Effects of Measuring Devices and Sampling Strategies on the Interpretation of Monitoring Data for Long-Term Trend Analysis

被引:6
作者
Fischer, Philipp [1 ,2 ]
Dietrich, Peter [3 ,4 ]
Achterberg, Eric P. [5 ]
Anselm, Norbert [6 ]
Brix, Holger [7 ]
Bussmann, Ingeborg [1 ,6 ]
Eickelmann, Laura
Floeser, Goetz [7 ]
Friedrich, Madlen [6 ]
Rust, Hendrik
Schuetze, Claudia [3 ]
Koedel, Uta [3 ]
机构
[1] Helmholtz Ctr Polar & Marine Res, Ctr Sci Div, Alfred Wegener Inst, Helgoland, Germany
[2] Jacobs Univ Bremen, Bremen, Germany
[3] UFZ Helmholtz Ctr Environm Res, Leipzig, Germany
[4] Eberhard Karls Univ Tubingen, Tubingen, Germany
[5] Helmholtz Ctr Ocean Res, GEOMAR, Kiel, Germany
[6] Helmholtz Ctr Polar & Marine Res, Alfred Wegener Inst, Bremerhaven, Germany
[7] Helmholtz Zentrum Hereon, Geesthacht, Germany
基金
欧盟地平线“2020”;
关键词
precision; accuracy; sensor selection; sampling scheme; environmental monitoring; Kongsfjorden; long-term data; coastal waters; CLIMATE-CHANGE; KONGSFJORDEN;
D O I
10.3389/fmars.2021.770977
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
A thorough and reliable assessment of changes in sea surface water temperatures (SSWTs) is essential for understanding the effects of global warming on long-term trends in marine ecosystems and their communities. The first long-term temperature measurements were established almost a century ago, especially in coastal areas, and some of them are still in operation. However, while in earlier times these measurements were done by hand every day, current environmental long-term observation stations (ELTOS) are often fully automated and integrated in cabled underwater observatories (UWOs). With this new technology, year-round measurements became feasible even in remote or difficult to access areas, such as coastal areas of the Arctic Ocean in winter, where measurements were almost impossible just a decade ago. In this context, there is a question over what extent the sampling frequency and accuracy influence results in long-term monitoring approaches. In this paper, we address this with a combination of lab experiments on sensor accuracy and precision and a simulated sampling program with different sampling frequencies based on a continuous water temperature dataset from Svalbard, Arctic, from 2012 to 2017. Our laboratory experiments showed that temperature measurements with 12 different temperature sensor types at different price ranges all provided measurements accurate enough to resolve temperature changes over years on a level discussed in the literature when addressing climate change effects in coastal waters. However, the experiments also revealed that some sensors are more suitable for measuring absolute temperature changes over time, while others are more suitable for determining relative temperature changes. Our simulated sampling program in Svalbard coastal waters over 5 years revealed that the selection of a proper sampling frequency is most relevant for discriminating significant long-term temperature changes from random daily, seasonal, or interannual fluctuations. While hourly and daily sampling could deliver reliable, stable, and comparable results concerning temperature increases over time, weekly sampling was less able to reliably detect overall significant trends. With even lower sampling frequencies (monthly sampling), no significant temperature trend over time could be detected. Although the results were obtained for a specific site, they are transferable to other aquatic research questions and non-polar regions.
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页数:18
相关论文
共 44 条
  • [1] MEASUREMENT IN MEDICINE - THE ANALYSIS OF METHOD COMPARISON STUDIES
    ALTMAN, DG
    BLAND, JM
    [J]. JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES D-THE STATISTICIAN, 1983, 32 (03) : 307 - 317
  • [2] AMAP, 2012, SWIPA 2011 OV REP AR
  • [3] AMAP, 2021, AMAP Arctic climate change update 2021: key trends and impacts
  • [4] An Evaluation of Autonomous In Situ Temperature Loggers in a Coastal Region of the Eastern Mediterranean Sea for Use in the Validation of Near-Shore Satellite Sea Surface Temperature Measurements
    Androulakis, Dimitrios N.
    Banks, Andrew Clive
    Dounas, Costas
    Margaris, Dionissios P.
    [J]. REMOTE SENSING, 2020, 12 (07)
  • [5] [Anonymous], 2008, Vocabulaire International de Metrologie-Concepts Fondamentaux et Generaux et Termes Associes (VIM) (p. 48
  • [6] Anselm, 2020, EGU GEN ASS, VU2020, P15961, DOI [10.5194/egusphere-egu2020-15961, DOI 10.5194/EGUSPHERE-EGU2020-15961]
  • [7] Baschek, 2018, HYDROGRAPHICAL TIME
  • [8] The Coastal Observing System for Northern and Arctic Seas (COSYNA)
    Baschek, Burkard
    Schroeder, Friedhelm
    Brix, Holger
    Riethmueller, Rolf
    Badewien, Thomas H.
    Breitbach, Gisbert
    Bruegge, Bernd
    Colijn, Franciscus
    Doerffer, Roland
    Eschenbach, Christiane
    Friedrich, Jana
    Fischer, Philipp
    Garthe, Stefan
    Horstmann, Jochen
    Krasemann, Hajo
    Metfies, Katja
    Merckelbach, Lucas
    Ohle, Nino
    Petersen, Wilhelm
    Proefrock, Daniel
    Roettgers, Ruediger
    Schlueter, Michael
    Schulz, Jan
    Schulz-Stellenfleth, Johannes
    Stanev, Emil
    Staneva, Joanna
    Winter, Christian
    Wirtz, Kai
    Wollschlaeger, Jochen
    Zielinski, Oliver
    Ziemer, Friedwart
    [J]. OCEAN SCIENCE, 2017, 13 (03) : 379 - 410
  • [9] STATISTICAL METHODS FOR ASSESSING AGREEMENT BETWEEN TWO METHODS OF CLINICAL MEASUREMENT
    BLAND, JM
    ALTMAN, DG
    [J]. LANCET, 1986, 1 (8476) : 307 - 310
  • [10] Ocean Data Product Integration Through Innovation-The Next Level of Data Interoperability
    Buck, Justin J. H.
    Bainbridge, Scott J.
    Burger, Eugene F.
    Kraberg, Alexandra C.
    Casari, Matthew
    Casey, Kenneth S.
    Darroch, Louise
    Del Rio, Joaquin
    Metfies, Katja
    Delory, Eric
    Fischer, Philipp F.
    Gardner, Thomas
    Heffernan, Ryan
    Jirka, Simon
    Kokkinaki, Alexandra
    Loebl, Martina
    Buttigieg, Pier Luigi
    Pearlman, Jay S.
    Schewe, Ingo
    [J]. FRONTIERS IN MARINE SCIENCE, 2019, 6