The MOD16A2 Version 6.1 Evapotranspiration/Latent Heat Flux product is an 8-day composite product produced at 500 meter pixel resolution. The algorithm used for the MOD16 data product collection is based on the logic of the Penman-Monteith equation, which includes inputs of daily meteorological reanalysis data along with MODIS remotely sensed …

Google Earth Engine implementation of the Mapping Evapotranspiration at high Resolution with Internalized Calibration model (eeMETRIC) eeMETRIC applies the advanced METRIC algorithms and process of Allen et al. (2007; 2015) and Allen et al. (2013b), where a singular relationship between the near surface air temperature difference (dT) and delapsed land surface temperature (TsDEM) is used to estimate sensible heat flux (H) and is applied to each Landsat scene. Automated selection of the hot and cold pixels for an image generally follows a statistical isolation procedure described by Allen et al. (2013a) and ReVelle, Kilic and Allen (2019a,b). The calibration of H in eeMETRIC utilizes alfalfa reference ET calculated from the NLDAS gridded weather dataset using a fixed 15% reduction in computed reference ET to account for known biases in the gridded data set. The fixed reduction does not impact the calibration accuracy of eeMETRIC and mostly reduces impacts of boundary layer buoyancy correction. The identification of candidates for pools of hot and cold pixels has evolved in the eeMETRIC implementation of METRIC. The new automated calibration process incorporates the combination of methodologies and approaches that stem from two development branches of EEFlux (Allen et al., 2015). The first branch focused on improving the automated pixel selection process using standard lapse rates for land surface temperature (LST) without any further spatial delapsing (ReVelle et al., 2019b). The second branch incorporated a secondary spatial delapsing of LST as well as changes to the pixel selection process (ReVelle et al., 2019a). The final, combined approach is described by Kilic et al. (2021). eeMETRIC employs the aerodynamic-related functions in complex terrain (mountains) developed by Allen et al. (2013b) to improve estimates for aerodynamic roughness, wind speed and boundary layer stability as related to estimated terrain roughness, position on a slope and wind direction. These functions tend to increase estimates for H (and reduce ET) on windward slopes and may reduce H (and increase ET) on leeward slopes. Other METRIC functions employed in eeMETRIC that have been added since the descriptions provided in Allen et al. (2007 and 2011) include reduction in soil heat flux (G) in the presence of organic mulch on the ground surface, use of an excess aerodynamic resistance for shrublands, use of the Perrier function for trees identified as forest (Allen et al., 2018; Santos et al., 2012) and aerodynamic estimation of evaporation from open water rather than using energy balance (Jensen and Allen 2016; Allen et al., 2018). In 2022, the Perrier function was applied to tree (orchard) crops and a 3-source partitioning of bulk surface temperature into canopy temperature, shaded soil temperature and sunlit soil temperature was applied to both orchards and vineyards. These latter applications were made where orchards and vineyards are identified by CDL or, in California, by a state-sponsored land use system. These functions and other enhancements to the original METRIC model are described in the most current METRIC users manual (Allen et al., 2018). eeMETRIC uses the atmospherically corrected surface reflectance and LST from Landsat Collection 2 Level 2, with fallback to Collection 2 Level 1 when needed for near real-time estimates. Additional information

The MOD16A2 V105 product provides information about 8-day global terrestrial evapotranspiration at 1km pixel resolution. Evapotranspiration (ET) is the sum of evaporation and plant transpiration from the Earth's surface to the atmosphere. With long-term ET data, the effects of changes in climate, land use, and ecosystems disturbances can be quantified. The MOD16A2 product is produced by the Numerical Terradynamic Simulation Group NTSG, University of Montana (UMT) in conjunction with NASA Earth Observing System. For more details about the algorithm used see the algorithm theoretical basis document. The period of coverage is 8 days with the exception of the last period at the end of the year which is either 5 or 6 days. ET/PET units are 0.1mm/5-day for December 27-31 of 2001, 2002, 2003, 2005, 2006, 2007, 2009, 2010, and 0.1mm/6-day for December 26-31 of 2000, 2004, 2008 (leap years). ** For some pixels in African rainforest, the MODIS albedo data from MCD43B2/MCD43B3 have no cloud-free data throughout an entire year. As a result, pixels for that year in all data bands are masked out.

The spreadsheet includes a tab for each figure and table in the publication titled "Mapping Actual Evapotranspiration using Landsat for the Conterminous United States: Google Earth Engine Implementation and Assessment of the SSEBop Model" by Senay et al. 2021. Each tab includes the graphic and the data used to create it.

