• Abatzoglou, J. T., 2013: Development of gridded surface meteorological data for ecological applications and modelling. Int. J. Climatol., 33, 121131, https://doi.org/10.1002/joc.3413.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Alam, M., and T. Trooien, 2001: Estimating reference evapotranspiration with an atmometer. Appl. Eng. Agric., 17, 153158, https://doi.org/10.13031/2013.5458.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Allen, R. G., 2009: Manual: REF-ET: Reference evapotranspiration calculation software for FAO and ASCE standardized equations. University of Idaho, 76 pp., https://www.webpages.uidaho.edu/ce325bae355/references/manual_prn.pdf.

  • Allen, R. G., L. S. Pereira, D. Raes, and M. Smith, 1998: Crop evapotranspiration: Guidelines for computing crop water requirements. FAO Irrigation and Drainage Paper 56, 300 pp., www.fao.org/docrep/X0490E/X0490E00.htm.

  • Allen, R. G., I. A. Walter, R. L. Elliott, T. A. Howell, D. Itenfisu, M. E. Jensen, and R. L. Snyder, Eds., 2005: The ASCE standardized reference evapotranspiration equation. ASCE-EWRI Technical Committee Rep., 173 pp., https://doi.org/10.1061/9780784408056.

    • Crossref
    • Export Citation
  • Allen, R. G., M. Tasumi, and R. Trezza, 2007: Satellite-based energy balance for Mapping Evapotranspiration with Internalized Calibration (METRIC)—Model. J. Irrig. Drain. Eng., 133, 380394, https://doi.org/10.1061/(ASCE)0733-9437(2007)133:4(380).

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Anderson, M. C., and Coauthors, 2011: Mapping daily evapotranspiration at field to continental scales using geostationary and polar orbiting satellite imagery. Hydrol. Earth Syst. Sci., 15, 223239, https://doi.org/10.5194/hess-15-223-2011.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Anderson, M. C., R. G. Allen, A. Morse, and W. P. Kustas, 2012: Use of Landsat thermal imagery in monitoring evapotranspiration and managing water resources. Remote Sens. Environ., 122, 5065, https://doi.org/10.1016/j.rse.2011.08.025.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Aumann, H. H., and Coauthors, 2003: AIRS/AMSU/HSB on the Aqua mission: Design, science objectives, data products, and processing systems. IEEE Trans. Geosci. Remote Sens., 41, 253264, https://doi.org/10.1109/TGRS.2002.808356.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Azorin-Molina, C., and Coauthors, 2015: Atmospheric evaporative demand observations, estimates and driving factors in Spain (1961–2011). J. Hydrol., 523, 262277, https://doi.org/10.1016/j.jhydrol.2015.01.046.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bandyopadhyay, A., A. Bhadra, N. Raghuwanshi, and R. Singh, 2009: Temporal trends in estimates of reference evapotranspiration over India. J. Hydrol. Eng., 14, 508515, https://doi.org/10.1061/(ASCE)HE.1943-5584.0000006.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Barr, A., G. Van der Kamp, T. Black, J. McCaughey, and Z. Nesic, 2012: Energy balance closure at the BERMS flux towers in relation to the water balance of the White Gull Creek watershed 1999–2009. Agric. For. Meteor., 153, 313, https://doi.org/10.1016/j.agrformet.2011.05.017.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bastiaanssen, W. G., M. Menenti, R. Feddes, and A. Holtslag, 1998: A remote sensing Surface Energy Balance Algorithm for Land (SEBAL). 1. Formulation. J. Hydrol., 212–213, 198212, https://doi.org/10.1016/S0022-1694(98)00253-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Broner, I., and R. Law, 1991: Evaluation of a modified atmometer for estimating reference ET. Irrig. Sci., 12, 2126, https://doi.org/10.1007/BF00190705.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cavalcante, R. B. L., D. B. da Silva Ferreira, P. R. M. Pontes, R. G. Tedeschi, C. P. W. da Costa, and E. B. de Souza, 2020: Evaluation of extreme rainfall indices from CHIRPS precipitation estimates over the Brazilian Amazonia. Atmos. Res., 238, 104879, https://doi.org/10.1016/j.atmosres.2020.104879.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, F., and Coauthors, 1996: Modeling of land surface evaporation by four schemes and comparison with FIFE observations. J. Geophys. Res., 101, 72517268, https://doi.org/10.1029/95JD02165.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, N., and Coauthors, 2020: Drought propagation in northern China plain: A comparative analysis of GLDAS and MERRA-2 datasets. J. Hydrol., 588, 125026, https://doi.org/10.1016/j.jhydrol.2020.125026.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Clark, M. P., and Coauthors, 2011: Representing spatial variability of snow water equivalent in hydrologic and land-surface models: A review. Water Resour. Res., 47, W07539, https://doi.org/10.1029/2011WR010745.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cobaner, M., H. Citakoğlu, T. Haktanir, and O. Kisi, 2017: Modifying Hargreaves–Samani equation with meteorological variables for estimation of reference evapotranspiration in Turkey. Hydrol. Res., 48, 480497, https://doi.org/10.2166/nh.2016.217.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Di Bella, C., C. Rebella, and J. M. Paruelo, 2000: Evapotranspiration estimates using NOAA AVHRR imagery in the Pampa region of Argentina. Int. J. Remote Sens., 21, 791797, https://doi.org/10.1080/014311600210579.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Doorenbos, J., and W. O. Pruitt, 1975: Guidelines for predicting crop water requirements. FAO Irrigation and Drainage Paper 24, 192 pp., https://www.nrc.gov/docs/ML1821/ML18215A282.pdf.

