Retrieving dynamics of the surface water extent in the upper reach of Yellow River

Haowei Zhou; Suxia Liu; Shi Hu; Xingguo Mo

doi:10.1016/j.scitotenv.2021.149348

Retrieving dynamics of the surface water extent in the upper reach of Yellow River

Sci Total Environ. 2021 Dec 15:800:149348. doi: 10.1016/j.scitotenv.2021.149348. Epub 2021 Jul 31.

Authors

Haowei Zhou¹, Suxia Liu², Shi Hu³, Xingguo Mo⁴

Affiliations

¹ Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China.
² Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China; Sino-Danish Center, University of Chinese Academy of Sciences, Beijing 100049, China.
³ Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China.
⁴ Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China; Sino-Danish Center, University of Chinese Academy of Sciences, Beijing 100049, China. Electronic address: moxg@igsnrr.ac.cn.

PMID: 34399339
DOI: 10.1016/j.scitotenv.2021.149348

Abstract

Multi-time scale surface water extent (SWE) dynamics are very important to understand climate change impacts on water resources. With Landsat 5/7/8 images and Google Earth Engine (GEE), an improved threshold-based water extraction algorithm and a novel surface water gaps (SWGs) interpolation method based on historical water frequency were applied to build surface water area (SWA, namely SWE without ice) and water body area (WBA, namely SWE with ice) monthly (January 2001-December 2019) and annual (1986-2019) time series in the upper reaches of the Yellow River (UYR). The Mann-Kendall test was used to analyse SWE trends, and the ridge regression was performed to figure out the relative contributions of meteorological factors to SWE dynamics. The pixels with modified normalized difference water index (MNDWI) higher than normalized difference vegetation index (NDVI) or enhanced vegetation index (EVI) were identified as SWE. The mean relative error (MRE) of the SWGs interpolation results was below 10%. At the annual scale, the average SWA and number of lakes over 1 ha showed significant upward trends of 4.4 km² yr^-1 and 7.53 yr^-1, respectively. The monthly WBA increased in summer and autumn while decreased in spring and winter. The maximum freezing and thawing ratios were 53.74% in December and 37.32% in May, respectively. Attribution analysis showed that precipitation and wind speed were the foremost factors dominating the dynamics of annual SWA and monthly WBA, respectively. Our findings confirmed that climatic changes have altered the dynamics of water bodies in the UYR.

Keywords: Google Earth Engine; Landsat images; Surface water extent dynamics; Surface water gaps interpolation; Water extraction.

MeSH terms

Climate Change
Lakes
Rivers*
Water Resources
Water*

Substances

Water