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

  • Alley, W. M., 1984: On the treatment of evapotranspiration, soil moisture accounting, and aquifer recharge in monthly water balance models. Water Resour. Res., 20, 11371149, doi:10.1029/WR020i008p01137.

    • Search Google Scholar
    • Export Citation
  • Almorox, J., Quej V. H. , and Martí P. , 2015: Global performance ranking of temperature-based approaches for evapotranspiration estimation considering Köppen climate classes. J. Hydrol., 528, 514522, doi:10.1016/j.jhydrol.2015.06.057.

    • Search Google Scholar
    • Export Citation
  • Amatya, D., Skaggs R. , and Gregory J. , 1995: Comparison of methods for estimating REF-ET. J. Irrig. Drain. Eng., 121, 427435, doi:10.1061/(ASCE)0733-9437(1995)121:6(427).

    • Search Google Scholar
    • Export Citation
  • Andersson, L., 1992: Improvements of runoff model: What way to go? J. Hydrol., 23 (5), 315332.

  • Andreadis, K. M., Clark E. A. , Wood A. W. , Hamlet A. F. , and Lettenmaier D. P. , 2005: Twentieth-century drought in the conterminous United States. J. Hydrometeor., 6, 9851001, doi:10.1175/JHM450.1.

    • Search Google Scholar
    • Export Citation
  • Andréassian, V., Perrin C. , and Michel C. , 2004: Impact of imperfect potential evapotranspiration knowledge on the efficiency and parameters of watershed models. J. Hydrol., 286, 1935, doi:10.1016/j.jhydrol.2003.09.030.

    • Search Google Scholar
    • Export Citation
  • Apip, Takara K. , Yamashiki Y. , Sassa K. , Ibrahim A. B. , and Fukuoka H. , 2010: A distributed hydrological–geotechnical model using satellite-derived rainfall estimates for shallow landslide prediction system at a catchment scale. Landslides, 7, 237258, doi:10.1007/s10346-010-0214-z.

    • Search Google Scholar
    • Export Citation
  • Arnold, J. G., Srinivasan R. , Muttiah R. S. , and Williams J. R. , 1998: Large area hydrologic modeling and assessment part I: Model development. J. Amer. Water Resour. Assoc., 34, 7389, doi:10.1111/j.1752-1688.1998.tb05961.x.

    • Search Google Scholar
    • Export Citation
  • Bai, P., Liu X. , Liang K. , and Liu C. , 2015: Comparison of performance of twelve monthly water balance models in different climatic catchments of China. J. Hydrol., 529, 10301040, doi:10.1016/j.jhydrol.2015.09.015.

    • Search Google Scholar
    • Export Citation
  • Bormann, H., 2011: Sensitivity analysis of 18 different potential evapotranspiration models to observed climatic change at German climate stations. Climatic Change, 104, 729753, doi:10.1007/s10584-010-9869-7.

    • Search Google Scholar
    • Export Citation
  • Chen, D., Gao G. , Xu C.-Y. , Guo J. , and Ren G. , 2005: Comparison of the Thornthwaite method and pan data with the standard Penman–Monteith estimates of reference evapotranspiration in China. Climate Res., 28, 123132, doi:10.3354/cr028123.

    • Search Google Scholar
    • Export Citation
  • Cheng, C.-T., Ou C. , and Chau K. , 2002: Combining a fuzzy optimal model with a genetic algorithm to solve multi-objective rainfall–runoff model calibration. J. Hydrol., 268, 7286, doi:10.1016/S0022-1694(02)00122-1.

    • Search Google Scholar
    • Export Citation
  • Donohue, R., Roderick M. , and McVicar T. , 2007: On the importance of including vegetation dynamics in Budyko’s hydrological model. Hydrol. Earth Syst. Sci., 11, 983995, doi:10.5194/hess-11-983-2007.

