Inverse Estimation of Soil Hydraulic and Transport Parameters of Layered Soils from Water Stable Isotope and Lysimeter Data

Accurate estimates of soil hydraulic parameters and dispersivities are crucial to simulate water flow and solute transport in terrestrial systems, particularly in the vadose zone. However, parameters obtained from inverse modeling can be ambiguous when identifying multiple parameters simultaneously...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Jannis Groh [verfasserIn] Christine Stumpp [verfasserIn] Andreas Lücke [verfasserIn] Thomas Pütz [verfasserIn] Jan Vanderborght [verfasserIn] Harry Vereecken [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Übergeordnetes Werk:	In: Vadose Zone Journal - Wiley, 2019, 17(2018), 1
Übergeordnetes Werk:	volume:17 ; year:2018 ; number:1

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.2136/vzj2017.09.0168

Katalog-ID:	DOAJ003120716

Internformat


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520			\|a Accurate estimates of soil hydraulic parameters and dispersivities are crucial to simulate water flow and solute transport in terrestrial systems, particularly in the vadose zone. However, parameters obtained from inverse modeling can be ambiguous when identifying multiple parameters simultaneously and when boundary conditions are not well known. Here, we performed an inverse modeling study in which we estimated soil hydraulic parameters and dispersivities of layered soils from soil water content, matric potential, and stable water isotope (δO) measurements in weighable lysimeter systems. We used different optimization strategies to investigate which observation types are necessary for simultaneously estimating soil hydraulic and solute transport parameters. Combining water content, matric potential, and tracer (e.g., δO) data in one objective function (OF) was found to be the best strategy for estimating parameters that can simulate all observed water flow and solute transport variables. A sequential optimization, in which first an OF with only water flow variables and subsequently an OF with transport variables was optimized, performed slightly worse indicating that transport variables contained additional information for estimating soil hydraulic parameters. Hydraulic parameters that were obtained from optimizing OFs that used either water contents or matric potential could not predict non-measured water flow variables. When a bromide (Br) tracer experiment was simulated using the optimized parameters, the arrival time of the bromide pulse was underestimated. This suggested that Br sorbed onto clay minerals and amorphous oxides under the prevailing geochemical conditions with low pH values. When accounting for anion adsorption in the simulation, Br concentrations were well predicted, which validated the dispersivity parameterization.
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spelling	10.2136/vzj2017.09.0168 doi (DE-627)DOAJ003120716 (DE-599)DOAJ19f6fe7ae26c469187a8c617e7ea2fb4 DE-627 ger DE-627 rakwb eng GE1-350 QE1-996.5 Jannis Groh verfasserin aut Inverse Estimation of Soil Hydraulic and Transport Parameters of Layered Soils from Water Stable Isotope and Lysimeter Data 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Accurate estimates of soil hydraulic parameters and dispersivities are crucial to simulate water flow and solute transport in terrestrial systems, particularly in the vadose zone. However, parameters obtained from inverse modeling can be ambiguous when identifying multiple parameters simultaneously and when boundary conditions are not well known. Here, we performed an inverse modeling study in which we estimated soil hydraulic parameters and dispersivities of layered soils from soil water content, matric potential, and stable water isotope (δO) measurements in weighable lysimeter systems. We used different optimization strategies to investigate which observation types are necessary for simultaneously estimating soil hydraulic and solute transport parameters. Combining water content, matric potential, and tracer (e.g., δO) data in one objective function (OF) was found to be the best strategy for estimating parameters that can simulate all observed water flow and solute transport variables. A sequential optimization, in which first an OF with only water flow variables and subsequently an OF with transport variables was optimized, performed slightly worse indicating that transport variables contained additional information for estimating soil hydraulic parameters. Hydraulic parameters that were obtained from optimizing OFs that used either water contents or matric potential could not predict non-measured water flow variables. When a bromide (Br) tracer