5. Ship-Based Sampling
Ship-based instrument sampling refers to the collection of oceanographic data using instruments installed or deployed on research vessels. This type of sampling is used to collect ocean data such as salinity, temperature, pressure, and nutrient concentrations in the ocean. Ship-based instrument sampling is a critical tool for understanding the ocean and its dynamics. It provides researchers with a unique view of the ocean, enabling them to observe the environment and oceanographic processes from different perspectives.
Ship-based instrument sampling can occur in both coastal and open ocean settings. In coastal environments, scientists may deploy instruments onto a research vessel in order to collect data from the nearby seas. This data can be used to inform coastal management decisions, understand nutrient and pollutant concentrations, and document changes in the local ocean environment.
In open ocean settings, instrument measurements are used to survey wider regions. This method of instrument sampling entails the deployment of floats and drifters in the open ocean. These instruments can measure parameters such as temperature, salinity, and nutrient concentrations as they drift throughout the ocean. This data helps scientists to better understand ocean dynamics and how they are changing over space and time. Instrument sampling also helps to identify key ocean habitats, providing essential information for fisheries management.
Here’s an example — one of the most common ship-based instrument (and perhaps one of the most fundamental ocean instrument) is a conductivity, temperature, and depth (CTD) sensor. Usually, this sensor is placed below a ring of sampling bottles (called Rosette), which can be triggered to close at various depths by an operator in a control room on board. The CTD autonomously records the profiles of temperature, conductivity (which is converted to salinity), and pressure (which is converted to depth) as the entire sampling package travels slowly down and then up the water column (i.e., down-cast, and then up-cast). The Rosette bottles is closed and sealed at pre-determined depth by the operator to capture water samples from specific depths — this technique is used to target features of interest, such as a deep chlorophyll maximum or temperature anomaly (more on this later). The entire CTD cast operation is done while the research vessel “holds station” by constantly making position adjustment; this can be extremely challenging in rough seas which is not uncommon in polar oceanographic deployments. Once the water samples are collected, they are either preserved(chemically, freezing, and/or simply bottled and stored under room temperature depending on the sampling protocols) which would then be analyzed in a lab onshore, or, quite often, the samples are analyzed directly on board of the research vessel.
Ship-based sampling is an extremely robust and versatile method for collecting ocean data. However, there are also some challenges. These sampling schemes only capture a “snapshot” of the ocean. In order to achieve a better understanding of the ocean, we need to supplement ship-based sampling with autonomous technology. While ship-based sampling remains a vital part of the ocean sampling process, investing in autonomous technology will help contribute a more comprehensive understanding of the ocean. With the use of autonomous instruments, it is possible to monitor the ocean over extended space and periods of time. This helps to improve our understanding of the ocean, both spatially and temporally, and thus provide a more robust view of the complex world beneath the waves. Ocean Motion Tech is here to support this important development by providing more sustained power for autonomous sampling and enable vessel-based projects to do more with their resources. By combining, ship-based sampling and autonomous ocean monitoring techniques, a more complete picture of the ocean can be achieved.
Please visit www.oceanmotion.tech to learn more.
The editing of this article was powered by deepsage.ai
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