    • Search Google Scholar
    • Export Citation
  • Federer, C., Vörösmarty C. , and Fekete B. , 1996: Intercomparison of methods for calculating potential evaporation in regional and global water balance models. Water Resour. Res., 32, 23152321, doi:10.1029/96WR00801.

    • Search Google Scholar
    • Export Citation
  • Franchini, M., 1996: Use of a genetic algorithm combined with a local search method for the automatic calibration of conceptual rainfall–runoff models. Hydrol. Sci. J., 41, 2139, doi:10.1080/02626669609491476.

    • Search Google Scholar
    • Export Citation
  • Franchini, M., and Galeati G. , 1997: Comparing several genetic algorithm schemes for the calibration of conceptual rainfall–runoff models. Hydrol. Sci. J., 42, 357379, doi:10.1080/02626669709492034.

    • Search Google Scholar
    • Export Citation
  • Franz, K. J., and Karsten L. R. , 2013: Calibration of a distributed snow model using MODIS snow covered area data. J. Hydrol., 494, 160175, doi:10.1016/j.jhydrol.2013.04.026.

    • Search Google Scholar
    • Export Citation
  • Goderniaux, P., Brouyère S. , Fowler H. J. , Blenkinsop S. , Therrien R. , Orban P. , and Dassargues A. , 2009: Large scale surface–subsurface hydrological model to assess climate change impacts on groundwater reserves. J. Hydrol., 373, 122138, doi:10.1016/j.jhydrol.2009.04.017.

    • Search Google Scholar
    • Export Citation
  • Güntner, A., 2008: Improvement of global hydrological models using GRACE data. Surv. Geophys., 29, 375397, doi:10.1007/s10712-008-9038-y.

    • Search Google Scholar
    • Export Citation
  • Hamon, W. R., 1961: Estimating potential evapotranspiration. J. Hydraul. Div., 87, 107120.

  • Hamon, W. R., 1963: Computation of direct runoff amounts from storm rainfall. IAHS Publ., 63, 5262. [Available online at http://iahs.info/uploads/dms/063006.pdf.]

    • Search Google Scholar
    • Export Citation
  • Hargreaves, G. H., and Samani Z. A. , 1985: Reference crop evapotranspiration from temperature. Appl. Eng. Agric., 1 (2), 9699, doi:10.13031/2013.26773.

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

    • Search Google Scholar
    • Export Citation
  • Hobbins, M. T., Dai A. , Roderick M. L. , and Farquhar G. D. , 2008: Revisiting the parameterization of potential evaporation as a driver of long‐term water balance trends. Geophys. Res. Lett., 35, L12403, doi:10.1029/2008GL033840.

    • Search Google Scholar
    • Export Citation
  • Immerzeel, W., and Droogers P. , 2008: Calibration of a distributed hydrological model based on satellite evapotranspiration. J. Hydrol., 349, 411424, doi:10.1016/j.jhydrol.2007.11.017.

    • Search Google Scholar
    • Export Citation
  • Itenfisu, D., Elliott R. L. , Allen R. G. , and Walter I. A. , 2003: Comparison of reference evapotranspiration calculations as part of the ASCE standardization effort. J. Irrig. Drain. Eng., 129, 440448, doi:10.1061/(ASCE)0733-9437(2003)129:6(440).

    • Search Google Scholar
    • Export Citation
  • Jacobs, J. M., Lowry B. , Choi M. , and Bolster C. H. , 2009: GOES solar radiation for evapotranspiration estimation and streamflow prediction. J. Hydrol. Eng., 14, 293298, doi:10.1061/(ASCE)1084-0699(2009)14:3(293).

    • Search Google Scholar
    • Export Citation
  • Jensen, M. E., and Haise H. R. , 1963: Estimating evapotranspiration from solar radiation. J. Irrig. Drain. Div., 89, 1544.

  • Klemeš, V., 1986: Operational testing of hydrological simulation models. Hydrol. Sci. J., 31, 1324, doi:10.1080/02626668609491024.

