| 英文摘要： |
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 km(2) 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. (C) 2021 Elsevier B.V. All rights reserved. |
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