Assessment of Input Uncertainty in SWAT Using Latent Variables

Abstract Applications of the Soil and Water Assessment Tool (SWAT) require a large amount of input data to perform model simulations. Consequently, uncertainty in input data tends to influence the accuracy of SWAT hydrologic and water quality outputs. It has been shown that input uncertainty can be...
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Autor*in:	Yen, Haw [verfasserIn] Jeong, Jaehak [verfasserIn] Feng, QingYu [verfasserIn] Deb, Debjani [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2014

Schlagwörter:	Input uncertainty Model calibration SWAT Uncertainty analysis

Übergeordnetes Werk:	Enthalten in: Water resources management - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987, 29(2014), 4 vom: 05. Nov., Seite 1137-1153
Übergeordnetes Werk:	volume:29 ; year:2014 ; number:4 ; day:05 ; month:11 ; pages:1137-1153

Links:	Volltext

DOI / URN:	10.1007/s11269-014-0865-y

Katalog-ID:	SPR018386903

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520			\|a Abstract Applications of the Soil and Water Assessment Tool (SWAT) require a large amount of input data to perform model simulations. Consequently, uncertainty in input data tends to influence the accuracy of SWAT hydrologic and water quality outputs. It has been shown that input uncertainty can be quantified explicitly during model calibration with latent variables. In this study, latent variables were explored to examine their sensitivity to SWAT outputs and further the potential impact of input uncertainty to model predictions. Results show that the increases in the range of latent variables pose a significant influence to streamflow and ammonia predictions while the impact was less significant in sediment responses. The performance of SWAT in predicting streamflow and ammonia declined with wider ranges of latent variables. In addition, the increase in the range of latent variables did not present noticeable effect on the corresponding predictive uncertainty in sediment predictions. In this study, the calibration results did not improve significantly with the applications of wider ranges of latent variables which are different from the findings in previous research work. The use of latent variables to incorporate input uncertainty may not be the proper alternative choice in terms of generating better results and should be carefully evaluated in the implementations of complex watershed simulation models.
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700	1		\|a Deb, Debjani \|e verfasserin \|4 aut
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matchkey_str	article:15731650:2014----::sesetfnuucranynwtsn
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bklnumber	43.33
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allfields	10.1007/s11269-014-0865-y doi (DE-627)SPR018386903 (SPR)s11269-014-0865-y-e DE-627 ger DE-627 rakwb eng 550 630 ASE 43.33 bkl Yen, Haw verfasserin aut Assessment of Input Uncertainty in SWAT Using Latent Variables 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Applications of the Soil and Water Assessment Tool (SWAT) require a large amount of input data to perform model simulations. Consequently, uncertainty in input data tends to influence the accuracy of SWAT hydrologic and water quality outputs. It has been shown that input uncertainty can be quantified explicitly during model calibration with latent variables. In this study, latent variables were explored to examine their sensitivity to SWAT outputs and further the potential impact of input uncertainty to model predictions. Results show that the increases in the range of latent variables pose a significant influence to streamflow and ammonia predictions while the impact was less significant in sediment responses. The performance of SWAT in predicting streamflow and ammonia declined with wider ranges of latent variables. In addition, the increase in the range of latent variables did not present noticeable effect on the corresponding predictive uncertainty in sediment predictions. In this study, the calibration results did not improve significantly with the applications of wider ranges of latent variables which are different from the findings in previous research work. The use of latent variables to incorporate input uncertainty may not be the proper alternative choice in terms of generating better results and should be carefully evaluated in the implementations of complex watershed simulation models. Input uncertainty (dpeaa)DE-He213 Model calibration (dpeaa)DE-He213 SWAT (dpeaa)DE-He213 Uncertainty analysis (dpeaa)DE-He213 Jeong, Jaehak verfasserin aut Feng, QingYu verfasserin aut Deb, Debjani verfasserin aut Enthalten in Water resources management Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 29(2014), 4 vom: 05. Nov., Seite 1137-1153 (DE-627)315299924 (DE-600)2016360-5 1573-1650 nnns volume:29 year:2014 number:4 day:05 month:11 pages:1137-1153 https://dx.doi.org/10.1007/s11269-014-0865-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 43.33 ASE AR 29 2014 4 05 11 1137-1153
