Matthew P Schauer Gabriel B Senay Stefanie Kagone 20221101 raster digital data https://doi.org/10.5066/P9R877Q8 Global alfalfa-reference potential evapotranspiration (ETr) is a key model parameter in actual evapotranspiration (ETa) modeling for worldwide applications. This dataset was constructed for use with the Operational Simplified Surface Energy Balance (SSEBop) model as a key driver of the final ETa magnitude. SSEBop is a parametric energy balance-based model that determines actual ET as the product of two independent estimates: 1) the SSEBop modeled ET fraction (ETf), an index nominally varying between 0 and 1 and derived from observed Landsat surface temperature using satellite psychrometry, and 2) the potential ET (maximum) under environmental conditions for an alfalfa crop (in millimeters). As SSEBop ETf can now be modeled for any Landsat scene across the globe, a suitable global ETr climatology dataset needed to be created. This global ETr data is a fusion of several different remote sensing and modeling products: 1981-2010 climatological normal (daily mean) ETr from Gridmet over the continental United States and 1981-2010 climatological normal MERRA-2 Fine Resolution ETr for all areas outside of the continental United States that has been scaled and corrected via terrestrial ecoregions from OneEarth and scaled using Worldclim Version 3 ETo (Abatzoglou 2013; Dinerstein et al., 2017; Hobbins et al., 2022; Zomer et al., 2022). The final mosaic has been smoothed and resampled to 1-km spatial resolution. The final dataset is a daily dataset of 366 GeoTIFF raster files for each day of the year including the leap day and representing a climatological normal (1981-2010) alfalfa-reference potential ET (ETr) for the entire global extent. This dataset was constructed for use with the Operational Simplified Surface Energy Balance (SSEBop) model or another Evapotranspiration remote-sensing-based model as a key driver of the final ETa magnitude. Individual rasters are scaled. Please multiply each raster file by a factor of 0.01 to scale them to the proper millimeters of evapotranspiration. 1981 2010 observed Complete None planned Global -179.9958 179.8842 86.0042 -89.9958 ISO 19115 Topic Category boundaries elevation environment USGS Thesaurus evaporation transpiration potential evapotranspiration ET modeling Reference Evapotranspiration input parameter for SSEBop model USGS Metadata Identifier USGS:6356d760d34ebe442502d912 None. Please see 'Distribution Info' for details. None. Users are advised to read the dataset's metadata thoroughly to understand appropriate use and data limitations. Matthew P Schauer (CTR) U.S. Geological Survey, CORE SCIENCE SYSTEMS mailing address

