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  • 2020-2024  (32)
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  • 1
    Publication Date: 2024-02-07
    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.
    Type: Article , PeerReviewed , info:eu-repo/semantics/article
    Format: text
    Format: text
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  • 2
    Publication Date: 2024-02-07
    Description: Highlights • Nutrient and carbon fluxes are key processes in land-ocean interactions. • We sampled along the river-estuary-ocean system according to travel time of water. • The river was autotrophic with phytoplankton growth, high pH and oxygen concentration, and CO2 undersaturation. • Phytoplankton died off in the estuary causing low pH and oxygen concentration, CO2 supersaturation, and nutrient release. • The approach is suitable to investigate single events such as hydrological extremes. Nutrient and carbon dynamics within the river-estuary-coastal water systems are key processes in understanding the flux of matter from the terrestrial environment to the ocean. Here, we analysed those dynamics by following a sampling approach based on the travel time of water and an advanced calculation of nutrient fluxes in the tidal part. We started with a nearly Lagrangian sampling of the river (River Elbe, Germany; 580 km within 8 days). After a subsequent investigation of the estuary, we followed the plume of the river by raster sampling the German Bight (North Sea) using three ships simultaneously. In the river, we detected intensive longitudinal growth of phytoplankton connected with high oxygen saturation and pH values and an undersaturation of CO2, whereas concentrations of dissolved nutrients declined. In the estuary, the Elbe shifted from an autotrophic to a heterotrophic system: Phytoplankton died off upstream of the salinity gradient, causing minima in oxygen saturation and pH, supersaturation of CO2, and a release of nutrients. In the shelf region, phytoplankton and nutrient concentrations were low, oxygen was close to saturation, and pH was within a typical marine range. Over all sections, oxygen saturation was positively related to pH and negatively to pCO2. Corresponding to the significant particulated nutrient flux via phytoplankton, flux rates of dissolved nutrients from river into estuary were low and determined by depleted concentrations. In contrast, fluxes from the estuary to the coastal waters were higher and the pattern was determined by tidal current. Overall, the approach is appropriate to better understand land-ocean fluxes, particularly to illuminate the importance of these fluxes under different seasonal and hydrological conditions, including flood and drought events.
    Type: Article , PeerReviewed
    Format: text
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  • 3
    Publication Date: 2023-09-28
    Description: On 25 and 26 of June in 2019 transects between Cuxhaven and Bremerhaven towards Helgoland were performed with the RV Prandtl and Uthörn. Basic hydrographic parameters were measured with two ferry boxes using the ships water supply. Data were saved once per minute. Dissolved methane was determined continuously. We used a degassing unit which was using surface water from the ship's water supply in an overflowing bucket. The gas mixture was subsequently analyzed with a Greenhouse Gas Analyzer from LosGatos. Conversion to methane concentration was performed with water samples, from which the methane content was determined with gas chromatography.
    Keywords: CT; DATE/TIME; DEPTH, water; FBOX; FerryBox; Greenhouse Gas Analyzer, LosGatos; KON_stern_2; KON_stern_2-track; LATITUDE; LONGITUDE; Ludwig Prandtl; Methane; Methane, flux; Modular Observation Solutions for Earth Systems; MOSES; Oxygen saturation; Platform; Salinity; Sternfahrt 2, KON, 20190244; Sternfahrt 2, UT05/2019; Temperature, water; Underway cruise track measurements; UT05_stern_2-track; UT05/2019_stern_2; Uthörn; Wind speed
    Type: Dataset
    Format: text/tab-separated-values, 10768 data points
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  • 4
    Publication Date: 2023-09-28
    Description: Surface water samples of the river Elbe were taken in May 2021 with the vessel Zwergseeschwalbe between Geesthacht and Neu Darchau. Connecting cruises were performed from colleagues from Geesthacht towards Hamburg and from Elster towards Dömitz.
    Keywords: 2021_ELBE_53760; 2021_ELBE_54840; 2021_ELBE_55840; 2021_ELBE_56770; 2021_ELBE_57260; 2021_ELBE_57860; 2021_ELBE_58380; 2021_ELBE_58790; 2021_ELBE_59777; 2021_ELBE_60640; Ammonium; Chl a; Chlorophyll a; DATE/TIME; DEPTH, water; Distance; Elbe; Event label; High Performance Liquid Chromatography (HPLC); Nitrate; nutrient; OPTIMARE Precision Salinometer System; Oxygen; Phosphate; Salinity; SEAL Analytical, AutoAnalyzer 3 HR (AA3 HR), XY-2 Sampler, method No. G-177-96 Rev. 8; Silicate; Station 1; Station 10; Station 2; Station 3; Station 4; Station 5; Station 6; Station 7; Station 8; Station 9; Titration, Winkler; turbidity; Turbidity; Turbidity meter, Hach, 2100N IS; Water sample; WS
    Type: Dataset
    Format: text/tab-separated-values, 100 data points
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  • 5
    Publication Date: 2023-09-28
    Description: The vessel Ludwig Prandtl was anchored for 48 h off Cuxhaven at Medem Reede. Water samples were taken approx. every 2 hours during daytime, while sensors were recording continuously. Water samples were taken from ferrybox outlet from surface (1 m).