spelling	10.1007/s11269-014-0865-y doi (DE-627)SPR018386903 (SPR)s11269-014-0865-y-e DE-627 ger DE-627 rakwb eng 550 630 ASE 43.33 bkl Yen, Haw verfasserin aut Assessment of Input Uncertainty in SWAT Using Latent Variables 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Applications of the Soil and Water Assessment Tool (SWAT) require a large amount of input data to perform model simulations. Consequently, uncertainty in input data tends to influence the accuracy of SWAT hydrologic and water quality outputs. It has been shown that input uncertainty can be quantified explicitly during model calibration with latent variables. In this study, latent variables were explored to examine their sensitivity to SWAT outputs and further the potential impact of input uncertainty to model predictions. Results show that the increases in the range of latent variables pose a significant influence to streamflow and ammonia predictions while the impact was less significant in sediment responses. The performance of SWAT in predicting streamflow and ammonia declined with wider ranges of latent variables. In addition, the increase in the range of latent variables did not present noticeable effect on the corresponding predictive uncertainty in sediment predictions. In this study, the calibration results did not improve significantly with the applications of wider ranges of latent variables which are different from the findings in previous research work. The use of latent variables to incorporate input uncertainty may not be the proper alternative choice in terms of generating better results and should be carefully evaluated in the implementations of complex watershed simulation models. Input uncertainty (dpeaa)DE-He213 Model calibration (dpeaa)DE-He213 SWAT (dpeaa)DE-He213 Uncertainty analysis (dpeaa)DE-He213 Jeong, Jaehak verfasserin aut Feng, QingYu verfasserin aut Deb, Debjani verfasserin aut Enthalten in Water resources management Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 29(2014), 4 vom: 05. Nov., Seite 1137-1153 (DE-627)315299924 (DE-600)2016360-5 1573-1650 nnns volume:29 year:2014 number:4 day:05 month:11 pages:1137-1153 https://dx.doi.org/10.1007/s11269-014-0865-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 43.33 ASE AR 29 2014 4 05 11 1137-1153
allfields_unstemmed	10.1007/s11269-014-0865-y doi (DE-627)SPR018386903 (SPR)s11269-014-0865-y-e DE-627 ger DE-627 rakwb eng 550 630 ASE 43.33 bkl Yen, Haw verfasserin aut Assessment of Input Uncertainty in SWAT Using Latent Variables 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Applications of the Soil and Water Assessment Tool (SWAT) require a large amount of input data to perform model simulations. Consequently, uncertainty in input data tends to influence the accuracy of SWAT hydrologic and water quality outputs. It has been shown that input uncertainty can be quantified explicitly during model calibration with latent variables. In this study, latent variables were explored to examine their sensitivity to SWAT outputs and further the potential impact of input uncertainty to model predictions. Results show that the increases in the range of latent variables pose a significant influence to streamflow and ammonia predictions while the impact was less significant in sediment responses. The performance of SWAT in predicting streamflow and ammonia declined with wider ranges of latent variables. In addition, the increase in the range of latent variables did not present noticeable effect on the corresponding predictive uncertainty in sediment predictions. In this study, the calibration results did not improve significantly with the applications of wider ranges of latent variables which are different from the findings in previous research work. The use of latent variables to incorporate input uncertainty may not be the proper alternative choice in terms of generating better results and should be carefully evaluated in the implementations of complex watershed simulation models. Input uncertainty (dpeaa)DE-He213 Model calibration (dpeaa)DE-He213 SWAT (dpeaa)DE-He213 Uncertainty analysis (dpeaa)DE-He213 Jeong, Jaehak verfasserin aut Feng, QingYu verfasserin aut Deb, Debjani verfasserin aut Enthalten in Water resources management Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 29(2014), 4 vom: 05. Nov., Seite 1137-1153 (DE-627)315299924 (DE-600)2016360-5 1573-1650 nnns volume:29 year:2014 number:4 day:05 month:11 pages:1137-1153 https://dx.doi.org/10.1007/s11269-014-0865-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 43.33 ASE AR 29 2014 4 05 11 1137-1153
allfieldsGer	10.1007/s11269-014-0865-y doi (DE-627)SPR018386903 (SPR)s11269-014-0865-y-e DE-627 ger DE-627 rakwb eng 550 630 ASE 43.33 bkl Yen, Haw verfasserin aut Assessment of Input Uncertainty in SWAT Using Latent Variables 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Applications of the Soil and Water Assessment Tool (SWAT) require a large amount of input data to perform model simulations. Consequently, uncertainty in input data tends to influence the accuracy of SWAT hydrologic and water quality outputs. It has been shown that input uncertainty can be quantified explicitly during model calibration with latent variables. In this study, latent variables were explored to examine their sensitivity to SWAT outputs and further the potential impact of input uncertainty to model predictions. Results show that the increases in the range of latent variables pose a significant influence to streamflow and ammonia predictions while the impact was less significant in sediment responses. The performance of SWAT in predicting streamflow and ammonia declined with wider ranges of latent variables. In addition, the increase in the range of latent variables did not present noticeable effect on the corresponding predictive uncertainty in sediment predictions. In this study, the calibration results did not improve