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Sioux Falls SD 57198 US 605-215-9697 mschauer@contractor.usgs.gov The system used to create this dataset used Microsoft Windows 10 with ESRI ArcGIS 10.8.2 software. The final dataset is a daily dataset of 366 GeoTIFF raster files for each day of the year including the leap day and representing a climatological normal (1981-2010) alfalfa-reference potential ET (ETr) for the entire global extent. The filenames start with "pet_" and then the month and day of year and file sizes range between 70-95 MB. John T. Abatzoglou 20111221 publication International Journal of Climatology vol. 33, issue 1 n/a Wiley ppg. 121-131 https://doi.org/10.1002/joc.3413 Eric Dinerstein David Olson Anup Joshi Carly Vynne Neil D. Burgess Eric Wikramanayake Nathan Hahn Suzanne Palminteri Prashant Hedao Reed Noss Matt Hansen Harvey Locke Erle C Ellis Benjamin Jones Charles Victor Barber Randy Hayes Cyril Kormos Vance Martin Eileen Crist Wes Sechrest Lori Price Jonathan E. M. Baillie Don Weeden Kierán Suckling Crystal Davis Nigel Sizer Rebecca Moore David Thau Tanya Birch Peter Potapov Svetlana Turubanova Alexandra Tyukavina Nadia de Souza Lilian Pintea José C. Brito Othman A. Llewellyn Anthony G. Miller Annette Patzelt Shahina A. Ghazanfar Jonathan Timberlake Heinz Klöser Yara Shennan-Farpón Roeland Kindt Jens-Peter Barnekow Lillesø Paulo van Breugel Lars Graudal Maianna Voge Khalaf F. Al-Shammari Muhammad Saleem 20170405 publication BioScience vol. 67, issue 6 n/a Oxford University Press (OUP) ppg. 534-545 https://doi.org/10.1093/biosci/bix014 Mike Hobbins Candida Dewes Timen Jansma 2022 dataset https://www.sciencebase.gov U.S. Geological Survey https://doi.org/10.5066/p9iiqmv1 Robert J. Zomer Jianchu Xu Antonio Trabucco 20220715 publication Scientific Data vol. 9, issue 1 n/a Springer Science and Business Media LLC https://doi.org/10.1038/s41597-022-01493-1 Gabriel B Senay 2018 publication Applied Engineering in Agriculture vol. 34, issue 3 n/a American Society of Agricultural and Biological Engineers (ASABE) ppg. 555-566 https://doi.org/10.13031/aea.12614 Accuracy testing was completed within the continental United States (CONUS) for the 1981-2010 climatological normal of Gridmet ETr against ground-based station potential ET calculated from 23 eddy covariance flux towers from the Ameriflux network. The Gridmet ETr showed very low bias of -0.2%, an RMSE of 1.86 mm, and Pearson's r correlation coefficient of 0.78 across 925 individual observations from 23 tower locations. The data has been checked to ensure that it conforms to the information provided and values for ETr fall within expected ranges across the globe. This dataset provides ETr for every land area across the globe except for certain areas of Antarctica where ETr cannot be modeled with remote sensing. John T. Abatzoglou 20111221 publication International Journal of Climatology vol. 33, issue 1 n/a Wiley ppg. 121-131 https://doi.org/10.1002/joc.3413 Digital and/or Hardcopy 19810101 20101231 observed Gridmet This provided the high resolution ETr over CONUS that was used to scale the global ETr and then also used for the areas within CONUS for the final ETr. Eric Dinerstein David Olson Anup Joshi Carly Vynne Neil D. Burgess Eric Wikramanayake Nathan Hahn Suzanne Palminteri Prashant Hedao Reed Noss Matt Hansen Harvey Locke Erle C Ellis Benjamin Jones Charles Victor Barber Randy Hayes Cyril Kormos Vance Martin Eileen Crist Wes Sechrest Lori Price Jonathan E. M. Baillie Don Weeden Kierán Suckling Crystal Davis Nigel Sizer Rebecca Moore David Thau Tanya Birch Peter Potapov Svetlana Turubanova Alexandra Tyukavina Nadia de Souza Lilian Pintea José C. Brito Othman A. Llewellyn Anthony G. Miller Annette Patzelt Shahina A. Ghazanfar Jonathan Timberlake Heinz Klöser Yara Shennan-Farpón Roeland Kindt Jens-Peter Barnekow Lillesø Paulo van Breugel Lars Graudal Maianna Voge Khalaf F. Al-Shammari Muhammad Saleem 20170405 publication BioScience vol. 67, issue 6 n/a Oxford University Press (OUP) ppg. 534-545 https://doi.org/10.1093/biosci/bix014 Digital and/or Hardcopy 20170101 publication date Ecoregions This provided the shapefile of ecoregions which were used as the zones for scaling the MERRA-2 ETr to the corrected WorldClim potential ET. Mike Hobbins Candida Dewes Timen Jansma 2022 dataset https://www.sciencebase.gov U.S. Geological Survey https://doi.org/10.5066/p9iiqmv1 Digital and/or Hardcopy 19810101 20101231 observed MERRA-2 ETr This provided the ETr for the globe after it had been scaled and smoothed with the k-factor grid. Robert J. Zomer Jianchu Xu Antonio Trabucco 20220715 publication Scientific Data vol. 9, issue 1 n/a Springer Science and Business Media LLC https://doi.org/10.1038/s41597-022-01493-1 Digital and/or Hardcopy 19800101 20001231 publication date WorldClim Used to create a k-factor raster by determining the ratio between this and the Gridmet ETr over CONUS and then correcting it a the annual total. The corrected annual total was then used to scale the MERRA-2 daily climatological ETr Acquired global MERRA-2 ETr 1981-2010 ETr from Hobbins et al 2022 as well as WorldClimo ETo climatology from Zomer et al 2022 and terrestrial ecoregions from Dinerstein et al 2017 and Gridmet ETr 1981-2010 over CONUS from Abatzaglou 2011. 20220430 Stefanie Kagone (CTR) U.S. Geological Survey, CORE SCIENCE SYSTEMS mailing address

