Articles | Volume 30, issue 8
https://doi.org/10.5194/hess-30-2417-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-30-2417-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
28 Apr 2026
Research article |
| 28 Apr 2026
| 28 Apr 2026
Triple collocation validates CONUS-wide evapotranspiration inferred from atmospheric conditions
Department of Earth System Science, Stanford University, Stanford, CA, USA
Lillian E. Sanders
Department of Earth System Science, Stanford University, Stanford, CA, USA
Department of Computer Science, Stanford University, Stanford, CA, USA
Kaighin A. McColl
Department of Earth and Planetary Sciences, Harvard University, Cambridge, MA, USA
School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
Alexandra G. Konings
Department of Earth System Science, Stanford University, Stanford, CA, USA
Related authors
Meng Zhao, Erica L. McCormick, Geruo A, Alexandra G. Konings, and Bailing Li
Hydrol. Earth Syst. Sci., 29, 2293–2307, https://doi.org/10.5194/hess-29-2293-2025, https://doi.org/10.5194/hess-29-2293-2025, 2025
Short summary
Short summary
Root-zone water storage capacity (Sr) helps plants survive droughts and influences water and climate systems. Using GRACE (Gravity Recovery and Climate Experiment) satellite data, we estimated Sr globally and found that it exceeds 2 m soil storage in nearly half of the vegetated areas, far more than previously thought. Incorporating our Sr estimates into a global hydrological model improves evapotranspiration simulations, particularly during droughts, highlighting the value of our approach for advancing water resource and ecosystem modeling.
David N. Dralle, W. Jesse Hahm, K. Dana Chadwick, Erica McCormick, and Daniella M. Rempe
Hydrol. Earth Syst. Sci., 25, 2861–2867, https://doi.org/10.5194/hess-25-2861-2021, https://doi.org/10.5194/hess-25-2861-2021, 2021
Short summary
Short summary
Root zone water storage capacity determines how much water can be stored belowground to support plants during periods without precipitation. Here, we develop a satellite remote sensing method to estimate this key variable at large scales that matter for management. Importantly, our method builds on previous approaches by accounting for snowpack, which may bias estimates from existing approaches. Ultimately, our method will improve large-scale understanding of plant access to subsurface water.
Andrew F. Feldman, William K. Smith, Alexandra G. Konings, Martin J. Baur, Marvin Browne, David Chaparro, Charles Devine, Jennifer L. Diehl, Arthur Endsley, Thomas Jagdhuber, Kristine M. Larson, Coral del Mar Valle-Rodriguez, Konstantin Schellenberg, Ruxandra-Maria Zotta, and Shawn P. Serbin
EGUsphere, https://doi.org/10.5194/egusphere-2026-1759, https://doi.org/10.5194/egusphere-2026-1759, 2026
This preprint is open for discussion and under review for Biogeosciences (BG).
Short summary
Short summary
Vegetation water observations from 272 field-based GNSS sensors and satellite-based vegetation optical depth are compared across the western U.S. We find significant positive correlations across the sites and identify common conditions that increase the correlations including less forest cover, more homogeneous vegetation across landscapes, and large seasonal amplitudes. These field-based sensors are thus useful tools to evaluate grass and shrub water stress and validate satellite signals.
Meng Zhao, Erica L. McCormick, Geruo A, Alexandra G. Konings, and Bailing Li
Hydrol. Earth Syst. Sci., 29, 2293–2307, https://doi.org/10.5194/hess-29-2293-2025, https://doi.org/10.5194/hess-29-2293-2025, 2025
Short summary
Short summary
Root-zone water storage capacity (Sr) helps plants survive droughts and influences water and climate systems. Using GRACE (Gravity Recovery and Climate Experiment) satellite data, we estimated Sr globally and found that it exceeds 2 m soil storage in nearly half of the vegetated areas, far more than previously thought. Incorporating our Sr estimates into a global hydrological model improves evapotranspiration simulations, particularly during droughts, highlighting the value of our approach for advancing water resource and ecosystem modeling.
