Data Package Metadata   View Summary

Ecosystem Responses to Hurricanes across North America, the Caribbean, and Taiwan; 1985 to 2018

General Information
Data Package:
Local Identifier:edi.493.12
Title:Ecosystem Responses to Hurricanes across North America, the Caribbean, and Taiwan; 1985 to 2018
Alternate Identifier:DOI PLACE HOLDER
Abstract:

Tropical cyclones play an increasingly important role in shaping ecosystems. Understanding and generalizing their responses is challenging because of meteorological variability among storms and its interaction with ecosystems. Data here represent the responses of ecosystems to hurricanes from across North America, the Caribbean and Taiwan. Data across a variety of ecosystems are represented here including wetlands, estuaries, marine, terrestrial and fresh water systems. These data measure a variety of different parameters to understand ecosystem response such as biogeochemical, hydrological and physical measurements as well as the abundance of mobile and sedentary biota.    

Meteorological data characterizing tropical cyclones are also presented and are derived from:

IBTrACS: International Best Track Archive for Climate Stewardship
Global storm track data set for all recorded low pressure systems and tropical cyclones dating back to 1842. Data includes timestamped spatial information on storm center location as well as meteorological readings of wind speed, wind direction, and barometric pressure. Most importantly for the purposes of this data set, wind speeds at various distances from storm center are provided, which allows for the development of a model of wind speed with direction for different category storms. 
GRIDMET: University of Idaho Gridded Surface Meteorological Dataset
The Gridded Surface Meteorological dataset provides high spatial resolution (~4-km) daily surface fields of temperature, precipitation, winds, humidity and radiation across the contiguous United States from 1979. The dataset blends the high resolution spatial data from PRISM with the high temporal resolution data from the National Land Data Assimilation System (NLDAS) to produce spatially and temporally continuous fields that lend themselves to additional land surface modeling.
This dataset contains provisional products that are replaced with updated versions when the complete source data become available. Products can be distinguished by the value of the status property. At first, assets are ingested with status=early. After several days, they are replaced by assets with status=provisional. After about 2 months, they are replaced by the final assets with status=permanent.

Daymet V3: Daily Surface Weather and Climatological Summaries
Daymet V3 provides gridded estimates of daily weather parameters for United States, Mexico, Canada, Hawaii, and Puerto Rico. It is derived from selected meteorological station data and various supporting data sources.
Compared to the previous version, Daymet V3 uses an entirely new suite of inputs including:
•	NASA SRTM DEM version 2.1.
•	Land/Water Mask: MODIS 250 MOD44W_v2.NASA_ORNL_
•	Horizon files derived from the SRTM DEM.
•	Ground station weather inputs from several sources with QA/QC.

PERSIANN-CDR: Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-Climate Data Record
PERSIANN-CDR is a daily quasi-global precipitation product that spans the period from 1983-01-01 to present. The data is produced quarterly, with a typical lag of three months. The product is developed by the Center for Hydrometeorology and Remote Sensing at the University of California, Irvine (UC-IRVINE/CHRS) using Gridded Satellite (GridSat-B1) IR data that are derived from merging ISCCP B1 IR data, along with GPCP version 2.2.


 CHIRPS Daily: Climate Hazards Group InfraRed Precipitation with Station Data (version 2.0 final)
Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) is a 30+ year quasi-global rainfall dataset. CHIRPS incorporates 0.05° resolution satellite imagery with in-situ station data to create gridded rainfall time series for trend analysis and seasonal drought monitoring.

Publication Date:2020-03-18
Language:English

Time Period
Begin:
1985-07-25
End:
2018-09-13

People and Organizations
Contact:Leon, Miguel (Department of Natural Resources and the Environment, University of New Hampshire) 
Creator:Leon, Miguel (Department of Natural Resources and the Environment, University of New Hampshire)
Creator:Patrick, Christopher (Biology Department, Virginia Institute of Marine Science, College of William and Mary)
Creator:Branoff, Benjamin (National Institute for Mathematical and Biological Synthesis, University of Tennessee Knoxville)
Creator:Kominoski, John (Institute of Environment, Florida International University)
Creator:Armitage, Anna (Department of Marine Biology, Texas A&M University at Galveston)
Creator:Campos-Cerqueira, Marconi (Sieve Analytics, San Juan, PR)
Creator:Chapela Lara, María (Department of Natural Resources and the Environment, University of New Hampshire)
Creator:Congdon, Victoria (Department of Marine Science, University of Texas at Austin)
Creator:Crowl, Todd (Institute of Environment, Florida International University)
Creator:Devlin, Donna (Department of Life Sciences, Texas A&M University Corpus Christi)
Creator:Douglas, Sarah (Marine Sciences Institute, University of Texas at Austin)
Creator:Erisman, Brad (Marine Sciences Institute, University of Texas at Austin)
Creator:Feagin, Russell (Department of Ocean Engineering, Texas A&M University)
Creator:Fisher, Mark (Texas Parks and Wildlife)
Creator:Geist, Simon (Department of Life Sciences, Texas A&M University Corpus Christi)
Creator:Hall, Nathan (University of North Carolina at Chapel Hill, Institute of Marine Sciences)
Creator:Hardison, Amber (University of Texas at Austin, Department of Marine Science)
Creator:Hogan, James Aaron (Institute of Environment, Florida International University)
Creator:Hogan, James Derek (Texas A&M University Corpus Christi, Department of Life Sciences)
Creator:Lin, Teng-Chiu (National Taiwan Normal University, Department of Life Sciences)
Creator:Liu, Xianbin (The Institute for Tropical Ecosystem Studies (ITES), University of Puerto Rico)
Creator:Lu, Kaijun (Marine Sciences Institute, University of Texas at Austin)
Creator:Montagna, Paul (Harte Research Institute for Gulf of Mexico Studies, Texas A&M University-Corpus Christi)
Creator:O'Connell, Christine (Environmental Studies, Macalester College)
Creator:Pennings, Steven (Department of Biology and Biochemistry, University of Houston)
Creator:Proffitt, C (Department of Life Sciences, Texas A&M University Corpus Christi)
Creator:Rehage, Jennifer (Institute of Environment, Florida International University)
Creator:Reustle, Joseph (Department of Life Sciences, Texas A&M University Corpus Christi)
Creator:Robinson, Kelly (Department of Biology, University of Louisiana at Lafayette)
Creator:Rush, Scott (Department of Wildlife, Fisheries, and Aquaculture, Mississippi State University)
Creator:Santos, Rolando (Institute of Environment, Florida International University)
Creator:Smith, Rachel (Odum School of Ecology, University of Georgia)
Creator:Starr, Gregory (Department of Biological Sciences, University of Alabama)
Creator:Strazisar, Theresa (Biological Sciences Department, Florida Atlantic University)
Creator:Strickland, Bradley (Institute of Environment, Florida International University)
Creator:Wetz, Michael (Harte Research Institute for Gulf of Mexico Studies, Texas A&M University-Corpus Christi)
Creator:Kelly, Stephen (Department of Biology, University of Louisiana at Lafayette)
Creator:Wilson, Sara (Institute of Environment, Florida International University)
Creator:Xinping, Hu (Marine Sciences Institute, University of Texas at Austin)
Creator:Xue, Jianhong (Marine Sciences Institute, University of Texas at Austin)
Creator:Yeager, Lauren (Marine Sciences Institute, University of Texas at Austin)
Creator:Zou, Xiaoming (Institute for Tropical Ecosystem Studies, University of Puerto Rico, Rio Piedras Campus)
Creator:McDowell, William (Department of Natural Resources and the Environment, University of New Hampshire)

