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Linear Unit: Meter (1.000000)
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8999f57aaf36f71f2a737729672e07f5
Carrie Kappel
National Center for Ecological Analysis and Synthesis
Primary Investigator
Santa Barbara
CA
kappel@nceas.ucsb.edu
Please contact the Ocean Tipping Points project in advance of applying these data sets to project work so the PI can track and communicate data uses and ensure no duplicate efforts are underway.
True
Carrie Kappel
This layer was developed as part of a geospatial database of key anthropogenic pressures to coastal waters of the Main Hawaiian Islands for the Ocean Tipping Points project (http://oceantippingpoints.org/). Ocean tipping points occur when shifts in human use or environmental conditions result in large, and sometimes abrupt, impacts to marine ecosystems. The ability to predict and understand ocean tipping points can enhance ecosystem management, including critical coral reef management and policies to protect ecosystem services produced by coral reefs. The goal of the Ocean Tipping Points Hawaii case study was to gather, process and map spatial information on environmental and human-based drivers of coral reef ecosystem conditions.
<DIV STYLE="text-align:Left;"><DIV><DIV><P STYLE="text-align:Justify;margin:0 0 0 0;"><SPAN><SPAN>S</SPAN></SPAN><SPAN><SPAN>ea surface temperature (SST) plays an important role in a number of ecological processes and can vary over a wide range of time scales, from daily to decadal changes. SST influences primary production, species migration patterns, and coral health. If temperatures are anomalous warm for extended periods of time, drastic changes in the surrounding ecosystem can result, including harmful effects such as coral bleaching</SPAN></SPAN><SPAN><SPAN>. Degree Heating Weeks is a metric of this thermal stress on corals. This layer represents maximum weekly Degree Heating Week (DHW) of sea surface temperature (SST) from 2000 – 2013. A continuous, 5km gap-filled weekly </SPAN></SPAN><SPAN>SST data set was produced from a variety of sources. Please see Lineage Statement for more details. T</SPAN><SPAN>he Coral Reef Watch methodology was used to calculate Degree Heating Weeks (DHW) time-series. Please see the Coral Reef Watch website for details on how the DHW is product is calculated (http://coralreefwatch.noaa.gov). </SPAN></P></DIV></DIV></DIV>
SST Maximum Degree Heating Week
2016-03-31T00:00:00
Carrie Kappel
National Center for Ecological Analysis and Synthesis
Primary Investigator
Santa Barbara
CA
kappel@nceas.ucsb.edu
Please contact the Ocean Tipping Points project in advance of applying these data sets to project work so the PI can track and communicate data uses and ensure no duplicate efforts are underway.
external
2abe3f5bd45c066fc2f4a8b86dd3cee
Kim Selkoe
National Center for Ecological Analysis and Synthesis
Santa Barbara
CA
selkoe@nceas.ucsb.edu
Please contact the Ocean Tipping Points project in advance of applying these data sets to project work so the PI can track and communicate data uses and ensure no duplicate efforts are underway.
True
Kim Selkoe
Ocean Tipping Points
environmental driver
oceanographic driver
coastal impacts
coral reef
SST
sea surface temperature
Ground condition
Hawaii
Main Hawaiian Islands
The Ocean Tipping Points project, 2016. (Please acknowledge the Ocean Tipping Points project as a source when this data is used in the preparation of reports, papers, publications, maps, and other products.)
external
8999f57aaf36f71f2a737729672e07f5
Carrie Kappel
National Center for Ecological Analysis and Synthesis
Primary Investigator
Santa Barbara
CA
kappel@nceas.ucsb.edu
Please contact the Ocean Tipping Points project in advance of applying these data sets to project work so the PI can track and communicate data uses and ensure no duplicate efforts are underway.
True
Carrie Kappel
<DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN>Please contact the Ocean Tipping Points project in advance of applying these data sets to project work so the PI can track and communicate data uses and ensure no duplicate efforts are underway. When applying these data for publication, please reference and cite the complete journal article, </SPAN><A href="http://oceantippingpoints.org/our-work/publications"><SPAN>Wedding et al. 2017</SPAN></A><SPAN>.</SPAN></P></DIV></DIV></DIV>
Lineage statement here?
Version 6.2 (Build 9200) ; Esri ArcGIS 10.4.1.5686
1
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18.753961
Raster Dataset
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FALSE
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pixel codes
FGDBR
FALSE
row and column
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20170304
Analysis performed in Matlab and Python
The SST weekly Degree Heating Week (DHW) data layer represents the maximum DHW experienced at any point for a given satellite pixel over the 2000 – 2013 time frame. DHW represents an accumulation of thermal stress and was calculated using the NOAA’s Coral Reef Watch methodology. See Coral Reef Watch for more detail (http://coralreefwatch.noaa.gov).
Three SST datasets were combined to provide continuous coverage from 1985-2013. The concatenation applies bias adjustment derived from linear regression to the overlap periods of datasets, with the final representation matching the 0.05 degree (~5 km) near real-time SST product.
First, a weekly composite, gap-filled SST data from the NOAA Pathfinder v5.2 SST 1/24 (~4 km), daily dataset (a NOAA Climate Data Record) for each location was produced following Heron et al. (2010). This dataset covers the period January 1985 – December 2012 at the native spatial resolution (i.e., ~4 km). Next, weekly composite SST data from the NOAA/NESDIS/STAR Blended SST 0.1 (~11 km), daily dataset was produced. This dataset covers the period February 2009 – October 2013 at the native spatial resolution (i.e., ~11 km). Finally a weekly composite SST data from the NOAA/NESDIS/STAR Blended SST 0.05 (~5 km), daily dataset was produced. This dataset covers the period March 2012 – December 2013 at the native spatial resolution (i.e., ~5 km).
Using the overlap period between datasets, linearly regress paired (in time) data to determine the bias between datasets for each location. Bias-adjust the datasets to represent the 5 km dataset. Blend the datasets through overlap periods to complete a single SST time series dataset covering 1985 – 2013 for each location. This layer uses a subset of this time series from 2000 - 2013.
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