http://lod.bco-dmo.org/id/dataset/768110
eng; USA
utf8
dataset
Highest level of data collection, from a common set of sensors or instrumentation, usually within the same research project
Biological and Chemical Oceanography Data Management Office (BCO-DMO)
Unavailable
508-289-2009
WHOI MS#36
Woods Hole
MA
02543
USA
info@bco-dmo.org
http://www.bco-dmo.org
Monday - Friday 8:00am - 5:00pm
For questions regarding this resource, please contact BCO-DMO via the email address provided.
pointOfContact
2019-05-16
ISO 19115-2 Geographic Information - Metadata - Part 2: Extensions for Imagery and Gridded Data
ISO 19115-2:2009(E)
Physical description of marine lakes: surface area, distance to ocean, tidal efficiency, depth, and stratification
2019-05-13
publication
2019-05-13
revision
Marine Biological Laboratory/Woods Hole Oceanographic Institution Library (MBLWHOI DLA)
2019-07-08
publication
https://doi.org/10.1575/1912/bco-dmo.768110.1
Michael N. Dawson
University of California-Merced
principalInvestigator
Biological and Chemical Oceanography Data Management Office (BCO-DMO)
Unavailable
508-289-2009
WHOI MS#36
Woods Hole
MA
02543
USA
info@bco-dmo.org
http://www.bco-dmo.org
Monday - Friday 8:00am - 5:00pm
For questions regarding this resource, please contact BCO-DMO via the email address provided.
publisher
Cite this dataset as: Dawson, M. (2019) Physical description of marine lakes: surface area, distance to ocean, tidal efficiency, depth, and stratification. Biological and Chemical Oceanography Data Management Office (BCO-DMO). (Version 1) Version Date 2019-05-13 [if applicable, indicate subset used]. doi:10.1575/1912/bco-dmo.768110.1 [access date]
Palau lakes physical description Dataset Description: <p>Physical parameters&nbsp;describing Palau marine lakes, 2011-2015. Reported parameters include surface area, distance to ocean, tidal efficiency, depth, and stratification.</p>
<p>* NOTE:&nbsp;The P.I.'s are using this dataset to write papers. Please contact them before using these data to make sure you are not duplicating efforts.</p> Methods and Sampling: <p><strong>Lake Bathymetry Mapping</strong></p>
<p>The bathymetry of the lakes was recorded with a purpose-built lightweight, low-power echosounder unit with integrated GPS and AHRS (attitude and heading reference system), towed behind a kayak. (Data courtesy of Herwig Stibor, Thomas Stieglitz.)</p>
<p><strong>Lake Bathymetry Analysis</strong></p>
<p>Raw bathymetry data were down-sampled to include one sample every 2&nbsp;m distance along the survey track. Data were visually inspected and false soundings removed. In some lakes, GPS reception was partially compromised along the steep shorelines. Where required to not compromise data density, the survey track was reconstructed from field notes and AHRS data (less than 5% of data where applicable). During the bathymetry surveys, tidal water level was not recorded. Therefore, tidal water level variations were corrected for by modeling tidal water level in each lake. The water level for each lake was correlated with tidal water level measured at a reference station at CRRF or predicted tide (x-tide database), by determining tidal lag time and tidal efficiency (see below) from data previously collected concurrently in the respective lake and at this reference station. Subsequently, bathymetry data was reduced to a grid with 2m resolution by kriging of a further down-sampled subset of data using every third data point. Lake-specific variograms were applied, and contour lines and total lake volumes were calculated in a GIS. (Data courtesy of Thomas Stieglitz.)</p>
<p><strong>Distance from lake to ocean &amp; lake surface area</strong></p>
<p>Lake shorelines and island coastlines were manually extracted from satellite data (Microsoft Bing). The nearest, mean and median distance of each lake to the respective island’s coastline as well as lake surface area was calculated in a GIS. The perimeter was measured in meters using the ‘Measure Line’ tool in QGIS 3.4.</p>
<p>Habitable surface area for benthic organisms was estimated either as (1) a multiple of the perimeter and depth of the chemocline, i.e. assuming a cylindrical model for the lake, or (2) as the area of a frustum, i.e. a truncated cone, using the perimeter of the lake at the surface, the average length of transects, and the average angle of the transect from the vertical (estimated using the sine of maximum depth / transect length).</p>
<p>Tidal lag time and tidal efficiency were calculated from concurrently measured tidal water level in a lake and the adjacent ocean. The tidal lag time — the time between high tide in the adjacent ocean and high tide in the lake — was determined by least-square fit between lake and ocean tide measured using HOBO 30-foot depth Titanium water level data loggers (Part # U20-001-01-Ti).&nbsp; Tidal efficiency was calculated as the ratio between amplitude of ocean tide and lake tide (e.g. Ayers, JF and Vacher, HL, 1986.&nbsp; Hydrogeology of an Atoll Island: A Conceptual Model from a Detailed Study of a Micronesian Example. Groundwater 24(2) 185-198.). Larger tidal lag time and smaller tidal efficiency respectively indicate a less efficient hydrological connection between ocean and lake.</p>