Data Entities
Data Table Name:
Ecosystem responses to hurricanes L2 validated data with derived CHRIPS climate data
Description:
Tropical cyclones play an increasingly important role in shaping ecosystems. Understanding and generalizing their responses is challenging because of meteorological variability among storms and its interaction with ecosystems. Data here represent the responses of ecosystems to hurricanes from across North America, the Caribbean and Taiwan. Data across a variety of ecosystems are represented here including wetlands, estuaries, marine, terrestrial and fresh water systems. These data measure a variety of different parameters to understand ecosystem response such as biogeochemical, hydrological and physical measurements as well as the abundance of mobile and sedentary biota. Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) is a 30+ year quasi-global rainfall dataset. CHIRPS incorporates 0.05° resolution satellite imagery with in-situ station data to create gridded rainfall time series for trend analysis and seasonal drought monitoring. Terms of Use This datasets are in the public domain. To the extent possible under law, Pete Peterson has waived all copyright and related or neighboring rights to Climate Hazards Group Infrared Precipitation with Stations (CHIRPS). Suggested citation(s) • Funk, Chris, Pete Peterson, Martin Landsfeld, Diego Pedreros, James Verdin, Shraddhanand Shukla, Gregory Husak, James Rowland, Laura Harrison, Andrew Hoell and Joel Michaelsen. "The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes". Scientific Data 2, 150066. doi:10.1038/sdata.2015.66 2015.  
Data Table Name:
Ecosystem responses to hurricanes L2 validated data with derived DAYMET climate data
Description:
Tropical cyclones play an increasingly important role in shaping ecosystems. Understanding and generalizing their responses is challenging because of meteorological variability among storms and its interaction with ecosystems. Data here represent the responses of ecosystems to hurricanes from across North America, the Caribbean and Taiwan. Data across a variety of ecosystems are represented here including wetlands, estuaries, marine, terrestrial and fresh water systems. These data measure a variety of different parameters to understand ecosystem response such as biogeochemical, hydrological and physical measurements as well as the abundance of mobile and sedentary biota. Daymet V3 provides gridded estimates of daily weather parameters for United States, Mexico, Canada, Hawaii, and Puerto Rico. It is derived from selected meteorological station data and various supporting data sources. Terms of Use This dataset is in the public domain and is available without restriction on use and distribution. See NASAs Earth Science Data and Information Policy for additional information. Datasets DOI(s) • https://doi.org/10.3334/ORNLDAAC/1328 Suggested citation(s) • Thornton, P.E., M.M. Thornton, B.W. Mayer, Y. Wei, R. Devarakonda, R.S.Vose, and R.B. Cook. {YEAR}. Daymet: Daily Surface Weather Data on a 1-km Grid for North America, Version3. ORNL DAAC, Oak Ridge, Tennessee, USA
Data Table Name:
Ecosystem responses to hurricanes L2 validated data with derived GRIDMET climate data
Description:
Tropical cyclones play an increasingly important role in shaping ecosystems. Understanding and generalizing their responses is challenging because of meteorological variability among storms and its interaction with ecosystems. Data here represent the responses of ecosystems to hurricanes from across North America, the Caribbean and Taiwan. Data across a variety of ecosystems are represented here including wetlands, estuaries, marine, terrestrial and fresh water systems. These data measure a variety of different parameters to understand ecosystem response such as biogeochemical, hydrological and physical measurements as well as the abundance of mobile and sedentary biota. The Gridded Surface Meteorological dataset provides high spatial resolution (~4-km) daily surface fields of temperature, precipitation, winds, humidity and radiation across the contiguous United States from 1979. The dataset blends the high resolution spatial data from PRISM with the high temporal resolution data from the National Land Data Assimilation System (NLDAS) to produce spatially and temporally continuous fields that lend themselves to additional land surface modeling. This dataset contains provisional products that are replaced with updated versions when the complete source data become available. Products can be distinguished by the value of the status property. At first, assets are ingested with status=early. After several days, they are replaced by assets with status=provisional. After about 2 months, they are replaced by the final assets with status=permanent. Terms of Use This work (METDATA, by John Abatzoglou) is in the public domain and is free of known copyright restrictions. Users should properly cite the source used in the creation of any reports and publications resulting from the use of this dataset and note the date when the data was acquired. Suggested citation(s) • Abatzoglou J. T., Development of gridded surface meteorological data for ecological applications and modelling, International Journal of Climatology. (2012) doi: https://doi.org/10.1002/joc.3413
Data Table Name:
Ecosystem responses to hurricanes L2 validated data with derived PERSIANN climate data
Description:
Tropical cyclones play an increasingly important role in shaping ecosystems. Understanding and generalizing their responses is challenging because of meteorological variability among storms and its interaction with ecosystems. Data here represent the responses of ecosystems to hurricanes from across North America, the Caribbean and Taiwan. Data across a variety of ecosystems are represented here including wetlands, estuaries, marine, terrestrial and fresh water systems. These data measure a variety of different parameters to understand ecosystem response such as biogeochemical, hydrological and physical measurements as well as the abundance of mobile and sedentary biota. PERSIANN-CDR is a daily quasi-global precipitation product that spans the period from 1983-01-01 to present. The data is produced quarterly, with a typical lag of three months. The product is developed by the Center for Hydrometeorology and Remote Sensing at the University of California, Irvine (UC-IRVINE/CHRS) using Gridded Satellite (GridSat-B1) IR data that are derived from merging ISCCP B1 IR data, along with GPCP version 2.2. Terms of Use This work (METDATA, by John Abatzoglou) is in the public domain and is free of known copyright restrictions. Users should properly cite the source used in the creation of any reports and publications resulting from the use of this dataset and note the date when the data was acquired. Suggested citation(s) • Abatzoglou J. T., Development of gridded surface meteorological data for ecological applications and modelling, International Journal of Climatology. (2012) doi: https://doi.org/10.1002/joc.3413
Data Table Name:
Ecosystem responses to hurricanes L2 validated data with derived event wind statistics from IBTrACS
Description:
Tropical cyclones play an increasingly important role in shaping ecosystems. Understanding and generalizing their responses is challenging because of meteorological variability among storms and its interaction with ecosystems. Data here represent the responses of ecosystems to hurricanes from across North America, the Caribbean and Taiwan. Data across a variety of ecosystems are represented here including wetlands, estuaries, marine, terrestrial and fresh water systems. These data measure a variety of different parameters to understand ecosystem response such as biogeochemical, hydrological and physical measurements as well as the abundance of mobile and sedentary biota. Storm track data are derived from NOAA's International Best Track Archive for Climate Stewardship (IBTrACS) data: Knapp, K. R., M. C. Kruk, D. H. Levinson, H. J. Diamond, and C. J. Neumann, 2010: The International Best Track Archive for Climate Stewardship (IBTrACS): Unifying tropical cyclone best track data. Bulletin of the American Meteorological Society, 91, 363-376. non-gonvernment domain doi:10.1175/2009BAMS2755.1 Knapp, K. R., H. J. Diamond, J. P. Kossin, M. C. Kruk, C. J. Schreck, 2018: International Best Track Archive for Climate Stewardship (IBTrACS) Project, Version 4. R00. NOAA National Centers for Environmental Information. non-gonvernment domain https://doi.org/10.25921/82ty-9e16 [3-23-2020].
Data Table Name:
Ecosystem responses to hurricanes L0 validated data
Description:
Tropical cyclones play an increasingly important role in shaping ecosystems. Understanding and generalizing their responses is challenging because of meteorological variability among storms and its interaction with ecosystems. Data here represent the responses of ecosystems to hurricanes from across North America, the Caribbean and Taiwan. Data across a variety of ecosystems are represented here including wetlands, estuaries, marine, terrestrial and fresh water systems. These data measure a variety of different parameters to understand ecosystem response such as biogeochemical, hydrological and physical measurements as well as the abundance of mobile and sedentary biota.
Other Name:
ERTHS additional metadata
Description:
Excel file with metadata additional metadata describing variables, units, variable subcategories and other fields
Other Name:
ERTHS additional metadata for event wind statistics variable descriptions
Description:
Excel file with metadata additional metadata describing event wind statistics variable descriptions
Detailed Metadata

Data Entities


Data Table

Data:
 Inline Data
Name:Ecosystem responses to hurricanes L2 validated data with derived CHRIPS climate data
Description:Tropical cyclones play an increasingly important role in shaping ecosystems. Understanding and generalizing their responses is challenging because of meteorological variability among storms and its interaction with ecosystems. Data here represent the responses of ecosystems to hurricanes from across North America, the Caribbean and Taiwan. Data across a variety of ecosystems are represented here including wetlands, estuaries, marine, terrestrial and fresh water systems. These data measure a variety of different parameters to understand ecosystem response such as biogeochemical, hydrological and physical measurements as well as the abundance of mobile and sedentary biota. Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) is a 30+ year quasi-global rainfall dataset. CHIRPS incorporates 0.05° resolution satellite imagery with in-situ station data to create gridded rainfall time series for trend analysis and seasonal drought monitoring. Terms of Use This datasets are in the public domain. To the extent possible under law, Pete Peterson has waived all copyright and related or neighboring rights to Climate Hazards Group Infrared Precipitation with Stations (CHIRPS). Suggested citation(s) • Funk, Chris, Pete Peterson, Martin Landsfeld, Diego Pedreros, James Verdin, Shraddhanand Shukla, Gregory Husak, James Rowland, Laura Harrison, Andrew Hoell and Joel Michaelsen. "The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes". Scientific Data 2, 150066. doi:10.1038/sdata.2015.66 2015.  
Number of Records:29658
Number of Columns:24

Table Structure
Object Name:HurricaneWorkshopData_L2_CHIRPS.csv
Size:17285917
Text Format:
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Record Delimiter:\r\n
Line Delimiter:\r\n
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Table Column Descriptions
 IDContributor First NameContributor Last NameVariable CategorySystem NameSystem TypeVariableLatitudeLongitudeEvent YearEvent MonthEvent DayEventSystem MajorSystem MinorrainBaseline_2monthAnnual_countCHIRPSrainBaseline_2monthAnnual_maxCHIRPSrainBaseline_2monthAnnual_sumCHIRPSrainBaseline_2monthBeforeEvent_countCHIRPSrainBaseline_2monthBeforeEvent_maxCHIRPSrainBaseline_2monthBeforeEvent_sumCHIRPSrainEvent_countCHIRPSrainEvent_maxCHIRPSrainEvent_sumCHIRPS
Column Name:ID  
Contributor First Name  
Contributor Last Name  
Variable Category  
System Name  
System Type  
Variable  
Latitude  
Longitude  
Event Year  
Event Month  
Event Day  
Event  
System Major  
System Minor  
rainBaseline_2monthAnnual_countCHIRPS  
rainBaseline_2monthAnnual_maxCHIRPS  
rainBaseline_2monthAnnual_sumCHIRPS  
rainBaseline_2monthBeforeEvent_countCHIRPS  
rainBaseline_2monthBeforeEvent_maxCHIRPS  
rainBaseline_2monthBeforeEvent_sumCHIRPS  
rainEvent_countCHIRPS  
rainEvent_maxCHIRPS  
rainEvent_sumCHIRPS  
Definition:IDContributor First NameContributor Last NameThe category of the response. Classifications include biogeochemistry, hydrography, hydrology, mobile biota, sedentary biota, and phyiscal.Name of the ecosystem where samples were collected.The category of the ecosystems. Classifications include off-shore coastal, in-shore coastal, estuary, river, barrier island, riparian, terrestrial.The name of the response variable.The latitude of the data collection in the case of repeated fixed station sampling. Use a central spatial location if values were aggregated from randomly selected sampling locations within an ecosystem (e.g. embayment) whose precise location changed through time. Format is Signed Degrees Format (DDD.dddd), e.g. -120.9762The longitude of the data collection in the case of repeated fixed station sampling. Use a central spatial location if values were aggregated from randomly selected sampling locations within an ecosystem (e.g. embayment) whose precise location changed through time. Format is Signed Degrees Format (DDD.dddd), e.g. -120.9762Year the event took placeMonth the event took place (numeric), e.g. if October, then enter "10"Day (numeric) the event took place, e.g. if October 22nd, then enter "22"Name of the event (e.g. Hurricane Maria)Either terrestrial or aquaticminor system type is wetland, estuarine, freshwater, marine, or terrestrialrainBaseline_2monthAnnual_countCHIRPSMaximum daily rainfall between 60 days before to day of event in DOY for every year in the record prior to the event yearSum of daily rainfall from 60 days before to day of event in DOY for every year in the record prior to the event yearNumber of observations between 60 days before.Maximum daily rainfall between 60 days before to 2 days after eventSum of daily rainfall from 60 days before eventNumber of observations between 2 days before and 2 days after eventMaximum daily rainfall between 2 days before to 2 days after eventSum of daily rainfall from 2 days before to 2 days after event
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Measurement Type:nominalnominalnominalnominalnominalnominalnominalnominalnominalnominalnominalnominalnominalnominalnominalratioratioratioratioratioratioratioratioratio
Measurement Values Domain:
DefinitionID
DefinitionContributor First Name
DefinitionContributor Last Name
DefinitionThe category of the response. Classifications include biogeochemistry, hydrography, hydrology, mobile biota, sedentary biota, and phyiscal.
DefinitionName of the ecosystem where samples were collected.
DefinitionThe category of the ecosystems. Classifications include off-shore coastal, in-shore coastal, estuary, river, barrier island, riparian, terrestrial.
DefinitionThe name of the response variable.
DefinitionThe latitude of the data collection in the case of repeated fixed station sampling. Use a central spatial location if values were aggregated from randomly selected sampling locations within an ecosystem (e.g. embayment) whose precise location changed through time. Format is Signed Degrees Format (DDD.dddd), e.g. -120.9762
DefinitionThe longitude of the data collection in the case of repeated fixed station sampling. Use a central spatial location if values were aggregated from randomly selected sampling locations within an ecosystem (e.g. embayment) whose precise location changed through time. Format is Signed Degrees Format (DDD.dddd), e.g. -120.9762
DefinitionYear the event took place
DefinitionMonth the event took place (numeric), e.g. if October, then enter "10"
DefinitionDay (numeric) the event took place, e.g. if October 22nd, then enter "22"
DefinitionName of the event (e.g. Hurricane Maria)
DefinitionEither terrestrial or aquatic
Definitionminor system type is wetland, estuarine, freshwater, marine, or terrestrial
Unitcount
Precision1
Typeinteger
Unitmm per day
Precision0.0001
Typereal
Unitsum mm per day
Precision0.00001
Typereal
Unitcount
Precision1
Typeinteger
Unitcount
Precision1
Typeinteger
Unitsum mm per day
Precision0.00001
Typereal
Unitcount
Precision1
Typeinteger
Unitmm per day
Precision0.0001
Typereal
Unitsum mm per day
Precision0.0001
Typereal
Missing Value Code:                                                
Accuracy Report:                                                
Accuracy Assessment:                                                
Coverage:                                                
Methods:                                                