  • Kumar, K. K., Kumar K. R. , and Rakhecha P. R. , 1987: Comparison of Penman and Thornthwaite methods of estimating potential evapotranspiration for Indian conditions. Theor. Appl. Climatol., 38, 140146, doi:10.1007/BF00868097.

    • Search Google Scholar
    • Export Citation
  • Leavesley, G. H., Lichty R. , Thoutman B. , and Saindon L. , 1983: Precipitation–runoff modeling system: User’s manual. Water-Resources Investigations Rep. 834238, 206 pp. [Available online at http://pubs.usgs.gov/wri/1983/4238/report.pdf.]

  • Li, H., Zhang Y. , Chiew F. H. , and Xu S. , 2009: Predicting runoff in ungauged catchments by using Xinanjiang model with MODIS leaf area index. J. Hydrol., 370, 155162, doi:10.1016/j.jhydrol.2009.03.003.

    • Search Google Scholar
    • Export Citation
  • Lidén, R., and Harlin J. , 2000: Analysis of conceptual rainfall–runoff modelling performance in different climates. J. Hydrol., 238, 231247, doi:10.1016/S0022-1694(00)00330-9.

    • Search Google Scholar
    • Export Citation
  • Lindström, G., Johansson B. , Persson M. , Gardelin M. , and Bergström S. , 1997: Development and test of the distributed HBV-96 hydrological model. J. Hydrol., 201, 272288, doi:10.1016/S0022-1694(97)00041-3.

    • Search Google Scholar
    • Export Citation
  • Liu, C., Wang Z. , Zheng H. , Zhang L. , and Wu X. , 2008: Development of hydro-informatic modelling system and its application. Sci. China, 51E, 456466, doi:10.1007/s11431-008-0040-x.

    • Search Google Scholar
    • Export Citation
  • Liu, X., Luo Y. , Zhang D. , Zhang M. , and Liu C. , 2011: Recent changes in pan-evaporation dynamics in China. Geophys. Res. Lett., 38, L13404, doi:10.1029/2011GL047929.

    • Search Google Scholar
    • Export Citation
  • Liu, X., Luo Y. , Yang T. , Liang K. , Zhang M. , and Liu C. , 2015: Investigation of the probability of concurrent drought events between the water source and destination regions of China’s water diversion project. Geophys. Res. Lett., 42, 84248431, doi:10.1002/2015GL065904.

    • Search Google Scholar
    • Export Citation
  • Lu, J., Sun G. , McNulty S. G. , and Amatya D. M. , 2005: A comparison of six potential evapotranspiration methods for regional use in the southeastern United States. J. Amer. Water Resour. Assoc., 41, 621633, doi:10.1111/j.1752-1688.2005.tb03759.x.

    • Search Google Scholar
    • Export Citation
  • Madsen, H., 2003: Parameter estimation in distributed hydrological catchment modelling using automatic calibration with multiple objectives. Adv. Water Resour., 26, 205216, doi:10.1016/S0309-1708(02)00092-1.

    • Search Google Scholar
    • Export Citation
  • Malet, J.-P., Van Asch T. W. , Van Beek R. , and Maquaire O. , 2005: Forecasting the behaviour of complex landslides with a spatially distributed hydrological model. Nat. Hazards Earth Syst. Sci., 5, 7185, doi:10.5194/nhess-5-71-2005.

    • Search Google Scholar
    • Export Citation
  • Martinez, G. F., and Gupta H. V. , 2010: Toward improved identification of hydrological models: A diagnostic evaluation of the “abcd” monthly water balance model for the conterminous United States. Water Resour. Res., 46, W08507, doi:10.1029/2009WR008294.

    • Search Google Scholar
    • Export Citation
  • Mohawesh, O., 2010: Spatio-temporal calibration of Blaney–Criddle equation in arid and semiarid environment. Water Resour. Manage., 24, 21872201, doi:10.1007/s11269-009-9546-7.