<p>Error-checking: Estimates of basic dimensions (depth, distance, length, surface area) were double-checked manually for a subset of measurements by a second person using using Google Earth.&nbsp;</p>
Funding provided by NSF Division of Ocean Sciences (NSF OCE) Award Number: OCE-1241255 Award URL: https://www.nsf.gov/awardsearch/show-award?AWD_ID=1241255
completed
Michael N. Dawson
University of California-Merced
209-228-4056
School of Natural Sciences 5200 North Lake Road
Merced
CA
95343
United States
mdawson@ucmerced.edu
pointOfContact
asNeeded
Dataset Version: 1
Unknown
lake
lake_code
volume_m3
surface_area_m2
distance_to_ocean_min_m
distance_to_ocean_mean_m
distance_to_ocean_median_m
tidal_efficiency
mean_transect_length_m
max_actual_depth_m
perimeter_m
habitable_surface_area_cylinder
habitable_surface_area_frustum
stratified
tidal_lag_time_minutes
HOBO 30-foot depth Titanium water level data loggers (Part # U20-001-01-Ti)
theme
None, User defined
site
volume
surface_area
No BCO-DMO term
track length
depth
featureType
BCO-DMO Standard Parameters
Data Logger
instrument
BCO-DMO Standard Instruments
Palau_lakes
service
Deployment Activity
NW Pacific
place
Locations
otherRestrictions
otherRestrictions
Access Constraints: none. Use Constraints: Please follow guidelines at: http://www.bco-dmo.org/terms-use Distribution liability: Under no circumstances shall BCO-DMO be liable for any direct, incidental, special, consequential, indirect, or punitive damages that result from the use of, or the inability to use, the materials in this data submission. If you are dissatisfied with any materials in this data submission your sole and exclusive remedy is to discontinue use.
Dimensions of Biodiversity
http://www.nsf.gov/funding/pgm_summ.jsp?pims_id=503446
Dimensions of Biodiversity
(adapted from the NSF Synopsis of Program)
Dimensions of Biodiversity is a program solicitation from the NSF Directorate for Biological Sciences. FY 2010 was year one of the program. [MORE from NSF]
The NSF Dimensions of Biodiversity program seeks to characterize biodiversity on Earth by using integrative, innovative approaches to fill rapidly the most substantial gaps in our understanding. The program will take a broad view of biodiversity, and in its initial phase will focus on the integration of genetic, taxonomic, and functional dimensions of biodiversity. Project investigators are encouraged to integrate these three dimensions to understand the interactions and feedbacks among them. While this focus complements several core NSF programs, it differs by requiring that multiple dimensions of biodiversity be addressed simultaneously, to understand the roles of biodiversity in critical ecological and evolutionary processes.
Dimensions of Biodiversity
largerWorkCitation
program
Do Parallel Patterns Arise from Parallel Processes?
http://marinelakes.ucmerced.edu/
Do Parallel Patterns Arise from Parallel Processes?
<p>This project will survey the taxonomic, genetic, and functional diversity of the organisms found in marine lakes, and investigate the processes that cause gains and losses in this biodiversity. Marine lakes formed as melting ice sheets raised sea level after the last glacial maximum and flooded hundreds of inland valleys around the world. Inoculated with marine life from the surrounding sea and then isolated to varying degrees for the next 6,000 to 15,000 years, these marine lakes provide multiple, independent examples of how environments and interactions between species can drive extinction and speciation. Researchers will survey the microbes, algae, invertebrates, and fishes present in 40 marine lakes in Palau and Papua, and study how diversity has changed over time by retrieving the remains of organisms preserved in sediments on the lake bottoms. The project will test whether the number of species, the diversity of functional roles played by organisms, and the genetic diversity within species increase and decrease in parallel; whether certain species can greatly curtail diversity by changing the environment; whether the size of a lake determines its biodiversity; and whether the processes that control diversity in marine organisms are similar to those that operate on land.</p>
<p>Because biodiversity underlies the ecosystem services on which society depends, society has a great interest in understanding the processes that generate and retain biodiversity in nature. This project will also help conserve areas of economic importance. Marine lakes in the study region are important for tourism, and researchers will work closely with governmental and non-governmental conservation and education groups and with diving and tourism businesses to raise awareness of the value and threats to marine lakes in Indonesia and Palau.</p>
PaPaPro
largerWorkCitation
project
eng; USA
oceans
NW Pacific
134.3447
134.5089
7.1506
7.3237
2014-07-11
2015-07-27
Western Pacific; Palau; Indonesia (West Papua)
0
BCO-DMO catalogue of parameters from Physical description of marine lakes: surface area, distance to ocean, tidal efficiency, depth, and stratification
Biological and Chemical Oceanography Data Management Office (BCO-DMO)
Unavailable
508-289-2009
WHOI MS#36
Woods Hole
MA
02543
USA
info@bco-dmo.org
http://www.bco-dmo.org
Monday - Friday 8:00am - 5:00pm
For questions regarding this resource, please contact BCO-DMO via the email address provided.
