Assessing Parameter Uncertainty of a Semi‐Distributed Hydrology Model for a Shallow Aquifer Dominated Environmental System
This paper examines the performance of a semi‐distributed hydrology model (i.e., Soil and Water Assessment Tool [SWAT]) using Sequential Uncertainty FItting (SUFI‐2), generalized likelihood uncertainty estimation (GLUE), parameter solution (ParaSol), and particle swarm optimization (PSO). We applied...
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| Published in | Journal of the American Water Resources Association Vol. 53; no. 6; pp. 1368 - 1389 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Middleburg
Blackwell Publishing Ltd
01.12.2017
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1093-474X 1752-1688 |
| DOI | 10.1111/1752-1688.12596 |
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| Abstract | This paper examines the performance of a semi‐distributed hydrology model (i.e., Soil and Water Assessment Tool [SWAT]) using Sequential Uncertainty FItting (SUFI‐2), generalized likelihood uncertainty estimation (GLUE), parameter solution (ParaSol), and particle swarm optimization (PSO). We applied SWAT to the Waccamaw watershed, a shallow aquifer dominated Coastal Plain watershed in the Southeastern United States (U.S.). The model was calibrated (2003‐2005) and validated (2006‐2007) at two U.S. Geological Survey gaging stations, using significant parameters related to surface hydrology, hydrogeology, hydraulics, and physical properties. SWAT performed best during intervals with wet and normal antecedent conditions with varying sensitivity to effluent channel shape and characteristics. In addition, the calibration of all algorithms depended mostly on Manning's n‐value for the tributary channels as the surface friction resistance factor to generate runoff. SUFI‐2 and PSO simulated the same relative probability distribution tails to those observed at an upstream outlet, while all methods (except ParaSol) exhibited longer tails at a downstream outlet. The ParaSol model exhibited large skewness suggesting a global search algorithm was less capable of characterizing parameter uncertainty. Our findings provide insights regarding parameter sensitivity and uncertainty as well as modeling diagnostic analysis that can improve hydrologic theory and prediction in complex watersheds. Editor's note: This paper is part of the featured series on SWAT Applications for Emerging Hydrologic and Water Quality Challenges. See the February 2017 issue for the introduction and background to the series. |
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| AbstractList | This paper examines the performance of a semi‐distributed hydrology model (i.e., Soil and Water Assessment Tool [SWAT]) using Sequential Uncertainty FItting (SUFI‐2), generalized likelihood uncertainty estimation (GLUE), parameter solution (ParaSol), and particle swarm optimization (PSO). We applied SWAT to the Waccamaw watershed, a shallow aquifer dominated Coastal Plain watershed in the Southeastern United States (U.S.). The model was calibrated (2003‐2005) and validated (2006‐2007) at two U.S. Geological Survey gaging stations, using significant parameters related to surface hydrology, hydrogeology, hydraulics, and physical properties. SWAT performed best during intervals with wet and normal antecedent conditions with varying sensitivity to effluent channel shape and characteristics. In addition, the calibration of all algorithms depended mostly on Manning's n‐value for the tributary channels as the surface friction resistance factor to generate runoff. SUFI‐2 and PSO simulated the same relative probability distribution tails to those observed at an upstream outlet, while all methods (except ParaSol) exhibited longer tails at a downstream outlet. The ParaSol model exhibited large skewness suggesting a global search algorithm was less capable of characterizing parameter uncertainty. Our findings provide insights regarding parameter sensitivity and uncertainty as well as modeling diagnostic analysis that can improve hydrologic theory and prediction in complex watersheds. Editor's note: This paper is part of the featured series on SWAT Applications for Emerging Hydrologic and Water Quality Challenges. See the February 2017 issue for the introduction and background to the series. This paper examines the performance of a semi‐distributed hydrology model ( i.e ., Soil and Water Assessment Tool [ SWAT ]) using Sequential Uncertainty FI tting ( SUFI ‐2), generalized likelihood uncertainty estimation ( GLUE ), parameter solution (ParaSol), and particle swarm optimization ( PSO ). We applied SWAT to the Waccamaw watershed, a shallow aquifer dominated Coastal Plain watershed in the Southeastern United States (U.S.). The model was calibrated (2003‐2005) and validated (2006‐2007) at two U.S. Geological Survey gaging stations, using significant parameters related to surface hydrology, hydrogeology, hydraulics, and physical properties. SWAT performed best during intervals with wet and normal antecedent conditions with varying sensitivity to effluent channel shape and characteristics. In addition, the calibration of all algorithms depended mostly on Manning's n ‐value for the tributary channels as the surface friction resistance factor to generate runoff. SUFI ‐2 and PSO simulated the same relative probability distribution tails to those observed at an upstream outlet, while all methods (except ParaSol) exhibited longer tails at a downstream outlet. The ParaSol model exhibited large skewness suggesting a global search algorithm was less capable of characterizing parameter uncertainty. Our findings provide insights regarding parameter sensitivity and uncertainty as well as modeling diagnostic analysis that can improve hydrologic theory and prediction in complex watersheds. Editor's note : This paper is part of the featured series on SWAT Applications for Emerging Hydrologic and Water Quality Challenges. See the February 2017 issue for the introduction and background to the series . |
| Author | Carbone, G.J. Samadi, S. Tufford, D.L. |
| Author_xml | – sequence: 1 givenname: S. surname: Samadi fullname: Samadi, S. email: samadi@cec.sc.edu organization: University of South Carolina – sequence: 2 givenname: D.L. surname: Tufford fullname: Tufford, D.L. organization: University of South Carolina – sequence: 3 givenname: G.J. surname: Carbone fullname: Carbone, G.J. organization: University of South Carolina |
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| CitedBy_id | crossref_primary_10_1016_j_jhydrol_2023_129740 crossref_primary_10_1088_1748_9326_ac7247 crossref_primary_10_1016_j_ejrh_2018_12_005 crossref_primary_10_1007_s00477_022_02182_6 crossref_primary_10_1038_s41598_019_46963_6 crossref_primary_10_3390_w11040709 crossref_primary_10_1002_hyp_14955 crossref_primary_10_1029_2019MS001924 crossref_primary_10_1111_1752_1688_12601 crossref_primary_10_2166_wcc_2019_122 crossref_primary_10_3390_su15107778 crossref_primary_10_1007_s00477_017_1489_6 crossref_primary_10_3390_hydrology7020021 crossref_primary_10_1007_s00477_022_02377_x |
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| Snippet | This paper examines the performance of a semi‐distributed hydrology model (i.e., Soil and Water Assessment Tool [SWAT]) using Sequential Uncertainty FItting... This paper examines the performance of a semi‐distributed hydrology model ( i.e ., Soil and Water Assessment Tool [ SWAT ]) using Sequential Uncertainty FI... |
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| SubjectTerms | Algorithms Aquifers Coastal aquifers Coastal plains Coastal waters Computational fluid dynamics Computer simulation Diagnostic systems Discharge measurement Fluid flow Friction resistance Gaging Gaging stations Geological surveys Geology Hydraulics Hydrogeology Hydrologic models Hydrology Mathematical models modeling diagnostic analysis Modelling Parameter estimation Parameter sensitivity Parameter uncertainty Physical properties Probability distribution Probability theory Resistance factors Runoff Search algorithms Sensitivity analysis shallow aquifer Skewness Soil Stream discharge Surveying SWAT Uncertainty Water quality Watersheds |
| Title | Assessing Parameter Uncertainty of a Semi‐Distributed Hydrology Model for a Shallow Aquifer Dominated Environmental System |
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