experiment was simulated using the optimized parameters, the arrival time of the bromide pulse was underestimated. This suggested that Br sorbed onto clay minerals and amorphous oxides under the prevailing geochemical conditions with low pH values. When accounting for anion adsorption in the simulation, Br concentrations were well predicted, which validated the dispersivity parameterization. Environmental sciences Geology Christine Stumpp verfasserin aut Andreas Lücke verfasserin aut Thomas Pütz verfasserin aut Jan Vanderborght verfasserin aut Harry Vereecken verfasserin aut In Vadose Zone Journal Wiley, 2019 17(2018), 1 (DE-627)354193597 (DE-600)2088189-7 15391663 nnns volume:17 year:2018 number:1 https://doi.org/10.2136/vzj2017.09.0168 kostenfrei https://doaj.org/article/19f6fe7ae26c469187a8c617e7ea2fb4 kostenfrei https://dl.sciencesocieties.org/publications/vzj/articles/17/1/170168 kostenfrei https://doaj.org/toc/1539-1663 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 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_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 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_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 17 2018 1
allfields_unstemmed	10.2136/vzj2017.09.0168 doi (DE-627)DOAJ003120716 (DE-599)DOAJ19f6fe7ae26c469187a8c617e7ea2fb4 DE-627 ger DE-627 rakwb eng GE1-350 QE1-996.5 Jannis Groh verfasserin aut Inverse Estimation of Soil Hydraulic and Transport Parameters of Layered Soils from Water Stable Isotope and Lysimeter Data 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Accurate estimates of soil hydraulic parameters and dispersivities are crucial to simulate water flow and solute transport in terrestrial systems, particularly in the vadose zone. However, parameters obtained from inverse modeling can be ambiguous when identifying multiple parameters simultaneously and when boundary conditions are not well known. Here, we performed an inverse modeling study in which we estimated soil hydraulic parameters and dispersivities of layered soils from soil water content, matric potential, and stable water isotope (δO) measurements in weighable lysimeter systems. We used different optimization strategies to investigate which observation types are necessary for simultaneously estimating soil hydraulic and solute transport parameters. Combining water content, matric potential, and tracer (e.g., δO) data in one objective function (OF) was found to be the best strategy for estimating parameters that can simulate all observed water flow and solute transport variables. A sequential optimization, in which first an OF with only water flow variables and subsequently an OF with transport variables was optimized, performed slightly worse indicating that transport variables contained additional information for estimating soil hydraulic parameters. Hydraulic parameters that were obtained from optimizing OFs that used either water contents or matric potential could not predict non-measured water flow variables. When a bromide (Br) tracer experiment was simulated using the optimized parameters, the arrival time of the bromide pulse was underestimated. This suggested that Br sorbed onto clay minerals and amorphous oxides under the prevailing geochemical conditions with low pH values. When accounting for anion adsorption in the simulation, Br concentrations were well predicted, which validated the dispersivity parameterization. Environmental sciences Geology Christine Stumpp verfasserin aut Andreas Lücke verfasserin aut Thomas Pütz verfasserin aut Jan Vanderborght verfasserin aut Harry Vereecken verfasserin aut In Vadose Zone Journal Wiley, 2019 17(2018), 1 (DE-627)354193597 (DE-600)2088189-7 15391663 nnns volume:17 year:2018 number:1 https://doi.org/10.2136/vzj2017.09.0168 kostenfrei https://doaj.org/article/19f6fe7ae26c469187a8c617e7ea2fb4 kostenfrei https://dl.sciencesocieties.org/publications/vzj/articles/17/1/170168 kostenfrei https://doaj.org/toc/1539-1663 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 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_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 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_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 17 2018 1
