Lake surface water temperature in China from 2001 to 2021 based on GEE and HANTS

Song, Song; Yang, Jinxin; Liu, Linjie; Bai, Gale; Zhou, Jie; McKay, Deirdre

Abstract

Warming of lakes' surface water leads to accelerated loss of biodiversity and eco-environmental collapse of aquatic systems. Changes in lack surface water temperature (LSWT) are a crucial indicator of lake warming. LSWT growth potentially leads to a higher greenhouse gas emissions and deterioration of the ecological environment within lake systems. However, the magnitude of these changes remains uncertain due to data limitations, particularly for small lakes (1–5 km2). Small lakes will experience increasing perturbation with accelerating climate change and our methods demonstrate how the impacts of changes in lakes can be accurately measured and monitored. Our study assessed the spatial and temporal patterns of LSWT in China from 2001 to 2021. We utilized Google Earth Engine (GEE) and the Harmonic Analysis of Time Series (HANTS) algorithm to reconstruct LSWT series and detect spatiotemporal dynamics. The innovative connection of GEE and HANTS provides powerful tool for LSWT analysis. Our results show LSWT increased at a rate of 0.24 °C per decade, albeit with notable spatial and temporal variations. The nighttime rate of increase was greater than the daytime rate of increase. However, there was an abrupt change in daytime LSWT in approximately 2010 and this occurred earlier than an abrupt change in nighttime LSWT. Geographically, the lakes in the Eastern Plain zone exhibited the most significant LSWT warming trend. The majority of lakes warmed more rapidly between 2011 and 2021 as compared to 2001 to 2010. We found a concurrent and pronounced increase in the frequency of algal bloom occurrences after 2010. Our results demonstrate how GEE and HANTS can deliver the continued monitoring and assessment of LSWT trends needed to inform management strategies aimed at mitigating potential negative impacts of climate change on lake ecosystems, both locally and globally. Building on this method, future research should explore the underlying mechanisms driving LSWT trends and their long-term impacts on lake health.

Citation

Song, S., Yang, J., Liu, L., Bai, G., Zhou, J., & McKay, D. (2024). Lake surface water temperature in China from 2001 to 2021 based on GEE and HANTS. Ecological Informatics, Article 102903. https://doi.org/10.1016/j.ecoinf.2024.102903

• Journal Article Type: Article
• Acceptance Date: Nov 15, 2024
• Online Publication Date: Nov 17, 2024
• Publication Date: 2024-11
• Deposit Date: Nov 21, 2024
• Publicly Available Date: Nov 26, 2024
• Journal: Ecological Informatics
• Print ISSN: 1574-9541
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Article Number: 102903
• DOI: https://doi.org/10.1016/j.ecoinf.2024.102903
• Keywords: Spatiotemporal variation; Lake surface water temperature; China; GEE; HANTS; Climate change
• Public URL: https://keele-repository.worktribe.com/output/979913
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S157495412400445X?via%3Dihub
• Additional Information: This article is maintained by: Elsevier; Article Title: Lake surface water temperature in China from 2001 to 2021 based on GEE and HANTS; Journal Title: Ecological Informatics; CrossRef DOI link to publisher maintained version: https://doi.org/10.1016/j.ecoinf.2024.102903; Content Type: article; Copyright: © 2024 The Authors. Published by Elsevier B.V.