  • Ficklin, D. L., S. L. Letsinger, H. Gholizadeh, and J. T. Maxwell, 2015: Incorporation of the Penman–Monteith potential evapotranspiration method into a Palmer drought severity index tool. Comput. Geosci., 85, 136141, https://doi.org/10.1016/j.cageo.2015.09.013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Foken, T., 2008: The energy balance closure problem: An overview. Ecol. Appl., 18, 13511367, https://doi.org/10.1890/06-0922.1.

  • Funk, C., and Coauthors, 2015: The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes. Sci. Data, 2, 150066, https://doi.org/10.1038/sdata.2015.66.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gavilán, P., and F. Castillo-Llanque, 2009: Estimating reference evapotranspiration with atmometers in a semiarid environment. Agric. Water Manage., 96, 465472, https://doi.org/10.1016/j.agwat.2008.09.011.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gebler, S., H. H. Franssen, T. Pütz, H. Post, M. Schmidt, and H. Vereecken, 2015: Actual evapotranspiration and precipitation measured by lysimeters: A comparison with eddy covariance and tipping bucket. Hydrol. Earth Syst. Sci., 19, 21452161, https://doi.org/10.5194/hess-19-2145-2015.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gleason, D. J., 2013: Evapotranspiration-based irrigation scheduling tools for use in eastern Colorado. M.S. thesis, Department of Soil & Crop Sciences, Colorado State University, 235 pp., http://hdl.handle.net/10217/79053.

  • Gocic, M., and S. Trajkovic, 2014: Drought characterisation based on water surplus variability index. Water Resour. Manage., 28, 31793191, https://doi.org/10.1007/s11269-014-0665-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gonzalez Miralles, D., T. Holmes, R. De Jeu, J. Gash, A. Meesters, and A. Dolman, 2011: Global land-surface evaporation estimated from satellite-based observations. Hydrol. Earth Syst. Sci., 15, 453469, https://doi.org/10.5194/hess-15-453-2011.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Grismer, M., M. Orang, R. Snyder, and R. Matyac, 2002: Pan evaporation to reference evapotranspiration conversion methods. J. Irrig. Drain. Eng., 128, 180184, https://doi.org/10.1061/(ASCE)0733-9437(2002)128:3(180).

