Assessing the value of electrical resistivity derived soil water content: Insights from a case study in the Critical Zone of the Chinese Loess Plateau

Acquiring data for the soil water content (θ) is important for assessing vegetation growth, drought, and climate change. However, it is a time-consuming and labor-intensive task that is especially challenging in deep soils. Therefore, we introduce the noninvasive technique of electrical resistivity...
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Gespeichert in:

Autor*in:	Sun, Hui [verfasserIn] Wang, Yunqiang [verfasserIn] Zhao, Yali [verfasserIn] Zhang, Pingping [verfasserIn] Song, Yi [verfasserIn] He, Meina [verfasserIn] Zhang, Chencheng [verfasserIn] Tong, Yongping [verfasserIn] Zhou, Jingxiong [verfasserIn] Qi, Lijun [verfasserIn] Xu, Lan [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Deep soil Electrical resistivity Land use Loessial soil Soil condition Soil water content

Übergeordnetes Werk:	Enthalten in: Journal of hydrology - Amsterdam [u.a.] : Elsevier, 1963, 589
Übergeordnetes Werk:	volume:589

DOI / URN:	10.1016/j.jhydrol.2020.125132

Katalog-ID:	ELV00457026X

Internformat


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245	1	0	\|a Assessing the value of electrical resistivity derived soil water content: Insights from a case study in the Critical Zone of the Chinese Loess Plateau
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520			\|a Acquiring data for the soil water content (θ) is important for assessing vegetation growth, drought, and climate change. However, it is a time-consuming and labor-intensive task that is especially challenging in deep soils. Therefore, we introduce the noninvasive technique of electrical resistivity tomography (ERT) for indirectly determining θ through the development of prediction models for loessial regions under different site conditions (i.e., soil texture, land use, soil depth, and dry/wet conditions). We obtained 2769 pairs of electrical resistivity (ρ) and θ datasets using ERT (53 sites) and a neutron probe (69 access tubes) on the Chinese Loess Plateau. We built linear and nonlinear models correlating ρ and θ and selected the best model according to the coefficient of determination (R 2) and root-mean-square error (RMSE). The uncertainty and sensitivity of ERT-derived θ were further evaluated and acceptable results were obtained. The new models correlating ρ and θ under different site conditions are the first set of models based on field data from a loessial region, and their acceptable performance makes them applicable for measuring different soil parameters in the Loess Plateau and possibly other loessial regions around the world.
650		4	\|a Deep soil
650		4	\|a Electrical resistivity
650		4	\|a Land use
650		4	\|a Loessial soil
650		4	\|a Soil condition
650		4	\|a Soil water content
700	1		\|a Wang, Yunqiang \|e verfasserin \|0 (orcid)0000-0003-3380-549X \|4 aut
700	1		\|a Zhao, Yali \|e verfasserin \|4 aut
700	1		\|a Zhang, Pingping \|e verfasserin \|0 (orcid)0000-0003-4555-9708 \|4 aut
700	1		\|a Song, Yi \|e verfasserin \|4 aut
700	1		\|a He, Meina \|e verfasserin \|4 aut
700	1		\|a Zhang, Chencheng \|e verfasserin \|4 aut
700	1		\|a Tong, Yongping \|e verfasserin \|4 aut
700	1		\|a Zhou, Jingxiong \|e verfasserin \|4 aut
700	1		\|a Qi, Lijun \|e verfasserin \|4 aut
700	1		\|a Xu, Lan \|e verfasserin \|4 aut
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936	b	k	\|a 38.85 \|j Hydrologie: Allgemeines
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allfields	10.1016/j.jhydrol.2020.125132 doi (DE-627)ELV00457026X (ELSEVIER)S0022-1694(20)30592-8 DE-627 ger DE-627 rda eng 690 DE-600 38.85 bkl Sun, Hui verfasserin (orcid)0000-0002-9866-1792 aut Assessing the value of electrical resistivity derived soil water content: Insights from a case study in the Critical Zone of the Chinese Loess Plateau 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Acquiring data for the soil water content (θ) is important for assessing vegetation growth, drought, and climate change. However, it is a time-consuming and labor-intensive task that is especially challenging in deep soils. Therefore, we introduce the noninvasive technique of electrical resistivity tomography (ERT) for indirectly determining θ through the development of prediction models for loessial regions under different site conditions (i.e., soil texture, land use, soil depth, and dry/wet conditions). We obtained 2769 pairs of electrical resistivity (ρ) and θ datasets using ERT (53 sites) and a neutron probe (69 access tubes) on the Chinese Loess