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

Mohsen Tahmasebi Nasab, Kendall Grimm, Mohammad Hadi Bazrkar, Lan Zeng, Afshin Shabani, Xiaodong Zhang and Xuefeng Chu
Additional contact information
Mohsen Tahmasebi Nasab: Department of Civil and Environmental Engineering (Dept 2470), North Dakota State University, PO Box 6050, Fargo, ND 58108-6050, USA
Kendall Grimm: Department of Civil and Environmental Engineering (Dept 2470), North Dakota State University, PO Box 6050, Fargo, ND 58108-6050, USA
Mohammad Hadi Bazrkar: Department of Civil and Environmental Engineering (Dept 2470), North Dakota State University, PO Box 6050, Fargo, ND 58108-6050, USA
Lan Zeng: Department of Civil and Environmental Engineering (Dept 2470), North Dakota State University, PO Box 6050, Fargo, ND 58108-6050, USA
Afshin Shabani: Department of Earth System Science & Policy, University of North Dakota, 4149 University Ave Stop 9011, Grand Forks, ND 58202-6089, USA
Xiaodong Zhang: Department of Earth System Science & Policy, University of North Dakota, 4149 University Ave Stop 9011, Grand Forks, ND 58202-6089, USA
Xuefeng Chu: Department of Civil and Environmental Engineering (Dept 2470), North Dakota State University, PO Box 6050, Fargo, ND 58108-6050, USA

IJERPH, 2018, vol. 15, issue 11, 1-17

Abstract: 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.

Keywords: SWAT; hydrologic modeling; water quality modeling; depressions; watershed (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2018
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/1660-4601/15/11/2492/pdf (application/pdf)
https://www.mdpi.com/1660-4601/15/11/2492/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:15:y:2018:i:11:p:2492-:d:181379

Access Statistics for this article

IJERPH is currently edited by Ms. Jenna Liu

More articles in IJERPH from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().