• Energy Research
• 2021-2025close
• 14. Life underwater close

During three cruises in the Mid Atlantic Ridge area in 2016 and 2017, we studied the biomass of mesopelagic fish down to a depth of 600 m and identified and quantified the species composition of the catches. The biomass density was estimated considering the volume of water filtered by the cross-sectional area of the trawl blinded with 16 mm meshes (130 m–2), the distanced covered by the trawl (m) at a towing speed (transformed to m s–1) the effective tow time (min) and the catch (kg).

This is the dataset used to analyse biomass of fauna collected in farmed and wild kelp at the West coast of Norway (Søre Sunnmøre) in April 2019. Coordinates are given in the fil. 

The beach topography and nearshore bathymetry data contained in this object where used to create a sediment budget model and produce the data figures in the paper titled “A Climatic Sand Management Model for Cardiff State Beach, CA” by Gopal et al., 2023. The data set includes annual mean beach width and nearshore mobile sediment volume estimates between 2000-2019 at South Torrey Pines State Beach and Cardiff State Beach, CA. MATLAB program code to read the data files, model the sediment budget equation in Gopal et al, and recreate the data figures in the paper is also included. Abstract: An empirically based sediment budget model is developed for Cardiff State Beach CA to assess management strategies to maintain beach width subject to mean sea level rise (MSLR) and potentially more frequent El Niño storms. Two decades (2000-2019) of surveys support the hypothesis that the rocky reefs bounding this beach retain sand added to the nearshore zone, except during strong El Niño years with more severe storm waves. The subaerial beach has widened by ~60 m during the last 20 years owing to nourishment (~17,000 cubic m/yr) of imported sand, and sand bypassed annually by dredging a lagoon inlet at the beach's updrift end. The observed widening yields 1 m/yr of mean beach width increase for each 6 cubic m/m-shoreline of added sand. A strong El Niño year is modeled with a permanent volume loss coupled with a shoreline retreat that recovers partially as the beach profile adjusts between El Niño years. Calibrated with observations from Cardiff and South Torrey Pines (a control beach), the model is used to project beach change through 2050. All modeled scenarios suggest that no bypassing or nourishment (no “management”) will result in tens of meters of beach width loss. However, continued bypassing would partially mitigate MSLR and El Niño beach width losses. An artificially built (living shoreline) dune that backs the beach, if completely undermined during strong El Niño storm waves, stores enough sand to balance one-third of the expected volume loss that year, and may make the beach more resilient and speed subsequent recovery. Model program scripts were written using MATLAB R2023a.

The key datasets used and generated in the paper mentioned in the title (from now on "the paper").+++ Temperature_Profile.xlsx +++This file contains the processed surface and deep-sea water temperatures that were used as inputs for the off-design analyses of the OTEC system designs. Outliers are already removed in this data set. Outliers are data points that are 1.5 times the interquartile range away from the top or bottom of the box plot. The raw temperature data can be downloaded from the HYCOM database following the download instructions elaborated in the paper.Column A: TimeShows the timestamp of the temperature data, from 01.01.1994 00:00 until 31.12.2012 21:00 in 3-hourly time steps.Columns B-C, D-E, F-G, H-IThese pairs of columns show the surface seawater temperature at 20 m depth and deep-sea water temperature at 1,000 m depth for the four locations analysed in the paper, namely Jayapura, Tarakan, Ende, and Sabang.Columns K - OShow the main statistics of the temperature files, including minimum, median, and maximum values of the surface and deep-sea water temperatures at each of the four locations.+++ System_Designs_Ende_LC +++This file contains the data for Table 4 in the paper, showing the system designs based on nine different configurations of seawater temperatures as design parameters. See sections 2.1 and 2.2 of the paper to learn more about the methods used to deduce the nine temperature configurations. The system designs are created using the temperature profiles from Ende and low-cost assumptions (LC). Please note that we used the following sign convention:Work and heat entering the system: positiveWork and heat leaving the system: negativeRows 6 - 15: Energy balance and net thermal efficiencyShows the energy balance and net thermal efficiency of the Rankine cycle on which the OTEC plant is basedRows 6 - 14 show the heat flows to the evaporator and from the condenser, the work from the turbine and to the pumps, as well as the losses.Row 15 shows the net efficiency and is calculated as follows:Row 15 = |Row 14|/Row 6Rows 17 - 28 show the exergy analysis including exergy inflow from the warm surface seawater and the exergy destruction in the system components. Row 28: Net Exergy EfficiencyRow 28 = |Row 27|/SUM(Row 17 to 19)Rows 29 to 30 show the carnot efficiency and second law efficiency. Rows 32 to 34 show the mass flows of working fluid (here ammonia or NH3), warm water (WW) and cold water (CW).Rows 36 and to 37 show the temperature differences between heat exchanger inlet and outlet of the warm water (WW) and cold water (CW).Rows 39 to 44 show the dimensions and properties of evaporator (evap) and condenser (cond), namely the heat exchanger area A, saturation temperature T and saturation temperature p of the working fluid.Rows 46 to 49 show the inner diameter and the number of required seawater pipes. Note, that the number of outlet pipes is the same as the number of inlet pipes, so if for example the number of WW pipes is 6, there are 3 inlet pipes and 3 outlet pipes for the warm water.+++ Net_Power_Profiles.xlsx +++Shows the net power output of the turbine in [kW] for 30 years (1994 - 2023) in 3-hourly time steps at the location in Ende. The values are negative as in accordance to the sign convention described above. The file contains the data for Figure 4 in the paper. There are three sheets in the file containing the net power profiles for configuration 1, 2, and 9. Please note that the four-weeks downtime period mentioned in section 2.5 is not included here yet.Column A: TimeShows the time of the year as the x-th 3-hour interval of the year.Columns B - AEShow the annual net power profiles for the years 1994 until 2023.Column AFShows the average net power output at the x-th 3-hour interval of the year.Column AGShows the standard deviation of the net power output at the x-th 3-hour interval of the yearRow 1Shows the headers for each columnRows 2 to 2929Shows the net power output in 3-hour time steps. Note that rows 474 to 481 represent the 29th February. For leap-years, these rows are filled with data, for non-leap-years, these rows are NaN.Row 2930Shows the sum of values under each column. For the annual electricity production in [kWh], the values in this row must be multiplied by factor 3 because of the 3-hourly time interval.