Data Table

Data:
 Inline Data
Name:Ecosystem responses to hurricanes L2 validated data with derived DAYMET climate data
Description:Tropical cyclones play an increasingly important role in shaping ecosystems. Understanding and generalizing their responses is challenging because of meteorological variability among storms and its interaction with ecosystems. Data here represent the responses of ecosystems to hurricanes from across North America, the Caribbean and Taiwan. Data across a variety of ecosystems are represented here including wetlands, estuaries, marine, terrestrial and fresh water systems. These data measure a variety of different parameters to understand ecosystem response such as biogeochemical, hydrological and physical measurements as well as the abundance of mobile and sedentary biota. Daymet V3 provides gridded estimates of daily weather parameters for United States, Mexico, Canada, Hawaii, and Puerto Rico. It is derived from selected meteorological station data and various supporting data sources. Terms of Use This dataset is in the public domain and is available without restriction on use and distribution. See NASAs Earth Science Data and Information Policy for additional information. Datasets DOI(s) • https://doi.org/10.3334/ORNLDAAC/1328 Suggested citation(s) • Thornton, P.E., M.M. Thornton, B.W. Mayer, Y. Wei, R. Devarakonda, R.S.Vose, and R.B. Cook. {YEAR}. Daymet: Daily Surface Weather Data on a 1-km Grid for North America, Version3. ORNL DAAC, Oak Ridge, Tennessee, USA
Number of Records:29658
Number of Columns:31

Table Structure
Object Name:ERTHS_L2_DAYMET.csv
Size:47038666
Text Format:
Number of Header Lines:1
Record Delimiter:\r\n
Line Delimiter:\r\n
Orientation:column
Simple Delimited:
Field Delimiter:,
Quote Character:"

Table Column Descriptions
 IDContributor First NameContributor Last NameVariable CategorySystem NameSystem TypeVariableLatitudeLongitudeEvent YearEvent MonthEvent DayEventSystem MajorSystem MinorrainBaseline_2monthAnnual_countDAYMETrainBaseline_2monthAnnual_maxDAYMETrainBaseline_2monthAnnual_sumDAYMETrainBaseline_2monthBeforeEvent_countDAYMETrainBaseline_2monthBeforeEvent_maxDAYMETrainBaseline_2monthBeforeEvent_sumDAYMETrainEvent_countDAYMETrainEvent_maxDAYMETrainEvent_sumDAYMETtempBaseline_2monthAnnual_maxDAYMETtempBaseline_2monthAnnual_sumDAYMETtempBaseline_2monthBeforeEvent_maxDAYMETtempBaseline_2monthBeforeEvent_sumDAYMETtempEvent_maxDAYMETtempEvent_sumDAYMET.geo
Column Name:ID  
Contributor First Name  
Contributor Last Name  
Variable Category  
System Name  
System Type  
Variable  
Latitude  
Longitude  
Event Year  
Event Month  
Event Day  
Event  
System Major  
System Minor  
rainBaseline_2monthAnnual_countDAYMET  
rainBaseline_2monthAnnual_maxDAYMET  
rainBaseline_2monthAnnual_sumDAYMET  
rainBaseline_2monthBeforeEvent_countDAYMET  
rainBaseline_2monthBeforeEvent_maxDAYMET  
rainBaseline_2monthBeforeEvent_sumDAYMET  
rainEvent_countDAYMET  
rainEvent_maxDAYMET  
rainEvent_sumDAYMET  
tempBaseline_2monthAnnual_maxDAYMET  
tempBaseline_2monthAnnual_sumDAYMET  
tempBaseline_2monthBeforeEvent_maxDAYMET  
tempBaseline_2monthBeforeEvent_sumDAYMET  
tempEvent_maxDAYMET  
tempEvent_sumDAYMET  
.geo  
Definition:IDContributor First NameContributor Last NameThe category of the response. Classifications include biogeochemistry, hydrography, hydrology, mobile biota, sedentary biota, and phyiscal.Name of the ecosystem where samples were collected.The category of the ecosystems. Classifications include off-shore coastal, in-shore coastal, estuary, river, barrier island, riparian, terrestrial.The name of the response variable.The latitude of the data collection in the case of repeated fixed station sampling. Use a central spatial location if values were aggregated from randomly selected sampling locations within an ecosystem (e.g. embayment) whose precise location changed through time. Format is Signed Degrees Format (DDD.dddd), e.g. -120.9762The longitude of the data collection in the case of repeated fixed station sampling. Use a central spatial location if values were aggregated from randomly selected sampling locations within an ecosystem (e.g. embayment) whose precise location changed through time. Format is Signed Degrees Format (DDD.dddd), e.g. -120.9762Year the event took placeMonth the event took place (numeric), e.g. if October, then enter "10"Day (numeric) the event took place, e.g. if October 22nd, then enter "22"Name of the event (e.g. Hurricane Maria)Either terrestrial or aquaticminor system type is wetland, estuarine, freshwater, marine, or terrestrialrainBaseline_2monthAnnual_countDAYMETMaximum daily rainfall between 60 days before to day of event in DOY for every year in the record prior to the event yearSum of daily rainfall from 60 days before to day of event in DOY for every year in the record prior to the event yearNumber of observations between 60 days before.Maximum daily rainfall between 60 days before to 2 days after eventSum of daily rainfall from 60 days before eventNumber of observations between 2 days before and 2 days after eventMaximum daily rainfall between 2 days before to 2 days after eventSum of daily rainfall from 2 days before to 2 days after eventMaximum daily temperature between 60 days before to day of event in DOY for every year in the record prior to the event year yearSum of daily temperature from 60 days before to day of event in DOY for every year in the record prior to the event yearMaximum daily temperature between 60 days before to 2 days after eventSum of daily temperature from 60 days before to 2 days before eventMaximum daily temperature between 2 days before to 2 days after eventSum of daily temperature from 2 days before to 2 days after eventAt each plot location provided, a 1000 m radial buffer (2000 m diameter) was created around the center point. The Year, Month, and Day were calculated for each data value location to determine the day-of-year (DOY) of the event. Grid cells from the various datasets (e.g., DAYMET, GRIDMET, PERSIANN, CHIRPS) were extracted and averaged using the location buffer (note that each dataset has a different spatial resolution). Average and maximum precipitation and temperatures were calculated using the various gridded datasets. The hurricane event was defined as the 2 days before through 2 days after the “event” date. A 2-month baseline period before the hurricane “event” was defined as 60 days prior to the “event” through 2 days before the event. A long-term 2-month baseline was also calculated using the 60 day period prior to the “event’s” DOY for every year preceding and including the “event” year.
Storage Type:string  
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Measurement Type:nominalnominalnominalnominalnominalnominalnominalnominalnominalnominalnominalnominalnominalnominalnominalintervalintervalintervalintervalintervalintervalintervalintervalintervalintervalintervalintervalintervalintervalintervalnominal
Measurement Values Domain:
DefinitionID
DefinitionContributor First Name
DefinitionContributor Last Name
DefinitionThe category of the response. Classifications include biogeochemistry, hydrography, hydrology, mobile biota, sedentary biota, and phyiscal.
DefinitionName of the ecosystem where samples were collected.
DefinitionThe category of the ecosystems. Classifications include off-shore coastal, in-shore coastal, estuary, river, barrier island, riparian, terrestrial.
DefinitionThe name of the response variable.
DefinitionThe latitude of the data collection in the case of repeated fixed station sampling. Use a central spatial location if values were aggregated from randomly selected sampling locations within an ecosystem (e.g. embayment) whose precise location changed through time. Format is Signed Degrees Format (DDD.dddd), e.g. -120.9762
DefinitionThe longitude of the data collection in the case of repeated fixed station sampling. Use a central spatial location if values were aggregated from randomly selected sampling locations within an ecosystem (e.g. embayment) whose precise location changed through time. Format is Signed Degrees Format (DDD.dddd), e.g. -120.9762
DefinitionYear the event took place
DefinitionMonth the event took place (numeric), e.g. if October, then enter "10"
DefinitionDay (numeric) the event took place, e.g. if October 22nd, then enter "22"
DefinitionName of the event (e.g. Hurricane Maria)
DefinitionEither terrestrial or aquatic
Definitionminor system type is wetland, estuarine, freshwater, marine, or terrestrial
Unitcount
Precision1
Typeinteger
Unitmm per day
Precision0.0001
Typereal
Unitsum mm per day
Precision0.00001
Typereal
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Precision1
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Unitcount
Precision1
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Precision0.00001
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Unitmm per day
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Unitmm per day
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Typereal
Unitsum mm per day
Precision0.00001
Typereal
Unitmm per day
Precision0.0001
Typereal
Unitsum mm per day
Precision0.0001
Typereal
Definitiongeographic bounds around sampling point.
Missing Value Code:                                                              
Accuracy Report:                                                              
Accuracy Assessment:                                                              
Coverage:                                                              
Methods:                                                              

