This data package was submitted to a development environment for testing purposes only. Use of these data for anything other than testing is strongly discouraged.

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Name: Ecosystem Responses to Hurricanes across North America, the Caribbean, and Taiwan; 1985 to 2018
Published: 2024-07-16

Title:

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

Creators:

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

Publication Date:

2024-07-16

Citation:

Leon, M., C. Patrick, B. Branoff, J. Kominoski, A. Armitage, M. Campos-Cerqueira, M. Chapela Lara, V. Congdon, T. Crowl, D. Devlin, S. Douglas, B. Erisman, R. Feagin, M. Fisher, S. Geist, N. Hall, A. Hardison, J. Hogan, J. Hogan, T. Lin, X. Liu, K. Lu, P. Montagna, C. O'Connell, S. Pennings, C. Proffitt, J. Rehage, J. Reustle, K. Robinson, S. Rush, R. Santos, R. Smith, G. Starr, T. Strazisar, B. Strickland, M. Wetz, S. Kelly, S. Wilson, H. Xinping, J. Xue, L. Yeager, X. Zou, and W. McDowell. 2024. Ecosystem Responses to Hurricanes across North America, the Caribbean, and Taiwan; 1985 to 2018 ver 16. Environmental Data Initiative. https://doi.org/DOI_PLACE_HOLDER (Accessed 2024-11-21).

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 distance 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.

Package ID:

edi.493.16 (Uploaded 2024-07-16)

Resources:

View Full Metadata (25 views)
View Quality Report
Data Entities:
1. ERTHS additional metadata (78.7 KiB; 0 downloads)
2. ERTHS additional metadata for event wind statistics variable descriptions (56.5 KiB; 0 downloads)

Intellectual Rights:

Data Policies This data package is released to the “public domain” under Creative Commons CCBY 4.0 “Attribution 4.0 International (CC BY 4.0) ” (see: https://creativecommons.org/licenses/by/4.0/). You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. 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.

Digital Object Identifier:

DOI PLACE HOLDER

Provenance:

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Journal Citations:

This data package was submitted to a development environment for testing purposes only. Use of these data for anything other than testing is strongly discouraged.

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