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Sioux Falls SD 57198 US 605-594-2862 skagone@contractor.usgs.gov Calculated daily mean 1981-2010 (30-yr) climatology datasets for Gridmet ETr over CONUS and MERRA-2 ETr for the globe. 20220501 Matthew P Schauer (CTR) U.S. Geological Survey, CORE SCIENCE SYSTEMS mailing address

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Sioux Falls SD 57198 US 605-594-2557 mschauer@contractor.usgs.gov Calculated the annual total ETr for Gridmet over CONUS from the climatological 1981-2010 data as well as the annual total ETo from the WorldClim potential ET (Zomer et al 2022). Determined the ratio between Gridmet and WorldClim potential ET over the CONUS which turned out to be 0.95. The annual total Worldclim potential ET was multiplied by this factor of 0.95 to "scale" it to be comparable to Gridmet. 20220501 Matthew P Schauer (CTR) U.S. Geological Survey, CORE SCIENCE SYSTEMS mailing address

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Sioux Falls SD 57198 US 605-594-2557 mschauer@contractor.usgs.gov Using the annual total Worldclim potential ET scaled to Gridmet ETr from Step 3, determined a scaling-factor grid based on terrestrial ecoregions (Dinerstein et al 2017) using the ratio between corrected WorldClim climatology potential ET annual total and the annual total from the MERRA-2 1981-2010 climatology ETr annual total. The result was a k-factor (scaling factor) raster divided by ecoregion. 20220501 Matthew P Schauer (CTR) U.S. Geological Survey, CORE SCIENCE SYSTEMS mailing address

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Sioux Falls SD 57198 US 605-594-2557 mschauer@contractor.usgs.gov Corrected the daily MERRA ETr fine resolution climatology dataset by the k-factor grid from step 4. Smoothed the result with Focal Statistics with a 5x5 pixel window. Final result was resampled to 4-km resolution to match the resolution of Gridmet ETr. 20220501 Matthew P Schauer (CTR) U.S. Geological Survey, CORE SCIENCE SYSTEMS mailing address

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Sioux Falls SD 57198 US 605-594-2557 mschauer@contractor.usgs.gov Created a mosaic with Gridmet climatological ETr over CONUS on top of the final global ETr from Step 5. 20220501 Matthew P Schauer (CTR) U.S. Geological Survey, CORE SCIENCE SYSTEMS mailing address

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Sioux Falls SD 57198 US 605-594-2557 mschauer@contractor.usgs.gov Raster Grid Cell 17600 35988 1 0.01 0.01 Decimal seconds WGS_1984 WGS 84 6378137.0 298.257223563 U.S. Geological Survey GS ScienceBase mailing address

Denver Federal Center, Building 810, Mail Stop 302

Denver CO 80225 United States 1-888-275-8747 sciencebase@usgs.gov Unless otherwise stated, all data, metadata and related materials are considered to satisfy the quality standards relative to the purpose for which the data were collected. Although these data and associated metadata have been reviewed for accuracy and completeness and approved for release by the U.S. Geological Survey (USGS), no warranty expressed or implied is made regarding the display or utility of the data for other purposes, nor on all computer systems, nor shall the act of distribution constitute any such warranty. Digital Data https://doi.org/10.5066/P9R877Q8 None 20221101 Matthew P Schauer (CTR) U.S. Geological Survey, CORE SCIENCE SYSTEMS mailing address

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Sioux Falls SD 57198 US 605-215-9697 mschauer@contractor.usgs.gov FGDC Biological Data Profile of the Content Standard for Digital Geospatial Metadata FGDC-STD-001.1-1999