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gruber, A., W. A. Dorigo, W. Crow, and W. Wagner, 2017: Triple collocation-based merging of satellite soil moisture retrievals. IEEE Trans. Geosci. Remote Sens., 55, 67806792, https://doi.org/10.1109/TGRS.2017.2734070.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Guttman, N. B., and R. G. Quayle, 1996: A historical perspective of U.S. climate divisions. Bull. Amer. Meteor. Soc., 77, 293304, https://doi.org/10.1175/1520-0477(1996)077<0293:AHPOUC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hargreaves, G. H., and Z. A. Samani, 1985: Reference crop evapotranspiration from temperature. Appl. Eng. Agric., 1, 9699, https://doi.org/10.13031/2013.26773.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hidalgo, H. G., D. R. Cayan, and M. D. Dettinger, 2005: Sources of variability of evapotranspiration in California. J. Hydrometeor., 6, 319, https://doi.org/10.1175/JHM-398.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hirschi, M., D. Michel, I. Lehner, and S. I. Seneviratne, 2017: A site-level comparison of lysimeter and eddy covariance flux measurements of evapotranspiration. Hydrol. Earth Syst. Sci., 21, 18091825, https://doi.org/10.5194/hess-21-1809-2017.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hobbins, M. T., J. A. Ramírez, and T. C. Brown, 2004: Trends in pan evaporation and actual evapotranspiration across the conterminous U.S.: Paradoxical or complementary? Geophys. Res. Lett., 31, L13503, https://doi.org/10.1029/2004GL019846.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huffman, G. J., D. T. Bolvin, D. Braithwaite, K. Hsu, R. Joyce, P. Xie, and S.-H. Yoo, 2015: NASA Global Precipitation Measurement (GPM) Integrated Multi-satellite Retrievals for GPM (IMERG). NASA Algorithm Theoretical Basis Doc., version 4, 30 pp.

  • Jamshidi, S., S. Zand-Parsa, M. Naghdyzadegan Jahromi, and D. Niyogi, 2019: Application of a simple Landsat-MODIS fusion model to estimate evapotranspiration over a heterogeneous sparse vegetation region. Remote Sens., 11, 741, https://doi.org/10.3390/rs11070741.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jamshidi, S., S. Zand-Parsa, A. A. Kamgar-Haghighi, A. R. Shahsavar, and D. Niyogi, 2020: Evapotranspiration, crop coefficients, and physiological responses of citrus trees in semi-arid climatic conditions. Agric. Water Manage., 227, 105838, https://doi.org/10.1016/j.agwat.2019.105838.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jia, B., Z. Xie, A. Dai, C. Shi, and F. Chen, 2013: Evaluation of satellite and reanalysis products of downward surface solar radiation over East Asia: Spatial and seasonal variations. J. Geophys. Res. Atmos., 118, 34313446, https://doi.org/10.1002/jgrd.50353.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Khan, M. S., J. Baik, and M. Choi, 2020: Inter-comparison of evapotranspiration datasets over heterogeneous landscapes across Australia. Adv. Space Res., 66, 533545, https://doi.org/10.1016/j.asr.2020.04.037.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kistner, E., O. Kellner, J. Andresen, D. Todey, and L. W. Morton, 2018: Vulnerability of specialty crops to short-term climatic variability and adaptation strategies in the Midwestern USA. Climatic Change, 146, 145158, https://doi.org/10.1007/s10584-017-2066-1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lawrimore, J. H., and T. C. Peterson, 2000: Pan evaporation trends in dry and humid regions of the United States. J. Hydrometeor., 1, 543546, https://doi.org/10.1175/1525-7541(2000)001<0543:PETIDA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liang, X., D. P. Lettenmaier, E. F. Wood, and S. J. Burges, 1994: A simple hydrologically based model of land surface water and energy fluxes for general circulation models. J. Geophys. Res., 99, 14 41514 428, https://doi.org/10.1029/94JD00483.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lokupitiya, E., and Coauthors, 2016: Carbon and energy fluxes in cropland ecosystems: A model-data comparison. Biogeochemistry, 129, 5376, https://doi.org/10.1007/s10533-016-0219-3.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Long, D., L. Longuevergne, and B. R. Scanlon, 2014: Uncertainty in evapotranspiration from land surface modeling, remote sensing, and GRACE satellites. Water Resour. Res., 50, 11311151, https://doi.org/10.1002/2013WR014581.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Magliulo, V., R. d’Andria, and G. Rana, 2003: Use of the modified atmometer to estimate reference evapotranspiration in Mediterranean environments. Agric. Water Manage., 63 (1), 114, https://doi.org/10.1016/S0378-3774(03)00098-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Makkink, G., 1957: Testing the Penman formula by means of lysimeters. J. Inst. Water Eng., 11, 277288.