David N. Dralle, W. Jesse Hahm, K. Dana Chadwick, Erica McCormick, and Daniella M. Rempe
Hydrol. Earth Syst. Sci., 25, 2861–2867, https://doi.org/10.5194/hess-25-2861-2021, https://doi.org/10.5194/hess-25-2861-2021, 2021
Short summary
Short summary
Root zone water storage capacity determines how much water can be stored belowground to support plants during periods without precipitation. Here, we develop a satellite remote sensing method to estimate this key variable at large scales that matter for management. Importantly, our method builds on previous approaches by accounting for snowpack, which may bias estimates from existing approaches. Ultimately, our method will improve large-scale understanding of plant access to subsurface water.
Yanlan Liu, Nataniel M. Holtzman, and Alexandra G. Konings
Hydrol. Earth Syst. Sci., 25, 2399–2417, https://doi.org/10.5194/hess-25-2399-2021, https://doi.org/10.5194/hess-25-2399-2021, 2021
Short summary
Short summary
The flow of water through plants varies with species-specific traits. To determine how they vary across the world, we mapped the traits that best allowed a model to match microwave satellite data. We also defined average values across a few clusters of trait behavior. These form a tractable solution for use in large-scale models. Transpiration estimates using these clusters were more accurate than if using plant functional types. We expect our maps to improve transpiration forecasts.
Caroline A. Famiglietti, T. Luke Smallman, Paul A. Levine, Sophie Flack-Prain, Gregory R. Quetin, Victoria Meyer, Nicholas C. Parazoo, Stephanie G. Stettz, Yan Yang, Damien Bonal, A. Anthony Bloom, Mathew Williams, and Alexandra G. Konings
Biogeosciences, 18, 2727–2754, https://doi.org/10.5194/bg-18-2727-2021, https://doi.org/10.5194/bg-18-2727-2021, 2021
Short summary
Short summary
Model uncertainty dominates the spread in terrestrial carbon cycle predictions. Efforts to reduce it typically involve adding processes, thereby increasing model complexity. However, if and how model performance scales with complexity is unclear. Using a suite of 16 structurally distinct carbon cycle models, we find that increased complexity only improves skill if parameters are adequately informed. Otherwise, it can degrade skill, and an intermediate-complexity model is optimal.
Cited articles
Abatzoglou, J. T.: Development of gridded surface meteorological data for ecological applications and modelling, Int. J. Climatol., 33, 121–131, https://doi.org/10.1002/joc.3413, 2013.
Alemohammad, S. H., McColl, K. A., Konings, A. G., Entekhabi, D., and Stoffelen, A.: Characterization of precipitation product errors across the United States using multiplicative triple collocation, Hydrol. Earth Syst. Sci., 19, 3489–3503, https://doi.org/10.5194/hess-19-3489-2015, 2015.
Alemohammad, S. H., Fang, B., Konings, A. G., Aires, F., Green, J. K., Kolassa, J., Miralles, D., Prigent, C., and Gentine, P.: Water, Energy, and Carbon with Artificial Neural Networks (WECANN): a statistically based estimate of global surface turbulent fluxes and gross primary productivity using solar-induced fluorescence, Biogeosciences, 14, 4101–4124, https://doi.org/10.5194/bg-14-4101-2017, 2017.
ArcGIS Data and Maps: USA Detailed Water Bodies, https://hub.arcgis.com/datasets/esri::usa-detailed-water-bodies/explore (last access: 1 August 2025), 2023.
Burnett, M. W., Quetin, G. R., and Konings, A. G.: Data-driven estimates of evapotranspiration and its controls in the Congo Basin, Hydrol. Earth Syst. Sci., 24, 4189–4211, https://doi.org/10.5194/hess-24-4189-2020, 2020.
Caires, S. and Sterl, A.: Validation of ocean wind and wave data using triple collocation, J. Geophys. Res.-Oceans, 108, https://doi.org/10.1029/2002JC001491, 2003.