Data Table

Data:
 Inline Data
Name:Ecosystem responses to hurricanes L2 validated data with derived GRIDMET climate data
Description:Tropical cyclones play an increasingly important role in shaping ecosystems. Understanding and generalizing their responses is challenging because of meteorological variability among storms and its interaction with ecosystems. Data here represent the responses of ecosystems to hurricanes from across North America, the Caribbean and Taiwan. Data across a variety of ecosystems are represented here including wetlands, estuaries, marine, terrestrial and fresh water systems. These data measure a variety of different parameters to understand ecosystem response such as biogeochemical, hydrological and physical measurements as well as the abundance of mobile and sedentary biota. The Gridded Surface Meteorological dataset provides high spatial resolution (~4-km) daily surface fields of temperature, precipitation, winds, humidity and radiation across the contiguous United States from 1979. The dataset blends the high resolution spatial data from PRISM with the high temporal resolution data from the National Land Data Assimilation System (NLDAS) to produce spatially and temporally continuous fields that lend themselves to additional land surface modeling. This dataset contains provisional products that are replaced with updated versions when the complete source data become available. Products can be distinguished by the value of the status property. At first, assets are ingested with status=early. After several days, they are replaced by assets with status=provisional. After about 2 months, they are replaced by the final assets with status=permanent. Terms of Use This work (METDATA, by John Abatzoglou) is in the public domain and is free of known copyright restrictions. Users should properly cite the source used in the creation of any reports and publications resulting from the use of this dataset and note the date when the data was acquired. Suggested citation(s) • Abatzoglou J. T., Development of gridded surface meteorological data for ecological applications and modelling, International Journal of Climatology. (2012) doi: https://doi.org/10.1002/joc.3413
Number of Records:29658
Number of Columns:31

Table Structure
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Table Column Descriptions
 IDContributor First NameContributor Last NameVariable CategorySystem NameSystem TypeVariableLatitudeLongitudeEvent YearEvent MonthEvent DayEventSystem MajorSystem MinorrainBaseline_2monthAnnual_countGRIDMETrainBaseline_2monthAnnual_maxGRIDMETrainBaseline_2monthAnnual_sumGRIDMETrainBaseline_2monthBeforeEvent_countGRIDMETrainBaseline_2monthBeforeEvent_maxGRIDMETrainBaseline_2monthBeforeEvent_sumGRIDMETrainEvent_countGRIDMETrainEvent_maxGRIDMETrainEvent_sumGRIDMETtempBaseline_2monthAnnual_maxGRIDMETtempBaseline_2monthAnnual_sumGRIDMETtempBaseline_2monthBeforeEvent_maxGRIDMETtempBaseline_2monthBeforeEvent_sumGRIDMETtempEvent_maxGRIDMETtempEvent_sumGRIDMET.geo
Column Name:ID  
Contributor First Name  
Contributor Last Name  
Variable Category  
System Name  
System Type  
Variable  
Latitude  
Longitude  
Event Year  
Event Month  
Event Day  
Event  
System Major  
System Minor  
rainBaseline_2monthAnnual_countGRIDMET  
rainBaseline_2monthAnnual_maxGRIDMET  
rainBaseline_2monthAnnual_sumGRIDMET  
rainBaseline_2monthBeforeEvent_countGRIDMET  
rainBaseline_2monthBeforeEvent_maxGRIDMET  
rainBaseline_2monthBeforeEvent_sumGRIDMET  
rainEvent_countGRIDMET  
rainEvent_maxGRIDMET  
rainEvent_sumGRIDMET  
tempBaseline_2monthAnnual_maxGRIDMET  
tempBaseline_2monthAnnual_sumGRIDMET  
tempBaseline_2monthBeforeEvent_maxGRIDMET  
tempBaseline_2monthBeforeEvent_sumGRIDMET  
tempEvent_maxGRIDMET  
tempEvent_sumGRIDMET  
.geo  
Definition:IDContributor First NameContributor Last NameThe category of the response. Classifications include biogeochemistry, hydrography, hydrology, mobile biota, sedentary biota, and phyiscal.Name of the ecosystem where samples were collected.The category of the ecosystems. Classifications include off-shore coastal, in-shore coastal, estuary, river, barrier island, riparian, terrestrial.The name of the response variable.The latitude of the data collection in the case of repeated fixed station sampling. Use a central spatial location if values were aggregated from randomly selected sampling locations within an ecosystem (e.g. embayment) whose precise location changed through time. Format is Signed Degrees Format (DDD.dddd), e.g. -120.9762The longitude of the data collection in the case of repeated fixed station sampling. Use a central spatial location if values were aggregated from randomly selected sampling locations within an ecosystem (e.g. embayment) whose precise location changed through time. Format is Signed Degrees Format (DDD.dddd), e.g. -120.9762Year the event took placeMonth the event took place (numeric), e.g. if October, then enter "10"Day (numeric) the event took place, e.g. if October 22nd, then enter "22"Name of the event (e.g. Hurricane Maria)Either terrestrial or aquaticminor system type is wetland, estuarine, freshwater, marine, or terrestrialrainBaseline_2monthAnnual_countGRIDMETMaximum daily rainfall between 60 days before to day of event in DOY for every year in the record prior to the event yearSum of daily rainfall from 60 days before to day of event in DOY for every year in the record prior to the event yearNumber of observations between 60 days before.Maximum daily rainfall between 60 days before to 2 days after eventSum of daily rainfall from 60 days before eventNumber of observations between 2 days before and 2 days after eventMaximum daily rainfall between 2 days before to 2 days after eventSum of daily rainfall from 2 days before to 2 days after eventMaximum daily temperature between 60 days before to day of event in DOY for every year in the record prior to the event year yearSum of daily temperature from 60 days before to day of event in DOY for every year in the record prior to the event yearMaximum daily temperature between 60 days before to 2 days after eventSum of daily temperature from 60 days before to 2 days before eventMaximum daily temperature between 2 days before to 2 days after eventSum of daily temperature from 2 days before to 2 days after eventAt each plot location provided, a 1000 m radial buffer (2000 m diameter) was created around the center point. The Year, Month, and Day were calculated for each data value location to determine the day-of-year (DOY) of the event. Grid cells from the various datasets (e.g., DAYMET, GRIDMET, PERSIANN, CHIRPS) were extracted and averaged using the location buffer (note that each dataset has a different spatial resolution). Average and maximum precipitation and temperatures were calculated using the various gridded datasets. The hurricane event was defined as the 2 days before through 2 days after the “event” date. A 2-month baseline period before the hurricane “event” was defined as 60 days prior to the “event” through 2 days before the event. A long-term 2-month baseline was also calculated using the 60 day period prior to the “event’s” DOY for every year preceding and including the “event” year.
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DefinitionID
DefinitionContributor First Name
DefinitionContributor Last Name
DefinitionThe category of the response. Classifications include biogeochemistry, hydrography, hydrology, mobile biota, sedentary biota, and phyiscal.
DefinitionName of the ecosystem where samples were collected.
DefinitionThe category of the ecosystems. Classifications include off-shore coastal, in-shore coastal, estuary, river, barrier island, riparian, terrestrial.
DefinitionThe name of the response variable.
DefinitionThe latitude of the data collection in the case of repeated fixed station sampling. Use a central spatial location if values were aggregated from randomly selected sampling locations within an ecosystem (e.g. embayment) whose precise location changed through time. Format is Signed Degrees Format (DDD.dddd), e.g. -120.9762
DefinitionThe longitude of the data collection in the case of repeated fixed station sampling. Use a central spatial location if values were aggregated from randomly selected sampling locations within an ecosystem (e.g. embayment) whose precise location changed through time. Format is Signed Degrees Format (DDD.dddd), e.g. -120.9762
DefinitionYear the event took place
DefinitionMonth the event took place (numeric), e.g. if October, then enter "10"
DefinitionDay (numeric) the event took place, e.g. if October 22nd, then enter "22"
DefinitionName of the event (e.g. Hurricane Maria)
DefinitionEither terrestrial or aquatic
Definitionminor system type is wetland, estuarine, freshwater, marine, or terrestrial
Unitcount
Precision1
Typeinteger
Unitmm per day
Precision0.0001
Typereal
Unitsum mm per day
Precision0.00001
Typereal
Unitcount
Precision1
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Unitcount
Precision1
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Unitsum mm per day
Precision0.00001
Typereal
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Unitmm per day
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Unitmm per day
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Unitmm per day
Precision0.0001
Typereal
Unitsum mm per day
Precision0.00001
Typereal
Unitmm per day
Precision0.0001
Typereal
Unitsum mm per day
Precision0.0001
Typereal
Definitiongeographic bounds around sampling point.
Missing Value Code:                                                              
Accuracy Report:                                                              
Accuracy Assessment:                                                              
Coverage:                                                              
Methods:                                                              