allfieldsGer	10.2136/vzj2017.09.0168 doi (DE-627)DOAJ003120716 (DE-599)DOAJ19f6fe7ae26c469187a8c617e7ea2fb4 DE-627 ger DE-627 rakwb eng GE1-350 QE1-996.5 Jannis Groh verfasserin aut Inverse Estimation of Soil Hydraulic and Transport Parameters of Layered Soils from Water Stable Isotope and Lysimeter Data 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Accurate estimates of soil hydraulic parameters and dispersivities are crucial to simulate water flow and solute transport in terrestrial systems, particularly in the vadose zone. However, parameters obtained from inverse modeling can be ambiguous when identifying multiple parameters simultaneously and when boundary conditions are not well known. Here, we performed an inverse modeling study in which we estimated soil hydraulic parameters and dispersivities of layered soils from soil water content, matric potential, and stable water isotope (δO) measurements in weighable lysimeter systems. We used different optimization strategies to investigate which observation types are necessary for simultaneously estimating soil hydraulic and solute transport parameters. Combining water content, matric potential, and tracer (e.g., δO) data in one objective function (OF) was found to be the best strategy for estimating parameters that can simulate all observed water flow and solute transport variables. A sequential optimization, in which first an OF with only water flow variables and subsequently an OF with transport variables was optimized, performed slightly worse indicating that transport variables contained additional information for estimating soil hydraulic parameters. Hydraulic parameters that were obtained from optimizing OFs that used either water contents or matric potential could not predict non-measured water flow variables. When a bromide (Br) tracer experiment was simulated using the optimized parameters, the arrival time of the bromide pulse was underestimated. This suggested that Br sorbed onto clay minerals and amorphous oxides under the prevailing geochemical conditions with low pH values. When accounting for anion adsorption in the simulation, Br concentrations were well predicted, which validated the dispersivity parameterization. Environmental sciences Geology Christine Stumpp verfasserin aut Andreas Lücke verfasserin aut Thomas Pütz verfasserin aut Jan Vanderborght verfasserin aut Harry Vereecken verfasserin aut In Vadose Zone Journal Wiley, 2019 17(2018), 1 (DE-627)354193597 (DE-600)2088189-7 15391663 nnns volume:17 year:2018 number:1 https://doi.org/10.2136/vzj2017.09.0168 kostenfrei https://doaj.org/article/19f6fe7ae26c469187a8c617e7ea2fb4 kostenfrei https://dl.sciencesocieties.org/publications/vzj/articles/17/1/170168 kostenfrei https://doaj.org/toc/1539-1663 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 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_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 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_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 17 2018 1
allfieldsSound	10.2136/vzj2017.09.0168 doi (DE-627)DOAJ003120716 (DE-599)DOAJ19f6fe7ae26c469187a8c617e7ea2fb4 DE-627 ger DE-627 rakwb eng GE1-350 QE1-996.5 Jannis Groh verfasserin aut Inverse Estimation of Soil Hydraulic and Transport Parameters of Layered Soils from Water Stable Isotope and Lysimeter Data 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Accurate estimates of soil hydraulic parameters and dispersivities are crucial to simulate water flow and solute transport in terrestrial systems, particularly in the vadose zone. However, parameters obtained from inverse modeling can be ambiguous when identifying multiple parameters simultaneously and when boundary conditions are not well known. Here, we performed an inverse modeling study in which we estimated soil hydraulic parameters and dispersivities of layered soils from soil water content, matric potential, and stable water isotope (δO) measurements in weighable lysimeter systems. We used different optimization strategies to investigate which observation types are necessary for simultaneously estimating soil hydraulic and solute transport parameters. Combining water content, matric potential, and tracer (e.g., δO) data in one objective function (OF) was found to be the best strategy for estimating parameters that can simulate all observed water flow and solute transport variables. A sequential optimization, in which first an OF with only water flow variables and subsequently an OF with transport variables was optimized, performed slightly worse indicating that transport variables contained additional information for estimating soil hydraulic parameters. Hydraulic parameters that were obtained from optimizing OFs that used either water contents or matric potential could not predict non-measured water flow variables. When a bromide (Br) tracer experiment was simulated using the optimized parameters, the arrival time of the bromide pulse was underestimated. This suggested that Br sorbed onto clay minerals and amorphous oxides under the prevailing geochemical conditions with low pH values. When accounting for anion adsorption in the simulation, Br concentrations were well predicted, which