Chen, F., Crow, W. T., Bindlish, R., Colliander, A., Burgin, M. S., Asanuma, J., and Aida, K.: Global-scale evaluation of SMAP, SMOS and ASCAT soil moisture products using triple collocation, Remote Sens. Environ., 214, 1–13, https://doi.org/10.1016/j.rse.2018.05.008, 2018.
Chen, S., McColl, K. A., Berg, A., and Huang, Y.: Surface Flux Equilibrium Estimates of Evapotranspiration at Large Spatial Scales, J. Hydrometeorol., 22, 765–779, https://doi.org/10.1175/JHM-D-20-0204.1, 2021.
Clothier, B. E., Clawson, K. L., Pinter Jr., P. J., Moran, M. S., Reginato, R. J., and Jackson, R. D.: Estimation of soil heat flux from net radiation during the growth of alfalfa, Agr. Forest Meteorol., 37, 319–329, 1986.
Crow, W. T., Lei, F., Hain, C., Anderson, M. C., Scott, R. L., Billesbach, D., and Arkebauer, T.: Robust estimates of soil moisture and latent heat flux coupling strength obtained from triple collocation: Estimation of Land Coupling Strength, Geophys. Res. Lett., 42, 8415–8423, https://doi.org/10.1002/2015GL065929, 2015.
Dewitz: National Land Cover Database (NLCD) 2019 Products (ver. 3.0, February 2024), USGS [data set], https://doi.org/10.5066/P9KZCM54, 2024.
Doelling, D. R., Loeb, N. G., Keyes, D. F., Nordeen, M. L., Morstad, D., Nguyen, C., Wielicki, B. A., Young, D. F., and Sun, M.: Geostationary Enhanced Temporal Interpolation for CERES Flux Products, J. Atmospheric Ocean. Technol., 30, 1072–1090, https://doi.org/10.1175/JTECH-D-12-00136.1, 2013.
Dralle, D. N., Hahm, W. J., Chadwick, K. D., McCormick, E., and Rempe, D. M.: Technical note: Accounting for snow in the estimation of root zone water storage capacity from precipitation and evapotranspiration fluxes, Hydrol. Earth Syst. Sci., 25, 2861–2867, https://doi.org/10.5194/hess-25-2861-2021, 2021.
Draper, C., Reichle, R., De Jeu, R., Naeimi, V., Parinussa, R., and Wagner, W.: Estimating root mean square errors in remotely sensed soil moisture over continental scale domains, Remote Sens. Environ., 137, 288–298, https://doi.org/10.1016/j.rse.2013.06.013, 2013.
ECMWF: IFS Documentation CY45R1 – Part IV: Physical processes, https://doi.org/10.21957/4WHWO8JW0, 2018.
Entekhabi, D., Reichle, R. H., Koster, R. D., and Crow, W. T.: Performance Metrics for Soil Moisture Retrievals and Application Requirements, J. Hydrometeorol., 11, 832–840, https://doi.org/10.1175/2010JHM1223.1, 2010.
Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E.: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization, Geosci. Model Dev., 9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016, 2016.
Feldman, A. F., Short Gianotti, D. J., Dong, J., Akbar, R., Crow, W. T., McColl, K. A., Konings, A. G., Nippert, J. B., Tumber-Dávila, S. J., Holbrook, N. M., Rockwell, F. E., Scott, R. L., Reichle, R. H., Chatterjee, A., Joiner, J., Poulter, B., and Entekhabi, D.: Remotely Sensed Soil Moisture Can Capture Dynamics Relevant to Plant Water Uptake, Water Resour. Res., 59, e2022WR033814, https://doi.org/10.1029/2022WR033814, 2023.
Ferreira, V. G., Montecino, H. D. C., Yakubu, C. I., and Heck, B.: Uncertainties of the Gravity Recovery and Climate Experiment time-variable gravity-field solutions based on three-cornered hat method, J. Appl. Remote Sens., 10, 015015, https://doi.org/10.1117/1.JRS.10.015015, 2016.