Plateau. We built linear and nonlinear models correlating ρ and θ and selected the best model according to the coefficient of determination (R 2) and root-mean-square error (RMSE). The uncertainty and sensitivity of ERT-derived θ were further evaluated and acceptable results were obtained. The new models correlating ρ and θ under different site conditions are the first set of models based on field data from a loessial region, and their acceptable performance makes them applicable for measuring different soil parameters in the Loess Plateau and possibly other loessial regions around the world. Deep soil Electrical resistivity Land use Loessial soil Soil condition Soil water content Wang, Yunqiang verfasserin (orcid)0000-0003-3380-549X aut Zhao, Yali verfasserin aut Zhang, Pingping verfasserin (orcid)0000-0003-4555-9708 aut Song, Yi verfasserin aut He, Meina verfasserin aut Zhang, Chencheng verfasserin aut Tong, Yongping verfasserin aut Zhou, Jingxiong verfasserin aut Qi, Lijun verfasserin aut Xu, Lan verfasserin aut Enthalten in Journal of hydrology Amsterdam [u.a.] : Elsevier, 1963 589 Online-Ressource (DE-627)268761817 (DE-600)1473173-3 (DE-576)077610628 1879-2707 nnns volume:589 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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_150 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 38.85 Hydrologie: Allgemeines AR 589
spelling	10.1016/j.jhydrol.2020.125132 doi (DE-627)ELV00457026X (ELSEVIER)S0022-1694(20)30592-8 DE-627 ger DE-627 rda eng 690 DE-600 38.85 bkl Sun, Hui verfasserin (orcid)0000-0002-9866-1792 aut Assessing the value of electrical resistivity derived soil water content: Insights from a case study in the Critical Zone of the Chinese Loess Plateau 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Acquiring data for the soil water content (θ) is important for assessing vegetation growth, drought, and climate change. However, it is a time-consuming and labor-intensive task that is especially challenging in deep soils. Therefore, we introduce the noninvasive technique of electrical resistivity tomography (ERT) for indirectly determining θ through the development of prediction models for loessial regions under different site conditions (i.e., soil texture, land use, soil depth, and dry/wet conditions). We obtained 2769 pairs of electrical resistivity (ρ) and θ datasets using ERT (53 sites) and a neutron probe (69 access tubes) on the Chinese Loess Plateau. We built linear and nonlinear models correlating ρ and θ and selected the best model according to the coefficient of determination (R 2) and root-mean-square error (RMSE). The uncertainty and sensitivity of ERT-derived θ were further evaluated and acceptable results were obtained. The new models correlating ρ and θ under different site conditions are the first set of models based on field data from a loessial region, and their acceptable performance makes them applicable for measuring different soil parameters in the Loess Plateau and possibly other loessial regions around the world. Deep soil Electrical resistivity Land use Loessial soil Soil condition Soil water content Wang, Yunqiang verfasserin (orcid)0000-0003-3380-549X aut Zhao, Yali verfasserin aut Zhang, Pingping verfasserin (orcid)0000-0003-4555-9708 aut Song, Yi verfasserin aut He, Meina verfasserin aut Zhang, Chencheng verfasserin aut Tong, Yongping verfasserin aut Zhou, Jingxiong verfasserin aut Qi, Lijun verfasserin aut Xu, Lan verfasserin aut Enthalten in Journal of hydrology Amsterdam [u.a.] : Elsevier, 1963 589 Online-Ressource (DE-627)268761817 (DE-600)1473173-3 (DE-576)077610628 1879-2707 nnns volume:589 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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_150 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 38.85 Hydrologie: Allgemeines AR 589
allfields_unstemmed	10.1016/j.jhydrol.2020.125132 doi (DE-627)ELV00457026X (ELSEVIER)S0022-1694(20)30592-8 DE-627 ger DE-627 rda eng 690 DE-600 38.85 bkl Sun, Hui verfasserin (orcid)0000-0002-9866-1792 aut Assessing the value of electrical resistivity derived soil water content: Insights from a case study in the Critical Zone of the Chinese Loess Plateau 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Acquiring data for the soil water content (θ) is important for assessing vegetation growth, drought, and climate change. However, it is a time-consuming and labor-intensive task that is especially challenging in deep soils. Therefore, we introduce the noninvasive technique of electrical resistivity tomography (ERT) for indirectly determining