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引用本文:袁俊,曹志刚,马金戈,沈明,齐天赐,段洪涛.1980s以来巢湖藻华物候时空变化遥感分析.湖泊科学,2023,35(1):57-72. DOI:10.18307/2023.0103
Yuan Jun,Cao Zhigang,Ma Jin'ge,Shen Ming,Qi Tianci,Duan Hongtao.Remote sensed analysis of spatial and temporal variation in phenology of algal blooms in Lake Chaohu since 1980s. J. Lake Sci.2023,35(1):57-72. DOI:10.18307/2023.0103
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1980s以来巢湖藻华物候时空变化遥感分析
袁俊1,2,3, 曹志刚3, 马金戈3, 沈明3, 齐天赐3, 段洪涛1,2
1.西北大学城市与环境学院, 西安 710127;2.西北大学, 陕西省地表系统与环境承载力重点实验室, 西安 710127;3.中国科学院南京地理与湖泊研究所, 中国科学院流域地理学重点实验室, 南京 210008
摘要:
蓝藻水华暴发时间变化一定程度上表征了藻华物候特征,研究藻华物候变化可为湖泊水环境健康问题治理和缓解水生生态系统环境退化提供科学依据。以往巢湖蓝藻水华遥感监测主要基于2000年以来的MODIS卫星数据,限制了对巢湖蓝藻水华暴发时空变化过程的理解。本文利用Landsat扩展时间序列,联合MODIS数据,基于浮游藻类指数和阈值分割技术提取巢湖蓝藻水华,在评估二者藻华提取结果一致性的基础上,获取并分析了巢湖1987—2020年蓝藻水华暴发物候的规律及影响因子。结果表明:(1) 2000年前,巢湖蓝藻水华暴发规模较小,2000年后面积显著上升,大面积蓝藻水华出现频繁,2011年达到最高峰(608.4 km2);(2)1987—2020年间,巢湖蓝藻水华暴发可以分为3个阶段: ①1987—2004年,巢湖蓝藻水华年暴发开始时间显著提前,暴发持续时间显著增加;②2005—2010年,藻华年暴发开始时间显著延迟,但暴发持续时间变化不显著;③2011—2020年,巢湖藻华暴发开始、结束和持续时间呈现年际波动,年暴发开始时间、结束时间和持续时间有所提前,但不显著;(3)巢湖蓝藻水华暴发时间变化主要与气温、降雨量和日照时长等气候因子有关,受营养盐变化影响较弱。1987—2004年暴发开始时间提前受上年年平均温度主导;而2005—2010年的藻华暴发开始时间延迟受春季平均温度、平均日照和降雨量共同作用,暴发结束时间的提前和持续时间缩短则与年降雨量有关。本研究分析了近40年巢湖蓝藻水华暴发的长时间序列变化,有助于深入了解巢湖蓝藻水华暴发的时空变化特征和驱动力,提升气候变化背景下的巢湖蓝藻水华科学处置和应对能力。
关键词:  Landsat  MODIS  蓝藻水华  时空变化  巢湖  富营养化
DOI:10.18307/2023.0103
分类号:
基金项目:国家自然科学基金项目(41971309,42101378)和中国长江三峡集团有限公司科研项目(202003079)联合资助。
Remote sensed analysis of spatial and temporal variation in phenology of algal blooms in Lake Chaohu since 1980s
Yuan Jun1,2,3, Cao Zhigang3, Ma Jin'ge3, Shen Ming3, Qi Tianci3, Duan Hongtao1,2
1.College of Urban and Environment, Northwest University, Xi'an 710127, P. R. China;2.Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, Northwest University, Xi'an 710127, P. R. China;3.Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, P. R. China
Abstract:
The temporal changes of harmful algal blooms (HABs) indicate the phenological characteristics of algal blooms to some extent, the study of the phenological variations in HABs can provide a scientific reference for lake management and ecological restoration. Previous studies with MODIS data cannot monitor the situation before 2000, largely limiting the understandings on the spatiotemporal variation in HABs in Lake Chaohu. Here, we employed heritage Landsat missions and MODIS data to extend the time series of HABs based on the floating algae index and a threshold segmentation technique, the spatiotemporal changes and driving factors of HABs in Lake Chaohu from 1987 to 2020 were analyzed on the basis of ensuring the consistency of HABs results between Landsat and MODIS. The results showed that: (1) Comparing to 1987-1999, the area of algal blooms increased significantly after 2000 and a peak of 608.4 km2 was found in 2011. (2) The temporal variations in cyanobacterial blooms in Lake Chaohu were divided into three stages in the period of 1987-2020. From 1987 to 2004, the annual start time of HABs was earlier significantly, while the duration of HABs increased significantly. The start time of the outbreak delayed significantly, and the duration of HABs did not change significantly from 2005 to 2010. From 2011 to 2020, the start time, termination time, and duration of HABs showed fluctuating changes with an insignificant decrease. (3) Changes in the timing of HABs in Lake Chaohu were mainly related to climatic factors such as temperature, precipitation and sunshine duration and were influenced by nutrients as well. During 1987-2004, the earlier start of the outbreak was dominated by the mean annual temperature in the previous year. From 2005 to 2010, the delay in the start of the outbreak was due to the combined effect of temperature, sunshine and precipitation in the spring; the earlier termination time and shorter duration of outbreak were associated with annual precipitation. This study established a four decades of long-term series variations of algal blooms in Lake Chaohu which provided insights to the spatio-temporal characteristics and driving forces of cyanobacterial blooms and largely improved the scientific disposal and response of algal blooms under the context of climate changes.
Key words:  Landsat  MODIS  algal blooms  spatio-temporal change  Lake Chaohu  eutrophication
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