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

IF 5.8 2区环境科学与生态学 Q1 ECOLOGY

Ecological Informatics Pub Date : 2024-11-17 DOI:10.1016/j.ecoinf.2024.102903

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

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引用次数: 0

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.

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基于 GEE 和 HANTS 的 2001 至 2021 年中国湖泊地表水温度

湖泊表层水变暖会导致生物多样性加速丧失和水生系统生态环境崩溃。湖泊表层水温（LSWT）的变化是湖泊变暖的一个重要指标。缺水表层水温的增长可能会导致温室气体排放量增加和湖泊系统生态环境恶化。然而，由于数据的局限性，这些变化的幅度仍不确定，尤其是小湖泊（1-5 平方公里）。随着气候变化的加速，小型湖泊将受到越来越多的干扰，我们的方法展示了如何精确测量和监测湖泊变化的影响。我们的研究评估了 2001 年至 2021 年中国湖泊水量变化的时空模式。我们利用谷歌地球引擎（GEE）和时间序列谐波分析（HANTS）算法来重建LSWT序列并检测时空动态。GEE 和 HANTS 的创新连接为 LSWT 分析提供了强大的工具。我们的研究结果表明，尽管存在明显的时空变化，LSWT 的上升速率为每十年 0.24 °C。夜间的上升率大于白天的上升率。然而，大约在 2010 年，白天的 LSWT 突然发生了变化，这要早于夜间 LSWT 的突然变化。从地理位置上看，东部平原区的湖泊表现出最明显的整周最低温度变暖趋势。与 2001 年至 2010 年相比，大多数湖泊在 2011 年至 2021 年期间升温更快。我们发现，2010 年之后，藻华发生的频率也同时明显增加。我们的研究结果表明了 GEE 和 HANTS 如何能够对 LSWT 趋势进行持续监测和评估，从而为旨在减轻气候变化对当地和全球湖泊生态系统的潜在负面影响的管理策略提供依据。在此方法的基础上，未来的研究应探索驱动 LSWT 趋势的潜在机制及其对湖泊健康的长期影响。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Ecological Informatics 环境科学-生态学

CiteScore

8.30

自引率

11.80%

发文量

346

审稿时长

46 days

期刊介绍： The journal Ecological Informatics is devoted to the publication of high quality, peer-reviewed articles on all aspects of computational ecology, data science and biogeography. The scope of the journal takes into account the data-intensive nature of ecology, the growing capacity of information technology to access, harness and leverage complex data as well as the critical need for informing sustainable management in view of global environmental and climate change. The nature of the journal is interdisciplinary at the crossover between ecology and informatics. It focuses on novel concepts and techniques for image- and genome-based monitoring and interpretation, sensor- and multimedia-based data acquisition, internet-based data archiving and sharing, data assimilation, modelling and prediction of ecological data.