Monthly OpenET Image Collections (v2.0) Summarized by 12-Digit Hydrologic Unit Codes, 2008-2023

This dataset provides monthly summaries of evapotranspiration (ET) data from OpenET v2.0 image collections for the period 2008-2023 for all National Watershed Boundary Dataset subwatersheds (12-digit hydrologic unit codes [HUC12s]) in the US that overlap the spatial extent of OpenET datasets. For ea...

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Hauptverfasser: Jordan L Wilson, Peter ReVelle, MacKenzie O Friedrichs, Charles Morton, Will Carrara, Bruno Andrade, Matt Bromley, Christian Dunkerly, Alberto Guzman, Gregory Halverson, Jordan Harding, Leonardo Laipelt, Blake Minor, Gabriel E Parrish, Christopher Pearson, John Volk, Yun Yang, Richard Allen, Martha Anderson, Conor Doherty, Joshua Fisher, Justin Huntington, Lee Johnson, Yanghui Kang, Ayse Kilic, Kyle Knipper, Forrest Melton, Francisco Munoz-Arriola, Samuel Ortega-Salazar, Adam Purdy, Anderson Ruhoff, Mitch Schull, Gabriel Senay, Garshaw Amidi-Abraham, Robyn Grimm, Maurice Hall, Rachel O'Connor, Gopal Penny, Jonathan Seefeldt
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creator Jordan L Wilson
Peter ReVelle
MacKenzie O Friedrichs
Charles Morton
Will Carrara
Bruno Andrade
Matt Bromley
Christian Dunkerly
Alberto Guzman
Gregory Halverson
Jordan Harding
Leonardo Laipelt
Blake Minor
Gabriel E Parrish
Christopher Pearson
John Volk
Yun Yang
Richard Allen
Martha Anderson
Conor Doherty
Joshua Fisher
Justin Huntington
Lee Johnson
Yanghui Kang
Ayse Kilic
Kyle Knipper
Forrest Melton
Francisco Munoz-Arriola
Samuel Ortega-Salazar
Adam Purdy
Anderson Ruhoff
Mitch Schull
Gabriel Senay
Garshaw Amidi-Abraham
Robyn Grimm
Maurice Hall
Rachel O'Connor
Gopal Penny
Jonathan Seefeldt
description This dataset provides monthly summaries of evapotranspiration (ET) data from OpenET v2.0 image collections for the period 2008-2023 for all National Watershed Boundary Dataset subwatersheds (12-digit hydrologic unit codes [HUC12s]) in the US that overlap the spatial extent of OpenET datasets. For each HUC12, this dataset contains spatial aggregation statistics (minimum, mean, median, and maximum) for each of the ET variables from each of the publicly available image collections from OpenET for the six available models (DisALEXI, eeMETRIC, geeSEBAL, PT-JPL, SIMS, SSEBop) and the Ensemble image collection, which is a pixel-wise ensemble of all 6 individual models after filtering and removal of outliers according to the median absolute deviation approach (Melton and others, 2022). Data are available in this data release in two different formats: comma-separated values (CSV) and parquet, a high-performance format that is optimized for storage and processing of columnar data. CSV files containing data for each 4-digit HUC are grouped by 2-digit HUCs for easier access of regional data, and the single parquet file provides convenient access to the entire dataset.    For each of the ET models (DisALEXI, eeMETRIC, geeSEBAL, PT-JPL, SIMS, SSEBop), variables in the model-specific CSV data files include: huc12: The 12-digit hydrologic unit code ET: Actual evapotranspiration (in millimeters) over the HUC12 area in the month calculated as the sum of daily ET interpolated between Landsat overpasses statistic: Max, mean, median, or min.  