Data_Sheet_1_Effects of Measuring Devices and Sampling Strategies on the Interpretation of Monitoring Data for Long-Term Trend Analysis.pdf
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, esp...
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Online Access: | https://doi.org/10.3389/fmars.2021.770977.s001 |
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ftsmithonian:oai:figshare.com:article/17111531 2023-05-15T15:04:53+02:00 Data_Sheet_1_Effects of Measuring Devices and Sampling Strategies on the Interpretation of Monitoring Data for Long-Term Trend Analysis.pdf Philipp Fischer (11099057) Peter Dietrich (5148866) Eric P. Achterberg (337732) Norbert Anselm (11781827) Holger Brix (11447773) Ingeborg Bussmann (11447770) Laura Eickelmann (11781830) Götz Flöser (11447776) Madlen Friedrich (8453250) Hendrik Rust (8453256) Claudia Schütze (11781833) Uta Koedel (11781836) 2021-12-02T10:54:44Z https://doi.org/10.3389/fmars.2021.770977.s001 unknown https://figshare.com/articles/dataset/Data_Sheet_1_Effects_of_Measuring_Devices_and_Sampling_Strategies_on_the_Interpretation_of_Monitoring_Data_for_Long-Term_Trend_Analysis_pdf/17111531 doi:10.3389/fmars.2021.770977.s001 CC BY 4.0 CC-BY Oceanography Marine Biology Marine Geoscience Biological Oceanography Chemical Oceanography Physical Oceanography Marine Engineering precision accuracy sensor selection sampling scheme environmental monitoring Kongsfjorden long-term data coastal waters Dataset 2021 ftsmithonian https://doi.org/10.3389/fmars.2021.770977.s001 2021-12-19T20:22:34Z 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. Dataset Arctic Arctic Ocean Climate change Global warming Kongsfjord* Kongsfjorden Svalbard Unknown Arctic Arctic Ocean Svalbard |
institution |
Open Polar |
collection |
Unknown |
op_collection_id |
ftsmithonian |
language |
unknown |
topic |
Oceanography Marine Biology Marine Geoscience Biological Oceanography Chemical Oceanography Physical Oceanography Marine Engineering precision accuracy sensor selection sampling scheme environmental monitoring Kongsfjorden long-term data coastal waters |
spellingShingle |
Oceanography Marine Biology Marine Geoscience Biological Oceanography Chemical Oceanography Physical Oceanography Marine Engineering precision accuracy sensor selection sampling scheme environmental monitoring Kongsfjorden long-term data coastal waters Philipp Fischer (11099057) Peter Dietrich (5148866) Eric P. Achterberg (337732) Norbert Anselm (11781827) Holger Brix (11447773) Ingeborg Bussmann (11447770) Laura Eickelmann (11781830) Götz Flöser (11447776) Madlen Friedrich (8453250) Hendrik Rust (8453256) Claudia Schütze (11781833) Uta Koedel (11781836) Data_Sheet_1_Effects of Measuring Devices and Sampling Strategies on the Interpretation of Monitoring Data for Long-Term Trend Analysis.pdf |
topic_facet |
Oceanography Marine Biology Marine Geoscience Biological Oceanography Chemical Oceanography Physical Oceanography Marine Engineering precision accuracy sensor selection sampling scheme environmental monitoring Kongsfjorden long-term data coastal waters |
description |
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. |
format |
Dataset |
author |
Philipp Fischer (11099057) Peter Dietrich (5148866) Eric P. Achterberg (337732) Norbert Anselm (11781827) Holger Brix (11447773) Ingeborg Bussmann (11447770) Laura Eickelmann (11781830) Götz Flöser (11447776) Madlen Friedrich (8453250) Hendrik Rust (8453256) Claudia Schütze (11781833) Uta Koedel (11781836) |
author_facet |
Philipp Fischer (11099057) Peter Dietrich (5148866) Eric P. Achterberg (337732) Norbert Anselm (11781827) Holger Brix (11447773) Ingeborg Bussmann (11447770) Laura Eickelmann (11781830) Götz Flöser (11447776) Madlen Friedrich (8453250) Hendrik Rust (8453256) Claudia Schütze (11781833) Uta Koedel (11781836) |
author_sort |
Philipp Fischer (11099057) |
title |
Data_Sheet_1_Effects of Measuring Devices and Sampling Strategies on the Interpretation of Monitoring Data for Long-Term Trend Analysis.pdf |
title_short |
Data_Sheet_1_Effects of Measuring Devices and Sampling Strategies on the Interpretation of Monitoring Data for Long-Term Trend Analysis.pdf |
title_full |
Data_Sheet_1_Effects of Measuring Devices and Sampling Strategies on the Interpretation of Monitoring Data for Long-Term Trend Analysis.pdf |
title_fullStr |
Data_Sheet_1_Effects of Measuring Devices and Sampling Strategies on the Interpretation of Monitoring Data for Long-Term Trend Analysis.pdf |
title_full_unstemmed |
Data_Sheet_1_Effects of Measuring Devices and Sampling Strategies on the Interpretation of Monitoring Data for Long-Term Trend Analysis.pdf |
title_sort |
data_sheet_1_effects of measuring devices and sampling strategies on the interpretation of monitoring data for long-term trend analysis.pdf |
publishDate |
2021 |
url |
https://doi.org/10.3389/fmars.2021.770977.s001 |
geographic |
Arctic Arctic Ocean Svalbard |
geographic_facet |
Arctic Arctic Ocean Svalbard |
genre |
Arctic Arctic Ocean Climate change Global warming Kongsfjord* Kongsfjorden Svalbard |
genre_facet |
Arctic Arctic Ocean Climate change Global warming Kongsfjord* Kongsfjorden Svalbard |
op_relation |
https://figshare.com/articles/dataset/Data_Sheet_1_Effects_of_Measuring_Devices_and_Sampling_Strategies_on_the_Interpretation_of_Monitoring_Data_for_Long-Term_Trend_Analysis_pdf/17111531 doi:10.3389/fmars.2021.770977.s001 |
op_rights |
CC BY 4.0 |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.3389/fmars.2021.770977.s001 |
_version_ |
1766336625408737280 |