  • Maleki, A., A. Naderi, R. Naseri, A. Fathi, S. Bahamin, and R. Maleki, 2013: Physiological performance of soybean cultivars under drought stress. Bull. Env. Pharmacol. Life Sci, 2, 3844.

    • Search Google Scholar
    • Export Citation
  • Mansouri-Far, C., S. A. M. M. Sanavy, and S. F. Saberali, 2010: Maize yield response to deficit irrigation during low-sensitive growth stages and nitrogen rate under semi-arid climatic conditions. Agric. Water Manage., 97, 1222, https://doi.org/10.1016/j.agwat.2009.08.003.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Martens, B., and Coauthors, 2017: GLEAM v3: Satellite-based land evaporation and root-zone soil moisture. Geosci. Model Dev., 10, 19031925, https://doi.org/10.5194/gmd-10-1903-2017.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Martinez, C. J., and M. Thepadia, 2010: Estimating reference evapotranspiration with minimum data in Florida. J. Irrig. Drain. Eng., 136, 494501, https://doi.org/10.1061/(ASCE)IR.1943-4774.0000214.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Matli, G., 2018: Indiana Agricultural Statistics 2017-2018. USDA National Agricultural Statistics Service Great Lakes Region Bull., 148 pp., https://www.nass.usda.gov/Statistics_by_State/Indiana/Publications/Annual_Statistical_Bulletin/1718/IN1718Bulletin.pdf.

  • Meyers, T., 2016: AmeriFlux US-Bo1 Bondville. AmeriFlux, accessed 1 July 2019, https://doi.org/10.17190/AMF/1246036.

  • Miralles, D., T. Holmes, R. De Jeu, J. Gash, A. Meesters, and A. Dolman, 2011: Global land-surface evaporation estimated from satellite-based observations. Hydrol. Earth Syst. Sci., 15, 453469, https://doi.org/10.5194/hess-15-453-2011.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Morley, S. K., T. V. Brito, and D. T. Welling, 2018: Measures of model performance based on the log accuracy ratio. Space Wea., 16, 6988, https://doi.org/10.1002/2017SW001669.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mu, Q., M. Zhao, and S. W. Running, 2011: Improvements to a MODIS global terrestrial evapotranspiration algorithm. Remote Sens. Environ., 115, 17811800, https://doi.org/10.1016/j.rse.2011.02.019.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Navarro-Hellín, H., J. Martínez-del-Rincon, R. Domingo-Miguel, F. Soto-Valles, and R. Torres-Sánchez, 2016: A decision support system for managing irrigation in agriculture. Comput. Electron. Agric., 124, 121131, https://doi.org/10.1016/j.compag.2016.04.003.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Niu, G. Y., and Coauthors, 2011: The community Noah land surface model with multiparameterization options (Noah-MP): 1. Model description and evaluation with local-scale measurements. J. Geophys. Res., 116, D12109, https://doi.org/10.1029/2010JD015139.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ohmura, A., and M. Wild, 2002: Is the hydrological cycle accelerating? Science, 298, 13451346, https://doi.org/10.1126/science.1078972.

  • Oliver, J. E., 2009: Indiana’s Weather and Climate. Indiana University Press, 272 pp.

  • Peng, L., Y. Li, and H. Feng, 2017: The best alternative for estimating reference crop evapotranspiration in different sub-regions of mainland China. Sci. Rep., 7, 5458, https://doi.org/10.1038/s41598-017-05660-y.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Peterson, T. C., V. S. Golubev, and P. Ya. Groisman, 1995: Evaporation losing its strength. Nature, 377, 687688, https://doi.org/10.1038/377687b0.

  • Philip, R., and K. Novick, 2016: AmeriFlux US-MMS Morgan Monroe State Forest. AmeriFlux, accessed 1 July 2019, https://doi.org/10.17190/AMF/1246080.

  • Prasad, P., S. Staggenborg, and Z. Ristic, 2008: Impacts of drought and/or heat stress on physiological, developmental, growth, and yield processes of crop plants. Response of Crops to Limited Water: Understanding and Modeling Water Stress Effects on Plant Growth Processes, L. R. Ahuja et al., Eds., American Society of Agronomy, Crop Science Society of America, Soil Science Society of America, 301–355.

