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Sci Data. 2017 Jul 25;4:170095. doi: 10.1038/sdata.2017.95.

A comprehensive data set of lake surface water temperature over the Tibetan Plateau derived from MODIS LST products 2001-2015.

Wan W1,2, Li H1,2, Xie H3, Hong Y1,2,4, Long D1,2, Zhao L5,6, Han Z1,2, Cui Y1,2, Liu B7, Wang C1,2, Yang W1,2.

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State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing 100084, China.
Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China.
Department of Geological Sciences, University of Texas at San Antonio, San Antonio, Texas 78249, USA.
Department of Civil Engineering and Environmental Science, University of Oklahoma, Norman, Oklahoma 73019, USA.
Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China.
The Center for National Spaceborne Demonstration, Beijing 100101, China.
School of Earth and Space Sciences, Peking University, Beijing 100871, China.


Lake surface water temperature (LSWT) is sensitive to long-term changes in thermal structure of lakes and regional air temperature. In the context of global climate change, recent studies showed a significant warming trend of LSWT based on investigating 291 lakes (71% are large lakes, ≥50 km2 each) globally. However, further efforts are needed to examine variation in LSWT at finer regional spatial and temporal scales. The Tibetan Plateau (TP), known as 'the Roof of the World' and 'Asia's water towers', exerts large influences on and is sensitive to regional and even global climates. Aiming to examine detailed changing patterns and potential driven mechanisms for temperature variations of lakes across the TP region, this paper presents the first comprehensive data set of 15-year (2001-2015) nighttime and daytime LSWT for 374 lakes (≥10 km2 each), using MODIS (Moderate Resolution Imaging Spectroradiometer) Land Surface Temperature (LST) products as well as four lake boundary shapefiles (i.e., 2002, 2005, 2009, and 2014) derived from Landsat/CBERS/GaoFen-1 satellite images. The data set itself reveals significant information on LSWT and its changes over the TP and is an indispensable variable for numerous applications related to climate change, water budget analysis (particularly lake evaporation), water storage changes, glacier melting and permafrost degradation, etc.

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