This dataset contains Landsat-derived images of Evaporative Fraction (ETf), Reference Evapotranspiration (ETo), and Actual Evapotranspiration (ETa) over a portion of California’s Central Valley for 15 dates in 2016. Each of the 15 images used in this study had three corresponding Tif files representing ETf, ETo, and ETa. Data used in this project was sourced from Landsat 8 Surface Reflectance Tier 1 images processed in Google Earth Engine (GEE). These images contain five visible and near-infrared (VNIR) bands and two short-wave infrared (SWIR) bands processed to orthorectified surface reflectance, and two thermal infrared (TIR) bands processed to orthorectified brightness temperature. To determine thermal properties of images to aid in ET calculation, the TIR Band 10 (B10) containing brightness temperature was chosen to determine Land Surface Temperature (LST).

We apply a research approach that can inform riparian restoration planning by developing products that show recent trends in vegetation conditions identifying areas potentially more at risk for degradation and the associated relationship between riparian vegetation dynamics and climate conditions. The full suite of data products and a link to the associated publication addressing this analysis can be found on the Parent data release. To characterize the climate conditions across the study period, we use the Standardized Precipitation Evapotranspiration Index (SPEI). The SPEI is a water balance index which includes both precipitation and evapotranspiration in its calculation. Conditions from the prior n months, generally ranging from 1 to 60, are compared to the same respective period over the prior years to identify the index value (Vicente-Serrano et al., 2010). Values generally range from -3 to 3, where values less than 0 suggest drought conditions while values greater than 0 suggest wetter than normal conditions. For this study, we are using the 12-month, or 1-year, SPEI to compare annual conditions within the larger Upper Gila River watershed. The SPEI data was extracted into a CSV spreadsheet using data from the Gridded Surface Meteorological (GRIDMET) dataset, which provides a spatially explicit SPEI product in Google Earth Engine (GEE) at a 5-day interval and a spatial resolution of 4-km (Abatzoglou, 2013). In GEE, we quantify overall mean values of SPEI across each 5-day period for the watershed from January 1980 to December 2021. Using R software, we reduced the 5-day values to represent monthly mean values and constrained the analysis to water year 1980 (i.e., October 1980) through water year 2021 (i.e., October 2021). Using the monthly timeseries, we completed the breakpoint analysis in R to identify breaks within the SPEI time series. The algorithm identifies a seasonal pattern within the timeseries. When the seasonal pattern deviates, a breakpoint is then detected. These breaks can be used to pinpoint unique climate periods in the time series. This is a Child Item for the Parent data release, Mapping Riparian Vegetation Response to Climate Change on the San Carlos Apache Reservation and Upper Gila River Watershed to Inform Restoration Priorities: 1935 to Present - Database of Trends in Vegetation Properties and Climate Adaptation Variables. The spreadsheet attached to this Child Item consists of 5 columns, including the (i) month from January 1985 through October 2021, (ii) the 1-year SPEI monthly time series, (iii) the dates identified as breaks within the breakpoint algorithm, (iv) the breakpoint trend identified within the breakpoint algorithm, and (v) the dates that were used as the climate period breaks in this study. The climate periods identified in this spreadsheet using the SPEI data were used as the climate periods in our riparian study.

TerraClimate is a dataset of monthly climate and climatic water balance for global terrestrial surfaces. It uses climatically aided interpolation, combining high-spatial resolution climatological normals from the WorldClim dataset, with coarser spatial resolution, but time-varying data from CRU Ts4.0 and the Japanese 55-year Reanalysis (JRA55). Conceptually, the procedure applies interpolated time-varying anomalies from CRU Ts4.0/JRA55 to the high-spatial resolution climatology of WorldClim to create a high-spatial resolution dataset that covers a broader temporal record. Temporal information is inherited from CRU Ts4.0 for most global land surfaces for temperature, precipitation, and vapor pressure. However, JRA55 data is used for regions where CRU data had zero climate stations contributing (including all of Antarctica, and parts of Africa, South America, and scattered islands). For primary climate variables of temperature, vapor pressure, and precipitation, the University of Idaho provides additional data on the number of stations (between 0 and 8) that contributed to the CRU Ts4.0 data used by TerraClimate. JRA55 was used exclusively for solar radiation and wind speeds. TerraClimate additionally produces monthly surface water balance datasets using a water balance model that incorporates reference evapotranspiration, precipitation, temperature, and interpolated plant extractable soil water capacity. A modified Thornthwaite-Mather climatic water-balance model and extractable soil water storage capacity data was used at a 0.5° grid from Wang-Erlandsson et al. (2016). Data Limitations: Long-term trends in data are inherited from parent datasets. TerraClimate should not be used directly for independent assessments of trends. TerraClimate will not capture temporal variability at finer scales than parent datasets and thus is not able to capture variability in orographic precipitation ratios and inversions. The water balance model is very simple and does not account for heterogeneity in vegetation types or their physiological response to changing environmental conditions. Limited validation in data-sparse regions (e.g., Antarctica).

