Effects of Climate Change and Urbanization on Spatiotemporal Variations of Lake Surface Water Temperature

Abstract

Lake surface water temperature (LSWT) is a crucial ecological indicator, impacting water quality, and aquatic life. Understanding its spatiotemporal trends and driving mechanisms is fundamental for lake water environment protection and management. Previous research has been limited by low-resolution satellite data and numerical simulations, hindering in-depth understanding of LSWT. This article fills the research gap by reconstructing a high-resolution LSWT dataset spanning 2000 to 2020. Employing data fusion techniques, we combined moderate resolution imaging spectroradiometer (MODIS) and Landsat observations, achieving a spatial resolution of 30 m and a revisit cycle of eight days. Seven major lakes in Yunnan Province, China, varying in urbanization intensity, were selected to investigate the impacts and mechanisms of urbanization and climate change on LSWT. The results showed that: First, the high spatiotemporal LSWT dataset reconstructed on the ubESTARFM data fusion model outperformed the existing product datasets in terms of accuracy evaluation and spatial details. Over the past 20 years, all LSWT in the study area exhibited a warming trend in both temporal and spatial dimensions; lakes in basins with higher urbanization intensity had significantly higher warming rates than the warming rates of near-surface air temperature, and the lakes showed a global warming trend. Second, the warming trend of LSWT is not only related to lake morphology and climate change, but also closely associated with urbanization; higher spatiotemporal resolution LSWT data revealed better spatiotemporal correlations between urbanization and LSWT. Third, active ecological management and enhanced watershed vegetation coverage could effectively mitigate the rate of lake warming.

Keywords

• Data fusion
• driving mechanism
• high spatiotemporal resolution
• lake surface water temperature (LSWT)