Statistic used in the spatial aggregation within each HUC12. For example, maximum ET is the maximum monthly pixel ET value occurring within the HUC12 boundary after summing daily ET in the month year: 4-digit year month: 2-digit month count: Number of Landsat overpasses included in the ET calculation in the month et_coverage_pct: Integer percentage of the HUC12 with ET data, which can be used to determine how representative the ET statistic is of the entire HUC12 count_coverage_pct: Integer percentage of the HUC12 with count data, which can be different than the et_coverage_pct value because the “count” band in the source image collection extends beyond the “et” band in the eastern portion of the image collection extent   For the Ensemble data, these additional variables are included in the CSV files: et_mad: Ensemble ET value, computed as the mean of the ensemble after filtering outliers using the median absolute deviation (MAD) et_mad_c
doi_str_mv 10.5066/p13y9hxj
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For each HUC12, this dataset contains spatial aggregation statistics (minimum, mean, median, and maximum) for each of the ET variables from each of the publicly available image collections from OpenET for the six available models (DisALEXI, eeMETRIC, geeSEBAL, PT-JPL, SIMS, SSEBop) and the Ensemble image collection, which is a pixel-wise ensemble of all 6 individual models after filtering and removal of outliers according to the median absolute deviation approach (Melton and others, 2022). Data are available in this data release in two different formats: comma-separated values (CSV) and parquet, a high-performance format that is optimized for storage and processing of columnar data. CSV files containing data for each 4-digit HUC are grouped by 2-digit HUCs for easier access of regional data, and the single parquet file provides convenient access to the entire dataset.    For each of the ET models (DisALEXI, eeMETRIC, geeSEBAL, PT-JPL, SIMS, SSEBop), variables in the model-specific CSV data files include: huc12: The 12-digit hydrologic unit code ET: Actual evapotranspiration (in millimeters) over the HUC12 area in the month calculated as the sum of daily ET interpolated between Landsat overpasses statistic: Max, mean, median, or min.  Statistic used in the spatial aggregation within each HUC12. For example, maximum ET is the maximum monthly pixel ET value occurring within the HUC12 boundary after summing daily ET in the month year: 4-digit year month: 2-digit month count: Number of Landsat overpasses included in the ET calculation in the month et_coverage_pct: Integer percentage of the HUC12 with ET data, which can be used to determine how representative the ET statistic is of the entire HUC12 count_coverage_pct: Integer percentage of the HUC12 with count data, which can be different than the et_coverage_pct value because the “count” band in the source image collection extends beyond the “et” band in the eastern portion of the image collection extent   For the Ensemble data, these additional variables are included in the CSV files: et_mad: Ensemble ET value, computed as the mean of the ensemble after filtering outliers using the median absolute deviation (MAD) et_mad_count: The number of models used to compute the ensemble ET value after filtering for outliers using the MAD et_mad_max: The maximum value in the ensemble range, after filtering for outliers using the MAD et_mad_min: The minimum value in the ensemble range, after filtering for outliers using the MAD et_sam: A simple arithmetic mean (across the 6 models) of actual ET average without outlier removal Below are the locations of each OpenET image collection used in this summary:   DisALEXI: https://developers.google.com/earth-engine/datasets/catalog/OpenET_DISALEXI_CONUS_GRIDMET_MONTHLY_v2_0   eeMETRIC: https://developers.google.com/earth-engine/datasets/catalog/OpenET_EEMETRIC_CONUS_GRIDMET_MONTHLY_v2_0   geeSEBAL: https://developers.google.com/earth-engine/datasets/catalog/OpenET_GEESEBAL_CONUS_GRIDMET_MONTHLY_v2_0   