon dynamics, aggregate stability and corn yield. Soil Science and Plant Nutrition 2014, 60, 108 -118.

AMA Style

S. Kumar, T. Nakajima, E.G. Mbonimpa, Sagar Gautam, U.R. Somireddy, A. Kadono, R. Lal, R. Chintala, R. Rafique, N. Fausey. Long-term tillage and drainage influences on soil organic carbon dynamics, aggregate stability and corn yield. Soil Science and Plant Nutrition. 2014; 60 (1):108-118.

Chicago/Turabian Style

S. Kumar; T. Nakajima; E.G. Mbonimpa; Sagar Gautam; U.R. Somireddy; A. Kadono; R. Lal; R. Chintala; R. Rafique; N. Fausey. 2014. "Long-term tillage and drainage influences on soil organic carbon dynamics, aggregate stability and corn yield." Soil Science and Plant Nutrition 60, no. 1: 108-118.