    • Crossref
    • Export Citation
  • Priestley, C. H. B., and R. Taylor, 1972: On the assessment of surface heat flux and evaporation using large-scale parameters. Mon. Wea. Rev., 100, 8192, https://doi.org/10.1175/1520-0493(1972)100<0081:OTAOSH>2.3.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Purdy, A., J. Fisher, M. Goulden, and J. Famiglietti, 2016: Ground heat flux: An analytical review of 6 models evaluated at 88 sites and globally. J. Geophys. Res. Biogeosci., 121, 30453059, https://doi.org/10.1002/2016JG003591.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Roderick, M. L., and G. D. Farquhar, 2002: The cause of decreased pan evaporation over the past 50 years. Science, 298, 14101411, https://doi.org/10.1126/science.1075390-a.

    • Search Google Scholar
    • Export Citation
  • Rojas, J. P., and R. E. Sheffield, 2013: Evaluation of daily reference evapotranspiration methods as compared with the ASCE-EWRI Penman–Monteith equation using limited weather data in northeast Louisiana. J. Irrig. Drain. Eng., 139, 285292, https://doi.org/10.1061/(ASCE)IR.1943-4774.0000523.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Running, S., Q. Mu, and M. Zhao, 2017: MOD16A2 MODIS/Terra Net Evapotranspiration 8-Day L4 Global 500m SIN Grid V006. NASA EOSDIS Land Processes DAAC, accessed 1 July 2019, https://doi.org/10.5067/MODIS/MOD16A2.006.

    • Crossref
    • Export Citation
  • Senay, G. B., S. Bohms, R. K. Singh, P. H. Gowda, N. M. Velpuri, H. Alemu, and J. P. Verdin, 2013: Operational evapotranspiration mapping using remote sensing and weather datasets: A new parameterization for the SSEB approach. J. Amer. Water Resour. Assoc., 49, 577591, https://doi.org/10.1111/jawr.12057.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sharifnezhadazizi, Z., H. Norouzi, S. Prakash, C. Beale, and R. Khanbilvardi, 2019: A global analysis of land surface temperature diurnal cycle using MODIS observations. J. Appl. Meteor. Climatol., 58, 12791291, https://doi.org/10.1175/JAMC-D-18-0256.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shiri, J., Ö. Kişi, G. Landeras, J. J. López, A. H. Nazemi, and L. C. Stuyt, 2012: Daily reference evapotranspiration modeling by using genetic programming approach in the Basque Country (northern Spain). J. Hydrol., 414–415, 302316, https://doi.org/10.1016/j.jhydrol.2011.11.004.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Straatmann, Z., G. Stevens, E. Vories, P. Guinan, J. Travlos, and M. Rhine, 2018: Measuring short-crop reference evapotranspiration in a humid region using electronic atmometers. Agric. Water Manage., 195, 180186, https://doi.org/10.1016/j.agwat.2017.10.007.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sun, S., P. Wu, Y. Wang, and X. Zhao, 2015: Impact of changing cropping pattern on the regional agricultural water productivity. J. Agric. Sci., 153, 767778, https://doi.org/10.1017/S0021859614000938.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Szilagyi, J., G. G. Katul, and M. B. Parlange, 2001: Evapotranspiration intensifies over the conterminous United States. J. Water Resour. Plann. Manage., 127, 354362, https://doi.org/10.1061/(ASCE)0733-9496(2001)127:6(354).

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Taylor, K. E., 2001: Summarizing multiple aspects of model performance in a single diagram. J. Geophys. Res., 106, 71837192, https://doi.org/10.1029/2000JD900719.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Todd, R. W., S. R. Evett, and T. A. Howell, 2000: The Bowen ratio-energy balance method for estimating latent heat flux of irrigated alfalfa evaluated in a semi-arid, advective environment. Agric. For. Meteor., 103, 335348, https://doi.org/10.1016/S0168-1923(00)00139-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tofallis, C., 2015: A better measure of relative prediction accuracy for model selection and model estimation. J. Oper. Res. Soc., 66, 13521362, https://doi.org/10.1057/jors.2014.103.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Trajkovic, S., 2007: Hargreaves versus Penman–Monteith under humid conditions. J. Irrig. Drain. Eng., 133, 3842, https://doi.org/10.1061/(ASCE)0733-9437(2007)133:1(38).

