<i>Multi-variable sensitivity analysis, calibration, and validation of a field-scale SWAT model: Building Stakeholder Trust in Hydrologic/Water Quality Modeling</i>

2019 ◽  
Author(s):  
Ritesh Karki ◽  
Puneet Srivastava ◽  
Latif Kalin ◽  
Jasmeet Lamba ◽  
David D. Bosch
Keyword(s):  
Water Quality ◽  
Sensitivity Analysis ◽  
Model Building ◽  
Swat Model ◽  
Water Quality Modeling ◽  
Field Scale ◽  
Calibration And Validation ◽  
Quality Modeling ◽  
Variable Sensitivity

Multi-Variable Sensitivity Analysis, Calibration, and Validation of a Field-Scale SWAT Model: Building Stakeholder Trust in Hydrologic and Water Quality Modeling

2020 ◽  
Vol 63 (2) ◽  
pp. 523-539 ◽  
Author(s):  
Ritesh Karki ◽  
Puneet Srivastava ◽  
David D. Bosch ◽  
Latif Kalin ◽  
Jasmeet Lamba ◽  
...  
Keyword(s):  
Water Quality ◽  
Soil Moisture ◽  
Crop Yield ◽  
Swat Model ◽  
Nutrient Transport ◽  
Agricultural Productivity ◽  
Water Quality Modeling ◽  
Field Scale ◽  
Management Scenarios ◽  
Model Robustness

HighlightsSWAT can adequately simulate runoff, soil moisture, cotton and peanut yields, and nitrate at field scale.Muskingum routing and adjusting DIS_STREAM are important to simulate fields as watersheds rather than HRUs.Crop yield calibration is critical for improving SWAT model robustness in nutrient transport simulations and for building stakeholder trust.SWAT can quantify the impacts of different management scenarios at the field scale.Abstract. Multi-variable calibration of a field-scale Soil and Water Assessment (SWAT) model is critical for understanding the true impacts of irrigation and nutrient best management practices (BMPs) on hydrology, water quality, and agricultural productivity and for building stakeholder trust for eventual BMP implementation at the watershed scale. This study evaluated the ability of SWAT to simulate runoff, soil moisture, cotton and peanut yields, and nitrate in conventionally tilled and strip-tilled plots while also evaluating the differences in hydrological and nutrient simulation parameters for the two tillage practices. Modeling results showed that SWAT adequately simulated runoff, soil moisture, cotton and peanut yields, and nitrate at the field scale and that calibrated values for the curve number of operation (CNOP) were different for the conventionally tilled and strip-tilled plots and critical to runoff calibration. It was also important to change the routing method from variable storage to Muskingum and to adjust DIS_STREAM for runoff simulation if the field was to be simulated as a watershed rather than as an HRU. Sequential calibration of surface runoff, soil moisture, crop yield, and nitrate showed that crop yield can be an important consideration for improving SWAT model robustness in nutrient transport simulations. Soil moisture calibration did not have a significant effect on runoff simulations. Evaluation of the impacts of different management scenarios showed that soil moisture sensor-based irrigation, cover crop, and strip tillage had the highest potential for reducing nutrient loss and conserving water while maintaining agricultural productivity in southern Georgia. This study also demonstrated to stakeholders that the SWAT model can successfully quantify the impacts of different management scenarios on their farm fields. Keywords: Agricultural BMPs, Field-scale SWAT, Multi-variable calibration, SWAT, SWAT-CUP.

scholarly journals Applying Monte Carlo and classification tree sensitivity analysis to the Zayandehrood River

2011 ◽  
Vol 14 (1) ◽  
pp. 236-250 ◽  
Author(s):  
Nader Nakhaei ◽  
Amir Etemad-Shahidi
Keyword(s):  
Water Quality ◽  
Sensitivity Analysis ◽  
Monte Carlo ◽  
Decay Rate ◽  
Classification Tree ◽  
Monte Carlo Analysis ◽  
Water Quality Modeling ◽  
Classification Tree Analysis ◽  
Tree Analysis ◽  
Quality Modeling

Water quality modeling is an important issue for both engineers and scientists. The QUAL2K model is a simulation tool that has been used widely for this purpose. Uncertainty and sensitivity analysis is a major step in water quality modeling. This article reports application of Monte Carlo analysis and classification tree sensitivity analysis in the modeling of the Zayandehrood River. First the model was calibrated and validated using two sets of data. Then, three input values (stream flow, roughness and decay rate) were considered for both analyses. The Monte Carlo analysis was conducted using triangular distribution of probability of occurrence for the input parameters. The classification tree analysis classifies outcome values into non-numeric categories. Considering the relationships between the input parameters in the classification tree analysis is the most important advantage of it. The analyses demonstrated the key input variables for three points of the river. The dissolved oxygen levels were mainly sensitive to the decay rate coefficient along the river.

scholarly journals SWAT Modeling of Non-Point Source Pollution in Depression-Dominated Basins under Varying Hydroclimatic Conditions

2018 ◽  
Vol 15 (11) ◽  
pp. 2492 ◽  
Author(s):  
Mohsen Tahmasebi Nasab ◽  
Kendall Grimm ◽  
Mohammad Bazrkar ◽  
Lan Zeng ◽  
Afshin Shabani ◽  
...  
Keyword(s):  
Water Quality ◽  
Point Source ◽  
Assessment Tool ◽  
The United States ◽  
Water Quality Modeling ◽  
Water Quantity ◽  
Basic Scenario ◽  
Calibration And Validation ◽  
Calibration Scheme ◽  
Quality Modeling

Non-point source (NPS) pollution from agricultural lands is the leading cause of various water quality problems across the United States. Particularly, surface depressions often alter the releasing patterns of NPS pollutants into the environment. However, most commonly-used hydrologic models may not be applicable to such depression-dominated regions. The objective of this study is to improve water quantity/quality modeling and its calibration for depression-dominated basins under wet and dry hydroclimatic conditions. Specifically, the Soil and Water Assessment Tool (SWAT) was applied for hydrologic and water quality modeling in the Red River of the North Basin (RRB). Surface depressions across the RRB were incorporated into the model by employing a surface delineation method and the impacts of depressions were evaluated for two modeling scenarios, MS1 (basic scenario) and MS2 (depression-oriented scenario). Moreover, a traditional calibration scheme (CS1) was compared to a wet-dry calibration scheme (CS2) that accounted for the effects of hydroclimatic variations on hydrologic and water quality modeling. Results indicated that the surface runoff simulation and the associated water quality modeling were improved when topographic characteristics of depressions were incorporated into the model (MS2). The Nash–Sutcliffe efficiency (NSE) coefficient indicated an average increase of 30.4% and 19.6% from CS1 to CS2 for the calibration and validation periods, respectively. Additionally, the CS2 provided acceptable simulations of water quality, with the NSE values of 0.50 and 0.74 for calibration and validation periods, respectively. These results highlight the enhanced capability of the proposed approach for simulating water quantity and quality for depression-dominated basins under the influence of varying hydroclimatic conditions.

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