Data Table

Data:
 Inline Data
Name:Ecosystem responses to hurricanes L2 validated data with derived PERSIANN climate data
Description:Tropical cyclones play an increasingly important role in shaping ecosystems. Understanding and generalizing their responses is challenging because of meteorological variability among storms and its interaction with ecosystems. Data here represent the responses of ecosystems to hurricanes from across North America, the Caribbean and Taiwan. Data across a variety of ecosystems are represented here including wetlands, estuaries, marine, terrestrial and fresh water systems. These data measure a variety of different parameters to understand ecosystem response such as biogeochemical, hydrological and physical measurements as well as the abundance of mobile and sedentary biota. PERSIANN-CDR is a daily quasi-global precipitation product that spans the period from 1983-01-01 to present. The data is produced quarterly, with a typical lag of three months. The product is developed by the Center for Hydrometeorology and Remote Sensing at the University of California, Irvine (UC-IRVINE/CHRS) using Gridded Satellite (GridSat-B1) IR data that are derived from merging ISCCP B1 IR data, along with GPCP version 2.2. Terms of Use This work (METDATA, by John Abatzoglou) is in the public domain and is free of known copyright restrictions. Users should properly cite the source used in the creation of any reports and publications resulting from the use of this dataset and note the date when the data was acquired. Suggested citation(s) • Abatzoglou J. T., Development of gridded surface meteorological data for ecological applications and modelling, International Journal of Climatology. (2012) doi: https://doi.org/10.1002/joc.3413
Number of Records:29658
Number of Columns:24

Table Structure
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Table Column Descriptions
 IDContributor First NameContributor Last NameVariable CategorySystem NameSystem TypeVariableLatitudeLongitudeEvent YearEvent MonthEvent DayEventSystem MajorSystem MinorrainBaseline_2monthAnnual_countPERSIANNrainBaseline_2monthAnnual_maxPERSIANNrainBaseline_2monthAnnual_sumPERSIANNrainBaseline_2monthBeforeEvent_countPERSIANNrainBaseline_2monthBeforeEvent_maxPERSIANNrainBaseline_2monthBeforeEvent_sumPERSIANNrainEvent_countPERSIANNrainEvent_maxPERSIANNrainEvent_sumPERSIANN
Column Name:ID  
Contributor First Name  
Contributor Last Name  
Variable Category  
System Name  
System Type  
Variable  
Latitude  
Longitude  
Event Year  
Event Month  
Event Day  
Event  
System Major  
System Minor  
rainBaseline_2monthAnnual_countPERSIANN  
rainBaseline_2monthAnnual_maxPERSIANN  
rainBaseline_2monthAnnual_sumPERSIANN  
rainBaseline_2monthBeforeEvent_countPERSIANN  
rainBaseline_2monthBeforeEvent_maxPERSIANN  
rainBaseline_2monthBeforeEvent_sumPERSIANN  
rainEvent_countPERSIANN  
rainEvent_maxPERSIANN  
rainEvent_sumPERSIANN  
Definition:IDContributor First NameContributor Last NameThe category of the response. Classifications include biogeochemistry, hydrography, hydrology, mobile biota, sedentary biota, and phyiscal.Name of the ecosystem where samples were collected.The category of the ecosystems. Classifications include off-shore coastal, in-shore coastal, estuary, river, barrier island, riparian, terrestrial.The name of the response variable.The latitude of the data collection in the case of repeated fixed station sampling. Use a central spatial location if values were aggregated from randomly selected sampling locations within an ecosystem (e.g. embayment) whose precise location changed through time. Format is Signed Degrees Format (DDD.dddd), e.g. -120.9762The longitude of the data collection in the case of repeated fixed station sampling. Use a central spatial location if values were aggregated from randomly selected sampling locations within an ecosystem (e.g. embayment) whose precise location changed through time. Format is Signed Degrees Format (DDD.dddd), e.g. -120.9762Year the event took placeMonth the event took place (numeric), e.g. if October, then enter "10"Day (numeric) the event took place, e.g. if October 22nd, then enter "22"Name of the event (e.g. Hurricane Maria)Either terrestrial or aquaticminor system type is wetland, estuarine, freshwater, marine, or terrestrialrainBaseline_2monthAnnual_countPERSIANNMaximum daily rainfall between 60 days before to day of event in DOY for every year in the record prior to the event yearSum of daily rainfall from 60 days before to day of event in DOY for every year in the record prior to the event yearNumber of observations between 60 days before.Maximum daily rainfall between 60 days before to 2 days after eventSum of daily rainfall from 60 days before eventNumber of observations between 2 days before and 2 days after eventMaximum daily rainfall between 2 days before to 2 days after eventSum of daily rainfall from 2 days before to 2 days after event
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DefinitionID
DefinitionContributor First Name
DefinitionContributor Last Name
DefinitionThe category of the response. Classifications include biogeochemistry, hydrography, hydrology, mobile biota, sedentary biota, and phyiscal.
DefinitionName of the ecosystem where samples were collected.
DefinitionThe category of the ecosystems. Classifications include off-shore coastal, in-shore coastal, estuary, river, barrier island, riparian, terrestrial.
DefinitionThe name of the response variable.
DefinitionThe latitude of the data collection in the case of repeated fixed station sampling. Use a central spatial location if values were aggregated from randomly selected sampling locations within an ecosystem (e.g. embayment) whose precise location changed through time. Format is Signed Degrees Format (DDD.dddd), e.g. -120.9762
DefinitionThe longitude of the data collection in the case of repeated fixed station sampling. Use a central spatial location if values were aggregated from randomly selected sampling locations within an ecosystem (e.g. embayment) whose precise location changed through time. Format is Signed Degrees Format (DDD.dddd), e.g. -120.9762
DefinitionYear the event took place
DefinitionMonth the event took place (numeric), e.g. if October, then enter "10"
DefinitionDay (numeric) the event took place, e.g. if October 22nd, then enter "22"
DefinitionName of the event (e.g. Hurricane Maria)
DefinitionEither terrestrial or aquatic
Definitionminor system type is wetland, estuarine, freshwater, marine, or terrestrial
Unitcount
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Missing Value Code:                                                
Accuracy Report:                                                
Accuracy Assessment:                                                
Coverage:                                                
Methods:                                                

Data Table

Data:
 Inline Data
Name:Ecosystem responses to hurricanes L2 validated data with derived event wind statistics from IBTrACS
Description:Tropical cyclones play an increasingly important role in shaping ecosystems. Understanding and generalizing their responses is challenging because of meteorological variability among storms and its interaction with ecosystems. Data here represent the responses of ecosystems to hurricanes from across North America, the Caribbean and Taiwan. Data across a variety of ecosystems are represented here including wetlands, estuaries, marine, terrestrial and fresh water systems. These data measure a variety of different parameters to understand ecosystem response such as biogeochemical, hydrological and physical measurements as well as the abundance of mobile and sedentary biota. Storm track data are derived from NOAA's International Best Track Archive for Climate Stewardship (IBTrACS) data: Knapp, K. R., M. C. Kruk, D. H. Levinson, H. J. Diamond, and C. J. Neumann, 2010: The International Best Track Archive for Climate Stewardship (IBTrACS): Unifying tropical cyclone best track data. Bulletin of the American Meteorological Society, 91, 363-376. non-gonvernment domain doi:10.1175/2009BAMS2755.1 Knapp, K. R., H. J. Diamond, J. P. Kossin, M. C. Kruk, C. J. Schreck, 2018: International Best Track Archive for Climate Stewardship (IBTrACS) Project, Version 4. R00. NOAA National Centers for Environmental Information. non-gonvernment domain https://doi.org/10.25921/82ty-9e16 [3-23-2020].
Number of Records:29658
Number of Columns:43