PT-JPL: https://developers.google.com/earth-engine/datasets/catalog/OpenET_PTJPL_CONUS_GRIDMET_MONTHLY_v2_0   SIMS: https://developers.google.com/earth-engine/datasets/catalog/OpenET_SIMS_CONUS_GRIDMET_MONTHLY_v2_0   SSEBop: https://developers.google.com/earth-engine/datasets/catalog/OpenET_SSEBOP_CONUS_GRIDMET_MONTHLY_v2_0   Ensemble: https://developers.google.com/earth-engine/datasets/catalog/OpenET_ENSEMBLE_CONUS_GRIDMET_MONTHLY_v2_0</description><identifier>DOI: 10.5066/p13y9hxj</identifier><language>eng</language><publisher>U.S. Geological Survey</publisher><subject>climatologyMeteorologyAtmosphere ; Hydrology ; Remote Sensing ; Water Resources</subject><creationdate>2024</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><orcidid>0000-0002-9602-321X ; 0000-0003-4078-3516 ; 0000-0002-8810-8539 ; 0000-0003-0490-9062 ; 0000-0003-2249-8666 ; 0000-0003-3592-4118 ; 0000-0001-8676-7592</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>776,1888</link.rule.ids><linktorsrc>$$Uhttps://commons.datacite.org/doi.org/10.5066/p13y9hxj$$EView_record_in_DataCite.org$$FView_record_in_$$GDataCite.org$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>Jordan L Wilson</creatorcontrib><creatorcontrib>Peter ReVelle</creatorcontrib><creatorcontrib>MacKenzie O Friedrichs</creatorcontrib><creatorcontrib>Charles Morton</creatorcontrib><creatorcontrib>Will Carrara</creatorcontrib><creatorcontrib>Bruno Andrade</creatorcontrib><creatorcontrib>Matt Bromley</creatorcontrib><creatorcontrib>Christian Dunkerly</creatorcontrib><creatorcontrib>Alberto Guzman</creatorcontrib><creatorcontrib>Gregory Halverson</creatorcontrib><creatorcontrib>Jordan Harding</creatorcontrib><creatorcontrib>Leonardo Laipelt</creatorcontrib><creatorcontrib>Blake Minor</creatorcontrib><creatorcontrib>Gabriel E Parrish</creatorcontrib><creatorcontrib>Christopher Pearson</creatorcontrib><creatorcontrib>John Volk</creatorcontrib><creatorcontrib>Yun Yang</creatorcontrib><creatorcontrib>Richard Allen</creatorcontrib><creatorcontrib>Martha Anderson</creatorcontrib><creatorcontrib>Conor Doherty</creatorcontrib><creatorcontrib>Joshua Fisher</creatorcontrib><creatorcontrib>Justin Huntington</creatorcontrib><creatorcontrib>Lee Johnson</creatorcontrib><creatorcontrib>Yanghui Kang</creatorcontrib><creatorcontrib>Ayse Kilic</creatorcontrib><creatorcontrib>Kyle Knipper</creatorcontrib><creatorcontrib>Forrest Melton</creatorcontrib><creatorcontrib>Francisco Munoz-Arriola</creatorcontrib><creatorcontrib>Samuel Ortega-Salazar</creatorcontrib><creatorcontrib>Adam Purdy</creatorcontrib><creatorcontrib>Anderson Ruhoff</creatorcontrib><creatorcontrib>Mitch Schull</creatorcontrib><creatorcontrib>Gabriel Senay</creatorcontrib><creatorcontrib>Garshaw Amidi-Abraham</creatorcontrib><creatorcontrib>Robyn Grimm</creatorcontrib><creatorcontrib>Maurice Hall</creatorcontrib><creatorcontrib>Rachel O'Connor</creatorcontrib><creatorcontrib>Gopal Penny</creatorcontrib><creatorcontrib>Jonathan Seefeldt</creatorcontrib><title>Monthly OpenET Image Collections (v2.0) Summarized by 12-Digit Hydrologic Unit Codes, 2008-2023</title><description>This dataset provides monthly summaries of evapotranspiration (ET) data from OpenET v2.0 image collections for the period 2008-2023 for all National Watershed Boundary Dataset subwatersheds (12-digit hydrologic unit codes [HUC12s]) in the US that overlap the spatial extent of OpenET datasets. For each HUC12, this dataset contains spatial aggregation statistics (minimum, mean, median, and maximum) for each of the ET variables from each of the publicly available image collections from OpenET