    Keywords: DATE/TIME; DEPTH, water; dissolved methane; Headspace Equilibration; LATITUDE; LONGITUDE; Methane; Modular Observation Solutions for Earth Systems; MOSES; Oxygen; stern_7_prandtl2021; Titration, Winkler; turbidity; Turbidity; Turbidity meter, Hach, 2100N IS; Water sample; WS
    Type: Dataset
    Format: text/tab-separated-values, 16 data points
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  • 6
    Publication Date: 2023-09-28
    Description: The vessel Reykjanes was anchored for 48 h off Cuxhaven at Medem Reede. Water samples were taken approx. every 2 hours during daytime, while sensors were recording continuously. Water samples were taken from different sources: ship's water supply, in-situ pump and niskin bottle, from surface (1 m) and bottom water (12 m).
    Keywords: DATE/TIME; DEPTH, water; dissolved methane; Headspace Equilibration; LATITUDE; LONGITUDE; Methane; Method comment; Modular Observation Solutions for Earth Systems; MOSES; Oxygen; Sample comment; stern_7_reyk2021; Titration, Winkler; Water sample; WS
    Type: Dataset
    Format: text/tab-separated-values, 117 data points
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  • 7
    Publication Date: 2023-09-28
    Description: On the MOSES cruise Sternfahrt_1 water samples were taken on 8 stations. Samples were exchanged between the institutes Alfred-Wegener-Institut Zentrum für Polar- und Meeresforschung (AWI), Helmholtz Centre for Ocean Research Kiel (Geomar) and Helmholtz-Zentrum Geesthacht Centre for Materials and Coastal Research (HZG) for later comparison of the nutrient data.
    Keywords: Ammonium; AWI; Colorimetric using QuAAtro39 AA (Seal Analytical); Comment; Cruise/expedition; DATE/TIME; DEPTH, water; Depth comment; Event label; Geomar; HZG; KON_stern_1; L19-03_stern_1; LATITUDE; Littorina; LONGITUDE; Ludwig Prandtl; Modular Observation Solutions for Earth Systems; MOSES; MOSES_stern1_nutrients; MYA2019/04_stern_1; Mya II; Name; Nitrate; Nitrite; Nitrogen, organic, total; NO2; NO3; Phosphate; PO4; Sample ID; SEAL Analytical, AutoAnalyzer 3 HR (AA3 HR), XY-2 Sampler; SEAL Analytical, AutoAnalyzer 3 HR (AA3 HR), XY-2 Sampler, method No. G-062-92 Rev. 2; SEAL Analytical, AutoAnalyzer 3 HR (AA3 HR), XY-2 Sampler, method No. G-170-96 Rev. 1; SEAL Analytical, AutoAnalyzer 3 HR (AA3 HR), XY-2 Sampler, method No. G-171-96 Rev. 14; SEAL Analytical, AutoAnalyzer 3 HR (AA3 HR), XY-2 Sampler, method No. G-177-96 Rev. 8; SEAL Analytical, AutoAnalyzer QuAAtro39, method No. Q-064-05 Rev. 4; SEAL Analytical, AutoAnalyzer QuAAtro39, method No. Q-066-05 Rev. 3; SEAL Analytical, AutoAnalyzer QuAAtro39, method No. Q-068-05 Rev. 6; SEAL Analytical, AutoAnalyzer QuAAtro39, method No. Q-080-06 Rev. 2; Si; Silicate; stern_1-100; stern_1-101; stern_1-102; stern_1-103; stern_1-104; stern_1-105; stern_1-56; stern_1-57; stern_1-58; stern_1-59; stern_1-60; stern_1-61; stern_1-62; stern_1-63; stern_1-64; stern_1-65; stern_1-66; stern_1-67; stern_1-68; stern_1-69; stern_1-70; stern_1-71; stern_1-72; stern_1-73; stern_1-74; stern_1-75; stern_1-76; stern_1-77; stern_1-78; stern_1-79; stern_1-80; stern_1-81; stern_1-82; stern_1-83; stern_1-84; stern_1-85; stern_1-86; stern_1-87; stern_1-88; stern_1-89; stern_1-90; stern_1-91; stern_1-92; stern_1-93; stern_1-94; stern_1-95; stern_1-96; stern_1-97; stern_1-98; stern_1-99; Sternfahrt 1, KON, 20190239; Sternfahrt 1, L19-03, 20190193; Sternfahrt 1, MYA2019/04; Type; Water sample; WS
    Type: Dataset
    Format: text/tab-separated-values, 935 data points
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  • 8
    Publication Date: 2023-09-28