θ through the development of prediction models for loessial regions under different site conditions (i.e., soil texture, land use, soil depth, and dry/wet conditions). We obtained 2769 pairs of electrical resistivity (ρ) and θ datasets using ERT (53 sites) and a neutron probe (69 access tubes) on the Chinese Loess Plateau. We built linear and nonlinear models correlating ρ and θ and selected the best model according to the coefficient of determination (R 2) and root-mean-square error (RMSE). The uncertainty and sensitivity of ERT-derived θ were further evaluated and acceptable results were obtained. The new models correlating ρ and θ under different site conditions are the first set of models based on field data from a loessial region, and their acceptable performance makes them applicable for measuring different soil parameters in the Loess Plateau and possibly other loessial regions around the world. Deep soil Electrical resistivity Land use Loessial soil Soil condition Soil water content Wang, Yunqiang verfasserin (orcid)0000-0003-3380-549X aut Zhao, Yali verfasserin aut Zhang, Pingping verfasserin (orcid)0000-0003-4555-9708 aut Song, Yi verfasserin aut He, Meina verfasserin aut Zhang, Chencheng verfasserin aut Tong, Yongping verfasserin aut Zhou, Jingxiong verfasserin aut Qi, Lijun verfasserin aut Xu, Lan verfasserin aut Enthalten in Journal of hydrology Amsterdam [u.a.] : Elsevier, 1963 589 Online-Ressource (DE-627)268761817 (DE-600)1473173-3 (DE-576)077610628 1879-2707 nnns volume:589 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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_150 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 38.85 Hydrologie: Allgemeines AR 589
allfieldsGer	10.1016/j.jhydrol.2020.125132 doi (DE-627)ELV00457026X (ELSEVIER)S0022-1694(20)30592-8 DE-627 ger DE-627 rda eng 690 DE-600 38.85 bkl Sun, Hui verfasserin (orcid)0000-0002-9866-1792 aut Assessing the value of electrical resistivity derived soil water content: Insights from a case study in the Critical Zone of the Chinese Loess Plateau 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Acquiring data for the soil water content (θ) is important for assessing vegetation growth, drought, and climate change. However, it is a time-consuming and labor-intensive task that is especially challenging in deep soils. Therefore, we introduce the noninvasive technique of electrical resistivity tomography (ERT) for indirectly determining θ through the development of prediction models for loessial regions under different site conditions (i.e., soil texture, land use, soil depth, and dry/wet conditions). We obtained 2769 pairs of electrical resistivity (ρ) and θ datasets using ERT (53 sites) and a neutron probe (69 access tubes) on the Chinese Loess Plateau. We built linear and nonlinear models correlating ρ and θ and selected the best model according to the coefficient of determination (R 2) and root-mean-square error (RMSE). The uncertainty and sensitivity of ERT-derived θ were further evaluated and acceptable results were obtained. The new models correlating ρ and θ under different site conditions are the first set of models based on field data from a loessial region, and their acceptable performance makes them applicable for measuring different soil parameters in the Loess Plateau and possibly other loessial regions around the world. Deep soil Electrical resistivity Land use Loessial soil Soil condition Soil water content Wang, Yunqiang verfasserin (orcid)0000-0003-3380-549X aut Zhao, Yali verfasserin aut Zhang, Pingping verfasserin (orcid)0000-0003-4555-9708 aut Song, Yi verfasserin aut He, Meina verfasserin aut Zhang, Chencheng verfasserin aut Tong, Yongping verfasserin aut Zhou, Jingxiong verfasserin aut Qi, Lijun verfasserin aut Xu, Lan verfasserin aut Enthalten in Journal of hydrology Amsterdam [u.a.] : Elsevier, 1963 589 Online-Ressource (DE-627)268761817 (DE-600)1473173-3 (DE-576)077610628 1879-2707 nnns volume:589 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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_150 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 38.85 Hydrologie: Allgemeines AR 589