pointOfContact
http://lod.bco-dmo.org/id/dataset-parameter/768121.rdf
Name: lake
Units: unitless
Description: <p>common name for lake</p>
http://lod.bco-dmo.org/id/dataset-parameter/768122.rdf
Name: lake_code
Units: unitless
Description: <p>3-letter code for sampled lake name</p>
http://lod.bco-dmo.org/id/dataset-parameter/768123.rdf
Name: volume_m3
Units: meters^3
Description: <p>estimated volume of the lake (m3)</p>
http://lod.bco-dmo.org/id/dataset-parameter/768124.rdf
Name: surface_area_m2
Units: meters^2
Description: <p>Surface area of the lake (m2).</p>
http://lod.bco-dmo.org/id/dataset-parameter/768125.rdf
Name: distance_to_ocean_min_m
Units: meters
Description: <p>Minimum distance from a lake’s edge to the surrounding lagoon (m)</p>
http://lod.bco-dmo.org/id/dataset-parameter/768126.rdf
Name: distance_to_ocean_mean_m
Units: meters
Description: <p>Mean distance from a lake’s edge to the surrounding lagoon (m).</p>
http://lod.bco-dmo.org/id/dataset-parameter/768127.rdf
Name: distance_to_ocean_median_m
Units: meters
Description: <p>Median distance from a lake’s edge to the surrounding lagoon (m).</p>
http://lod.bco-dmo.org/id/dataset-parameter/768128.rdf
Name: tidal_efficiency
Units: unitless
Description: <p>the ratio between amplitude of ocean tide and lake tide.</p>
http://lod.bco-dmo.org/id/dataset-parameter/768129.rdf
Name: mean_transect_length_m
Units: meters
Description: <p>average of distance_trans for the lake from <a href="https://www.bco-dmo.org/dataset/541181">https://www.bco-dmo.org/dataset/541181</a></p>
http://lod.bco-dmo.org/id/dataset-parameter/768130.rdf
Name: max_actual_depth_m
Units: meters
Description: <p>maximum depth_m for the lake from <a href="https://www.bco-dmo.org/dataset/541181">https://www.bco-dmo.org/dataset/541181</a></p>
http://lod.bco-dmo.org/id/dataset-parameter/768131.rdf
Name: perimeter_m
Units: meters
Description: <p>circumference of lake (m).</p>
http://lod.bco-dmo.org/id/dataset-parameter/768132.rdf
Name: habitable_surface_area_cylinder
Units: meters^2
Description: <p>An estimate of the area of the benthic zone above the chemocline; based on the model of lake shape as a cylinder.</p>
http://lod.bco-dmo.org/id/dataset-parameter/768133.rdf
Name: habitable_surface_area_frustum
Units: meters^2
Description: <p>An estimate of the area of the benthic zone above the chemocline; based on the model of lake shape as a truncated cone.</p>
http://lod.bco-dmo.org/id/dataset-parameter/768134.rdf
Name: stratified
Units: unitless
Description: <p>whether a lake is vertically stratified (1) or holomictic (0)</p>
http://lod.bco-dmo.org/id/dataset-parameter/768135.rdf
Name: tidal_lag_time_minutes
Units: minutes
Description: <p>the time between high tide in the adjacent ocean and high tide in the lake</p>
GB/NERC/BODC > British Oceanographic Data Centre, Natural Environment Research Council, United Kingdom
Biological and Chemical Oceanography Data Management Office (BCO-DMO)
Unavailable
508-289-2009
WHOI MS#36
Woods Hole
MA
02543
USA
info@bco-dmo.org
http://www.bco-dmo.org
Monday - Friday 8:00am - 5:00pm
For questions regarding this resource, please contact BCO-DMO via the email address provided.