validated the dispersivity parameterization. Environmental sciences Geology Christine Stumpp verfasserin aut Andreas Lücke verfasserin aut Thomas Pütz verfasserin aut Jan Vanderborght verfasserin aut Harry Vereecken verfasserin aut In Vadose Zone Journal Wiley, 2019 17(2018), 1 (DE-627)354193597 (DE-600)2088189-7 15391663 nnns volume:17 year:2018 number:1 https://doi.org/10.2136/vzj2017.09.0168 kostenfrei https://doaj.org/article/19f6fe7ae26c469187a8c617e7ea2fb4 kostenfrei https://dl.sciencesocieties.org/publications/vzj/articles/17/1/170168 kostenfrei https://doaj.org/toc/1539-1663 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 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_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 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_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 17 2018 1
language	English
source	In Vadose Zone Journal 17(2018), 1 volume:17 year:2018 number:1
sourceStr	In Vadose Zone Journal 17(2018), 1 volume:17 year:2018 number:1
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Environmental sciences Geology
isfreeaccess_bool	true
container_title	Vadose Zone Journal
authorswithroles_txt_mv	Jannis Groh @@aut@@ Christine Stumpp @@aut@@ Andreas Lücke @@aut@@ Thomas Pütz @@aut@@ Jan Vanderborght @@aut@@ Harry Vereecken @@aut@@
publishDateDaySort_date	2018-01-01T00:00:00Z
hierarchy_top_id	354193597
id	DOAJ003120716
language_de	englisch
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callnumber-first	G - Geography, Anthropology, Recreation
author	Jannis Groh
spellingShingle	Jannis Groh misc GE1-350 misc QE1-996.5 misc Environmental sciences misc Geology Inverse Estimation of Soil Hydraulic and Transport Parameters of Layered Soils from Water Stable Isotope and Lysimeter Data
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topic_title	GE1-350 QE1-996.5 Inverse Estimation of Soil Hydraulic and Transport Parameters of Layered Soils from Water Stable Isotope and Lysimeter Data
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title	Inverse Estimation of Soil Hydraulic and Transport Parameters of Layered Soils from Water Stable Isotope and Lysimeter Data
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title_full	Inverse Estimation of Soil Hydraulic and Transport Parameters of Layered Soils from Water Stable Isotope and Lysimeter Data
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author_browse	Jannis Groh Christine Stumpp Andreas Lücke Thomas Pütz Jan Vanderborght Harry Vereecken
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title_sort	inverse estimation of soil hydraulic and transport parameters of layered soils from water stable isotope and lysimeter data
callnumber	GE1-350
title_auth	Inverse Estimation of Soil Hydraulic and Transport Parameters of Layered Soils from Water Stable Isotope and Lysimeter Data
abstract	Accurate estimates of soil hydraulic parameters and dispersivities are crucial to simulate water flow and solute transport in terrestrial systems, particularly in the vadose zone. However, parameters obtained from inverse modeling can be ambiguous when identifying multiple parameters simultaneously and when boundary conditions are not well known. Here, we performed an inverse modeling study in which we estimated soil hydraulic parameters and dispersivities of layered soils from soil water content, matric potential, and stable water isotope (δO) measurements in weighable lysimeter systems. We used different optimization strategies to investigate which observation types are necessary for simultaneously estimating soil hydraulic and solute transport parameters. Combining water content, matric potential, and tracer (e.g., δO) data in one objective function (OF) was found to be the best strategy for estimating parameters that can simulate all observed water flow and solute transport variables. A sequential optimization, in which first an OF with only water flow variables and subsequently an OF with transport variables was optimized, performed slightly worse indicating that transport variables contained additional information for estimating soil hydraulic parameters. Hydraulic parameters that were obtained from optimizing OFs that used either water contents or matric potential could not predict non-measured water flow variables. When a bromide (Br) tracer experiment was simulated using the optimized parameters, the arrival time of the bromide pulse was underestimated. This suggested that Br sorbed onto clay minerals and amorphous oxides under the prevailing geochemical conditions with low pH values. When accounting for anion adsorption in the simulation, Br concentrations were well predicted, which validated the dispersivity parameterization.
