Spatiotemporal variability in global lakes turbidity derived from satellite imageries

被引:0
|
作者
Wu, Defeng [1 ,2 ,3 ]
Tang, Ting [4 ,5 ]
Odermatt, Daniel [6 ,7 ]
Liu, Wenfeng [1 ,2 ,3 ]
机构
[1] China Agr Univ, State Key Lab Efficient Utilizat Agr Water Resourc, Beijing 100083, Peoples R China
[2] Natl Field Sci Observat & Res Stn Efficient Water, Wuwei 733000, Peoples R China
[3] China Agr Univ, Coll Water Resources & Civil Engn, Ctr Agr Water Res China, Beijing 100083, Peoples R China
[4] King Abdullah Univ Sci & Technol, Biol & Environm Sci & Engn Div, Thuwal 23955, Saudi Arabia
[5] Int Inst Appl Syst Anal, Biodivers & Nat Resources Program, Austria Water Secur Res Grp, Schlosspl 1, A-2361 Laxenburg, Austria
[6] Swiss Fed Inst Aquat Sci & Technol, Eawag, CH-8600 Dubendorf, Switzerland
[7] Univ Zurich, Dept Geog, Winterthurerstr 190, CH-8057 Zurich, Switzerland
来源
基金
中国国家自然科学基金;
关键词
global assessment; lake turbidity; climate change impact; lake management; temporal variation analysis; SUSPENDED PARTICULATE MATTER; WATER-QUALITY PARAMETERS; CLARITY; PHOSPHORUS; RESERVOIR; DECLINE; RUNOFF; INLAND; RECORD; TESTS;
D O I
10.1088/2515-7620/adb941
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Turbidity is a key indicator of water quality and has significant impacts on underwater light availability of lakes. But the spatiotemporal variability of turbidity, which is important for understanding comprehensive changes in the water quality and status of aquatic ecosystems, remains unclear on a global scale. In this study, the spatial distribution pattern, seasonal variability, spatiotemporal variability, and influencing factors of turbidity in 774 lakes worldwide have been investigated using the turbidity product of Copernicus Global Land Service (CGLS) derived from Sentinel-3 OLCI. We found that 63.4% of lakes show low turbidity (<= 5 Nephelometric Turbidity Units). The ranking of turbidity by climate zone is as follows: arid climate > tropical climate > temperate climate similar to polar climate > cold climate. Turbidity decreased significantly in 40% of studied lakes, and increased significantly in 32% lakes. The lake with low turbidity has less seasonal variation, and there is a large seasonal variation in lake turbidity in the tropical and polar climate zones of Northern Hemisphere. Positive covariates to turbidity of global lakes include wind speed of lake, slope, surface runoff, and population in the catchment. Conversely, negative covariates include lake area, volume, discharge, inflow of lake, and GDP. Abundant water volume, favorable flow conditions, and more financial investments in lake management can help to reduce turbidity. These findings highlight the spatiotemporal changes of global lake turbidity and underlying mechanisms in controlling the variability, providing valuable insights for future lake water quality management.
引用
收藏
页数:14
相关论文
共 50 条
  • [31] Global ammonia distribution derived from infrared satellite observations
    Clarisse, Lieven
    Clerbaux, Cathy
    Dentener, Frank
    Hurtmans, Daniel
    Coheur, Pierre-Francois
    NATURE GEOSCIENCE, 2009, 2 (07) : 479 - 483
  • [32] Global surface eddy diffusivities derived from satellite altimetry
    Abernathey, R. P.
    Marshall, J.
    JOURNAL OF GEOPHYSICAL RESEARCH-OCEANS, 2013, 118 (02) : 901 - 916
  • [33] Global ammonia distribution derived from infrared satellite observations
    Lieven Clarisse
    Cathy Clerbaux
    Frank Dentener
    Daniel Hurtmans
    Pierre-François Coheur
    Nature Geoscience, 2009, 2 : 479 - 483
  • [34] Confronting turbidity, the major challenge for satellite-derived coastal bathymetry
    Caballero, Isabel
    Stumpf, Richard P.
    SCIENCE OF THE TOTAL ENVIRONMENT, 2023, 870
  • [35] Conditions of spatiotemporal variability of the thickness of the ice cover on lakes in the Tatra Mountains
    Solarski, Maksymilian
    Szumny, Miroslaw
    JOURNAL OF MOUNTAIN SCIENCE, 2020, 17 (10) : 2369 - 2386
  • [36] Conditions of spatiotemporal variability of the thickness of the ice cover on lakes in the Tatra Mountains
    Maksymilian SOLARSKI
    Miros?aw SZUMNY
    Journal of Mountain Science, 2020, 17 (10) : 2369 - 2386
  • [37] Patterns of CO2 Variability from Global Satellite Data
    Ruzmaikin, Alexander
    Aumann, Hartmut H.
    Pagano, Thomas S.
    JOURNAL OF CLIMATE, 2012, 25 (18) : 6383 - 6393
  • [38] Global analysis of sea surface salinity variability from satellite data
    Michel, S
    Chapron, B
    Tournadre, J
    Reul, N
    OCEANS 2005 - EUROPE, VOLS 1 AND 2, 2005, : 11 - 16
  • [39] Conditions of spatiotemporal variability of the thickness of the ice cover on lakes in the Tatra Mountains
    Maksymilian Solarski
    Mirosław Szumny
    Journal of Mountain Science, 2020, 17 : 2369 - 2386
  • [40] The Global Surface Area Variations of Lakes and Reservoirs as Seen From Satellite Remote Sensing
    Bonnema, Matthew
    David, Cedric H.
    Frasson, Renato Prata de Moraes
    Oaida, Catalina
    Yun, Sang-Ho
    GEOPHYSICAL RESEARCH LETTERS, 2022, 49 (15)