Table Structure
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Table Column Descriptions
 IDContributor First NameContributor Last NameVariable CategorySystem NameSystem TypeVariableLatitudeLongitudeEvent YearEvent MonthEvent DayEventSystem MajorSystem MinorMinDistance_kmCatAtMinDistMaxWind_knotsmaxGust_knotsminPress_mbarQuadAtClosestQuadAtClosestTropStormDur_hrsCat1Dur_hrsCat2Dur_hrsCat3Dur_hrsCat4Dur_hrsCat5Dur_hrsEventEnergy_MJPrevTropStorm_YearsAgoPrevCat1_YearsAgoPrevCat2_YearsAgoPrevCat3_YearsAgoPrevCat4_YearsAgoPrevCat5_YearsAgoTropStormReturnPeriod_yearsHurReturnPeriod_yearsCat1ReturnPeriod_yearsCat2ReturnPeriod_yearsCat3ReturnPeriod_yearsCat4ReturnPeriod_yearsCat5ReturnPeriod_yearsPrevTotEnergy_MJ
Column Name:ID  
Contributor First Name  
Contributor Last Name  
Variable Category  
System Name  
System Type  
Variable  
Latitude  
Longitude  
Event Year  
Event Month  
Event Day  
Event  
System Major  
System Minor  
MinDistance_km  
CatAtMinDist  
MaxWind_knots  
maxGust_knots  
minPress_mbar  
QuadAtClosest  
QuadAtClosest  
TropStormDur_hrs  
Cat1Dur_hrs  
Cat2Dur_hrs  
Cat3Dur_hrs  
Cat4Dur_hrs  
Cat5Dur_hrs  
EventEnergy_MJ  
PrevTropStorm_YearsAgo  
PrevCat1_YearsAgo  
PrevCat2_YearsAgo  
PrevCat3_YearsAgo  
PrevCat4_YearsAgo  
PrevCat5_YearsAgo  
TropStormReturnPeriod_years  
HurReturnPeriod_years  
Cat1ReturnPeriod_years  
Cat2ReturnPeriod_years  
Cat3ReturnPeriod_years  
Cat4ReturnPeriod_years  
Cat5ReturnPeriod_years  
PrevTotEnergy_MJ  
Definition:IDContributor First NameContributor Last NameThe category of the response. Classifications include biogeochemistry, hydrography, hydrology, mobile biota, sedentary biota, and phyiscal.Name of the ecosystem where samples were collected.The category of the ecosystems. Classifications include off-shore coastal, in-shore coastal, estuary, river, barrier island, riparian, terrestrial.The name of the response variable.The latitude of the data collection in the case of repeated fixed station sampling. Use a central spatial location if values were aggregated from randomly selected sampling locations within an ecosystem (e.g. embayment) whose precise location changed through time. Format is Signed Degrees Format (DDD.dddd), e.g. -120.9762The longitude of the data collection in the case of repeated fixed station sampling. Use a central spatial location if values were aggregated from randomly selected sampling locations within an ecosystem (e.g. embayment) whose precise location changed through time. Format is Signed Degrees Format (DDD.dddd), e.g. -120.9762Year the event took placeMonth the event took place (numeric), e.g. if October, then enter "10"Day (numeric) the event took place, e.g. if October 22nd, then enter "22"Name of the event (e.g. Hurricane Maria)Either terrestrial or aquaticminor system type is wetland, estuarine, freshwater, marine, or terrestrialThe closest passing of the storm's center to the provided coordinates.The provided category of the storm at the closest distance. This is depednent on the wind speed at the center and not on the calculates wind speed at the site.The estimated maximum wind speed at the given coordinates, given the distance to the center of the storm and the wind speed at the center. This is computed using the specific maximum radius of wind speeds provided by the IBTrACS dataset for each quadrant (NE,SE,SW,NW) of each cetegory of each storm, when available. These variables from IBTrACS are "USA_R34_NE", "USA_R34_SE", "USA_R50_NE" etc., which are the maximum radial distances to sustained 34 and 50 knot winds in each quadrant. When the specific numbers for each storm are not available, mean values are used for similar storms of the same category and same quadrant. The wind speed follows a logarithmic relationship with distance such that PredictedWind = WindAtCenter + m*log(distance +1). The slope (m) is different for each storm category and quadrant. This predicted wind is used to calculate a number of the other variables in the dataset. The estimated maximum wind speed at the given coordinates, given the distance to the center of the storm and the wind speed at the center. This is computed using the specific maximum radius of wind speeds provided by the IBTrACS dataset for each quadrant (NE,SE,SW,NW) of each cetegory of each storm, when available. These variables from IBTrACS are "USA_R34_NE", "USA_R34_SE", "USA_R50_NE" etc., which are the maximum radial distances to sustained 34 and 50 knot winds in each quadrant. When the specific numbers for each storm are not available, mean values are used for similar storms of the same category and same quadrant. The wind speed follows a logarithmic relationship with distance such that PredictedWind = WindAtCenter + m*log(distance +1). The slope (m) is different for each storm category and quadrant. This predicted wind is used to calculate a number of the other variables in the dataset. the minimum atmospheric pressure predicted at the site. Like the wind velocity, the predicted pressure is also a function of the relationship between the distance to the center and the pressure at the center such that PredictedPressure = PressureAtCenter + m*log(distance +1). Again, the slope (m) is dependent on the storm category. These relationships are computed using variables in the IBTrACS data set (USA_PRES is the minimum sea level pressure, assumed to be at the storms center. USA_POCI is the pressure at the outermost closed isobar, or the furthest extent of the storms pressure system. USA_ROCI is the radius to the last closed isobar. The quadrant of the storm over the site at the minimum distance. The quadrant of the storm over the site at the maximum windspeed at the site. The duration of tropical storm force winds (>= 34 knots and < 64 knots) at the site. The duration of Category 1 hurricane force winds (>= 64 knots and < 83 knots) at the site. The duration of Category 2 hurricane force winds (>= 83 knots and < 96 knots) at the site. The duration of Category 3 hurricane force winds (>= 96 knots and < 114 knots) at the site. The duration of Category 4 hurricane force winds (>= 114 knots and < 135 knots) at the site. The duration of Category 5 hurricane force winds (>= 135 knots) at the site. The total tropical storm and hurricane wind kinetic energy at the site. This is dependent on the wind speeds, the air density, and the duration of the winds at the site as calculated by E = 0.5*A*t*p*v^3, where A is the area in m^2, t is the duration in seconds, p is the air density in kg/m^3, and v is the wind velocity in m/s. In this case, the area is constant at 1m^2. The density of the air is calculated as the a function of the pressure, the temperature, and the humidity. Because neither humidity or temperature are available, they are used as constants and the mean values from multiple tropical cyclones (https://journals.ametsoc.org/doi/pdf/10.1175/1520-0493(2000)128%3C1550%3ASOITHE%3E2.0.CO%3B2) are used as .0205 kg/kg and 297.65 K, respectively. Given these constant humidity and temperature values, along with the specific gas constant for dry air (Ra=286.9 J/kg K) and humid air (Rw = 461.5 J/kg K), the density of the air at variable pressure (in Pa) is given as Press/ (Ra*Temp))*(1 + Hum))) / (1 + Hum*(Rw / Ra)) . The density could also be taken as a constant (1.225 kg/m3 at sea level) and this would result in around a 10% difference in the calculated energy. The number of years since the most recent previous tropical storm. The number of years since the most recent category 1 storm. The number of years since the most recent category 2 storm. The number of years since the most recent category 3 storm. The number of years since the most recent category 4 storm. The number of years since the most recent category 5 storm. The average return period for tropical storm force winds in the previous 100 years. The average return period for hurricane force winds in the previous 100 years. The average return period for category 1 storm force winds in the previous 100 years. The average return period for category 2 storm force winds in the previous 100 years. The average return period for category 3 storm force winds in the previous 100 years. The average return period for category 4 storm force winds in the previous 100 years. The average return period for category 5 storm force winds in the previous 100 years. The total energy of all previous tropical storm force winds or greater at the site in the previous 100 years.
Storage Type:string  
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Measurement Type:nominalnominalnominalnominalnominalnominalnominalnominalnominalnominalnominalnominalnominalnominalnominalintervalintervalintervalintervalintervalintervalintervalintervalintervalintervalintervalintervalintervalintervalintervalintervalintervalintervalintervalintervalintervalintervalintervalintervalintervalintervalintervalinterval
Measurement Values Domain:
DefinitionID
DefinitionContributor First Name
DefinitionContributor Last Name
DefinitionThe category of the response. Classifications include biogeochemistry, hydrography, hydrology, mobile biota, sedentary biota, and phyiscal.
DefinitionName of the ecosystem where samples were collected.
DefinitionThe category of the ecosystems. Classifications include off-shore coastal, in-shore coastal, estuary, river, barrier island, riparian, terrestrial.
DefinitionThe name of the response variable.
DefinitionThe latitude of the data collection in the case of repeated fixed station sampling. Use a central spatial location if values were aggregated from randomly selected sampling locations within an ecosystem (e.g. embayment) whose precise location changed through time. Format is Signed Degrees Format (DDD.dddd), e.g. -120.9762
DefinitionThe longitude of the data collection in the case of repeated fixed station sampling. Use a central spatial location if values were aggregated from randomly selected sampling locations within an ecosystem (e.g. embayment) whose precise location changed through time. Format is Signed Degrees Format (DDD.dddd), e.g. -120.9762
DefinitionYear the event took place
DefinitionMonth the event took place (numeric), e.g. if October, then enter "10"
DefinitionDay (numeric) the event took place, e.g. if October 22nd, then enter "22"
DefinitionName of the event (e.g. Hurricane Maria)
DefinitionEither terrestrial or aquatic
Definitionminor system type is wetland, estuarine, freshwater, marine, or terrestrial
Unitkm
Precision0.0001
Typereal
UnitSaffir-Simpson Category
Precision0.0001
Typereal
Unitknots
Precision0.0001
Typereal
Unitknots
Precision0.0001
Typereal
Unitmili bar
Precision0.0001
Typereal
UnitCardinal Directions
Precision0.0001
Typereal
UnitCardinal Directions
Precision0.0001
Typereal
Unithours
Precision0.01
Typereal
Unithours
Precision0.01
Typereal
Unithours
Precision0.01
Typereal
Unithours
Precision0.01
Typereal
Unithours
Precision0.01
Typereal
Unithours
Precision0.01
Typereal
UnitMega Joules
Precision0.0001
Typereal
Unityears
Precision0.0001
Typereal
Unityears
Precision0.0001
Typereal
Unityears
Precision0.0001
Typereal
Unityears
Precision0.0001
Typereal
Unityears
Precision0.0001
Typereal
Unityears
Precision0.0001
Typereal
Unityears
Precision0.0001
Typereal
Unityears
Precision0.0001
Typereal
Unityears
Precision0.0001
Typereal
Unityears
Precision0.0001
Typereal
Unityears
Precision0.0001
Typereal
Unityears
Precision0.0001
Typereal
Unityears
Precision0.0001
Typereal
UnitMega Joules
Precision0.0001
Typereal
Missing Value Code:                                                                                      
Accuracy Report:                                                                                      
Accuracy Assessment:                                                                                      
Coverage:                                                                                      
Methods:                                                                                      

Data Table

Data:
 Inline Data
Name:Ecosystem responses to hurricanes L0 validated data
Description:Tropical cyclones play an increasingly important role in shaping ecosystems. Understanding and generalizing their responses is challenging because of meteorological variability among storms and its interaction with ecosystems. Data here represent the responses of ecosystems to hurricanes from across North America, the Caribbean and Taiwan. Data across a variety of ecosystems are represented here including wetlands, estuaries, marine, terrestrial and fresh water systems. These data measure a variety of different parameters to understand ecosystem response such as biogeochemical, hydrological and physical measurements as well as the abundance of mobile and sedentary biota.
Number of Records:29658
Number of Columns:43

Table Structure
Object Name:EcosystemResponsesToHurricanesSynthesis-ERTHS_L0.csv
Size:13388836
Text Format:
Number of Header Lines:1
Record Delimiter:\r\n
Line Delimiter:\r\n
Orientation:column
Simple Delimited:
Field Delimiter:,
Quote Character:"

