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Detection and Attribution of Changes in Terrestrial Water Storage across China: Climate Change versus Vegetation Greening

Rui Kong,Zeng-xin Zhang,Ying Zhang,Yiming V. Wang,Zhenhua Peng,Xi Chen,Chong-yu Xu

Published 2023 in Remote Sensing

ABSTRACT

Whether or not large-scale vegetation restoration will lead to a decrease in regional terrestrial water storage is a controversial topic. This study employed the Geodetector model, in conjunction with observed and satellite hydro-meteorological data, to detect the changes in terrestrial water storage anomaly (TWSA) and to identify the contributions of climate change and vegetation greening across China during the years 1982–2019. The results revealed that: (1) during the period of 1982–2019, TWSA showed a downward trend in about two thirds of the country, with significant declines in North China, southeast Tibet, and northwest Xinjiang, and an upward trend in the remaining third of the country, with significant increases mainly in the Qaidam Basin, the Yangtze River, and the Songhua River; (2) the positive correlation between normalized vegetation index (NDVI) and TWSA accounts for 48.64% of the total vegetation area across China. In addition, the response of vegetation greenness lags behind the TWSA and precipitation, and the lag time was shorter in arid and semi-arid regions dominated by grasslands, and longer in relatively humid regions dominated by forests and savannas; (3) furthermore, TWSAs decreased with the increase in NDVI and evapotranspiration (ET) in arid and semi-arid areas, and increased with the rise in NDVI and ET in the humid regions. The Geodetector model was used to detect the effects of climate, vegetation, and human factors on TWSA. It is worth mentioning that NDVI, precipitation, and ET were some of the main factors affecting TWSA. Therefore, it is essential to implement rational ecological engineering to mitigate climate change’s negative effects and maintain water resources’ sustainability in arid and semi-arid regions.

PUBLICATION RECORD

  • Publication year

    2023

  • Venue

    Remote Sensing

  • Publication date

    2023-06-14

  • Fields of study

    Computer Science, Environmental Science

  • Identifiers
    DOI 10.3390/rs15123104
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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