These datasets contain five estuarine flood risk lines relating to flooding from the inland for estuaries in the South African Northern Cape Province, Western Cape Province, Eastern Cape Province and KwaZulu-Natal Province. The classification from 'very low' to 'very high' was based on expert knowledge. Refer to the Technical Report to the National Coastal Climate Change Vulnerability Assessment, November 2020, Section 4. Refer to the Technical Report to the National Coastal Climate Change Vulnerability Assessment, November 2020, Section 4: 10.15493/DEA.MIMS.09032022. National Coastal Climate Change Vulnerability Assessment

This repository contains spatial data and metadata for the research article: Thieme M.L. et al. (2021). Navigating trade-offs between dams and river conservation. Global Sustainability 4, e17, 1–7. https://doi.org/10.1017/sus.2021.15

The Pacific sector of the Arctic Ocean is experiencing major reductions in seasonal sea ice extent and increases in sea surface temperatures. One of the key uncertainties in this region is how the marine ecosystem will respond to seasonal shifts in the timing of spring sea ice retreat and/or delays in fall sea ice formation. Variations in upper ocean water hydrography, planktonic production, pelagic-benthic coupling and sediment carbon cycling are all influenced by sea ice and temperature change. To more systematically track the broad biological response to sea ice retreat and associated environmental change, an international consortium of scientists have developed a coordinated “Distributed Biological Observatory” (DBO) that includes selected biological measurements at multiple trophic levels, along with satellite and mooring measurements. The DBO currently focuses on five regional biological “hotspot” locations along a latitudinal gradient that allows for consistent sampling and monitoring at five biologically productive locations across a latitudinal gradient: DBO 1 (SLIP)-south of St. Lawrence Island (SLI), DBO2 (Chirikov)-north of SLI, DBO3 (southern Chukchi Sea), DBO4-NE Chukchi Sea,and DBO5-Barrow Canyon. This dataset contains benthic macroinfaunal population level from sediment samples collected at each station for the United States Coast Guard Cutter (USCGC) Healy cruise-Leg 1 in 2017, identified by station number (#), Station name (Stn. Name), Date (mm/dd/yy), latitude (°N), longitude (°W), and station depth (m). The following macroinfaunal parameters were determined: abundance, wet weight biomass (grams of wet weight per meters squared), dry weight biomass (grams of Carbon per meters squared), and taxon type.

This dataset contains measurements of downwelling short wave irradiance, measured in a small scale grid setup at Falkenberg: 20 sensors in 4 by 5 grid with a 50 meter grid spacing. Another 4 sensors were placed in all direction about 5 km away from the main grid at Falkenberg. The sampling rate is 10 Hz, to catch all irradiance variability, and is calibrated against a high quality sun tracker. The strength of this dataset is not the absolute accuracy, but rather the spatial measurements and ability to catch variability. Quality: Accuracy is estimated to be within 5% of a conventional pyranometer. Quality varies depending on weather type, but is best for high solar elevation angles (solar noon +/- 4 hours). Data is manually quality controlled, with detailed quality flags included in the dataset. Some anomalous data is not caught, in particular noisy data due to many insects on the sensor or small dirt from birds that reduces the signal slightly. These effects are much smaller than the driving weather patterns. The data is unsuitable for calculating radiation balances, but it is particularly useful for studying variability and patterns of solar irradiance on small scales. Funding: Dutch Research Council (NWO), Shedding Light On Cloud Shadows: VI.Vidi.192.068 Project: FESSTVaL (Field Experiment on submesoscale spatio-temporal variability in Lindenberg), a measurement campaign initiated by the Hans-Ertel-Center for Weather Research.