This dataset provides accurate actual evapotranspiration (AET or ETa) for Australia using the CMRSET algorithm. The AET band (named 'ETa') contains the average daily value from the CMRSET model for all cloud-free Landsat observations in that month (indicated with value 3 in the AET Data Source QA bits). After the Landsat 7 ETM+ Scan Line Corrector (SLC) failed on 31 May 2003, Landsat 7 ETM+ data are only used if there are no cloud-free Landsat 5 TM or Landsat 8 OLI data for that month. If there is no cloud-free Landsat data available, pixels are infilled with blended data. The blended data will be blended Landsat-MODIS until Feb 2012, then blended Landsat-VIIRS onwards (indicated with value 2 in the AET Data Source QA bits). If there is no blended data available in a month, then missing monthly AET values are linearly interpolated (indicated with value 1 in the AET Data Source QA bits). This means monthly 30 m AET data covering all Australia, with no gaps due to cloud, are available and ready to use. Accurate AET information is important for irrigation, food security, and environmental management. Like many other parts of the world, water availability in Australia is limited and AET is the largest consumptive component of the water balance. In Australia 70% of available water is used for crop and pasture irrigation. Better monitoring will support improved water use efficiency in this sector, with any water savings available as environmental flows. Additionally, ground-water dependent ecosystems (GDE) occupy a small area yet are "biodiversity hotspots". Knowing their water needs enables enhanced management of these critical areas. AET can also be used to model the catchment water balance. If used in water balance (mass balance) calculations, then this AET value needs to be multiplied by the number of days in the month. To let the developers know you are using this dataset, to get information on updates, or if you have any questions please contact: tim.mcvicar@csiro.au, tom.vanniel@csiro.au, jamie.vleeshouwer@csiro.au.

The aridity index also known as the dryness index is the ratio of potential evapotranspiration to precipitation. The aridity index indicates water deficiency. The aridity index is used to classify locations as humid or dry. The evaporation ratio (evaporation index) on the other hand indicates the availability of water in watersheds. The evaporation index is inversely proportional to water availability. For long periods renewable water resources availability is residual precipitation after evaporation loss is deducted. These two ratios provide very useful information about water availability. Understating the powerful potential of the aridity index and evaporation ratio, this app is developed on the Google Earth Engine using NLDAS-2 and MODIS products to map temporal variability of the Aridity Index and Evaporation ratio over CONUS. The app can be found at https://cartoscience.users.earthengine.app/view/aridity-index.

The actual evapotranspiration and interception (ETIa) (dekadal, in mm/day) is the sum of the soil evaporation (E), canopy transpiration (T), and evaporation from rainfall intercepted by leaves (I). The value of each pixel represents the average daily ETIa in a given dekad.

Operational Simplified Surface Energy Balance (SSEBop) The Operational Simplified Surface Energy Balance (SSEBop) model by Senay et al. (2013, 2017) is a thermal-based simplified surface energy model for estimating actual ET based on the principles of satellite psychrometry (Senay 2018). The OpenET SSEBop implementation uses land surface temperature (Ts) from Landsat (Collection 2 Level-2 Science Products) with key model parameters (cold/wet-bulb reference, Tc, and surface psychrometric constant, 1/dT) derived from a combination of observed surface temperature, normalized difference vegetation index (NDVI), climatological average (1980-2017) daily maximum air temperature (Ta, 1-km) from Daymet, and net radiation data from ERA-5. This model implementation uses the Google Earth Engine processing framework for connecting key SSEBop ET functions and algorithms together when generating both intermediate and aggregated ET results. A detailed study and evaluation of the SSEBop model across CONUS (Senay et al., 2022) informs both cloud implementation and assessment for water balance applications at broad scales. Notable model (v0.2.6) enhancements and performance against previous versions include additional compatibility with Landsat 9 (launched Sep 2021), global model extensibility, and improved parameterization of SSEBop using FANO (Forcing and Normalizing Operation) to better estimate ET in all landscapes and all seasons regardless of vegetation cover density, thereby improving model accuracy by avoiding extrapolation of Tc to non-calibration regions. Additional information

