Input uncertainty on watershed modeling: Evaluation of precipitation and air temperature data by latent variables using SWAT

Latent variables (i.e., normally distributed random noise) provide valuable information regarding model input uncertainty. Watershed processes have been explored with sophisticated simulation models in the past few decades and researchers have found that incorporating the uncertainty attributed to f...
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Autor*in:	Yen, Haw [verfasserIn] Wang, Ruoyu [verfasserIn] Feng, Qingyu [verfasserIn] Young, Chih-Chieh [verfasserIn] Chen, Shien-Tsung [verfasserIn] Tseng, Wen-Hsiao [verfasserIn] Wolfe, June E. [verfasserIn] White, Michael J. [verfasserIn] Arnold, Jeffrey G. [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Schlagwörter:	Input uncertainty Latent variables Model calibration Precipitation Air temperature SWAT IPEAT

Übergeordnetes Werk:	Enthalten in: Ecological engineering - Amsterdam [u.a.] : Elsevier Science, 1992, 122, Seite 16-26
Übergeordnetes Werk:	volume:122 ; pages:16-26

DOI / URN:	10.1016/j.ecoleng.2018.07.014

Katalog-ID:	ELV000474835

Internformat


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245	1	0	\|a Input uncertainty on watershed modeling: Evaluation of precipitation and air temperature data by latent variables using SWAT
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520			\|a Latent variables (i.e., normally distributed random noise) provide valuable information regarding model input uncertainty. Watershed processes have been explored with sophisticated simulation models in the past few decades and researchers have found that incorporating the uncertainty attributed to forcing inputs, model parameters, and measured data, can help improve simulation results, however, not in all cases. Latent variable use requires careful consideration to determine if results are better or worse. In this study, latent variables were implemented to both precipitation and air temperature data to investigate the influence on model predictions and associated predictive uncertainty by using the Soil and Water Assessment Tool (SWAT). Results indicated that model predictions in terms of statistics, behavior solutions, and predictive uncertainty were substantially affected by applying latent variables on precipitation data but it does not guarantee improved performance. On the other hand, model responses did not denote similar performance by conducting the same approach to air temperature data. Ultimately, incorporating latent variables a priori proportionally may or may not improve model predictive uncertainty. Researchers should carefully consider latent variable potential benefits on model predictions before committing to further work or making important model-supported decisions.
650		4	\|a Input uncertainty
650		4	\|a Latent variables
650		4	\|a Model calibration
650		4	\|a Precipitation
650		4	\|a Air temperature
650		4	\|a SWAT
650		4	\|a IPEAT
700	1		\|a Wang, Ruoyu \|e verfasserin \|4 aut
700	1		\|a Feng, Qingyu \|e verfasserin \|4 aut
700	1		\|a Young, Chih-Chieh \|e verfasserin \|4 aut
700	1		\|a Chen, Shien-Tsung \|e verfasserin \|4 aut
700	1		\|a Tseng, Wen-Hsiao \|e verfasserin \|4 aut
700	1		\|a Wolfe, June E. \|e verfasserin \|4 aut
700	1		\|a White, Michael J. \|e verfasserin \|4 aut
700	1		\|a Arnold, Jeffrey G. \|e verfasserin \|4 aut
773	0	8	\|i Enthalten in \|t Ecological engineering \|d Amsterdam [u.a.] : Elsevier Science, 1992 \|g 122, Seite 16-26 \|h Online-Ressource \|w (DE-627)320406938 \|w (DE-600)2000805-3 \|w (DE-576)259271063 \|x 0925-8574 \|7 nnns
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912			\|a GBV_ILN_4393
936	b	k	\|a 58.50 \|j Umwelttechnik: Allgemeines
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952			\|d 122 \|h 16-26