allfieldsSound	10.1016/j.jhydrol.2020.125132 doi (DE-627)ELV00457026X (ELSEVIER)S0022-1694(20)30592-8 DE-627 ger DE-627 rda eng 690 DE-600 38.85 bkl Sun, Hui verfasserin (orcid)0000-0002-9866-1792 aut Assessing the value of electrical resistivity derived soil water content: Insights from a case study in the Critical Zone of the Chinese Loess Plateau 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Acquiring data for the soil water content (θ) is important for assessing vegetation growth, drought, and climate change. However, it is a time-consuming and labor-intensive task that is especially challenging in deep soils. Therefore, we introduce the noninvasive technique of electrical resistivity tomography (ERT) for indirectly determining θ through the development of prediction models for loessial regions under different site conditions (i.e., soil texture, land use, soil depth, and dry/wet conditions). We obtained 2769 pairs of electrical resistivity (ρ) and θ datasets using ERT (53 sites) and a neutron probe (69 access tubes) on the Chinese Loess Plateau. We built linear and nonlinear models correlating ρ and θ and selected the best model according to the coefficient of determination (R 2) and root-mean-square error (RMSE). The uncertainty and sensitivity of ERT-derived θ were further evaluated and acceptable results were obtained. The new models correlating ρ and θ under different site conditions are the first set of models based on field data from a loessial region, and their acceptable performance makes them applicable for measuring different soil parameters in the Loess Plateau and possibly other loessial regions around the world. Deep soil Electrical resistivity Land use Loessial soil Soil condition Soil water content Wang, Yunqiang verfasserin (orcid)0000-0003-3380-549X aut Zhao, Yali verfasserin aut Zhang, Pingping verfasserin (orcid)0000-0003-4555-9708 aut Song, Yi verfasserin aut He, Meina verfasserin aut Zhang, Chencheng verfasserin aut Tong, Yongping verfasserin aut Zhou, Jingxiong verfasserin aut Qi, Lijun verfasserin aut Xu, Lan verfasserin aut Enthalten in Journal of hydrology Amsterdam [u.a.] : Elsevier, 1963 589 Online-Ressource (DE-627)268761817 (DE-600)1473173-3 (DE-576)077610628 1879-2707 nnns volume:589 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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_150 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 38.85 Hydrologie: Allgemeines AR 589
language	English
source	Enthalten in Journal of hydrology 589 volume:589
sourceStr	Enthalten in Journal of hydrology 589 volume:589
format_phy_str_mv	Article
bklname	Hydrologie: Allgemeines
institution	findex.gbv.de
topic_facet	Deep soil Electrical resistivity Land use Loessial soil Soil condition Soil water content
dewey-raw	690
isfreeaccess_bool	false
container_title	Journal of hydrology
authorswithroles_txt_mv	Sun, Hui @@aut@@ Wang, Yunqiang @@aut@@ Zhao, Yali @@aut@@ Zhang, Pingping @@aut@@ Song, Yi @@aut@@ He, Meina @@aut@@ Zhang, Chencheng @@aut@@ Tong, Yongping @@aut@@ Zhou, Jingxiong @@aut@@ Qi, Lijun @@aut@@ Xu, Lan @@aut@@
publishDateDaySort_date	2020-01-01T00:00:00Z
hierarchy_top_id	268761817
dewey-sort	3690
id	ELV00457026X
language_de	englisch
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author	Sun, Hui
spellingShingle	Sun, Hui ddc 690 bkl 38.85 misc Deep soil misc Electrical resistivity misc Land use misc Loessial soil misc Soil condition misc Soil water content Assessing the value of electrical resistivity derived soil water content: Insights from a case study in the Critical Zone of the Chinese Loess Plateau
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topic_title	690 DE-600 38.85 bkl Assessing the value of electrical resistivity derived soil water content: Insights from a case study in the Critical Zone of the Chinese Loess Plateau Deep soil Electrical resistivity Land use Loessial soil Soil condition Soil water content
topic	ddc 690 bkl 38.85 misc Deep soil misc Electrical resistivity misc Land use misc Loessial soil misc Soil condition misc Soil water content
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title	Assessing the value of electrical resistivity derived soil water content: Insights from a case study in the Critical Zone of the Chinese Loess Plateau
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title_full	Assessing the value of electrical resistivity derived soil water content: Insights from a case study in the Critical Zone of the Chinese Loess Plateau
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author_browse	Sun, Hui Wang, Yunqiang Zhao, Yali Zhang, Pingping Song, Yi He, Meina Zhang, Chencheng Tong, Yongping Zhou, Jingxiong Qi, Lijun Xu, Lan
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title_sort	assessing the value of electrical resistivity derived soil water content: insights from a case study in the critical zone of the chinese loess plateau
title_auth	Assessing the value of electrical resistivity derived soil water content: Insights from a case study in the Critical Zone of the Chinese Loess Plateau