Table Column Descriptions
 IDContributor First NameContributor Last NameSource of FundingAward Number (if applicable)Variable CategorySystem NameSystem TypeVariableUnitsLatitudeLongitudeEvent YearEvent MonthEvent DayEventPre-Storm Sampling Interval (Days)Post-Storm Sampling Interval (Days)Pre-Storm Sampling Series Length (Days)Post-Storm Sampling Series Length (Days)Max/Min Value Post-HurricaneSD Max/Min (NaN if no replication)Baseline ValueSD Baseline Value (NaN if no replication)Observed ChangeProportional Change %Return Time (Days)Adjusted Return Time (Days)LogResponseRatioLong-Term Average Max/Min (NaN if none available)Long-Term SD Max/Min - (NaN if none available or no replication)Historic Period Length (years) - NaN if none availableHistorical Observed ChangeHistorical Proportional Change %Historical LogResponseRatioNotesNotes 2System MajorSystem Minorabsolute proportional changeVariable Sub-CategoryResponse SignLRR ratio as magnitude with sign added
Column Name:ID  
Contributor First Name  
Contributor Last Name  
Source of Funding  
Award Number (if applicable)  
Variable Category  
System Name  
System Type  
Variable  
Units  
Latitude  
Longitude  
Event Year  
Event Month  
Event Day  
Event  
Pre-Storm Sampling Interval (Days)  
Post-Storm Sampling Interval (Days)  
Pre-Storm Sampling Series Length (Days)  
Post-Storm Sampling Series Length (Days)  
Max/Min Value Post-Hurricane  
SD Max/Min (NaN if no replication)  
Baseline Value  
SD Baseline Value (NaN if no replication)  
Observed Change  
Proportional Change %  
Return Time (Days)  
Adjusted Return Time (Days)  
LogResponseRatio  
Long-Term Average Max/Min (NaN if none available)  
Long-Term SD Max/Min - (NaN if none available or no replication)  
Historic Period Length (years) - NaN if none available  
Historical Observed Change  
Historical Proportional Change %  
Historical LogResponseRatio  
Notes  
Notes 2  
System Major  
System Minor  
absolute proportional change  
Variable Sub-Category  
Response Sign  
LRR ratio as magnitude with sign added  
Definition:IDContributor First NameContributor Last NameSource of FundingAward Number (if applicable)The category of the response. Classifications include biogeochemistry, hydrography, hydrology, mobile biota, sedentary biota, and phyiscal.Name of the ecosystem where samples were collected.The category of the ecosystems. Classifications include off-shore coastal, in-shore coastal, estuary, river, barrier island, riparian, terrestrial.The name of the response variable.The units of the response variable (if applicable).The latitude of the data collection in the case of repeated fixed station sampling. Use a central spatial location if values were aggregated from randomly selected sampling locations within an ecosystem (e.g. embayment) whose precise location changed through time. Format is Signed Degrees Format (DDD.dddd), e.g. -120.9762The longitude of the data collection in the case of repeated fixed station sampling. Use a central spatial location if values were aggregated from randomly selected sampling locations within an ecosystem (e.g. embayment) whose precise location changed through time. Format is Signed Degrees Format (DDD.dddd), e.g. -120.9762Year the event took placeMonth the event took place (numeric), e.g. if October, then enter "10"Day (numeric) the event took place, e.g. if October 22nd, then enter "22"Name of the event (e.g. Hurricane Maria)Sampling frequency for pre-storm data collection. If high frequency, e.g. 15 minute, then use fractional values such as 0.01041Sampling frequency for post-storm data collection. If high frequency, e.g. 15 minute, then use fractional values such as 0.01041Sampling period for pre-storm data collection. E.g. if 6 months of pre-storm data then report 180 daysSampling period for post-storm data collection. E.g. if 12 months of post-storm data then report 365 daysThe minimum or maximum value of the response variable after the hurricane. In the case of multiple peaks and valleys following the storm (no clear trend), the first peak or valley post storm was used (immediate response).If the minimum or maximum response was calculated from replicate samples, the standard deviation of those samples is reported here.The value of the response variable before the event. In the case of daily or weekly data, the mean value for month immediately before the event should be used account for natural variation.The standard deviation of the baseline value. In the case of temporal data, this is the standard devation for the month,week, etc for the period preceding the event (whichever is most appropriate or available). In the case of spatially replicated data taken before the storm this is the standard deviation of the replicates.The difference between the post-storm max/min and pre-storm baseline.The difference between the post-storm max/min and pre-storm baseline divided by the pre-storm baseline.The number of days post storm for values to return the baseline.In the case of variables that had not returned to baseline at end of sampling, assign the maximum post-event observation period in days. If return to baseline has been observed, then simply re-enter the value in the adjacent "Return Time (Days)" cellThe natural log of the min/max post hurricane value divided by the baseline value.The average value of the response variable, historically, at the time of year of first post storm sampling. For example, if monthly data was used to estimate the change in response between August 2017 (pre-storm for Hurricane Harvey) and September 2017 (post-storm for Hurricane Harvey), then this cell would contain the average value for September based on historical data.The standard deviation of the response variable, historically, at the time of year of first post storm sampling. For example, if monthly data was used to estimate the change in response between August 2017 (pre-storm for Hurricane Harvey) and September 2017 (post-storm for Hurricane Harvey), then this cell would contain the average value for September based on historical data.Number of years of data used to calculate the long term average valuesTypical change in response variable between the time of year when the storm occurred and the time of year when first post storm sampling occurredTypical percent change in response variable between the time of year when the storm occurred and the time of year when first post storm sampling occurredThe natural log of the long-term average min/max value divided by the baseline value.Any notes about the data set or values entered that the investigator believe are important. These may include caveates or concerns about estimate precision, moving baseline, etc, etcadditional notes fieldEither terrestrial or aquaticminor system type is wetland, estuarine, freshwater, marine, or terrestrialabsolute value of % proportional change, then a scalar value of 1 was added to force all values to be >= 1Variable Sub-CategorySign of % proportional change 1 if positive, -1 if negativeLog response ratio as a magnitude with sign added (for ease of interpretation)
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Measurement Type:nominalnominalnominalnominalnominalnominalnominalnominalnominalnominalnominalnominalnominalnominalnominalnominalnominalnominalnominalnominalnominalnominalnominalnominalnominalnominalnominalnominalnominalnominalnominalnominalnominalnominalnominalnominalnominalnominalnominalnominalnominalratioratio
Measurement Values Domain:
DefinitionID
DefinitionContributor First Name
DefinitionContributor Last Name
DefinitionSource of Funding
DefinitionAward Number (if applicable)
DefinitionThe category of the response. Classifications include biogeochemistry, hydrography, hydrology, mobile biota, sedentary biota, and phyiscal.
DefinitionName of the ecosystem where samples were collected.
DefinitionThe category of the ecosystems. Classifications include off-shore coastal, in-shore coastal, estuary, river, barrier island, riparian, terrestrial.
DefinitionThe name of the response variable.
DefinitionThe units of the response variable (if applicable).
DefinitionThe latitude of the data collection in the case of repeated fixed station sampling. Use a central spatial location if values were aggregated from randomly selected sampling locations within an ecosystem (e.g. embayment) whose precise location changed through time. Format is Signed Degrees Format (DDD.dddd), e.g. -120.9762
DefinitionThe longitude of the data collection in the case of repeated fixed station sampling. Use a central spatial location if values were aggregated from randomly selected sampling locations within an ecosystem (e.g. embayment) whose precise location changed through time. Format is Signed Degrees Format (DDD.dddd), e.g. -120.9762
DefinitionYear the event took place
DefinitionMonth the event took place (numeric), e.g. if October, then enter "10"
DefinitionDay (numeric) the event took place, e.g. if October 22nd, then enter "22"
DefinitionName of the event (e.g. Hurricane Maria)
DefinitionSampling frequency for pre-storm data collection. If high frequency, e.g. 15 minute, then use fractional values such as 0.01041
DefinitionSampling frequency for post-storm data collection. If high frequency, e.g. 15 minute, then use fractional values such as 0.01041
DefinitionSampling period for pre-storm data collection. E.g. if 6 months of pre-storm data then report 180 days
DefinitionSampling period for post-storm data collection. E.g. if 12 months of post-storm data then report 365 days
DefinitionThe minimum or maximum value of the response variable after the hurricane. In the case of multiple peaks and valleys following the storm (no clear trend), the first peak or valley post storm was used (immediate response).
DefinitionIf the minimum or maximum response was calculated from replicate samples, the standard deviation of those samples is reported here.
DefinitionThe value of the response variable before the event. In the case of daily or weekly data, the mean value for month immediately before the event should be used account for natural variation.
DefinitionThe standard deviation of the baseline value. In the case of temporal data, this is the standard devation for the month,week, etc for the period preceding the event (whichever is most appropriate or available). In the case of spatially replicated data taken before the storm this is the standard deviation of the replicates.
DefinitionThe difference between the post-storm max/min and pre-storm baseline.
DefinitionThe difference between the post-storm max/min and pre-storm baseline divided by the pre-storm baseline.
DefinitionThe number of days post storm for values to return the baseline.
DefinitionIn the case of variables that had not returned to baseline at end of sampling, assign the maximum post-event observation period in days. If return to baseline has been observed, then simply re-enter the value in the adjacent "Return Time (Days)" cell
DefinitionThe natural log of the min/max post hurricane value divided by the baseline value.
DefinitionThe average value of the response variable, historically, at the time of year of first post storm sampling. For example, if monthly data was used to estimate the change in response between August 2017 (pre-storm for Hurricane Harvey) and September 2017 (post-storm for Hurricane Harvey), then this cell would contain the average value for September based on historical data.
DefinitionThe standard deviation of the response variable, historically, at the time of year of first post storm sampling. For example, if monthly data was used to estimate the change in response between August 2017 (pre-storm for Hurricane Harvey) and September 2017 (post-storm for Hurricane Harvey), then this cell would contain the average value for September based on historical data.
DefinitionNumber of years of data used to calculate the long term average values
DefinitionTypical change in response variable between the time of year when the storm occurred and the time of year when first post storm sampling occurred
DefinitionTypical percent change in response variable between the time of year when the storm occurred and the time of year when first post storm sampling occurred
DefinitionThe natural log of the long-term average min/max value divided by the baseline value.
DefinitionAny notes about the data set or values entered that the investigator believe are important. These may include caveates or concerns about estimate precision, moving baseline, etc, etc
Definitionadditional notes field
DefinitionEither terrestrial or aquatic
Definitionminor system type is wetland, estuarine, freshwater, marine, or terrestrial
Definitionabsolute value of % proportional change, then a scalar value of 1 was added to force all values to be >= 1
DefinitionVariable Sub-Category
UnitSign of % proportional change 1 if positive, -1 if negative
Precision1
Typeinteger
UnitLog response ratio as a magnitude with sign added (for ease of interpretation)
Precision0.00001
Typereal
Missing Value Code:                                                                                      
Accuracy Report:                                                                                      
Accuracy Assessment:                                                                                      
Coverage:                                                                                      
Methods:                                                                                      

