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.493.9
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 L0 validated data with 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 L0 validated data with 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 L0 validated data with 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 L0 validated data with 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 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.

Detailed Metadata

Data Entities

Data Table


Data:
	Inline Data
Name:	Ecosystem responses to hurricanes L0 validated data with 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:

Number of Header Lines:

Record Delimiter:

\r\n

Line Delimiter:

\r\n

Orientation:

column

Simple Delimited:

Field Delimiter:	,
Quote Character:	"

Table Column Descriptions

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

Column Name:

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:

Contributor First Name

Contributor Last Name

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 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

The 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

Year the event took place

Month 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 aquatic

minor system type is wetland, estuarine, freshwater, marine, or terrestrial

rainBaseline_2monthAnnual_countCHIRPS

Maximum daily rainfall between 60 days before to day of event in DOY for every year in the record prior to the event year

Sum of daily rainfall from 60 days before to day of event in DOY for every year in the record prior to the event year

Number of observations between 60 days before.

Maximum daily rainfall between 60 days before to 2 days after event

Sum of daily rainfall from 60 days before event

Number of observations between 2 days before and 2 days after event

Maximum daily rainfall between 2 days before to 2 days after event

Sum of daily rainfall from 2 days before to 2 days after event

Storage Type:

string

float

Measurement Type:

nominal

ratio

Measurement Values Domain:

Definition

Contributor First Name

Definition

Contributor Last Name

Definition

The category of the response. Classifications include biogeochemistry, hydrography, hydrology, mobile biota, sedentary biota, and phyiscal.

Definition

Name of the ecosystem where samples were collected.

Definition

The category of the ecosystems. Classifications include off-shore coastal, in-shore coastal, estuary, river, barrier island, riparian, terrestrial.

Definition

The name of the response variable.

Definition

Year the event took place

Definition

Month the event took place (numeric), e.g. if October, then enter "10"

Definition

Day (numeric) the event took place, e.g. if October 22nd, then enter "22"

Definition

Name of the event (e.g. Hurricane Maria)

Definition

Either terrestrial or aquatic

Definition

minor system type is wetland, estuarine, freshwater, marine, or terrestrial

Unit	count
Precision	1
Type	integer

Unit	mm per day
Precision	0.0001
Type	real

Unit	sum mm per day
Precision	0.00001
Type	real

Unit	count
Precision	1
Type	integer

Unit	count
Precision	1
Type	integer

Unit	sum mm per day
Precision	0.00001
Type	real

Unit	count
Precision	1
Type	integer

Unit	mm per day
Precision	0.0001
Type	real

Unit	sum mm per day
Precision	0.0001
Type	real

Missing Value Code:

Accuracy Report:

Accuracy Assessment:

Coverage:

Methods:

Data Table


Data:
	Inline Data
Name:	Ecosystem responses to hurricanes L0 validated data with 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:

HurricaneWorkshopData_L2_DAYMET.csv

Size:

47038666

Text Format:

Number of Header Lines:

Record Delimiter:

\r\n

Line Delimiter:

\r\n

Orientation:

column

Simple Delimited:

Field Delimiter:	,
Quote Character:	"

Table Column Descriptions

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

Column Name:

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:

Contributor First Name

Contributor Last Name

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.

Year the event took place

Month 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 aquatic

minor system type is wetland, estuarine, freshwater, marine, or terrestrial

rainBaseline_2monthAnnual_countDAYMET

Maximum daily rainfall between 60 days before to day of event in DOY for every year in the record prior to the event year

Sum of daily rainfall from 60 days before to day of event in DOY for every year in the record prior to the event year

Number of observations between 60 days before.

Maximum daily rainfall between 60 days before to 2 days after event

Sum of daily rainfall from 60 days before event

Number of observations between 2 days before and 2 days after event

Maximum daily rainfall between 2 days before to 2 days after event

Sum of daily rainfall from 2 days before to 2 days after event

Maximum daily temperature between 60 days before to day of event in DOY for every year in the record prior to the event year year

Sum of daily temperature from 60 days before to day of event in DOY for every year in the record prior to the event year

Maximum daily temperature between 60 days before to 2 days after event

Sum of daily temperature from 60 days before to 2 days before event

Maximum daily temperature between 2 days before to 2 days after event

Sum of daily temperature from 2 days before to 2 days after event

At 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

float

string

Measurement Type:

nominal

interval

nominal

Measurement Values Domain:

Definition

Contributor First Name

Definition

Contributor Last Name

Definition

The category of the response. Classifications include biogeochemistry, hydrography, hydrology, mobile biota, sedentary biota, and phyiscal.

Definition

Name of the ecosystem where samples were collected.

Definition

The category of the ecosystems. Classifications include off-shore coastal, in-shore coastal, estuary, river, barrier island, riparian, terrestrial.

Definition

The name of the response variable.

Definition

Year the event took place

Definition

Month the event took place (numeric), e.g. if October, then enter "10"

Definition

Day (numeric) the event took place, e.g. if October 22nd, then enter "22"

Definition

Name of the event (e.g. Hurricane Maria)

Definition

Either terrestrial or aquatic

Definition

minor system type is wetland, estuarine, freshwater, marine, or terrestrial

Unit	count
Precision	1
Type	integer

Unit	mm per day
Precision	0.0001
Type	real

Unit	sum mm per day
Precision	0.00001
Type	real

Unit	count
Precision	1
Type	integer

Unit	count
Precision	1
Type	integer

Unit	sum mm per day
Precision	0.00001
Type	real

Unit	count
Precision	1
Type	integer

Unit	mm per day
Precision	0.0001
Type	real

Unit	sum mm per day
Precision	0.0001
Type	real

Unit	mm per day
Precision	0.0001
Type	real

Unit	sum mm per day
Precision	0.00001
Type	real

Unit	mm per day
Precision	0.0001
Type	real

Unit	sum mm per day
Precision	0.00001
Type	real

Unit	mm per day
Precision	0.0001
Type	real

Unit	sum mm per day
Precision	0.0001
Type	real

Definition

geographic bounds around sampling point.

Missing Value Code:

Accuracy Report:

Accuracy Assessment:

Coverage:

Methods:

Data Table


Data:
	Inline Data
Name:	Ecosystem responses to hurricanes L0 validated data with 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

Object Name:

HurricaneWorkshopData_L2_GRIDMET.csv

Size:

49869091

Text Format:

Number of Header Lines:

Record Delimiter:

\r\n

Line Delimiter:

\r\n

Orientation:

column

Simple Delimited:

Field Delimiter:	,
Quote Character:	"

Table Column Descriptions

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