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.493.6
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, Aaron (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.
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.
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.
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.
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:	https://pasta-s.lternet.edu/package/data/eml/edi/493/6/04ff07fbb02cb13b6331aa7bfb53fd94
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.
Number of Records:	29658
Number of Columns:	52

Table Structure

Object Name:

HurricaneWorkshopData_L0_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

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

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

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

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

Source of Funding

Award 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. -120.9762

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)

Sampling frequency for pre-storm data collection. If high frequency, e.g. 15 minute, then use fractional values such as 0.01041

Sampling frequency for post-storm data collection. If high frequency, e.g. 15 minute, then use fractional values such as 0.01041

Sampling period for pre-storm data collection. E.g. if 6 months of pre-storm data then report 180 days

Sampling period for post-storm data collection. E.g. if 12 months of post-storm data then report 365 days

The 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)" cell

The 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 values

Typical change in response variable between the time of year when the storm occurred and the time of year when first post storm sampling occurred

Typical 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

The 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, etc

additional notes field

Either terrestrial or aquatic

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

absolute value of % proportional change, then a scalar value of 1 was added to force all values to be >= 1

Variable Sub-Category

Sign of % proportional change 1 if positive, -1 if negative

Log response ratio as a magnitude with sign added (for ease of interpretation)

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

Source of Funding

Definition

Award Number (if applicable)

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

The units of the response variable (if applicable).

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

Sampling frequency for pre-storm data collection. If high frequency, e.g. 15 minute, then use fractional values such as 0.01041

Definition

Sampling frequency for post-storm data collection. If high frequency, e.g. 15 minute, then use fractional values such as 0.01041

Definition

Sampling period for pre-storm data collection. E.g. if 6 months of pre-storm data then report 180 days

Definition

Sampling period for post-storm data collection. E.g. if 12 months of post-storm data then report 365 days

Definition

If the minimum or maximum response was calculated from replicate samples, the standard deviation of those samples is reported here.

Definition

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.

Definition

The difference between the post-storm max/min and pre-storm baseline.

Definition

The difference between the post-storm max/min and pre-storm baseline divided by the pre-storm baseline.

Definition

The number of days post storm for values to return the baseline.

Definition

The natural log of the min/max post hurricane value divided by the baseline value.

Definition

Number of years of data used to calculate the long term average values

Definition

Typical change in response variable between the time of year when the storm occurred and the time of year when first post storm sampling occurred

Definition

Typical 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

Definition

The natural log of the long-term average min/max value divided by the baseline value.

Definition

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, etc

Definition

additional notes field

Definition

Either terrestrial or aquatic

Definition

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

Definition

absolute value of % proportional change, then a scalar value of 1 was added to force all values to be >= 1

Definition

Variable Sub-Category

Definition

Sign of % proportional change 1 if positive, -1 if negative

Definition

Log response ratio as a magnitude with sign added (for ease of interpretation)

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:	https://pasta-s.lternet.edu/package/data/eml/edi/493/6/e7080b7cc2fda8ebd5faa96cf3abe0e4
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.
Number of Records:	29658
Number of Columns:	59

Table Structure

Object Name:

HurricaneWorkshopData_L0_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

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

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