Non-Categorized Data Resource

Name:ERTHS additional metadata
Entity Type:document
Description:Excel file with metadata additional metadata describing variables, units, variable subcategories and other fields
Physical Structure Description:
Object Name:EcosystemResponsesToHurricanesSynthesis-ERTHS-AdditionalMetadata.xlsx
Size:80571
Externally Defined Format:
Format Name:application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
Data:https://pasta-s.lternet.edu/package/data/eml/edi/493/12/b0a997b39bc9c4fab1783bf8a3cb1e11

Non-Categorized Data Resource

Name:ERTHS additional metadata for event wind statistics variable descriptions
Entity Type:document
Description:Excel file with metadata additional metadata describing event wind statistics variable descriptions
Physical Structure Description:
Object Name:ERTHS-Event Wind Stats Variable Descriptions.xlsx
Size:57896
Externally Defined Format:
Format Name:application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
Data:https://pasta-s.lternet.edu/package/data/eml/edi/493/12/2cd546df95c7030291ada0af63a27642

Data Package Usage Rights

Data Policies

		  This data package is released to the “public domain” under Creative Commons CC0 1.0 “No Rights Reserved” (see: https://creativecommons.org/publicdomain/zero/1.0/). It is considered professional etiquette to provide attribution of the original work if this data package is shared in whole or by individual components. A generic citation is provided for this data package on the website https://portal.edirepository.org (herein “website”) in the summary metadata page. Communication (and collaboration) with the creators of this data package is recommended to prevent duplicate research or publication. This data package (and its components) is made available “as is” and with no warranty of accuracy or fitness for use. The creators of this data package and the website shall not be liable for any damages resulting from misinterpretation or misuse of the data package or its components. Periodic updates of this data package may be available from the website. Thank you.

Keywords

By Thesaurus:
Core Areasdisturbance
LTER Controlled Vocabularyecosystem ecology, productivity

Methods and Protocols

These methods, instrumentation and/or protocols apply to all data in this dataset:

Methods and protocols used in the collection of this data package
Description:

Ecosystem response to hurricanes data were collected from 42 different researchers across 142 monitored ecosystem sites where 289 different observation types were inspected for responses to hurricanes that effected the sites. These ecosystem responses span North America, the Caribbean, and Taiwan. Data were reorganized and reclassified to be more consistent between entries and to achieve the ecosystem groupings we are looking for. Variables were divided into groups of Biogeochemistry, Physical, Sedentary Biota, Mobile Biota and Microfauna.  Ecosystems were classified into groups of Forest, Lotic, Estuarine/In-Shore waters, Estuarine marshes, Riparian, and Off-Shore. Data was quality checked and site locations where verified.

People and Organizations

Creators:
Individual: Miguel Leon
Organization:Department of Natural Resources and the Environment, University of New Hampshire
Individual: Christopher Patrick
Organization:Biology Department, Virginia Institute of Marine Science, College of William and Mary
Individual: Benjamin Branoff
Organization:National Institute for Mathematical and Biological Synthesis, University of Tennessee Knoxville
Individual: John Kominoski
Organization:Institute of Environment, Florida International University
Individual: Anna Armitage
Organization:Department of Marine Biology, Texas A&M University at Galveston
Individual: Marconi Campos-Cerqueira
Organization:Sieve Analytics, San Juan, PR
Individual: María Chapela Lara
Organization:Department of Natural Resources and the Environment, University of New Hampshire
Individual: Victoria Congdon
Organization:Department of Marine Science, University of Texas at Austin
Individual: Todd Crowl
Organization:Institute of Environment, Florida International University
Individual: Donna Devlin
Organization:Department of Life Sciences, Texas A&M University Corpus Christi
Individual: Sarah Douglas
Organization:Marine Sciences Institute, University of Texas at Austin
Individual: Brad Erisman
Organization:Marine Sciences Institute, University of Texas at Austin
Individual: Russell Feagin
Organization:Department of Ocean Engineering, Texas A&M University
Individual: Mark Fisher
Organization:Texas Parks and Wildlife
Individual: Simon Geist
Organization:Department of Life Sciences, Texas A&M University Corpus Christi
Individual: Nathan Hall
Organization:University of North Carolina at Chapel Hill, Institute of Marine Sciences
Individual: Amber Hardison
Organization:University of Texas at Austin, Department of Marine Science
Individual: James Aaron Hogan
Organization:Institute of Environment, Florida International University
Individual: James Derek Hogan
Organization:Texas A&M University Corpus Christi, Department of Life Sciences
Individual: Teng-Chiu Lin
Organization:National Taiwan Normal University, Department of Life Sciences
Individual: Xianbin Liu
Organization:The Institute for Tropical Ecosystem Studies (ITES), University of Puerto Rico
Individual: Kaijun Lu
Organization:Marine Sciences Institute, University of Texas at Austin
Individual: Paul Montagna
Organization:Harte Research Institute for Gulf of Mexico Studies, Texas A&M University-Corpus Christi
Individual: Christine O'Connell
Organization:Environmental Studies, Macalester College
Individual: Steven Pennings
Organization:Department of Biology and Biochemistry, University of Houston
Individual: C Proffitt
Organization:Department of Life Sciences, Texas A&M University Corpus Christi
Individual: Jennifer Rehage
Organization:Institute of Environment, Florida International University
Individual: Joseph Reustle
Organization:Department of Life Sciences, Texas A&M University Corpus Christi
Individual: Kelly Robinson
Organization:Department of Biology, University of Louisiana at Lafayette
Individual: Scott Rush
Organization:Department of Wildlife, Fisheries, and Aquaculture, Mississippi State University
Individual: Rolando Santos
Organization:Institute of Environment, Florida International University
Individual: Rachel Smith
Organization:Odum School of Ecology, University of Georgia
Individual: Gregory Starr
Organization:Department of Biological Sciences, University of Alabama
Individual: Theresa Strazisar
Organization:Biological Sciences Department, Florida Atlantic University
Individual: Bradley Strickland
Organization:Institute of Environment, Florida International University
Individual: Michael Wetz
Organization:Harte Research Institute for Gulf of Mexico Studies, Texas A&M University-Corpus Christi
Individual: Stephen Kelly
Organization:Department of Biology, University of Louisiana at Lafayette
Individual: Sara Wilson
Organization:Institute of Environment, Florida International University
Individual: Hu Xinping
Organization:Marine Sciences Institute, University of Texas at Austin
Individual: Jianhong Xue
Organization:Marine Sciences Institute, University of Texas at Austin
Individual: Lauren Yeager
Organization:Marine Sciences Institute, University of Texas at Austin
Individual: Xiaoming Zou
Organization:Institute for Tropical Ecosystem Studies, University of Puerto Rico, Rio Piedras Campus
Address:
University of Puerto Rico at Rio Piedras, Ponce de Leon Ave.,,
San Juan, PR 00931 US
Phone:
(787) 764-0000 x2868 (voice)
Phone:
(787) 772-1481 (facsimile)
Email Address:
xzou2011@gmail.com
Individual: William McDowell
Organization:Department of Natural Resources and the Environment, University of New Hampshire
Contacts:
Individual: Miguel Leon
Organization:Department of Natural Resources and the Environment, University of New Hampshire

Temporal, Geographic and Taxonomic Coverage

Temporal, Geographic and/or Taxonomic information that applies to all data in this dataset:

Time Period
Begin:
1985-07-25
End:
2018-09-13
Geographic Region:
Description:Data here represent the responses of ecosystems to hurricanes from across North America, the Caribbean and Taiwan. Data across a variety of ecosystems are represented here including wetlands, estuaries, marine, terrestrial and fresh water systems.
Bounding Coordinates:
Northern:  -90Southern:  90
Western:  -180Eastern:  180

Project

Parent Project Information:

Title: Ecosystem Responses to Hurricanes Synthesis Workshop
Personnel:
Individual:Dr. Christopher Patrick
Address:
William and Mary, Virginia Institue of Marine Science,
1370 Greate Rd.,
Gloucester Point, Virginia 23062 United States
Email Address:
cjpatster@gmail.com
Role:Principal Investigator
Abstract:

Three major hurricanes (Harvey, Irma, and Maria) made landfall in the United States during the fall of 2017. Catastrophic human, economic, and ecological effects occurred from the storm surge, saltwater intrusion, wind damage, and flooding. The number of storms, their intensity, the number that made landfall, and the catastrophic damage was remarkable. Global models predict hurricanes will increase and effected areas will change over the next century. Thus, synthesizing responses to severe tropical storm disturbances is needed to understand general patterns of impact and recovery. This award will provide workshop funds for researchers, experts, managers, and graduate students to develop a synthetic understanding of hurricane impacts on freshwater, estuarine, and terrestrial ecosystems. The proposed workshop will provide important opportunities for career advancement and training of underrepresented and early career scientists. The unprecedented landfall of multiple major hurricanes in the United States presents a rare opportunity to document generalizable patterns in ecosystem response to extreme disturbance. The workshop will leverage the significant investment of the National Science Foundation in Hurricane Research following the devastating impacts of storms Harvey, Irma, and Maria in 2017 to generate a novel cross-system disturbance ecology synthesis. The synthesis will result from three major activities including: recruitment and workflow design, a three-day workshop, and post-workshop analysis and synthesis. The workshop will bring together research teams studying multiple ecosystem types (estuarine, freshwater, terrestrial) and ecosystem responses (physical, biogeochemical, organismal ? mobile vs sedentary, microbial, animal, and plant) to these hurricanes. The participants will merge diverse datasets into a common statistical framework and conduct synthetic analyses to identify shared and unique responses to different types of hurricane stressors. Both workshop and post workshop activities will yield an understanding of how ecosystems respond to severe disturbance and contribute a generalizable conceptual framework that is applicable across ecosystems and ecosystem components.

Funding:

NSF Award DEB-1903760

Maintenance

Maintenance:
Description:
Frequency:
Other Metadata

EDI is a collaboration between the University of New Mexico and the University of Wisconsin – Madison, Center for Limnology:

UNM logo UW-M logo