for the six available models (DisALEXI, eeMETRIC, geeSEBAL, PT-JPL, SIMS, SSEBop) and the Ensemble image collection, which is a pixel-wise ensemble of all 6 individual models after filtering and removal of outliers according to the median absolute deviation approach (Melton and others, 2022). Data are available in this data release in two different formats: comma-separated values (CSV) and parquet, a high-performance format that is optimized for storage and processing of columnar data. CSV files containing data for each 4-digit HUC are grouped by 2-digit HUCs for easier access of regional data, and the single parquet file provides convenient access to the entire dataset.    For each of the ET models (DisALEXI, eeMETRIC, geeSEBAL, PT-JPL, SIMS, SSEBop), variables in the model-specific CSV data files include: huc12: The 12-digit hydrologic unit code ET: Actual evapotranspiration (in millimeters) over the HUC12 area in the month calculated as the sum of daily ET interpolated between Landsat overpasses statistic: Max, mean, median, or min.  Statistic used in the spatial aggregation within each HUC12. For example, maximum ET is the maximum monthly pixel ET value occurring within the HUC12 boundary after summing daily ET in the month year: 4-digit year month: 2-digit month count: Number of Landsat overpasses included in the ET calculation in the month et_coverage_pct: Integer percentage of the HUC12 with ET data, which can be used to determine how representative the ET statistic is of the entire HUC12 count_coverage_pct: Integer percentage of the HUC12 with count data, which can be different than the et_coverage_pct value because the “count” band in the source image collection extends beyond the “et” band in the eastern portion of the image collection extent   For the Ensemble data, these additional variables are included in the CSV files: et_mad: Ensemble ET value, computed as the mean of the ensemble after filtering outliers using the median absolute deviation (MAD) et_mad_count: The number of models used to compute the ensemble ET value after filtering for outliers using the MAD et_mad_max: The maximum value in the ensemble range, after filtering for outliers using the MAD et_mad_min: The minimum value in the ensemble range, after filtering for outliers using the MAD et_sam: A simple arithmetic mean (across the 6 models) of actual ET average without outlier removal Below are the locations of each OpenET image collection used in this summary:   DisALEXI: https://developers.google.com/earth-engine/datasets/catalog/OpenET_DISALEXI_CONUS_GRIDMET_MONTHLY_v2_0   eeMETRIC: https://developers.google.com/earth-engine/datasets/catalog/OpenET_EEMETRIC_CONUS_GRIDMET_MONTHLY_v2_0   geeSEBAL: https://developers.google.com/earth-engine/datasets/catalog/OpenET_GEESEBAL_CONUS_GRIDMET_MONTHLY_v2_0   PT-JPL: https://developers.google.com/earth-engine/datasets/catalog/OpenET_PTJPL_CONUS_GRIDMET_MONTHLY_v2_0   SIMS: https://developers.google.com/earth-engine/datasets/catalog/OpenET_SIMS_CONUS_GRIDMET_MONTHLY_v2_0   SSEBop: https://developers.google.com/earth-engine/datasets/catalog/OpenET_SSEBOP_CONUS_GRIDMET_MONTHLY_v2_0   Ensemble: https://developers.google.com/earth-engine/datasets/catalog/OpenET_ENSEMBLE_CONUS_GRIDMET_MONTHLY_v2_0</description><subject>climatologyMeteorologyAtmosphere</subject><subject>Hydrology</subject><subject>Remote Sensing</subject><subject>Water Resources</subject><fulltext>true</fulltext><rsrctype>dataset</rsrctype><creationdate>2024</creationdate><recordtype>dataset</recordtype><sourceid>PQ8</sourceid><recordid>eNqVjrEKwjAURbM4iAp-whsr2JqkWHSulTqIg3UOsYltJE1KG8X49SroBzgdLhwuB6EpwdESJ8miJbFf14_rELG9Na7WHg6tNFkBu4ZXElKrtSydsqaH4E4jPIPjrWl4p55SwNkDoeFGVcpB7kVnta1UCSfz3qkVsp8DxXgVUkzjMRpcuO7l5MsRCrZZkeah4I6XyknWdup97BnB7NPGfm3xH-oLmzFDxQ</recordid><startdate>2024</startdate><enddate>2024</enddate><creator>Jordan