Fisher, J. B., Lee, B., Purdy, A. J., Halverson, G. H., Dohlen, M. B., Cawse-Nicholson, K., Wang, A., Anderson, R. G., Aragon, B., Arain, M. A., Baldocchi, D. D., Baker, J. M., Barral, H., Bernacchi, C. J., Bernhofer, C., Biraud, S. C., Bohrer, G., Brunsell, N., Cappelaere, B., Castro- Contreras, S., Chun, J., Conrad, B. J., Cremonese, E., Demarty, J., Desai, A. R., De Ligne, A., Foltýnová, L., Goulden, M. L., Griffis, T. J., Grünwald, T., Johnson, M. S., Kang, M., Kelbe, D., Kowalska, N., Lim, J., Maïnassara, I., McCabe, M. F., Missik, J. E. C., Mohanty, B. P., Moore, C. E., Morillas, L., Morrison, R., Munger, J. W., Posse, G., Richardson, A. D., Russell, E. S., Ryu, Y., Sanchez-Azofeifa, A., Schmidt, M., Schwartz, E., Sharp, I., Šigut, L., Tang, Y., Hulley, G., Anderson, M., Hain, C., French, A., Wood, E., and Hook, S.: ECOSTRESS: NASA's Next Generation Mission to Measure Evapotranspiration From the International Space Station, Water Resour. Res., 56, e2019WR026058, https://doi.org/10.1029/2019WR026058, 2020.
Good, S. P., Noone, D., and Bowen, G.: Hydrologic connectivity constrains partitioning of global terrestrial water fluxes, Science, 349, 175–177, https://doi.org/10.1126/science.aaa5931, 2015.
Green, J. K., Konings, A. G., Alemohammad, S. H., Berry, J., Entekhabi, D., Kolassa, J., Lee, J.-E., and Gentine, P.: Regionally strong feedbacks between the atmosphere and terrestrial biosphere, Nat. Geosci., 10, 410–414, https://doi.org/10.1038/ngeo2957, 2017.
Gruber, A., Su, C.-H., Zwieback, S., Crow, W., Dorigo, W., and Wagner, W.: Recent advances in (soil moisture) triple collocation analysis, Int. J. Appl. Earth Obs. Geoinformation, 45, 200–211, https://doi.org/10.1016/j.jag.2015.09.002, 2016.
He, Y., Wang, C., Hu, J., Mao, H., Duan, Z., Qu, C., Li, R., Wang, M., and Song, X.: Discovering Optimal Triplets for Assessing the Uncertainties of Satellite-Derived Evapotranspiration Products, Remote Sens., 15, 3215, https://doi.org/10.3390/rs15133215, 2023.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., De Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020.
Huffman, G. J., Adler, R. F., Morrissey, M. M., Bolvin, D. T., Curtis, S., Joyce, R., McGavock, B., and Susskind, J.: Global Precipitation at One-Degree Daily Resolution from Multisatellite Observations, J. Hydrometeorol., 2, 36–50, https://doi.org/10.1175/1525-7541(2001)002<0036:GPAODD>2.0.CO;2, 2001.
Jung, M., Koirala, S., Weber, U., Ichii, K., Gans, F., Camps-Valls, G., Papale, D., Schwalm, C., Tramontana, G., and Reichstein, M.: The FLUXCOM ensemble of global land-atmosphere energy fluxes, Sci. Data, 6, 74, https://doi.org/10.1038/s41597-019-0076-8, 2019.
Khan, M. S., Waqas Liaqat, U., Baik, J., and Choi, M.: Stand-alone uncertainty characterization of GLEAM, GLDAS and MOD16 evapotranspiration products using an extended triple collocation approach, Agr. Forest Meteorol., 252, 256–268, https://doi.org/10.1016/j.agrformet.2018.01.022, 2018.
Koppa, A., Rains, D., Hulsman, P., Poyatos, R., and Miralles, D. G.: A deep learning-based hybrid model of global terrestrial evaporation, Nat. Commun., 13, 1912, https://doi.org/10.1038/s41467-022-29543-7, 2022.