Indexfelder

author_variant	h y hy r w rw q f qf c c y ccy s t c stc w h t wht j e w je jew m j w mj mjw j g a jg jga
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publishDate	2018
allfields	10.1016/j.ecoleng.2018.07.014 doi (DE-627)ELV000474835 (ELSEVIER)S0925-8574(18)30252-0 DE-627 ger DE-627 rda eng 690 DE-600 BIODIV DE-30 fid 58.50 bkl Yen, Haw verfasserin (orcid)0000-0002-5509-8792 aut Input uncertainty on watershed modeling: Evaluation of precipitation and air temperature data by latent variables using SWAT 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Latent variables (i.e., normally distributed random noise) provide valuable information regarding model input uncertainty. Watershed processes have been explored with sophisticated simulation models in the past few decades and researchers have found that incorporating the uncertainty attributed to forcing inputs, model parameters, and measured data, can help improve simulation results, however, not in all cases. Latent variable use requires careful consideration to determine if results are better or worse. In this study, latent variables were implemented to both precipitation and air temperature data to investigate the influence on model predictions and associated predictive uncertainty by using the Soil and Water Assessment Tool (SWAT). Results indicated that model predictions in terms of statistics, behavior solutions, and predictive uncertainty were substantially affected by applying latent variables on precipitation data but it does not guarantee improved performance. On the other hand, model responses did not denote similar performance by conducting the same approach to air temperature data. Ultimately, incorporating latent variables a priori proportionally may or may not improve model predictive uncertainty. Researchers should carefully consider latent variable potential benefits on model predictions before committing to further work or making important model-supported decisions. Input uncertainty Latent variables Model calibration Precipitation Air temperature SWAT IPEAT Wang, Ruoyu verfasserin aut Feng, Qingyu verfasserin aut Young, Chih-Chieh verfasserin aut Chen, Shien-Tsung verfasserin aut Tseng, Wen-Hsiao verfasserin aut Wolfe, June E. verfasserin aut White, Michael J. verfasserin aut Arnold, Jeffrey G. verfasserin aut Enthalten in Ecological engineering Amsterdam [u.a.] : Elsevier Science, 1992 122, Seite 16-26 Online-Ressource (DE-627)320406938 (DE-600)2000805-3 (DE-576)259271063 0925-8574 nnns volume:122 pages:16-26 GBV_USEFLAG_U SYSFLAG_U GBV_ELV FID-BIODIV SSG-OPC-GGO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 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_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 58.50 Umwelttechnik: Allgemeines AR 122 16-26
spelling	10.1016/j.ecoleng.2018.07.014 doi (DE-627)ELV000474835 (ELSEVIER)S0925-8574(18)30252-0 DE-627 ger DE-627 rda eng 690 DE-600 BIODIV DE-30 fid 58.50 bkl Yen, Haw verfasserin (orcid)0000-0002-5509-8792 aut Input uncertainty on watershed modeling: Evaluation of precipitation and air temperature data by latent variables using SWAT 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Latent variables (i.e., normally distributed random noise) provide valuable information regarding model input uncertainty. Watershed processes have been explored with sophisticated simulation models in the past few decades and researchers have found that incorporating the uncertainty attributed to forcing inputs, model parameters, and measured data, can help improve simulation results, however, not in all cases. Latent variable use requires careful consideration to determine if results are better or worse. In this study, latent variables were implemented to both precipitation and air temperature data to investigate the influence on model predictions and associated predictive uncertainty by using the Soil and Water Assessment Tool (SWAT). Results indicated that model predictions in terms of statistics, behavior solutions, and predictive uncertainty were substantially affected by applying latent variables on precipitation data but it does not guarantee improved performance. On the other hand, model responses did not denote similar performance by conducting the same approach to air temperature data. Ultimately, incorporating latent variables a priori proportionally may or may not improve model predictive uncertainty. Researchers should carefully consider latent variable potential benefits on model predictions before committing to further work or making important model-supported decisions. Input uncertainty Latent variables Model calibration Precipitation Air temperature SWAT IPEAT Wang, Ruoyu verfasserin aut Feng, Qingyu verfasserin aut Young, Chih-Chieh verfasserin aut Chen, Shien-Tsung verfasserin aut Tseng, Wen-Hsiao verfasserin aut Wolfe, June E. verfasserin aut White, Michael J. verfasserin aut Arnold, Jeffrey G. verfasserin aut Enthalten in Ecological engineering Amsterdam [u.a.] : Elsevier Science, 1992 122, Seite 16-26 Online-Ressource (DE-627)320406938 (DE-600)2000805-3 (DE-576)259271063 0925-8574 nnns volume:122 pages:16-26 GBV_USEFLAG_U SYSFLAG_U GBV_ELV FID-BIODIV SSG-OPC-GGO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 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_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 58.50 Umwelttechnik: Allgemeines AR 122 16-26