L Wilson</creator><creator>Peter ReVelle</creator><creator>MacKenzie O Friedrichs</creator><creator>Charles Morton</creator><creator>Will Carrara</creator><creator>Bruno Andrade</creator><creator>Matt Bromley</creator><creator>Christian Dunkerly</creator><creator>Alberto Guzman</creator><creator>Gregory Halverson</creator><creator>Jordan Harding</creator><creator>Leonardo Laipelt</creator><creator>Blake Minor</creator><creator>Gabriel E Parrish</creator><creator>Christopher Pearson</creator><creator>John Volk</creator><creator>Yun Yang</creator><creator>Richard Allen</creator><creator>Martha Anderson</creator><creator>Conor Doherty</creator><creator>Joshua Fisher</creator><creator>Justin Huntington</creator><creator>Lee Johnson</creator><creator>Yanghui Kang</creator><creator>Ayse Kilic</creator><creator>Kyle Knipper</creator><creator>Forrest Melton</creator><creator>Francisco Munoz-Arriola</creator><creator>Samuel Ortega-Salazar</creator><creator>Adam Purdy</creator><creator>Anderson Ruhoff</creator><creator>Mitch Schull</creator><creator>Gabriel Senay</creator><creator>Garshaw Amidi-Abraham</creator><creator>Robyn Grimm</creator><creator>Maurice Hall</creator><creator>Rachel O'Connor</creator><creator>Gopal Penny</creator><creator>Jonathan Seefeldt</creator><general>U.S. Geological Survey</general><scope>DYCCY</scope><scope>PQ8</scope><orcidid>https://orcid.org/0000-0002-9602-321X</orcidid><orcidid>https://orcid.org/0000-0003-4078-3516</orcidid><orcidid>https://orcid.org/0000-0002-8810-8539</orcidid><orcidid>https://orcid.org/0000-0003-0490-9062</orcidid><orcidid>https://orcid.org/0000-0003-2249-8666</orcidid><orcidid>https://orcid.org/0000-0003-3592-4118</orcidid><orcidid>https://orcid.org/0000-0001-8676-7592</orcidid></search><sort><creationdate>2024</creationdate><title>Monthly OpenET Image Collections (v2.0) Summarized by 12-Digit Hydrologic Unit Codes, 2008-2023</title><author>Jordan L Wilson ; Peter ReVelle ; MacKenzie O Friedrichs ; Charles Morton ; Will Carrara ; Bruno Andrade ; Matt Bromley ; Christian Dunkerly ; Alberto Guzman ; Gregory Halverson ; Jordan Harding ; Leonardo Laipelt ; Blake Minor ; Gabriel E Parrish ; Christopher Pearson ; John Volk ; Yun Yang ; Richard Allen ; Martha Anderson ; Conor Doherty ; Joshua Fisher ; Justin Huntington ; Lee Johnson ; Yanghui Kang ; Ayse Kilic ; Kyle Knipper ; Forrest Melton ; Francisco Munoz-Arriola ; Samuel Ortega-Salazar ; Adam Purdy ; Anderson Ruhoff ; Mitch Schull ; Gabriel Senay ; Garshaw Amidi-Abraham ; Robyn Grimm ; Maurice Hall ; Rachel O'Connor ; Gopal Penny ; Jonathan Seefeldt</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-datacite_primary_10_5066_p13y9hxj3</frbrgroupid><rsrctype>datasets</rsrctype><prefilter>datasets</prefilter><language>eng</language><creationdate>2024</creationdate><topic>climatologyMeteorologyAtmosphere</topic><topic>Hydrology</topic><topic>Remote Sensing</topic><topic>Water Resources</topic><toplevel>online_resources</toplevel><creatorcontrib>Jordan L Wilson</creatorcontrib><creatorcontrib>Peter ReVelle</creatorcontrib><creatorcontrib>MacKenzie O Friedrichs</creatorcontrib><creatorcontrib>Charles Morton</creatorcontrib><creatorcontrib>Will Carrara</creatorcontrib><creatorcontrib>Bruno Andrade</creatorcontrib><creatorcontrib>Matt Bromley</creatorcontrib><creatorcontrib>Christian Dunkerly</creatorcontrib><creatorcontrib>Alberto Guzman</creatorcontrib><creatorcontrib>Gregory