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Turc, L., 1961: Evaluation des besoins en eau d’irrigation, évapotranspiration potentielle. Ann. Agron., 12, 1349.

  • USDA, 2019: Quick Stats. National Agricultural Statistics Service, https://www.nass.usda.gov/Quick_Stats/index.php.

  • Velpuri, N. M., G. B. Senay, R. K. Singh, S. Bohms, and J. P. Verdin, 2013: A comprehensive evaluation of two MODIS evapotranspiration products over the conterminous United States: Using point and gridded FLUXNET and water balance ET. Remote Sens. Environ., 139, 3549, https://doi.org/10.1016/j.rse.2013.07.013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Walter, I. A., and Coauthors, 2000: ASCE’s standardized reference evapotranspiration equation. Watershed Management and Operations Management Conf. 2000, 11 pp., https://doi.org/10.1061/40499(2000)126.

    • Crossref
    • Export Citation
  • Walter, M. T., D. S. Wilks, J.-Y. Parlange, and R. L. Schneider, 2004: Increasing evapotranspiration from the conterminous United States. J. Hydrometeor., 5, 405408, https://doi.org/10.1175/1525-7541(2004)005<0405:IEFTCU>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wielicki, B. A., B. R. Barkstrom, E. F. Harrison, R. B. Lee III, G. L. Smith, and J. E. Cooper, 1996: Clouds and the Earth’s Radiant Energy System (CERES): An earth observing system experiment. Bull. Amer. Meteor. Soc., 77, 853868, https://doi.org/10.1175/1520-0477(1996)077<0853:CATERE>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wilson, K., and Coauthors, 2002: Energy balance closure at FLUXNET sites. Agric. For. Meteor., 113, 223243, https://doi.org/10.1016/S0168-1923(02)00109-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wright, J. L., 1982: New evapotranspiration crop coefficients. J. Irrig. Drain. Div., 108, 5774.

  • Wright, J. L, 1996: Derivation of alfalfa and grass reference evapotranspiration. Proc. Int. Conf. on Evapotranspiration and Irrigation Scheduling, San Antonio, TX, ASAE, 133–140.

  • Xia, Y., and Coauthors, 2012: 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., 117, D03109, https://doi.org/10.1029/2011JD016048.

    • Search Google Scholar
    • Export Citation
  • Yeh, P. J. F., and C. Wu, 2018: Recent acceleration of the terrestrial hydrologic cycle in the US Midwest. J. Geophys. Res. Atmos., 123, 29933008, https://doi.org/10.1002/2017JD027706.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, B., Y. Xia, B. Long, M. Hobbins, X. Zhao, C. Hain, Y. Li, and M. C. Anderson, 2020: Evaluation and comparison of multiple evapotranspiration data models over the contiguous United States: Implications for the next phase of NLDAS (NLDAS-Testbed) development. Agric. For. Meteor., 280, 107810, https://doi.org/10.1016/j.agrformet.2019.107810.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, X., N. Lu, H. Jiang, and L. Yao, 2020: Evaluation of reanalysis surface incident solar radiation data in China. Sci. Rep., 10, 3494, https://doi.org/10.1038/s41598-020-60460-1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, Z., F. Tian, H. Hu, and P. Yang, 2014: A comparison of methods for determining field evapotranspiration: Photosynthesis system, sap flow, and eddy covariance. Hydrol. Earth Syst. Sci., 18, 10531072, https://doi.org/10.5194/hess-18-1053-2014.

    • Crossref
    • Search Google Scholar
    • Export Citation
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Evapotranspiration Climatology of Indiana Using In Situ and Remotely Sensed Products

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  • 1 Department of Agronomy, Purdue University, West Lafayette, Indiana
  • | 2 Department of Earth, Atmospheric and Planetary Sciences, Purdue University, West Lafayette, Indiana
  • | 3 Indiana Department of Natural Resources, Division of Water, Indianapolis, Indiana
  • | 4 Climate Impact Company, Plymouth, Massachusetts
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Abstract