significantly with the applications of wider ranges of latent variables which are different from the findings in previous research work. The use of latent variables to incorporate input uncertainty may not be the proper alternative choice in terms of generating better results and should be carefully evaluated in the implementations of complex watershed simulation models. Input uncertainty (dpeaa)DE-He213 Model calibration (dpeaa)DE-He213 SWAT (dpeaa)DE-He213 Uncertainty analysis (dpeaa)DE-He213 Jeong, Jaehak verfasserin aut Feng, QingYu verfasserin aut Deb, Debjani verfasserin aut Enthalten in Water resources management Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 29(2014), 4 vom: 05. Nov., Seite 1137-1153 (DE-627)315299924 (DE-600)2016360-5 1573-1650 nnns volume:29 year:2014 number:4 day:05 month:11 pages:1137-1153 https://dx.doi.org/10.1007/s11269-014-0865-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 43.33 ASE AR 29 2014 4 05 11 1137-1153
allfieldsSound	10.1007/s11269-014-0865-y doi (DE-627)SPR018386903 (SPR)s11269-014-0865-y-e DE-627 ger DE-627 rakwb eng 550 630 ASE 43.33 bkl Yen, Haw verfasserin aut Assessment of Input Uncertainty in SWAT Using Latent Variables 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Applications of the Soil and Water Assessment Tool (SWAT) require a large amount of input data to perform model simulations. Consequently, uncertainty in input data tends to influence the accuracy of SWAT hydrologic and water quality outputs. It has been shown that input uncertainty can be quantified explicitly during model calibration with latent variables. In this study, latent variables were explored to examine their sensitivity to SWAT outputs and further the potential impact of input uncertainty to model predictions. Results show that the increases in the range of latent variables pose a significant influence to streamflow and ammonia predictions while the impact was less significant in sediment responses. The performance of SWAT in predicting streamflow and ammonia declined with wider ranges of latent variables. In addition, the increase in the range of latent variables did not present noticeable effect on the corresponding predictive uncertainty in sediment predictions. In this study, the calibration results did not improve significantly with the applications of wider ranges of latent variables which are different from the findings in previous research work. The use of latent variables to incorporate input uncertainty may not be the proper alternative choice in terms of generating better results and should be carefully evaluated in the implementations of complex watershed simulation models. Input uncertainty (dpeaa)DE-He213 Model calibration (dpeaa)DE-He213 SWAT (dpeaa)DE-He213 Uncertainty analysis (dpeaa)DE-He213 Jeong, Jaehak verfasserin aut Feng, QingYu verfasserin aut Deb, Debjani verfasserin aut Enthalten in Water resources management Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 29(2014), 4 vom: 05. Nov., Seite 1137-1153 (DE-627)315299924 (DE-600)2016360-5 1573-1650 nnns volume:29 year:2014 number:4 day:05 month:11 pages:1137-1153 https://dx.doi.org/10.1007/s11269-014-0865-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 43.33 ASE AR 29 2014 4 05 11 1137-1153
language	English
source	Enthalten in Water resources management 29(2014), 4 vom: 05. Nov., Seite 1137-1153 volume:29 year:2014 number:4 day:05 month:11 pages:1137-1153
sourceStr	Enthalten in Water resources management 29(2014), 4 vom: 05. Nov., Seite 1137-1153 volume:29 year:2014 number:4 day:05 month:11 pages:1137-1153
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Input uncertainty Model calibration SWAT Uncertainty analysis
dewey-raw	550
isfreeaccess_bool	false
container_title	Water resources management
authorswithroles_txt_mv	Yen, Haw @@aut@@ Jeong, Jaehak @@aut@@ Feng, QingYu @@aut@@ Deb, Debjani @@aut@@
publishDateDaySort_date	2014-11-05T00:00:00Z
hierarchy_top_id	315299924
dewey-sort	3550
id	SPR018386903
language_de	englisch
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Consequently, uncertainty in input data tends to influence the accuracy of SWAT hydrologic and water quality outputs. It has been shown that input uncertainty can be quantified explicitly during model calibration with latent variables. In this study, latent variables were explored to examine their sensitivity to SWAT outputs and further the potential impact of input uncertainty to model predictions. Results show that the increases in the range of latent variables pose a significant influence to streamflow and ammonia predictions while the impact was less significant in sediment responses. The performance of SWAT in predicting streamflow and ammonia declined with wider ranges of latent variables. In addition, the increase in the range of latent variables did not present noticeable effect on the corresponding predictive uncertainty in sediment predictions. In this study, the calibration results did not improve significantly with the applications of wider ranges of latent variables which are different from the findings in previous research work. 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author	Yen, Haw
spellingShingle	Yen, Haw ddc 550 bkl 43.33 misc Input uncertainty misc Model calibration misc SWAT misc Uncertainty analysis Assessment of Input Uncertainty in SWAT Using Latent Variables