This dataset provides accurate, high-resolution (30 m) / high-frequency (monthly) / continuous (no gaps due to cloud) actual evapotranspiration (AET) for Australia using the CMRSET algorithm. The CMRSET algorithm uses reflective remotely sensed indices to estimate AET from potential evapotranspiration (PET; calculated using daily gridded meteorological data generated by the Bureau of Meteorology). Blending high-resolution / low-frequency AET estimates (e.g., Landsat and Sentinel-2) with low-resolution / high-frequency AET estimates (e.g., MODIS and VIIRS) results in AET data that are high-resolution / high-frequency / continuous (no gaps due to cloud) and accurate. These are all ideal characteristics when calculating the water balance for a wetland, paddock, river reach, irrigation area, landscape or catchment.

Accurate AET information is important for irrigation, food security and environmental management. Like many other parts of the world, water availability in Australia is limited and AET is the largest consumptive component of the water balance. In Australia 70% of available water is used for crop and pasture irrigation and better monitoring will support improved water use efficiency in this sector, with any water savings available as environmental flows. Additionally, ground-water dependent ecosystems (GDE) occupy a small area yet are "biodiversity hotspots", and knowing their water needs allows for enhanced management of these critical areas in the landscape. Having high-resolution, frequent and accurate AET estimates for all of Australia means this AET data source can be used to model the water balance for any catchment / groundwater system in Australia.

Details of the CMRSET algorithm and its independent validation are provided in Guerschman, J.P., McVicar, T.R., Vleeshouwer, J., Van Niel, T.G., Peña-Arancibia, J.L. and Chen, Y. (2022) Estimating actual evapotranspiration at field-to-continent scales by calibrating the CMRSET algorithm with MODIS, VIIRS, Landsat and Sentinel-2 data. Journal of Hydrology. 605, 127318, doi:10.1016/j.jhydrol.2021.127318

We strongly recommend users to use the TERN CMRSET AET V2.2. Details of the TERN CMRSET AET V2.2 data product generation are provided in McVicar, T.R., Vleeshouwer, J., Van Niel, T.G., Guerschman, J.P., Peña-Arancibia, J.L. and Stenson, M.P. (2022) Generating a multi-decade gap-free high-resolution monthly actual evapotranspiration dataset for Australia using Landsat, MODIS and VIIRS data in the Google Earth Engine platform: Development and use cases. Journal of Hydrology (In Preparation).

This dataset includes measured data used for developing hybrid-predictive-modeling (HPM) approach and simulated evapotranspiration and ecosystem respiration data across several Fluxnet sites, SNOTEL sites and East River locations (Chen et al., 2020 in review). Fluxnet sites considered in this study include: CA-OAS, CA-OBS, US-NR1, US-TON, US-VAR, US-SRM, US-WHS, US-WKG. Snotel sites Butte (ER-BT), Porphyry Creek (ER-PK) and Schofield Pass (ER-SP) are included as well 16 east river locations with different vegetation types denoted by evergreen forests (EF), deciduous forests (DF), riparian shrublands (RS) and meadow grassland (MS). This data package includes: 1) A summary spread sheet of the sites and acronyms for data variables. 2) Processed Fluxnet datasets that are obtained from Fluxnet.fluxdata.org with HPM estimated evapotranspiration (ET) and ecosystem respiration (RECO). Trained models and metrics for parameters for the Fluxnet sites were added in an update on November 2, 2020; 3) Processed SNOTEL datasets obtained from www.nrcs.usda.gov/snow/ and HPM estimated ET and RECO; 4) Weather data at east river locations obtained from DAYMET (daymet.ornl.gov) as well as HPM estimated ET and RECO. NDVI datasets are obtained from Landsat 5, 7 and 8 on Google Earth Engine platform. Detailed QA/QC and sampling methods of measured data are available at the original data source.

