Water clarity response to climate warming and wetting of the Inner Mongolia-Xinjiang Plateau: A remote sensing approach

Yibo Zhang; Kun Shi; Yunlin Zhang; Max Jacobo Moreno-Madriñán; Xuan Xu; Yongqiang Zhou; Boqiang Qin; Guangwei Zhu; Erik Jeppesen

doi:10.1016/j.scitotenv.2021.148916

Water clarity response to climate warming and wetting of the Inner Mongolia-Xinjiang Plateau: A remote sensing approach

Yibo Zhang, Kun Shi, Yunlin Zhang^*, Max Jacobo Moreno-Madriñán, Xuan Xu, Yongqiang Zhou, Boqiang Qin, Guangwei Zhu, Erik Jeppesen

^*Corresponding author for this work

Arctic Research Centre - Arctic Research Center, ECOS Water

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaper › Journal article › Research › peer-review

15 Citations (Scopus)

Abstract

Water clarity (generally quantified as the Secchi disk depth: SDD) is a key variable for assessing environmental changes in lakes. Using remote sensing we calculated and elucidated the SDD dynamics in lakes in the Inner Mongolia-Xinjiang Lake Zone (IMXL) from 1986 to 2018 in response to variations in temperature, rainfall, lake area, normalized difference vegetation index (NDVI) and Palmer's drought severity index (PDSI). The results showed that the lakes with high SDD values are primarily located in the Xinjiang region at longitudes of 75°–93° E. In contrast, the lakes in Inner Mongolia at longitudes of 93°–118° E generally have low SDD values. In total, 205 lakes show significant increasing SDD trends (P < 0.05), with a mean rate of 0.15 m per decade. In contrast, 75 lakes, most of which are located in Inner Mongolia, exhibited significant decreasing trends with a mean rate of 0.08 m per decade (P < 0.05). Pooled together, an overall increase is found with a mean rate of 0.14 m per decade. Multiple linear regression reveals that among the five variables selected to explain the variations in SDD, lake area accounts for the highest proportion of variance (25%), while temperature and rainfall account for 12% and 10%, respectively. In addition, rainfall accounts for 52% of the variation in humidity, 8% of the variation in lake area and 7% of the variation in NDVI. Temperature accounts for 27% of the variation in NDVI, 39% of the variation in lake area and 22% of the variation in PDSI. Warming and wetting conditions in IMXL thus promote the growth of vegetation and cause melting of glaciers and expansion of lake area, which eventually leads to improved water quality in the lakes in terms of higher SDD. In contrast, lakes facing more severe drought conditions, became more turbid.

Original language	English
Article number	148916
Journal	Science of the Total Environment
Volume	796
ISSN	0048-9697
DOIs	https://doi.org/10.1016/j.scitotenv.2021.148916
Publication status	Published - Nov 2021

Keywords

Climate change
Inner Mongolia-Xinjiang Plateau
Lakes
Transparency

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10.1016/j.scitotenv.2021.148916

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@article{0cb4dba4691c4e7cb7bf7a8f2d3a7458,

title = "Water clarity response to climate warming and wetting of the Inner Mongolia-Xinjiang Plateau: A remote sensing approach",

abstract = "Water clarity (generally quantified as the Secchi disk depth: SDD) is a key variable for assessing environmental changes in lakes. Using remote sensing we calculated and elucidated the SDD dynamics in lakes in the Inner Mongolia-Xinjiang Lake Zone (IMXL) from 1986 to 2018 in response to variations in temperature, rainfall, lake area, normalized difference vegetation index (NDVI) and Palmer's drought severity index (PDSI). The results showed that the lakes with high SDD values are primarily located in the Xinjiang region at longitudes of 75°–93° E. In contrast, the lakes in Inner Mongolia at longitudes of 93°–118° E generally have low SDD values. In total, 205 lakes show significant increasing SDD trends (P < 0.05), with a mean rate of 0.15 m per decade. In contrast, 75 lakes, most of which are located in Inner Mongolia, exhibited significant decreasing trends with a mean rate of 0.08 m per decade (P < 0.05). Pooled together, an overall increase is found with a mean rate of 0.14 m per decade. Multiple linear regression reveals that among the five variables selected to explain the variations in SDD, lake area accounts for the highest proportion of variance (25%), while temperature and rainfall account for 12% and 10%, respectively. In addition, rainfall accounts for 52% of the variation in humidity, 8% of the variation in lake area and 7% of the variation in NDVI. Temperature accounts for 27% of the variation in NDVI, 39% of the variation in lake area and 22% of the variation in PDSI. Warming and wetting conditions in IMXL thus promote the growth of vegetation and cause melting of glaciers and expansion of lake area, which eventually leads to improved water quality in the lakes in terms of higher SDD. In contrast, lakes facing more severe drought conditions, became more turbid.",

keywords = "Climate change, Inner Mongolia-Xinjiang Plateau, Lakes, Transparency",

author = "Yibo Zhang and Kun Shi and Yunlin Zhang and Moreno-Madri{\~n}{\'a}n, {Max Jacobo} and Xuan Xu and Yongqiang Zhou and Boqiang Qin and Guangwei Zhu and Erik Jeppesen",

note = "Publisher Copyright: {\textcopyright} 2021 Elsevier B.V.",

year = "2021",

month = nov,

doi = "10.1016/j.scitotenv.2021.148916",

language = "English",

volume = "796",

journal = "Science of the Total Environment",

issn = "0048-9697",

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Water clarity response to climate warming and wetting of the Inner Mongolia-Xinjiang Plateau: A remote sensing approach. / Zhang, Yibo; Shi, Kun; Zhang, Yunlin et al.
In: Science of the Total Environment, Vol. 796, 148916, 11.2021.