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topic_title	550 630 ASE 43.33 bkl Assessment of Input Uncertainty in SWAT Using Latent Variables Input uncertainty (dpeaa)DE-He213 Model calibration (dpeaa)DE-He213 SWAT (dpeaa)DE-He213 Uncertainty analysis (dpeaa)DE-He213
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title	Assessment of Input Uncertainty in SWAT Using Latent Variables
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title_full	Assessment of Input Uncertainty in SWAT Using Latent Variables
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title_sort	assessment of input uncertainty in swat using latent variables
title_auth	Assessment of Input Uncertainty in SWAT Using Latent Variables
abstract	Abstract Applications of the Soil and Water Assessment Tool (SWAT) require a large amount of input data to perform model simulations. Consequently, uncertainty in input data tends to influence the accuracy of SWAT hydrologic and water quality outputs. It has been shown that input uncertainty can be quantified explicitly during model calibration with latent variables. In this study, latent variables were explored to examine their sensitivity to SWAT outputs and further the potential impact of input uncertainty to model predictions. Results show that the increases in the range of latent variables pose a significant influence to streamflow and ammonia predictions while the impact was less significant in sediment responses. The performance of SWAT in predicting streamflow and ammonia declined with wider ranges of latent variables. In addition, the increase in the range of latent variables did not present noticeable effect on the corresponding predictive uncertainty in sediment predictions. In this study, the calibration results did not improve significantly with the applications of wider ranges of latent variables which are different from the findings in previous research work. The use of latent variables to incorporate input uncertainty may not be the proper alternative choice in terms of generating better results and should be carefully evaluated in the implementations of complex watershed simulation models.
abstractGer	Abstract Applications of the Soil and Water Assessment Tool (SWAT) require a large amount of input data to perform model simulations. Consequently, uncertainty in input data tends to influence the accuracy of SWAT hydrologic and water quality outputs. It has been shown that input uncertainty can be quantified explicitly during model calibration with latent variables. In this study, latent variables were explored to examine their sensitivity to SWAT outputs and further the potential impact of input uncertainty to model predictions. Results show that the increases in the range of latent variables pose a significant influence to streamflow and ammonia predictions while the impact was less significant in sediment responses. The performance of SWAT in predicting streamflow and ammonia declined with wider ranges of latent variables. In addition, the increase in the range of latent variables did not present noticeable effect on the corresponding predictive uncertainty in sediment predictions. In this study, the calibration results did not improve significantly with the applications of wider ranges of latent variables which are different from the findings in previous research work. The use of latent variables to incorporate input uncertainty may not be the proper alternative choice in terms of generating better results and should be carefully evaluated in the implementations of complex watershed simulation models.
abstract_unstemmed	Abstract Applications of the Soil and Water Assessment Tool (SWAT) require a large amount of input data to perform model simulations. Consequently, uncertainty in input data tends to influence the accuracy of SWAT hydrologic and water quality outputs. It has been shown that input uncertainty can be quantified explicitly during model calibration with latent variables. In this study, latent variables were explored to examine their sensitivity to SWAT outputs and further the potential impact of input uncertainty to model predictions. Results show that the increases in the range of latent variables pose a significant influence to streamflow and ammonia predictions while the impact was less significant in sediment responses. The performance of SWAT in predicting streamflow and ammonia declined with wider ranges of latent variables. In addition, the increase in the range of latent variables did not present noticeable effect on the corresponding predictive uncertainty in sediment predictions. In this study, the calibration results did not improve significantly with the applications of wider ranges of latent variables which are different from the findings in previous research work. The use of latent variables to incorporate input uncertainty may not be the proper alternative choice in terms of generating better results and should be carefully evaluated in the implementations of complex watershed simulation models.
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container_issue	4
title_short	Assessment of Input Uncertainty in SWAT Using Latent Variables
url	https://dx.doi.org/10.1007/s11269-014-0865-y
remote_bool	true
author2	Jeong, Jaehak Feng, QingYu Deb, Debjani
author2Str	Jeong, Jaehak Feng, QingYu Deb, Debjani
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doi_str	10.1007/s11269-014-0865-y
up_date	2024-07-03T19:19:30.268Z
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Assessment of Input Uncertainty in SWAT Using Latent Variables

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