The Terra Moderate Resolution Imaging Spectroradiometer (MODIS) MOD16A2GF Version 6.1 Evapotranspiration/Latent Heat Flux (ET/LE) product is a year-end gap-filled 8-day composite dataset produced at 500 meter (m) pixel resolution. The algorithm used for the MOD16 data product collection is based on the logic of the Penman-Monteith equation, which includes inputs of daily meteorological reanalysis data along with MODIS remotely sensed data products such as vegetation property dynamics, albedo, and land cover. The pixel values for the two Evapotranspiration layers (ET and PET) are the sum of all eight days within the composite period, and the pixel values for the two Latent Heat layers (LE and PLE) are the average of all eight days within the composite period. The last acquisition period of each year is a 5 or 6-day composite period, depending on the year. Documentation: User's Guide Algorithm Theoretical Basis Document (ATBD) General Documentation

Monthly actual evapotranspiration (ETa) computed with the CMRSET model at 500 m resolution. The monthly data spans from March 2000 to December 2018. The data is geographically limited to the area subtended by the 48 main canal commands in the IBIS. The data was produced to support water resources assessments and the IBIS water balance (see e.g. Ahmad et al., 2020). The format is in unsigned 16 bit integer GeoTIFF in the WGS 1984 World Mercator projected coordinate system and uses a scaling factor of 100 (i.e. divide by 100 to obtain ETa in mm per month). Lineage: ETa estimates for the IBIS were computed on a 10-day basis via the CMRSET ETa model 2a (Guerschman et al., 2009), from 24 February 2000 to 31 December 2018 (Peña-Arancibia et al., 2020) and then aggregated to monthly. The parameters used had the following values: Kc_max=1.00, a=14.42, α=2.701, b=2.086, β=0.953. The IBIS ETa estimates used 10-day cloud free EVI and GVMI composites obtained via Google Earth Engine (Gorelick et al., 2017) from the daily Moderate Resolution Imaging Spectroradiometer (MODIS) surface spectral reflectance product (MOD09GA collection 6, Vermote and Wolfe, 2015) to obtain a crop factor which is the scaled by reference evapotranspiration (ET0). ET0 was estimated using the Hargreaves (1974) equation from a combination of local gridded ~2.5 km temperature analysis and post-processed GLDAS ~25 km reanalysis data (see Peña-Arancibia et al., 2020 for details; Rodell et al., 2004).

References

Ahmad, M. D., J. L. Peña Arancibia, J. P. Stewart, and J. M. Kirby (2020), Water balance trends in irrigated canal commands and its implications for sustainable water management in Pakistan: Evidence from 1981 to 2012, Agricultural Water Management, http://doi.org/10.1016/j.agwat.2020.106648 Cheema, M. J. M. (2012), Understanding water resources conditions in data scarce river basins using intelligent pixel information: Transboundary Indus Basin, Technische Universiteit Delft,http://resolver.tudelft.nl/uuid:7b569411-9934-4b23-b631-36a58f60363f, last access: July 2020. Gorelick, N., M. Hancher, M. Dixon, S. Ilyushchenko, D. Thau, and R. Moore (2017), Google Earth Engine: Planetary-scale geospatial analysis for everyone, Remote Sensing of Environment, 202, 18-27, http://doi.org/10.1016/j.rse.2017.06.031 Guerschman, J. P., A. I. J. M. Van Dijk, G. Mattersdorf, J. Beringer, L. B. Hutley, R. Leuning, R. C. Pipunic, and B. S. Sherman (2009), Scaling of potential evapotranspiration with MODIS data reproduces flux observations and catchment water balance observations across Australia, Journal of Hydrology, 369(1-2), 107-119, http://doi.org/10.1016/j.jhydrol.2009.02.013 Hargreaves, G. H. (1974), Estimation of Potential and Crop Evapotranspiration, Transactions of the ASAE, 17(4), 701-704, http://doi.org/10.13031/2013.30184 Peña-Arancibia, J. L., M. D. Ahmad, J. M. Kirby, and M. J. M. Cheema (2020), Remotely sensed time-series (2000‒2018) estimation of evapotranspiration in the Indus Basin: Implementation, evaluation and analysis 34 pp, CSIRO, Australia, https://publications.csiro.au/publications/#publication/PIcsiro:EP20787, last access: July 2020. Rodell, M., et al. (2004), The global land data assimilation system, Bulletin of the American Meteorological Society, 85(3), http://doi.org/10.1175/bams-85-3-381 Vermote, E., and R. Wolfe (2015), MOD09GA MODIS/Terra Surface Reflectance Daily L2G Global 1km and 500m SIN Grid V006 [Data set].