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaper › Journal article › Research › peer-review

TY - JOUR

T1 - Water clarity response to climate warming and wetting of the Inner Mongolia-Xinjiang Plateau

T2 - A remote sensing approach

AU - Zhang, Yibo

AU - Shi, Kun

AU - Zhang, Yunlin

AU - Moreno-Madriñán, Max Jacobo

AU - Xu, Xuan

AU - Zhou, Yongqiang

AU - Qin, Boqiang

AU - Zhu, Guangwei

AU - Jeppesen, Erik

PY - 2021/11

Y1 - 2021/11

N2 - Water clarity (generally quantified as the Secchi disk depth: SDD) is a key variable for assessing environmental changes in lakes. Using remote sensing we calculated and elucidated the SDD dynamics in lakes in the Inner Mongolia-Xinjiang Lake Zone (IMXL) from 1986 to 2018 in response to variations in temperature, rainfall, lake area, normalized difference vegetation index (NDVI) and Palmer's drought severity index (PDSI). The results showed that the lakes with high SDD values are primarily located in the Xinjiang region at longitudes of 75°–93° E. In contrast, the lakes in Inner Mongolia at longitudes of 93°–118° E generally have low SDD values. In total, 205 lakes show significant increasing SDD trends (P < 0.05), with a mean rate of 0.15 m per decade. In contrast, 75 lakes, most of which are located in Inner Mongolia, exhibited significant decreasing trends with a mean rate of 0.08 m per decade (P < 0.05). Pooled together, an overall increase is found with a mean rate of 0.14 m per decade. Multiple linear regression reveals that among the five variables selected to explain the variations in SDD, lake area accounts for the highest proportion of variance (25%), while temperature and rainfall account for 12% and 10%, respectively. In addition, rainfall accounts for 52% of the variation in humidity, 8% of the variation in lake area and 7% of the variation in NDVI. Temperature accounts for 27% of the variation in NDVI, 39% of the variation in lake area and 22% of the variation in PDSI. Warming and wetting conditions in IMXL thus promote the growth of vegetation and cause melting of glaciers and expansion of lake area, which eventually leads to improved water quality in the lakes in terms of higher SDD. In contrast, lakes facing more severe drought conditions, became more turbid.

AB - Water clarity (generally quantified as the Secchi disk depth: SDD) is a key variable for assessing environmental changes in lakes. Using remote sensing we calculated and elucidated the SDD dynamics in lakes in the Inner Mongolia-Xinjiang Lake Zone (IMXL) from 1986 to 2018 in response to variations in temperature, rainfall, lake area, normalized difference vegetation index (NDVI) and Palmer's drought severity index (PDSI). The results showed that the lakes with high SDD values are primarily located in the Xinjiang region at longitudes of 75°–93° E. In contrast, the lakes in Inner Mongolia at longitudes of 93°–118° E generally have low SDD values. In total, 205 lakes show significant increasing SDD trends (P < 0.05), with a mean rate of 0.15 m per decade. In contrast, 75 lakes, most of which are located in Inner Mongolia, exhibited significant decreasing trends with a mean rate of 0.08 m per decade (P < 0.05). Pooled together, an overall increase is found with a mean rate of 0.14 m per decade. Multiple linear regression reveals that among the five variables selected to explain the variations in SDD, lake area accounts for the highest proportion of variance (25%), while temperature and rainfall account for 12% and 10%, respectively. In addition, rainfall accounts for 52% of the variation in humidity, 8% of the variation in lake area and 7% of the variation in NDVI. Temperature accounts for 27% of the variation in NDVI, 39% of the variation in lake area and 22% of the variation in PDSI. Warming and wetting conditions in IMXL thus promote the growth of vegetation and cause melting of glaciers and expansion of lake area, which eventually leads to improved water quality in the lakes in terms of higher SDD. In contrast, lakes facing more severe drought conditions, became more turbid.

KW - Climate change

KW - Inner Mongolia-Xinjiang Plateau

KW - Lakes

KW - Transparency

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U2 - 10.1016/j.scitotenv.2021.148916

DO - 10.1016/j.scitotenv.2021.148916

M3 - Journal article

C2 - 34328890

AN - SCOPUS:85110332779

SN - 0048-9697

VL - 796

JO - Science of the Total Environment

JF - Science of the Total Environment

M1 - 148916

ER -

Water clarity response to climate warming and wetting of the Inner Mongolia-Xinjiang Plateau: A remote sensing approach

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