Martens, B., Miralles, D. G., Lievens, H., van der Schalie, R., de Jeu, R. A. M., Fernández-Prieto, D., Beck, H. E., Dorigo, W. A., and Verhoest, N. E. C.: GLEAM v3: satellite-based land evaporation and root-zone soil moisture, Geosci. Model Dev., 10, 1903–1925, https://doi.org/10.5194/gmd-10-1903-2017, 2017.
Martens, B., Schumacher, D. L., Wouters, H., Muñoz-Sabater, J., Verhoest, N. E. C., and Miralles, D. G.: Evaluating the land-surface energy partitioning in ERA5, Geosci. Model Dev., 13, 4159–4181, https://doi.org/10.5194/gmd-13-4159-2020, 2020.
McColl, K. A. and Rigden, A. J.: Emergent Simplicity of Continental Evapotranspiration, Geophys. Res. Lett., 47, https://doi.org/10.1029/2020GL087101, 2020.
McColl, K. A., Vogelzang, J., Konings, A. G., Entekhabi, D., Piles, M., and Stoffelen, A.: Extended triple collocation: Estimating errors and correlation coefficients with respect to an unknown target, Geophys. Res. Lett., 41, 6229–6236, https://doi.org/10.1002/2014GL061322, 2014.
McColl, K. A., Salvucci, G. D., and Gentine, P.: Surface Flux Equilibrium Theory Explains an Empirical Estimate of Water-Limited Daily Evapotranspiration, J. Adv. Model. Earth Syst., 11, 2036–2049, https://doi.org/10.1029/2019MS001685, 2019.
McCormick, E. L., Sanders, L., McColl, K., and Konings, A.: Daily surface flux equilibrium (SFE) ET across CONUS (v1.0.0), Zenodo [code and data set], https://doi.org/10.5281/zenodo.17903676, 2026.
Mesinger, F., DiMego, G., Kalnay, E., Mitchell, K., Shafran, P. C., Ebisuzaki, W., Jović, D., Woollen, J., Rogers, E., Berbery, E. H., Ek, M. B., Fan, Y., Grumbine, R., Higgins, W., Li, H., Lin, Y., Manikin, G., Parrish, D., and Shi, W.: North American Regional Reanalysis, B. Am. Meteorol. Soc., 87, 343–360, https://doi.org/10.1175/BAMS-87-3-343, 2006.
Miralles, D. G., Crow, W. T., and Cosh, M. H.: Estimating Spatial Sampling Errors in Coarse-Scale Soil Moisture Estimates Derived from Point-Scale Observations, J. Hydrometeorol., 11, 1423–1429, https://doi.org/10.1175/2010JHM1285.1, 2010.
Miralles, D. G., Holmes, T. R. H., De Jeu, R. A. M., Gash, J. H., Meesters, A. G. C. A., and Dolman, A. J.: Global land-surface evaporation estimated from satellite-based observations, Hydrol. Earth Syst. Sci., 15, 453–469, https://doi.org/10.5194/hess-15-453-2011, 2011.
Miralles, D. G., Jiménez, C., Jung, M., Michel, D., Ershadi, A., McCabe, M. F., Hirschi, M., Martens, B., Dolman, A. J., Fisher, J. B., Mu, Q., Seneviratne, S. I., Wood, E. F., and Fernández-Prieto, D.: The WACMOS-ET project – Part 2: Evaluation of global terrestrial evaporation data sets, Hydrol. Earth Syst. Sci., 20, 823–842, https://doi.org/10.5194/hess-20-823-2016, 2016.
Miralles, D. G., Bonte, O., Koppa, A., Baez-Villanueva, O. M., Tronquo, E., Zhong, F., Beck, H. E., Hulsman, P., Dorigo, W., Verhoest, N. E. C., and Haghdoost, S.: GLEAM4: global land evaporation and soil moisture dataset at 0.1° resolution from 1980 to near present, Sci. Data, 12, 416, https://doi.org/10.1038/s41597-025-04610-y, 2025.