Priestley-Taylor Jet Propulsion Laboratory (PT-JPL) The core formulation of the PT-JPL model within the OpenET framework has not changed from the original formulation detailed in Fisher et al. (2008). However, enhancements and updates to model inputs and time integration for PT-JPL were made to take advantage of contemporary gridded weather datasets, provide consistency with other models, improve open water evaporation estimates, and account for advection over crop and wetland areas in semiarid and arid environments. These changes include the use of Landsat surface reflectance and thermal radiation for calculating net radiation, photosynthetically active radiation, plant canopy and moisture variables, and use of NLDAS, Spatial CIMIS, and gridMET weather data for estimating insolation and ASCE reference ET. Similar to the implementation of other OpenET models, estimation of daily and monthly time integrated ET is based on the fraction of ASCE reference ET. Open water evaporation is estimated following a surface energy balance approach of Abdelrady et al. (2016) that is specific for water bodies by accounting for water heat flux as opposed to soil heat flux. Additional information

This data release provides a monthly irrigation water use reanalysis for the period 2000-20 for all U.S. Geological Survey (USGS) Watershed Boundary Dataset of Subwatersheds (Hydrologic Unit Code 12 [HUC12]) in the conterminous United States (CONUS). Results include reference evapotranspiration (ETo), actual evapotranspiration (ETa), irrigated areas, consumptive use, and effective precipitation for each HUC12. ETo and ETa were estimated using the operational Simplified Surface Energy Balance (SSEBop, Senay and others, 2013; Senay and others, 2020) model executed in the OpenET (Melton and others, 2021) web-based application implemented in Google Earth Engine. Results provided by OpenET/SSEBop were summarized to hydrologic response units (HRUs) in the National Hydrologic Model (NHM; Regan and others, 2019) to estimate consumptive use and effective precipitation on irrigated lands. Irrigated lands for the CONUS were provided by the Landsat-based Irrigation Dataset (LANID; Xie and others, 2019) for each year of the reanalysis period. Consumptive use estimates provided by the NHM were disaggregated to HUC12s using area weighted intersections with HRUs and the relative proportion of irrigated lands in each intersected area. The Landsat-based Irrigation Dataset (LANID) uses a random-forest machine-learning model with greenness and vegetative indices, climate data, and crop masks to identify irrigated crops (Xie and others, 2021, Xie and Lark, 2021). Separate western US and eastern US methods are used to train and validate the model. Annual LANID maps for 2018 -20 were created using the same techniques in Xie and others, 2021, and Xie and Lark, 2021.

This data release provides a monthly irrigation water use reanalysis for the period 2000-20 for all U.S. Geological Survey (USGS) Watershed Boundary Dataset of Subwatersheds (Hydrologic Unit Code 12 [HUC12]) in the conterminous United States (CONUS). Results include reference evapotranspiration (ETo), actual evapotranspiration (ETa), irrigated areas, consumptive use, and effective precipitation for each HUC12. ETo and ETa were estimated using the operational Simplified Surface Energy Balance (SSEBop, Senay and others, 2013; Senay and others, 2020) model executed in the OpenET (Melton and others, 2021) web-based application implemented in Google Earth Engine. Results provided by OpenET/SSEBop were summarized to hydrologic response units (HRUs) in the National Hydrologic Model (NHM; Regan and others, 2019) to estimate consumptive use and effective precipitation on irrigated lands. Irrigated lands for the CONUS were provided by the Landsat-based Irrigation Dataset (LANID; Xie and others, 2019) for each year of the reanalysis period. Consumptive use estimates provided by the NHM were disaggregated to HUC12s using area weighted intersections with HRUs and the relative proportion of irrigated lands in each intersected area. These datasets are generated during the irrigation reanalysis workflow (irrigation_reanalysis.7zip). The files actet_openet.cbh, potet_openet.cbh, and dyn_ag_frac.param are created in step one of the workflow, which involves converting daily OpenET/SSEBop results into inputs for the NHM. All other files are produced by the NHM and are utilized for calculating irrigation consumptive use and effective precipitation.