allfields_unstemmed	10.1016/j.ecoleng.2018.07.014 doi (DE-627)ELV000474835 (ELSEVIER)S0925-8574(18)30252-0 DE-627 ger DE-627 rda eng 690 DE-600 BIODIV DE-30 fid 58.50 bkl Yen, Haw verfasserin (orcid)0000-0002-5509-8792 aut Input uncertainty on watershed modeling: Evaluation of precipitation and air temperature data by latent variables using SWAT 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Latent variables (i.e., normally distributed random noise) provide valuable information regarding model input uncertainty. Watershed processes have been explored with sophisticated simulation models in the past few decades and researchers have found that incorporating the uncertainty attributed to forcing inputs, model parameters, and measured data, can help improve simulation results, however, not in all cases. Latent variable use requires careful consideration to determine if results are better or worse. In this study, latent variables were implemented to both precipitation and air temperature data to investigate the influence on model predictions and associated predictive uncertainty by using the Soil and Water Assessment Tool (SWAT). Results indicated that model predictions in terms of statistics, behavior solutions, and predictive uncertainty were substantially affected by applying latent variables on precipitation data but it does not guarantee improved performance. On the other hand, model responses did not denote similar performance by conducting the same approach to air temperature data. Ultimately, incorporating latent variables a priori proportionally may or may not improve model predictive uncertainty. Researchers should carefully consider latent variable potential benefits on model predictions before committing to further work or making important model-supported decisions. Input uncertainty Latent variables Model calibration Precipitation Air temperature SWAT IPEAT Wang, Ruoyu verfasserin aut Feng, Qingyu verfasserin aut Young, Chih-Chieh verfasserin aut Chen, Shien-Tsung verfasserin aut Tseng, Wen-Hsiao verfasserin aut Wolfe, June E. verfasserin aut White, Michael J. verfasserin aut Arnold, Jeffrey G. verfasserin aut Enthalten in Ecological engineering Amsterdam [u.a.] : Elsevier Science, 1992 122, Seite 16-26 Online-Ressource (DE-627)320406938 (DE-600)2000805-3 (DE-576)259271063 0925-8574 nnns volume:122 pages:16-26 GBV_USEFLAG_U SYSFLAG_U GBV_ELV FID-BIODIV SSG-OPC-GGO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 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_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 58.50 Umwelttechnik: Allgemeines AR 122 16-26
allfieldsGer	10.1016/j.ecoleng.2018.07.014 doi (DE-627)ELV000474835 (ELSEVIER)S0925-8574(18)30252-0 DE-627 ger DE-627 rda eng 690 DE-600 BIODIV DE-30 fid 58.50 bkl Yen, Haw verfasserin (orcid)0000-0002-5509-8792 aut Input uncertainty on watershed modeling: Evaluation of precipitation and air temperature data by latent variables using SWAT 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Latent variables (i.e., normally distributed random noise) provide valuable information regarding model input uncertainty. Watershed processes have been explored with sophisticated simulation models in the past few decades and researchers have found that incorporating the uncertainty attributed to forcing inputs, model parameters, and measured data, can help improve simulation results, however, not in all cases. Latent variable use requires careful consideration to determine if results are better or worse. In this study, latent variables were implemented to both precipitation and air temperature data to investigate the influence on model predictions and associated predictive uncertainty by using the Soil and Water Assessment Tool (SWAT). Results indicated that model predictions in terms of statistics, behavior solutions, and predictive uncertainty were substantially affected by applying latent variables on precipitation data but it does not guarantee improved performance. On the other hand, model responses did not denote similar performance by conducting the same approach to air temperature data. Ultimately, incorporating latent variables a priori proportionally may or may not improve model predictive uncertainty. Researchers should carefully consider latent variable potential benefits on model predictions before committing to further work or making important model-supported decisions. Input uncertainty Latent variables Model calibration Precipitation Air temperature SWAT IPEAT Wang, Ruoyu verfasserin aut Feng, Qingyu verfasserin aut Young, Chih-Chieh verfasserin aut Chen, Shien-Tsung verfasserin aut Tseng, Wen-Hsiao verfasserin aut Wolfe, June E. verfasserin aut White, Michael J. verfasserin aut Arnold, Jeffrey G. verfasserin aut Enthalten in Ecological engineering Amsterdam [u.a.] : Elsevier Science, 1992 122, Seite 16-26 Online-Ressource (DE-627)320406938 (DE-600)2000805-3 (DE-576)259271063 0925-8574 nnns volume:122 pages:16-26 GBV_USEFLAG_U SYSFLAG_U GBV_ELV FID-BIODIV SSG-OPC-GGO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 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_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 58.50 Umwelttechnik: Allgemeines AR 122 16-26
