Temporal and spatial heterogeneity of recent lake surface water temperature trends in the Qinghai-Tibet Plateau

Abstract

Lake surface water temperature (LSWT) monitoring is significant as it provides valuable information on climate changes and resultant changes in lakes. Here, monthly moderate resolution imaging spectroradiometer LSWT data of 364 lakes during 2001–2015 were analysed to determine spatiotemporal patterns of daytime (LSWT_d), nighttime (LSWT_n), daily LSWT (LSWT_m) and their drivers in the Qinghai-Tibet Plateau at monthly, seasonal and annual timescales. We focused on spatiotemporal heterogeneity of LSWT trends. Results showed climatological LSWT presented a saddle-shaped distribution along the northeast-southwest direction except for LSWT_d in March, while highly heterogeneous LSWT trends without spatial dependence were observed at all timescales. Diurnal asymmetry of LSWT trends was evident with 56.04–88.19% lakes showing smaller LSWT_d trends than LSWT_n trends except for August and September. Further, we found lake surface albedo trend could effectively explain spatial patterns of LSWT trends at different timescales, and its interactions with other variable trends generally were the largest or second-largest.

Keywords:

• Lake surface water temperature
• MODIS land surface temperature
• climate change
• water surface albedo
• Qinghai-Tibet Plateau

Disclosure statement

No potential conflict of interest was reported by the authors.

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Additional information

Funding

This research was supported by the National Key Research and Development Program of China [Grant No. 2019YFC0507801], the Second Tibetan Plateau Scientific Expedition and Research Program (STEP) [Grant No. 2019QZKK1003], the Strategic Priority Research Program of Chinese Academy of Sciences [Grant No. XDA20040301], the CAS Interdisciplinary Innovation Team [Grant No. JCTD-2019-04] and the National Natural Science Foundation of China [Grant No. 41890824]. Thanks are extended to Scientific Data, a data journal, for providing free monthly LSWT dataset and China meteorological forcing dataset, to NASA for offering MODIS albedo product, and to National Tibetan Plateau Data Center for providing elevation data.