Halverson</creatorcontrib><creatorcontrib>Jordan Harding</creatorcontrib><creatorcontrib>Leonardo Laipelt</creatorcontrib><creatorcontrib>Blake Minor</creatorcontrib><creatorcontrib>Gabriel E Parrish</creatorcontrib><creatorcontrib>Christopher Pearson</creatorcontrib><creatorcontrib>John Volk</creatorcontrib><creatorcontrib>Yun Yang</creatorcontrib><creatorcontrib>Richard Allen</creatorcontrib><creatorcontrib>Martha Anderson</creatorcontrib><creatorcontrib>Conor Doherty</creatorcontrib><creatorcontrib>Joshua Fisher</creatorcontrib><creatorcontrib>Justin Huntington</creatorcontrib><creatorcontrib>Lee Johnson</creatorcontrib><creatorcontrib>Yanghui Kang</creatorcontrib><creatorcontrib>Ayse Kilic</creatorcontrib><creatorcontrib>Kyle Knipper</creatorcontrib><creatorcontrib>Forrest Melton</creatorcontrib><creatorcontrib>Francisco Munoz-Arriola</creatorcontrib><creatorcontrib>Samuel Ortega-Salazar</creatorcontrib><creatorcontrib>Adam Purdy</creatorcontrib><creatorcontrib>Anderson Ruhoff</creatorcontrib><creatorcontrib>Mitch Schull</creatorcontrib><creatorcontrib>Gabriel Senay</creatorcontrib><creatorcontrib>Garshaw Amidi-Abraham</creatorcontrib><creatorcontrib>Robyn Grimm</creatorcontrib><creatorcontrib>Maurice Hall</creatorcontrib><creatorcontrib>Rachel O'Connor</creatorcontrib><creatorcontrib>Gopal Penny</creatorcontrib><creatorcontrib>Jonathan Seefeldt</creatorcontrib><collection>DataCite (Open Access)</collection><collection>DataCite</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Jordan L Wilson</au><au>Peter ReVelle</au><au>MacKenzie O Friedrichs</au><au>Charles Morton</au><au>Will Carrara</au><au>Bruno Andrade</au><au>Matt Bromley</au><au>Christian Dunkerly</au><au>Alberto Guzman</au><au>Gregory Halverson</au><au>Jordan Harding</au><au>Leonardo Laipelt</au><au>Blake Minor</au><au>Gabriel E Parrish</au><au>Christopher Pearson</au><au>John Volk</au><au>Yun Yang</au><au>Richard Allen</au><au>Martha Anderson</au><au>Conor Doherty</au><au>Joshua Fisher</au><au>Justin Huntington</au><au>Lee Johnson</au><au>Yanghui Kang</au><au>Ayse Kilic</au><au>Kyle Knipper</au><au>Forrest Melton</au><au>Francisco Munoz-Arriola</au><au>Samuel Ortega-Salazar</au><au>Adam Purdy</au><au>Anderson Ruhoff</au><au>Mitch Schull</au><au>Gabriel Senay</au><au>Garshaw Amidi-Abraham</au><au>Robyn Grimm</au><au>Maurice Hall</au><au>Rachel O'Connor</au><au>Gopal Penny</au><au>Jonathan Seefeldt</au><format>book</format><genre>unknown</genre><ristype>DATA</ristype><title>Monthly OpenET Image Collections (v2.0) Summarized by 12-Digit Hydrologic Unit Codes, 2008-2023</title><date>2024</date><risdate>2024</risdate><abstract>This dataset provides monthly summaries of evapotranspiration (ET) data from OpenET v2.0 image collections for the period 2008-2023 for all National Watershed Boundary Dataset subwatersheds (12-digit hydrologic unit codes [HUC12s]) in the US that overlap the spatial extent of OpenET datasets. For each HUC12, this dataset contains spatial aggregation statistics (minimum, mean, median, and maximum) for each of the ET variables from each of the publicly available image collections from OpenET for the six available models (DisALEXI, eeMETRIC, geeSEBAL, PT-JPL, SIMS, SSEBop) and the Ensemble image collection, which is a pixel-wise ensemble of all 6 individual models after filtering and removal of outliers according to the median absolute deviation approach (Melton and others, 2022). Data are available in this data release in two different formats: comma-separated values (CSV) and parquet, a high-performance format that is optimized for storage and processing of columnar data. CSV files containing data for each 4-digit HUC are grouped by 2-digit HUCs for easier access of regional data, and the single parquet file provides convenient access to the entire dataset.    