Mu, Q., Zhao, M., and Running, S. W.: Improvements to a MODIS global terrestrial evapotranspiration algorithm, Remote Sens. Environ., 115, 1781–1800, https://doi.org/10.1016/j.rse.2011.02.019, 2011.
Muñoz-Sabater, J., Dutra, E., Agustí-Panareda, A., Albergel, C., Arduini, G., Balsamo, G., Boussetta, S., Choulga, M., Harrigan, S., Hersbach, H., Martens, B., Miralles, D. G., Piles, M., Rodríguez-Fernández, N. J., Zsoter, E., Buontempo, C., and Thépaut, J.-N.: ERA5-Land: a state-of-the-art global reanalysis dataset for land applications, Earth Syst. Sci. Data, 13, 4349–4383, https://doi.org/10.5194/essd-13-4349-2021, 2021.
Oki, T. and Kanae, S.: Global Hydrological Cycles and World Water Resources, Freshw. Resour., 313, https://doi.org/10.1126/science.1128845, 2006.
Salvucci, G. D. and Gentine, P.: Emergent relation between surface vapor conductance and relative humidity profiles yields evaporation rates from weather data, Proc. Natl. Acad. Sci. USA, https://doi.org/10.1073/pnas.1215844110, 2013.
Santanello Jr., J. A. and Friedl, M. A.: Diurnal covariation in soil heat flux and net radiation, J. Appl. Meteorol., 42, 851–862, 2003.
Savoca, M. E., Senay, G. B., Maupin, M. A., Kenny, J. F., and Perry, C. A.: Actual evapotranspiration modeling using the operational Simplified Surface Energy Balance (SSEBop) approach, Reston, VA, https://doi.org/10.3133/sir20135126, 2013.
Scipal, K., Holmes, T., De Jeu, R., Naeimi, V., and Wagner, W.: A possible solution for the problem of estimating the error structure of global soil moisture data sets, Geophys. Res. Lett., 35, https://doi.org/10.1029/2008gl035599, 2008.
Stocker, B. D., Tumber-Dávila, S. J., Konings, A. G., Anderson, M. C., Hain, C., and Jackson, R. B.: Global patterns of water storage in the rooting zones of vegetation, Nat. Geosci., 16, 250–256, https://doi.org/10.1038/s41561-023-01125-2, 2023.
Stoffelen, A.: Toward the true near-surface wind speed: Error modeling and calibration using triple collocation, J. Geophys. Res. Oceans, 103, 7755–7766, https://doi.org/10.1029/97JC03180, 1998.
Su, C. H., Ryu, D., Crow, W. T., and Western, A. W.: Beyond triple collocation: Applications to soil moisture monitoring, J. Geophys. Res.-Atmos., 119, 6419–6439, 2014.
Sun, J., McColl, K. A., Wang, Y., Rigden, A. J., Lu, H., Yang, K., Li, Y., and Santanello, J. A.: Global evaluation of terrestrial near-surface air temperature and specific humidity retrievals from the Atmospheric Infrared Sounder (AIRS), Remote Sens. Environ., 252, 112146, https://doi.org/10.1016/j.rse.2020.112146, 2021.
Teuling, A. J., Seneviratne, S. I., Stöckli, R., Reichstein, M., Moors, E., Ciais, P., Luyssaert, S., Van Den Hurk, B., Ammann, C., Bernhofer, C., Dellwik, E., Gianelle, D., Gielen, B., Grünwald, T., Klumpp, K., Montagnani, L., Moureaux, C., Sottocornola, M., and Wohlfahrt, G.: Contrasting response of European forest and grassland energy exchange to heatwaves, Nat. Geosci., 3, 722–727, https://doi.org/10.1038/ngeo950, 2010.
Thakur, H., Raghav, P., Kumar, M., and Wolkeba, F.: Surface Flux Equilibrium Theory-Derived Evapotranspiration Estimate Outperforms ECOSTRESS, MODIS, and SSEBop Products, Geophys. Res. Lett., 52, e2025GL114822, https://doi.org/10.1029/2025GL114822, 2025.