allfieldsSound	10.1016/j.ecoleng.2018.07.014 doi (DE-627)ELV000474835 (ELSEVIER)S0925-8574(18)30252-0 DE-627 ger DE-627 rda eng 690 DE-600 BIODIV DE-30 fid 58.50 bkl Yen, Haw verfasserin (orcid)0000-0002-5509-8792 aut Input uncertainty on watershed modeling: Evaluation of precipitation and air temperature data by latent variables using SWAT 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Latent variables (i.e., normally distributed random noise) provide valuable information regarding model input uncertainty. Watershed processes have been explored with sophisticated simulation models in the past few decades and researchers have found that incorporating the uncertainty attributed to forcing inputs, model parameters, and measured data, can help improve simulation results, however, not in all cases. Latent variable use requires careful consideration to determine if results are better or worse. In this study, latent variables were implemented to both precipitation and air temperature data to investigate the influence on model predictions and associated predictive uncertainty by using the Soil and Water Assessment Tool (SWAT). Results indicated that model predictions in terms of statistics, behavior solutions, and predictive uncertainty were substantially affected by applying latent variables on precipitation data but it does not guarantee improved performance. On the other hand, model responses did not denote similar performance by conducting the same approach to air temperature data. Ultimately, incorporating latent variables a priori proportionally may or may not improve model predictive uncertainty. Researchers should carefully consider latent variable potential benefits on model predictions before committing to further work or making important model-supported decisions. Input uncertainty Latent variables Model calibration Precipitation Air temperature SWAT IPEAT Wang, Ruoyu verfasserin aut Feng, Qingyu verfasserin aut Young, Chih-Chieh verfasserin aut Chen, Shien-Tsung verfasserin aut Tseng, Wen-Hsiao verfasserin aut Wolfe, June E. verfasserin aut White, Michael J. verfasserin aut Arnold, Jeffrey G. verfasserin aut Enthalten in Ecological engineering Amsterdam [u.a.] : Elsevier Science, 1992 122, Seite 16-26 Online-Ressource (DE-627)320406938 (DE-600)2000805-3 (DE-576)259271063 0925-8574 nnns volume:122 pages:16-26 GBV_USEFLAG_U SYSFLAG_U GBV_ELV FID-BIODIV SSG-OPC-GGO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 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_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 58.50 Umwelttechnik: Allgemeines AR 122 16-26
language	English
source	Enthalten in Ecological engineering 122, Seite 16-26 volume:122 pages:16-26
sourceStr	Enthalten in Ecological engineering 122, Seite 16-26 volume:122 pages:16-26
format_phy_str_mv	Article
bklname	Umwelttechnik: Allgemeines
institution	findex.gbv.de
topic_facet	Input uncertainty Latent variables Model calibration Precipitation Air temperature SWAT IPEAT
dewey-raw	690
isfreeaccess_bool	false
container_title	Ecological engineering
authorswithroles_txt_mv	Yen, Haw @@aut@@ Wang, Ruoyu @@aut@@ Feng, Qingyu @@aut@@ Young, Chih-Chieh @@aut@@ Chen, Shien-Tsung @@aut@@ Tseng, Wen-Hsiao @@aut@@ Wolfe, June E. @@aut@@ White, Michael J. @@aut@@ Arnold, Jeffrey G. @@aut@@
publishDateDaySort_date	2018-01-01T00:00:00Z
hierarchy_top_id	320406938
dewey-sort	3690
id	ELV000474835
language_de	englisch
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author	Yen, Haw
spellingShingle	Yen, Haw ddc 690 fid BIODIV bkl 58.50 misc Input uncertainty misc Latent variables misc Model calibration misc Precipitation misc Air temperature misc SWAT misc IPEAT Input uncertainty on watershed modeling: Evaluation of precipitation and air temperature data by latent variables using SWAT
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topic_title	690 DE-600 BIODIV DE-30 fid 58.50 bkl Input uncertainty on watershed modeling: Evaluation of precipitation and air temperature data by latent variables using SWAT Input uncertainty Latent variables Model calibration Precipitation Air temperature SWAT IPEAT
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title	Input uncertainty on watershed modeling: Evaluation of precipitation and air temperature data by latent variables using SWAT
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title_full	Input uncertainty on watershed modeling: Evaluation of precipitation and air temperature data by latent variables using SWAT
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author_browse	Yen, Haw Wang, Ruoyu Feng, Qingyu Young, Chih-Chieh Chen, Shien-Tsung Tseng, Wen-Hsiao Wolfe, June E. White, Michael J. Arnold, Jeffrey G.
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title_sort	input uncertainty on watershed modeling: evaluation of precipitation and air temperature data by latent variables using swat