For each of the ET models (DisALEXI, eeMETRIC, geeSEBAL, PT-JPL, SIMS, SSEBop), variables in the model-specific CSV data files include: huc12: The 12-digit hydrologic unit code ET: Actual evapotranspiration (in millimeters) over the HUC12 area in the month calculated as the sum of daily ET interpolated between Landsat overpasses statistic: Max, mean, median, or min.  Statistic used in the spatial aggregation within each HUC12. For example, maximum ET is the maximum monthly pixel ET value occurring within the HUC12 boundary after summing daily ET in the month year: 4-digit year month: 2-digit month count: Number of Landsat overpasses included in the ET calculation in the month et_coverage_pct: Integer percentage of the HUC12 with ET data, which can be used to determine how representative the ET statistic is of the entire HUC12 count_coverage_pct: Integer percentage of the HUC12 with count data, which can be different than the et_coverage_pct value because the “count” band in the source image collection extends beyond the “et” band in the eastern portion of the image collection extent   For the Ensemble data, these additional variables are included in the CSV files: et_mad: Ensemble ET value, computed as the mean of the ensemble after filtering outliers using the median absolute deviation (MAD) et_mad_count: The number of models used to compute the ensemble ET value after filtering for outliers using the MAD et_mad_max: The maximum value in the ensemble range, after filtering for outliers using the MAD et_mad_min: The minimum value in the ensemble range, after filtering for outliers using the MAD et_sam: A simple arithmetic mean (across the 6 models) of actual ET average without outlier removal Below are the locations of each OpenET image collection used in this summary:   DisALEXI: https://developers.google.com/earth-engine/datasets/catalog/OpenET_DISALEXI_CONUS_GRIDMET_MONTHLY_v2_0   eeMETRIC: https://developers.google.com/earth-engine/datasets/catalog/OpenET_EEMETRIC_CONUS_GRIDMET_MONTHLY_v2_0   geeSEBAL: https://developers.google.com/earth-engine/datasets/catalog/OpenET_GEESEBAL_CONUS_GRIDMET_MONTHLY_v2_0   PT-JPL: https://developers.google.com/earth-engine/datasets/catalog/OpenET_PTJPL_CONUS_GRIDMET_MONTHLY_v2_0   SIMS: https://developers.google.com/earth-engine/datasets/catalog/OpenET_SIMS_CONUS_GRIDMET_MONTHLY_v2_0   SSEBop: https://developers.google.com/earth-engine/datasets/catalog/OpenET_SSEBOP_CONUS_GRIDMET_MONTHLY_v2_0   Ensemble: https://developers.google.com/earth-engine/datasets/catalog/OpenET_ENSEMBLE_CONUS_GRIDMET_MONTHLY_v2_0</abstract><pub>U.S. Geological Survey</pub><doi>10.5066/p13y9hxj</doi><orcidid>https://orcid.org/0000-0002-9602-321X</orcidid><orcidid>https://orcid.org/0000-0003-4078-3516</orcidid><orcidid>https://orcid.org/0000-0002-8810-8539</orcidid><orcidid>https://orcid.org/0000-0003-0490-9062</orcidid><orcidid>https://orcid.org/0000-0003-2249-8666</orcidid><orcidid>https://orcid.org/0000-0003-3592-4118</orcidid><orcidid>https://orcid.org/0000-0001-8676-7592</orcidid><oa>free_for_read</oa></addata></record>
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identifier DOI: 10.5066/p13y9hxj
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language eng
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subjects climatologyMeteorologyAtmosphere
Hydrology
Remote Sensing
Water Resources
title Monthly OpenET Image Collections (v2.0) Summarized by 12-Digit Hydrologic Unit Codes, 2008-2023
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