An intercomparison of multiresolution evapotranspiration (ET) datasets with reference to ground-based measurements for the development of regional reference (ETref) and actual (ETa) evapotranspiration maps over Indiana is presented. A representative ETref equation for the state is identified by evaluating 10 years of in situ measurements (2009–19). A statewide ETref climatology is developed using the ETref equation and high-resolution surface meteorological data from the gridded surface meteorological dataset (gridMET). For ETa analyses, MODIS, Simplified Surface Energy Balance Operational dataset (SSEBop), Global Land Evaporation Amsterdam Model (GLEAM) (versions 3.3a and 3.3b), and NLDAS (Noah and VIC) datasets are evaluated using AmeriFlux data. Thirty years of rainfall data from Climate Hazards Group Infrared Precipitation with Station Data Rainfall (CHIRPS) are used with the ET datasets to develop effective precipitation fields. Results show that the standardized Penman–Monteith equation performs as the best ETref equation with median symmetric accuracy (MSA) of 0.37, Taylor’s skill score (TSC) of 0.89, and r2 = 0.83. The analysis shows that the gridMET dataset overestimates wind speed and requires adjustment before a series of statewide ETref climatology maps are generated (1990–2020). For ETa, the MODIS and GLEAM (3.3b) datasets outperform the rest, with MSA = 0.5, TSC = 0.8, and r2 = 0.8. The state ETa dataset is generated using all MODIS data from 2003 and blending the MODIS data with GLEAM (3.3b) to cover data unavailability. Using the top-performing datasets, annual ETref for Indiana is computed as 1110 mm, ETa as 708 mm, and precipitation as 1091 mm. A marginal increasing climatological trend is found for Indiana’s ETref (0.013 mm yr−1) while ETa is found to be relatively stable. The state’s water availability, defined as rainfall minus ETa, has remained positive and stable at 0.99 mm day−1 (annual magnitude of +3820 mm).

Current affiliation: Department of Geological Sciences, Jackson School of Geosciences, University of Texas at Austin, Austin, Texas.

Current affiliation: Department of Civil, Architectural, and Environmental Engineering, Cockrell School of Engineering, University of Texas at Austin, Austin, Texas.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JAMC-D-20-0024.s1.

© 2020 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Dev Niyogi, happy1@utexas.edu

Abstract

An intercomparison of multiresolution evapotranspiration (ET) datasets with reference to ground-based measurements for the development of regional reference (ETref) and actual (ETa) evapotranspiration maps over Indiana is presented. A representative ETref equation for the state is identified by evaluating 10 years of in situ measurements (2009–19). A statewide ETref climatology is developed using the ETref equation and high-resolution surface meteorological data from the gridded surface meteorological dataset (gridMET). For ETa analyses, MODIS, Simplified Surface Energy Balance Operational dataset (SSEBop), Global Land Evaporation Amsterdam Model (GLEAM) (versions 3.3a and 3.3b), and NLDAS (Noah and VIC) datasets are evaluated using AmeriFlux data. Thirty years of rainfall data from Climate Hazards Group Infrared Precipitation with Station Data Rainfall (CHIRPS) are used with the ET datasets to develop effective precipitation fields. Results show that the standardized Penman–Monteith equation performs as the best ETref equation with median symmetric accuracy (MSA) of 0.37, Taylor’s skill score (TSC) of 0.89, and r2 = 0.83. The analysis shows that the gridMET dataset overestimates wind speed and requires adjustment before a series of statewide ETref climatology maps are generated (1990–2020). For ETa, the MODIS and GLEAM (3.3b) datasets outperform the rest, with MSA = 0.5, TSC = 0.8, and r2 = 0.8. The state ETa dataset is generated using all MODIS data from 2003 and blending the MODIS data with GLEAM (3.3b) to cover data unavailability. Using the top-performing datasets, annual ETref for Indiana is computed as 1110 mm, ETa as 708 mm, and precipitation as 1091 mm. A marginal increasing climatological trend is found for Indiana’s ETref (0.013 mm yr−1) while ETa is found to be relatively stable. The state’s water availability, defined as rainfall minus ETa, has remained positive and stable at 0.99 mm day−1 (annual magnitude of +3820 mm).

Current affiliation: Department of Geological Sciences, Jackson School of Geosciences, University of Texas at Austin, Austin, Texas.

Current affiliation: Department of Civil, Architectural, and Environmental Engineering, Cockrell School of Engineering, University of Texas at Austin, Austin, Texas.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JAMC-D-20-0024.s1.

© 2020 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Dev Niyogi, happy1@utexas.edu

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