title_auth	Input uncertainty on watershed modeling: Evaluation of precipitation and air temperature data by latent variables using SWAT
abstract	Latent variables (i.e., normally distributed random noise) provide valuable information regarding model input uncertainty. Watershed processes have been explored with sophisticated simulation models in the past few decades and researchers have found that incorporating the uncertainty attributed to forcing inputs, model parameters, and measured data, can help improve simulation results, however, not in all cases. Latent variable use requires careful consideration to determine if results are better or worse. In this study, latent variables were implemented to both precipitation and air temperature data to investigate the influence on model predictions and associated predictive uncertainty by using the Soil and Water Assessment Tool (SWAT). Results indicated that model predictions in terms of statistics, behavior solutions, and predictive uncertainty were substantially affected by applying latent variables on precipitation data but it does not guarantee improved performance. On the other hand, model responses did not denote similar performance by conducting the same approach to air temperature data. Ultimately, incorporating latent variables a priori proportionally may or may not improve model predictive uncertainty. Researchers should carefully consider latent variable potential benefits on model predictions before committing to further work or making important model-supported decisions.
abstractGer	Latent variables (i.e., normally distributed random noise) provide valuable information regarding model input uncertainty. Watershed processes have been explored with sophisticated simulation models in the past few decades and researchers have found that incorporating the uncertainty attributed to forcing inputs, model parameters, and measured data, can help improve simulation results, however, not in all cases. Latent variable use requires careful consideration to determine if results are better or worse. In this study, latent variables were implemented to both precipitation and air temperature data to investigate the influence on model predictions and associated predictive uncertainty by using the Soil and Water Assessment Tool (SWAT). Results indicated that model predictions in terms of statistics, behavior solutions, and predictive uncertainty were substantially affected by applying latent variables on precipitation data but it does not guarantee improved performance. On the other hand, model responses did not denote similar performance by conducting the same approach to air temperature data. Ultimately, incorporating latent variables a priori proportionally may or may not improve model predictive uncertainty. Researchers should carefully consider latent variable potential benefits on model predictions before committing to further work or making important model-supported decisions.
abstract_unstemmed	Latent variables (i.e., normally distributed random noise) provide valuable information regarding model input uncertainty. Watershed processes have been explored with sophisticated simulation models in the past few decades and researchers have found that incorporating the uncertainty attributed to forcing inputs, model parameters, and measured data, can help improve simulation results, however, not in all cases. Latent variable use requires careful consideration to determine if results are better or worse. In this study, latent variables were implemented to both precipitation and air temperature data to investigate the influence on model predictions and associated predictive uncertainty by using the Soil and Water Assessment Tool (SWAT). Results indicated that model predictions in terms of statistics, behavior solutions, and predictive uncertainty were substantially affected by applying latent variables on precipitation data but it does not guarantee improved performance. On the other hand, model responses did not denote similar performance by conducting the same approach to air temperature data. Ultimately, incorporating latent variables a priori proportionally may or may not improve model predictive uncertainty. Researchers should carefully consider latent variable potential benefits on model predictions before committing to further work or making important model-supported decisions.
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title_short	Input uncertainty on watershed modeling: Evaluation of precipitation and air temperature data by latent variables using SWAT
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author2	Wang, Ruoyu Feng, Qingyu Young, Chih-Chieh Chen, Shien-Tsung Tseng, Wen-Hsiao Wolfe, June E. White, Michael J. Arnold, Jeffrey G.
author2Str	Wang, Ruoyu Feng, Qingyu Young, Chih-Chieh Chen, Shien-Tsung Tseng, Wen-Hsiao Wolfe, June E. White, Michael J. Arnold, Jeffrey G.
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doi_str	10.1016/j.ecoleng.2018.07.014
up_date	2024-07-06T18:05:53.956Z
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Input uncertainty on watershed modeling: Evaluation of precipitation and air temperature data by latent variables using SWAT

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