United States Census Bureau: “tl_2023_us_coastline”, TIGER/Line Shapefiles, Nation, U.S., Coastline, 2023, https://catalog.data.gov/dataset/tiger-line-shapefile-2023-nation-u-s-coastline (last access: 1 August 2025), 2019.
Wei, Y., Liu, S., Huntzinger, D. N., Michalak, A. M., Viovy, N., Post, W. M., Schwalm, C. R., Schaefer, K., Jacobson, A. R., Lu, C., Tian, H., Ricciuto, D. M., Cook, R. B., Mao, J., and Shi, X.: The North American Carbon Program Multi-scale Synthesis and Terrestrial Model Intercomparison Project – Part 2: Environmental driver data, Geosci. Model Dev., 7, 2875–2893, https://doi.org/10.5194/gmd-7-2875-2014, 2014.
Xia, Y., Mitchell, K., Ek, M., Sheffield, J., Cosgrove, B., Wood, E., Luo, L., Alonge, C., Wei, H., Meng, J., Livneh, B., Lettenmaier, D., Koren, V., Duan, Q., Mo, K., Fan, Y., and Mocko, D.: Continental-scale water and energy flux analysis and validation for the North American Land Data Assimilation System project phase 2 (NLDAS-2): 1. Intercomparison and application of model products, J. Geophys. Res.-Atmos., 117, 2011JD016048, https://doi.org/10.1029/2011JD016048, 2012.
Yamazaki, D., Ikeshima, D., Sosa, J., Bates, P. D., Allen, G. H., and Pavelsky, T. M.: MERIT Hydro: A High-Resolution Global Hydrography Map Based on Latest Topography Dataset, Water Resour. Res., 55, 5053–5073, https://doi.org/10.1029/2019WR024873, 2019.
Yang, Y., Roderick, M. L., Guo, H., Miralles, D. G., Zhang, L., Fatichi, S., Luo, X., Zhang, Y., McVicar, T. R., Tu, Z., Keenan, T. F., Fisher, J. B., Gan, R., Zhang, X., Piao, S., Zhang, B., and Yang, D.: Evapotranspiration on a greening Earth, Nat. Rev. Earth Environ., 4, 626–641, https://doi.org/10.1038/s43017-023-00464-3, 2023.
Yilmaz, M. T. and Crow, W. T.: Evaluation of Assumptions in Soil Moisture Triple Collocation Analysis, J. Hydrometeorol., 15, 1293–1302, https://doi.org/10.1175/JHM-D-13-0158.1, 2014.
Yin, X., Jiang, B., Liang, S., Li, S., Zhao, X., Wang, Q., Xu, J., Han, J., Liang, H., Zhang, X., Liu, Q., Yao, Y., Jia, K., and Xie, X.: Significant discrepancies of land surface daily net radiation among ten remotely sensed and reanalysis products, Int. J. Digit. Earth, 16, 3725–3752, https://doi.org/10.1080/17538947.2023.2253211, 2023.
Zhao, M., A, G., Liu, Y., and Konings, A. G.: Evapotranspiration frequently increases during droughts, Nat. Clim. Change, 12, 1024–1030, https://doi.org/10.1038/s41558-022-01505-3, 807 2022.
Zhu, W., Yu, X., Wei, J., and Lv, A.: Surface flux equilibrium estimates of evaporative fraction and evapotranspiration at global scale: Accuracy evaluation and performance comparison, Agric. Water Manag., 291, 108609, https://doi.org/10.1016/j.agwat.2023.108609, 2024.
Short summary
We estimate daily evapotranspiration (ET) across the United States using the ‘surface flux equilibrium’ approach, which assumes that the balance of temperature and humidity in the atmosphere reflects recent ET on land. Using triple collocation, we compare our estimates to three other ET datasets and find that the surface flux equilibrium ET method performs well. Surface flux equilibrium ET may therefore be useful for hydrologic studies where simple, parameter-free ET estimates are advantageous.
We estimate daily evapotranspiration (ET) across the United States using the ‘surface flux...