    • Search Google Scholar
    • Export Citation
  • Monteith, J., 1965: Evaporation and environment. Symp. Soc. Exp. Biol., 19, 205234.

  • Narasimhan, B., and Srinivasan R. , 2005: Development and evaluation of soil moisture deficit index (SMDI) and evapotranspiration deficit index (ETDI) for agricultural drought monitoring. Agric. For. Meteor., 133, 6988, doi:10.1016/j.agrformet.2005.07.012.

    • Search Google Scholar
    • Export Citation
  • Nash, J., and Sutcliffe J. V. , 1970: River flow forecasting through conceptual models part I—A discussion of principles. J. Hydrol., 10, 282290, doi:10.1016/0022-1694(70)90255-6.

    • Search Google Scholar
    • Export Citation
  • Oudin, L., Michel C. , and Anctil F. , 2005a: Which potential evapotranspiration input for a lumped rainfall–runoff model?: Part 1—Can rainfall–runoff models effectively handle detailed potential evapotranspiration inputs? J. Hydrol., 303, 275289, doi:10.1016/j.jhydrol.2004.08.025.

    • Search Google Scholar
    • Export Citation
  • Oudin, L., Hervieu F. , Michel C. , Perrin C. , Andréassian V. , Anctil F. , and Loumagne C. , 2005b: Which potential evapotranspiration input for a lumped rainfall–runoff model?: Part 2—Towards a simple and efficient potential evapotranspiration model for rainfall–runoff modelling. J. Hydrol., 303, 290306, doi:10.1016/j.jhydrol.2004.08.026.

    • Search Google Scholar
    • Export Citation
  • Oudin, L., Perrin C. , Mathevet T. , Andréassian V. , and Michel C. , 2006: Impact of biased and randomly corrupted inputs on the efficiency and the parameters of watershed models. J. Hydrol., 320, 6283, doi:10.1016/j.jhydrol.2005.07.016.

    • Search Google Scholar
    • Export Citation
  • Parmele, L. H., 1972: Errors in output of hydrologic models due to errors in input potential evapotranspiration. Water Resour. Res., 8, 348359, doi:10.1029/WR008i002p00348.

    • Search Google Scholar
    • Export Citation
  • Paturel, J. E., Servat E. , and Vassiliadis A. , 1995: Sensitivity of conceptual rainfall–runoff algorithms to errors in input data—Case of the GR2M model. J. Hydrol., 168, 111125, doi:10.1016/0022-1694(94)02654-T.

    • Search Google Scholar
    • Export Citation
  • Penman, H. L., 1948: Natural evaporation from open water, bare soil and grass. Proc. Roy. Soc. London, A193, 120145, doi:10.1098/rspa.1948.0037.

    • Search Google Scholar
    • Export Citation
  • Picard, R. R., and Cook R. D. , 1984: Cross-validation of regression models. J. Amer. Stat. Assoc., 79, 575583, doi:10.1080/01621459.1984.10478083.

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

    • Search Google Scholar
    • Export Citation
  • Roderick, M. L., Hobbins M. T. , and Farquhar G. D. , 2009: Pan evaporation trends and the terrestrial water balance. II. Energy balance and interpretation. Geogr. Compass, 3, 761780, doi:10.1111/j.1749-8198.2008.00214.x.

    • Search Google Scholar
    • Export Citation
  • Samain, B., and Pauwels V. R. N. , 2013: Impact of potential and (scintillometer-based) actual evapotranspiration estimates on the performance of a lumped rainfall–runoff model. Hydrol. Earth Syst. Sci., 17, 45254540, doi:10.5194/hess-17-4525-2013.

    • Search Google Scholar
    • Export Citation
  • Sawada, Y., Koike T. , and Jaranilla‐Sanchez P. A. , 2014: Modeling hydrologic and ecologic responses using a new eco‐hydrological model for identification of droughts. Water Resour. Res., 50, 62146235, doi:10.1002/2013WR014847.