    Description: Water samples were taken in September 2019 in the southern German Bight (between Bremerhaven, Cuxhaven, Helgoland and Sylt). The surface water samples were later analyzed by AWI, Geomar and HZG laboratories for nutrients, carbon compounds and pigments. Samples were fixed by filtering and freezing (nutrients, POC, pigments) or poisoning (TA, DOC)
    Keywords: 19-Hexanoyloxyfucoxanthin; after Dickson et al. (2007); after Irving and Frey (1984); Alkalinity, total; Alloxanthin; Ammonium; beta-Carotene; Carbon, organic, dissolved; Carbon, organic, particulate; Chlorophyll a; Chlorophyll b; Chlorophyllide a; Comment; DEPTH, water; Diadinoxanthin; Diatoxanthin; Element analyse, Eurovector after Sharp (1974); Event label; Fucoxanthin; High Performance Liquid Chromatography (HPLC 2695), Wiltshire et al. 1998; Lutein; M1; M10; M11; M12; M13; M14; M15; M16; M17; M18; M19; M2; M20; M21; M22; M23; M24; M25; M26; M27; M28; M29; M3; M30; M31; M32; M4; M5; M6; M7; M8; M9; Modular Observation Solutions for Earth Systems; MOSES; MYA2020/08_stern_5; Mya II; Neoxanthin; Nitrate; Nitrite; OPTIMARE Precision Salinometer System; Oxygen, dissolved; Peridinin; Phosphate; Salinity; SEAL Analytical, AutoAnalyzer QuAAtro39, method No. Q-066-05 Rev. 3; Shimadzu TOC-VCPH total organic carbon analyzer; Silicate; stern_5-M1; stern_5-M10; stern_5-M11; stern_5-M12; stern_5-M13; stern_5-M14; stern_5-M15; stern_5-M16; stern_5-M17; stern_5-M18; stern_5-M19; stern_5-M2; stern_5-M20; stern_5-M21; stern_5-M22; stern_5-M23; stern_5-M24; stern_5-M25; stern_5-M26; stern_5-M27; stern_5-M28; stern_5-M29; stern_5-M3; stern_5-M30; stern_5-M31; stern_5-M32; stern_5-M4; stern_5-M5; stern_5-M6; stern_5-M7; stern_5-M8; stern_5-M9; Sternfahrt 5; Titration, Winkler; Turbidity; Violaxanthin; Water sample; WS; Zeaxanthin
    Type: Dataset
    Format: text/tab-separated-values, 560 data points
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  • 9
    Publication Date: 2023-09-28
    Keywords: dissolved methane; Modular Observation Solutions for Earth Systems; MOSES; Salinity; sediment methane; turbidity
    Type: Dataset
    Format: application/zip, 3 datasets
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  • 10
    Publication Date: 2023-09-28
    Keywords: 19-Hexanoyloxyfucoxanthin; after Dickson et al. (2007); Alkalinity, total; Alloxanthin; Astaxanthin; beta-Carotene; Carbon, inorganic, dissolved; Carbon, organic, dissolved; Carbon, organic, particulate; Chlorophyll a; Chlorophyll a, epimer; Chlorophyll a allomers; Chlorophyll b; Chlorophyll c2; Chlorophyllide a; Colorimetric using QuAAtro39 AA (Seal Analytical); Comment; DEPTH, water; Diadinoxanthin; Diatoxanthin; Element analyse, Eurovector after Sharp (1974); Event label; Fucoxanthin; L1; L11; L13; L15; L17; L19; L20_stern_5; L21; L23; L25; L27; L29; L30; L32; L33; L34; L36; L38; L4; L43; L45; L48; L51; L54; L57; L58; L61; L62; L63; L65; L7; L8; L9; Littorina; Lutein; Mg-2,4-divinyl pheoporphyrin a5 monomethyl ester; Modular Observation Solutions for Earth Systems; MOSES; Neoxanthin; Nitrate; Nitrite; OPTIMARE Precision Salinometer System; Oxygen, dissolved; Peridinin; Phosphate; Prasinoxanthin; Salinity; Shimadzu TOC-VCPH total organic carbon analyzer; Silicate; stern_5-L1; stern_5-L11; stern_5-L13; stern_5-L15; stern_5-L17; stern_5-L19; stern_5-L21; stern_5-L23; stern_5-L25; stern_5-L27; stern_5-L29; stern_5-L30; stern_5-L32; stern_5-L33; stern_5-L34; stern_5-L36; stern_5-L38; stern_5-L4; stern_5-L43; stern_5-L45; stern_5-L48; stern_5-L51; stern_5-L54; stern_5-L57; stern_5-L58; stern_5-L61; stern_5-L62; stern_5-L63; stern_5-L65; stern_5-L7; stern_5-L8; stern_5-L9; Sternfahrt 5; Thermo Scientific UltiMate 3000 high-performance liquid chromatography instrument (HPLC) (van Heukelem 2001); Titration, Winkler; Violaxanthin; Water sample; WS; Zeaxanthin
    Type: Dataset
    Format: text/tab-separated-values, 880 data points
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