The gridded annual evapotranspiration (ET) from 1985 to 2019 were calculated based on the correlations between eddy-covariance flux-tower measurements of annual evapotranspiration and satellite imagery derived Normalized Difference Vegetation Index (NDVI). Annual ET observations from 12 flux-towers across the Sierra Nevada and Southern California were collected from 2001 to 2016, resulting 97 site-years of ET observations in five main vegetation types (Evergreen Needleleaf Forest, Grasslands, Mixed Forest, Open Shrublands, and Woody Savannas). The NDVI was calculated at 30-m resolution from USGS Landsat Collection Tier 1 surface reflectance over the entire California and aggregated to annual mean values for each water year (Oct. to Sept.) from 1985 to 2019. The gridded annual ET was calculated at 30 m spatial resolution using the exponential relationship between NDVI and ET derived from flux-tower sites.

Methods The ET regression function (1) was estimated based on the method developed by (Goulden and Bales 2019) using exponential regression in R language. The flux-tower ET observations were summed annually by water year after filling data gaps (Rungee et al. 2018). The total 97 site-year ET observations also included some site-years that were impacted by drought, fire, forest thinning, prescribed fire, reflecting by the significant reduction in annual NDVI. The annual NDVI map was calculated as the mean of all Landsat scenes for a water year (Oct. to Sept.) after masking for shadow, snow or cloud (Zhu and Woodcock 2012). We homogenized Landsat 8 NDVI (L8, 2014-2019) and Landsat 7 (L7, 2012- 2013) to Landsat 5 NDVI (L5, 1985-2011) following equation 2 and 3 (Su et al. 2017; Sulla-Menashe et al. 2016). The NDVI and ET maps were generated using Google Earth Engine. The modeled ET showed a strong correlation to site-level flux-tower observations (coefficient of determination, R2=0.77). Most of the modeled ET fall within ±100 mm ranges of ET measurements, with a root mean square error (RMSE) at 108 mm, and mean absolute error (MAE) at 74 mm. The main estimation error is observed at site-years with high NDVI and ET values, due to NDVI saturation issue. The model’s temporal and spatial sensitivities were assessed using leave-one-out cross validation method by removing an individual water year or flux-tower site for model building and then evaluating on the site-year removed.

ET=112.3×e3.2×NDVI (1)

L5=0.9883×L7-0.0367 (2)

L5=0.8213×L8-0.0403 (3)

The data was developed for the USGS Water-Use and Data Research program grant opportunities G20AS00053 and G21AS00258, combined with fundnig from Oregon Water Resources Department to improve estimates of water use from irrigated lands in Oregon. These data contain attributes of irrigation status, irrigation source type, crop type, irrigation method, assumed irrigation efficiency, irrigation water source, evapotranspiration (ET) data from OpenET, and effective precipitation developed using the USBR ET Demands model. Thee data were aggregated in order to further the development of estimates of applied water at the field-scale., A single set of draft field boundaries for all agricultural lands were developed to represent the maximum extent of irrigated lands from 1985-2020 (digitized at the 1:5,000 scale). The approach used for this task was relatively straight forward yet time consuming and required careful attention to detail to avoid numerous potential pitfalls. Agricultural field boundaries were developed within a GIS system by modifying existing 2007 USDA Common Land Unit (CLU) data, OWRD drawn field boundaries (e.g., Malheur Lake Basin) and developing field boundaries from scratch where needed. This entailed: 1) using Common Land Unit (CLU) as-is where the quality and representativeness of the linework was deemed suitable; 2) modifying the CLU data to eliminate duplicates, overlaps, and slivers within the linework, and make representative of maximum agricultural extent; 3) manually digitizing new field boundaries where they do not currently exist; and 4) QAQC all results. Crop type and irrigation status r..., , # Oregon Statewide ET OpenET Field Boundary and HUC Watershed Geodatabases Processing

Blake Minor - DRI

12/22/23

The Oregon Statewide Evapotranspiration (ET) field boundary file ("Oregon_WUDR_II_DataPackage_Field_Scale.zip") and HUC file geodatabase ("Oregon_WUDR_II_DataPackage_HUC_Summary.zip") contains results of the Phase I and II of the USGS Water-Use and Data Research (WUDR) projects for the state of Oregon. These files contains summaries of ET, effective precipitation, net ET, and additional variables/attributes for the 2016-2022 time period on a per-field and per HUC-8 and HUC-12 basis. Elements of the field level geodatabase include the field boundary feature class, annual summary table CSV's, and relationship classes to relate the table records to the feature class dataset. The HUC file geodatabase contains two feature classes, with one of them being the HUC-8 summaries and the other being the HUC-12 summaries. Google Earth Engine (GEE) and the Python Application Programming...