abstractGer	Accurate estimates of soil hydraulic parameters and dispersivities are crucial to simulate water flow and solute transport in terrestrial systems, particularly in the vadose zone. However, parameters obtained from inverse modeling can be ambiguous when identifying multiple parameters simultaneously and when boundary conditions are not well known. Here, we performed an inverse modeling study in which we estimated soil hydraulic parameters and dispersivities of layered soils from soil water content, matric potential, and stable water isotope (δO) measurements in weighable lysimeter systems. We used different optimization strategies to investigate which observation types are necessary for simultaneously estimating soil hydraulic and solute transport parameters. Combining water content, matric potential, and tracer (e.g., δO) data in one objective function (OF) was found to be the best strategy for estimating parameters that can simulate all observed water flow and solute transport variables. A sequential optimization, in which first an OF with only water flow variables and subsequently an OF with transport variables was optimized, performed slightly worse indicating that transport variables contained additional information for estimating soil hydraulic parameters. Hydraulic parameters that were obtained from optimizing OFs that used either water contents or matric potential could not predict non-measured water flow variables. When a bromide (Br) tracer experiment was simulated using the optimized parameters, the arrival time of the bromide pulse was underestimated. This suggested that Br sorbed onto clay minerals and amorphous oxides under the prevailing geochemical conditions with low pH values. When accounting for anion adsorption in the simulation, Br concentrations were well predicted, which validated the dispersivity parameterization.
abstract_unstemmed	Accurate estimates of soil hydraulic parameters and dispersivities are crucial to simulate water flow and solute transport in terrestrial systems, particularly in the vadose zone. However, parameters obtained from inverse modeling can be ambiguous when identifying multiple parameters simultaneously and when boundary conditions are not well known. Here, we performed an inverse modeling study in which we estimated soil hydraulic parameters and dispersivities of layered soils from soil water content, matric potential, and stable water isotope (δO) measurements in weighable lysimeter systems. We used different optimization strategies to investigate which observation types are necessary for simultaneously estimating soil hydraulic and solute transport parameters. Combining water content, matric potential, and tracer (e.g., δO) data in one objective function (OF) was found to be the best strategy for estimating parameters that can simulate all observed water flow and solute transport variables. A sequential optimization, in which first an OF with only water flow variables and subsequently an OF with transport variables was optimized, performed slightly worse indicating that transport variables contained additional information for estimating soil hydraulic parameters. Hydraulic parameters that were obtained from optimizing OFs that used either water contents or matric potential could not predict non-measured water flow variables. When a bromide (Br) tracer experiment was simulated using the optimized parameters, the arrival time of the bromide pulse was underestimated. This suggested that Br sorbed onto clay minerals and amorphous oxides under the prevailing geochemical conditions with low pH values. When accounting for anion adsorption in the simulation, Br concentrations were well predicted, which validated the dispersivity parameterization.
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title_short	Inverse Estimation of Soil Hydraulic and Transport Parameters of Layered Soils from Water Stable Isotope and Lysimeter Data
url	https://doi.org/10.2136/vzj2017.09.0168 https://doaj.org/article/19f6fe7ae26c469187a8c617e7ea2fb4 https://dl.sciencesocieties.org/publications/vzj/articles/17/1/170168 https://doaj.org/toc/1539-1663
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up_date	2024-07-03T16:05:38.897Z
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A sequential optimization, in which first an OF with only water flow variables and subsequently an OF with transport variables was optimized, performed slightly worse indicating that transport variables contained additional information for estimating soil hydraulic parameters. Hydraulic parameters that were obtained from optimizing OFs that used either water contents or matric potential could not predict non-measured water flow variables. When a bromide (Br) tracer experiment was simulated using the optimized parameters, the arrival time of the bromide pulse was underestimated. This suggested that Br sorbed onto clay minerals and amorphous oxides under the prevailing geochemical conditions with low pH values. 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Inverse Estimation of Soil Hydraulic and Transport Parameters of Layered Soils from Water Stable Isotope and Lysimeter Data

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