abstract	Acquiring data for the soil water content (θ) is important for assessing vegetation growth, drought, and climate change. However, it is a time-consuming and labor-intensive task that is especially challenging in deep soils. Therefore, we introduce the noninvasive technique of electrical resistivity tomography (ERT) for indirectly determining θ through the development of prediction models for loessial regions under different site conditions (i.e., soil texture, land use, soil depth, and dry/wet conditions). We obtained 2769 pairs of electrical resistivity (ρ) and θ datasets using ERT (53 sites) and a neutron probe (69 access tubes) on the Chinese Loess Plateau. We built linear and nonlinear models correlating ρ and θ and selected the best model according to the coefficient of determination (R 2) and root-mean-square error (RMSE). The uncertainty and sensitivity of ERT-derived θ were further evaluated and acceptable results were obtained. The new models correlating ρ and θ under different site conditions are the first set of models based on field data from a loessial region, and their acceptable performance makes them applicable for measuring different soil parameters in the Loess Plateau and possibly other loessial regions around the world.
abstractGer	Acquiring data for the soil water content (θ) is important for assessing vegetation growth, drought, and climate change. However, it is a time-consuming and labor-intensive task that is especially challenging in deep soils. Therefore, we introduce the noninvasive technique of electrical resistivity tomography (ERT) for indirectly determining θ through the development of prediction models for loessial regions under different site conditions (i.e., soil texture, land use, soil depth, and dry/wet conditions). We obtained 2769 pairs of electrical resistivity (ρ) and θ datasets using ERT (53 sites) and a neutron probe (69 access tubes) on the Chinese Loess Plateau. We built linear and nonlinear models correlating ρ and θ and selected the best model according to the coefficient of determination (R 2) and root-mean-square error (RMSE). The uncertainty and sensitivity of ERT-derived θ were further evaluated and acceptable results were obtained. The new models correlating ρ and θ under different site conditions are the first set of models based on field data from a loessial region, and their acceptable performance makes them applicable for measuring different soil parameters in the Loess Plateau and possibly other loessial regions around the world.
abstract_unstemmed	Acquiring data for the soil water content (θ) is important for assessing vegetation growth, drought, and climate change. However, it is a time-consuming and labor-intensive task that is especially challenging in deep soils. Therefore, we introduce the noninvasive technique of electrical resistivity tomography (ERT) for indirectly determining θ through the development of prediction models for loessial regions under different site conditions (i.e., soil texture, land use, soil depth, and dry/wet conditions). We obtained 2769 pairs of electrical resistivity (ρ) and θ datasets using ERT (53 sites) and a neutron probe (69 access tubes) on the Chinese Loess Plateau. We built linear and nonlinear models correlating ρ and θ and selected the best model according to the coefficient of determination (R 2) and root-mean-square error (RMSE). The uncertainty and sensitivity of ERT-derived θ were further evaluated and acceptable results were obtained. The new models correlating ρ and θ under different site conditions are the first set of models based on field data from a loessial region, and their acceptable performance makes them applicable for measuring different soil parameters in the Loess Plateau and possibly other loessial regions around the world.
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title_short	Assessing the value of electrical resistivity derived soil water content: Insights from a case study in the Critical Zone of the Chinese Loess Plateau
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author2	Wang, Yunqiang Zhao, Yali Zhang, Pingping Song, Yi He, Meina Zhang, Chencheng Tong, Yongping Zhou, Jingxiong Qi, Lijun Xu, Lan
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up_date	2024-07-06T23:26:14.412Z
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Assessing the value of electrical resistivity derived soil water content: Insights from a case study in the Critical Zone of the Chinese Loess Plateau

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