pointOfContact
2067
https://darchive.mblwhoilibrary.org/bitstream/1912/24321/1/dataset-768110_palau-lakes-physical-description__v1.tsv
download
https://doi.org/10.1575/1912/bco-dmo.768110.1
download
onLine
dataset
<p><strong>Lake Bathymetry Mapping</strong></p>
<p>The bathymetry of the lakes was recorded with a purpose-built lightweight, low-power echosounder unit with integrated GPS and AHRS (attitude and heading reference system), towed behind a kayak. (Data courtesy of Herwig Stibor, Thomas Stieglitz.)</p>
<p><strong>Lake Bathymetry Analysis</strong></p>
<p>Raw bathymetry data were down-sampled to include one sample every 2&nbsp;m distance along the survey track. Data were visually inspected and false soundings removed. In some lakes, GPS reception was partially compromised along the steep shorelines. Where required to not compromise data density, the survey track was reconstructed from field notes and AHRS data (less than 5% of data where applicable). During the bathymetry surveys, tidal water level was not recorded. Therefore, tidal water level variations were corrected for by modeling tidal water level in each lake. The water level for each lake was correlated with tidal water level measured at a reference station at CRRF or predicted tide (x-tide database), by determining tidal lag time and tidal efficiency (see below) from data previously collected concurrently in the respective lake and at this reference station. Subsequently, bathymetry data was reduced to a grid with 2m resolution by kriging of a further down-sampled subset of data using every third data point. Lake-specific variograms were applied, and contour lines and total lake volumes were calculated in a GIS. (Data courtesy of Thomas Stieglitz.)</p>
<p><strong>Distance from lake to ocean &amp; lake surface area</strong></p>
<p>Lake shorelines and island coastlines were manually extracted from satellite data (Microsoft Bing). The nearest, mean and median distance of each lake to the respective island’s coastline as well as lake surface area was calculated in a GIS. The perimeter was measured in meters using the ‘Measure Line’ tool in QGIS 3.4.</p>
<p>Habitable surface area for benthic organisms was estimated either as (1) a multiple of the perimeter and depth of the chemocline, i.e. assuming a cylindrical model for the lake, or (2) as the area of a frustum, i.e. a truncated cone, using the perimeter of the lake at the surface, the average length of transects, and the average angle of the transect from the vertical (estimated using the sine of maximum depth / transect length).</p>
<p>Tidal lag time and tidal efficiency were calculated from concurrently measured tidal water level in a lake and the adjacent ocean. The tidal lag time — the time between high tide in the adjacent ocean and high tide in the lake — was determined by least-square fit between lake and ocean tide measured using HOBO 30-foot depth Titanium water level data loggers (Part # U20-001-01-Ti).&nbsp; Tidal efficiency was calculated as the ratio between amplitude of ocean tide and lake tide (e.g. Ayers, JF and Vacher, HL, 1986.&nbsp; Hydrogeology of an Atoll Island: A Conceptual Model from a Detailed Study of a Micronesian Example. Groundwater 24(2) 185-198.). Larger tidal lag time and smaller tidal efficiency respectively indicate a less efficient hydrological connection between ocean and lake.</p>
<p>Error-checking: Estimates of basic dimensions (depth, distance, length, surface area) were double-checked manually for a subset of measurements by a second person using using Google Earth.&nbsp;</p>
Specified by the Principal Investigator(s)
<p><strong>BCO-DMO Processing:</strong><br />
- added conventional header with dataset name, PI name, version date<br />
- reformatted date from dd-Mon-yy to yyyy-mm-dd (ISO date format)<br />
- replaced blanks records</p>
Specified by the Principal Investigator(s)
asNeeded
7.x-1.1
Biological and Chemical Oceanography Data Management Office (BCO-DMO)
Unavailable
508-289-2009
WHOI MS#36
Woods Hole
MA
02543
USA
info@bco-dmo.org
http://www.bco-dmo.org
Monday - Friday 8:00am - 5:00pm
For questions regarding this resource, please contact BCO-DMO via the email address provided.
pointOfContact
HOBO 30-foot depth Titanium water level data loggers (Part # U20-001-01-Ti)
HOBO 30-foot depth Titanium water level data loggers (Part # U20-001-01-Ti)
PI Supplied Instrument Name: HOBO 30-foot depth Titanium water level data loggers (Part # U20-001-01-Ti) PI Supplied Instrument Description:Used to determine tidal lag time, the time between high tide in the adjacent ocean and high tide in the lake. Instrument Name: Data Logger Instrument Short Name: Instrument Description: Electronic devices that record data over time or in relation to location either with a built-in instrument or sensor or via external instruments and sensors.
Cruise: Palau_lakes
Palau_lakes
Small boats - CRRF
vessel
Palau_lakes
Michael N. Dawson
University of California-Merced
Small boats - CRRF
vessel