Variations of deep soil moisture under different vegetation types and
influencing factors in a watershed of the Loess Plateau, China

Fang, Xuening; Zhao, Wenwu; Wang, Lixin; Feng, Qiang; Ding, Jingyi; Liu, Yuanxin; Zhang, Xiao

doi:10.5194/hess-20-3309-2016

Articles | Volume 20, issue 8

https://doi.org/10.5194/hess-20-3309-2016

© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Special issue:

Vegetation changes under a changing environment and the impacts...

https://doi.org/10.5194/hess-20-3309-2016

© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Articles | Volume 20, issue 8

Research article

|

12 Aug 2016

Research article |

| 12 Aug 2016

Variations of deep soil moisture under different vegetation types and influencing factors in a watershed of the Loess Plateau, China

Xuening Fang, Wenwu Zhao, Lixin Wang, Qiang Feng, Jingyi Ding, Yuanxin Liu, and Xiao Zhang

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PDF: 1,841
XML: 192
Total: 4,881
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BibTeX: 148
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Views and downloads (calculated since 19 Jan 2016)

Month	HTML	PDF	XML	Total
Jan 2016	57	26	1	84
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Mar 2016	36	10	0	46
Apr 2016	7	6	0	13
May 2016	9	4	0	13
Jun 2016	15	1	0	16
Jul 2016	16	4	0	20
Aug 2016	198	43	4	245
Sep 2016	81	28	4	113
Oct 2016	71	15	5	91
Nov 2016	37	4	7	48
Dec 2016	16	12	0	28
Jan 2017	28	13	3	44
Feb 2017	10	16	0	26
Mar 2017	34	20	2	56
Apr 2017	44	33	1	78
May 2017	26	14	2	42
Jun 2017	31	21	2	54
Jul 2017	18	26	0	44
Aug 2017	11	19	0	30
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Oct 2017	10	9	0	19
Nov 2017	10	14	0	24
Dec 2017	14	25	1	40
Jan 2018	4	24	1	29
Feb 2018	13	5	1	19
Mar 2018	30	18	3	51
Apr 2018	16	16	0	32
May 2018	11	19	0	30
Jun 2018	18	16	1	35
Jul 2018	12	5	3	20
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Nov 2018	19	13	4	36
Dec 2018	7	16	0	23
Jan 2019	5	7	0	12
Feb 2019	8	6	1	15
Mar 2019	7	7	0	14
Apr 2019	12	7	0	19
May 2019	8	6	1	15
Jun 2019	9	14	0	23
Jul 2019	17	12	2	31
Aug 2019	8	8	0	16
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Jan 2020	9	14	1	24
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Aug 2020	10	16	3	29
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Jan 2022	23	22	0	45
Feb 2022	15	8	0	23
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Apr 2022	18	12	1	31
May 2022	44	19	2	65
Jun 2022	32	16	1	49
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Sep 2022	19	13	2	34
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Nov 2022	21	11	0	32
Dec 2022	17	13	2	32
Jan 2023	18	19	2	39
Feb 2023	22	12	0	34
Mar 2023	35	19	1	55
Apr 2023	19	21	1	41
May 2023	25	14	0	39
Jun 2023	37	10	1	48
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Aug 2023	20	19	4	43
Sep 2023	15	21	0	36
Oct 2023	12	11	2	25
Nov 2023	10	5	3	18
Dec 2023	31	11	1	43
Jan 2024	31	16	3	50
Feb 2024	26	20	3	49
Mar 2024	33	13	5	51
Apr 2024	33	10	7	50
May 2024	37	16	6	59
Jun 2024	32	14	2	48
Jul 2024	31	9	3	43
Aug 2024	45	11	3	59
Sep 2024	23	10	0	33
Oct 2024	40	19	2	61
Nov 2024	23	13	3	39
Dec 2024	28	13	1	42
Jan 2025	25	10	3	38
Feb 2025	41	12	2	55
Mar 2025	40	15	3	58
Apr 2025	39	27	1	67
May 2025	31	13	0	44
Jun 2025	63	26	0	89
Jul 2025	37	12	3	52
Aug 2025	26	22	3	51
Sep 2025	44	33	3	80
Oct 2025	40	34	2	76
Nov 2025	56	26	2	84

Cumulative views and downloads (calculated since 19 Jan 2016)

Month	HTML	PDF	XML	Total
Jan 2016	57	26	1	84
Feb 2016	69	19	4	92
Mar 2016	36	10	0	46
Apr 2016	7	6	0	13
May 2016	9	4	0	13
Jun 2016	15	1	0	16
Jul 2016	16	4	0	20
Aug 2016	198	43	4	245
Sep 2016	81	28	4	113
Oct 2016	71	15	5	91
Nov 2016	37	4	7	48
Dec 2016	16	12	0	28
Jan 2017	28	13	3	44
Feb 2017	10	16	0	26
Mar 2017	34	20	2	56
Apr 2017	44	33	1	78
May 2017	26	14	2	42
Jun 2017	31	21	2	54
Jul 2017	18	26	0	44
Aug 2017	11	19	0	30
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Oct 2017	10	9	0	19
Nov 2017	10	14	0	24
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Jan 2018	4	24	1	29
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Oct 2022	18	13	3	34
Nov 2022	21	11	0	32
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Jan 2023	18	19	2	39
Feb 2023	22	12	0	34
Mar 2023	35	19	1	55
Apr 2023	19	21	1	41
May 2023	25	14	0	39
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Jul 2023	17	11	4	32
Aug 2023	20	19	4	43
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Oct 2023	12	11	2	25
Nov 2023	10	5	3	18
Dec 2023	31	11	1	43
Jan 2024	31	16	3	50
Feb 2024	26	20	3	49
Mar 2024	33	13	5	51
Apr 2024	33	10	7	50
May 2024	37	16	6	59
Jun 2024	32	14	2	48
Jul 2024	31	9	3	43
Aug 2024	45	11	3	59
Sep 2024	23	10	0	33
Oct 2024	40	19	2	61
Nov 2024	23	13	3	39
Dec 2024	28	13	1	42
Jan 2025	25	10	3	38
Feb 2025	41	12	2	55
Mar 2025	40	15	3	58
Apr 2025	39	27	1	67
May 2025	31	13	0	44
Jun 2025	63	26	0	89
Jul 2025	37	12	3	52
Aug 2025	26	22	3	51
Sep 2025	44	33	3	80
Oct 2025	40	34	2	76
Nov 2025	56	26	2	84

Latest update: 26 Nov 2025

Short summary

In this study, we focused on analyzing the variation and factors influencing deep soil moisture content based on a soil moisture survey of the Ansai watershed. Our results revealed the variation characteristics of deep soil moisture and its controlling mechanism at a moderate scale. The results of this study are of practical significance for vegetation restoration strategies and the sustainability of restored ecosystems.