    • Search Google Scholar
    • Export Citation
  • Schuurmans, J., Troch P. , Veldhuizen A. , Bastiaanssen W. , and Bierkens M. , 2003: Assimilation of remotely sensed latent heat flux in a distributed hydrological model. Adv. Water Resour., 26, 151159, doi:10.1016/S0309-1708(02)00089-1.

    • Search Google Scholar
    • Export Citation
  • Shaw, S. B., and Riha S. J. , 2011: Assessing temperature-based PET equations under a changing climate in temperate, deciduous forests. Hydrol. Processes, 25, 14661478, doi:10.1002/hyp.7913.

    • Search Google Scholar
    • Export Citation
  • Sheffield, J., Wood E. F. , and Roderick M. L. , 2012: Little change in global drought over the past 60 years. Nature, 491, 435438, doi:10.1038/nature11575.

    • Search Google Scholar
    • Export Citation
  • Shi, X. Z., Yu D. S. , Warner E. D. , Sun W. X. , Petersen G. W. , Gong Z. T. , and Lin H. , 2006: Cross-reference system for translating between genetic soil classification of China and soil taxonomy. Soil Sci. Soc. Amer. J., 70, 7883, doi:10.2136/sssaj2004.0318.

    • Search Google Scholar
    • Export Citation
  • Spies, R. R., Franz K. J. , Hogue T. S. , and Bowman A. L. , 2015: Distributed hydrologic modeling using satellite-derived potential evapotranspiration. J. Hydrometeor., 16, 129146, doi:10.1175/JHM-D-14-0047.1.

    • Search Google Scholar
    • Export Citation
  • Steinschneider, S., Polebitski A. , Brown C. , and Letcher B. H. , 2012: Toward a statistical framework to quantify the uncertainties of hydrologic response under climate change. Water Resour. Res., 48, W11525, doi:10.1029/2011WR011318.

    • Search Google Scholar
    • Export Citation
  • Stoelzle, M., Weiler M. , Stahl K. , Morhard A. , and Schuetz T. , 2015: Is there a superior conceptual groundwater model structure for baseflow simulation? Hydrol. Processes, 29, 13011313, doi:10.1002/hyp.10251.

    • Search Google Scholar
    • Export Citation
  • Thiessen, A. H., 1911: Precipitation averages for large areas. Mon. Wea. Rev., 39, 10821089, doi:10.1175/1520-0493(1911)39<1082b:PAFLA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Thomas, H. A., Jr., 1981: Improved methods for national water assessment. USGS Rep. WR15249270, 59 pp. [Available online at http://pubs.usgs.gov/unnumbered/70046351/report.pdf.]

  • Thornthwaite, C. W., 1948: An approach toward a rational classification of climate. Geogr. Rev., 38, 5594, doi:10.2307/210739.

  • UNEP, 1992: World Atlas of Desertification. Edward Arnold, 69 pp.

  • Vandewiele, G., and Xu C.-Y. , 1992: Methodology and comparative study of monthly water balance models in Belgium, China and Burma. J. Hydrol., 134, 315347, doi:10.1016/0022-1694(92)90041-S.

    • Search Google Scholar
    • Export Citation
  • Vázquez, R. F., 2003: Effect of potential evapotranspiration estimates on effective parameters and performance of the MIKE SHE-code applied to a medium-size catchment. J. Hydrol., 270, 309327, doi:10.1016/S0022-1694(02)00308-6.

    • Search Google Scholar
    • Export Citation
  • Vicente-Serrano, S. M., Sánchez S. , and Cuadrat J. M. , 2003: Comparative analysis of interpolation methods in the middle Ebro Valley (Spain): Application to annual precipitation and temperature. Climate Res., 24, 161180, doi:10.3354/cr024161.

    • Search Google Scholar
    • Export Citation
  • Vicente-Serrano, S. M., Azorin-Molina C. , Sanchez-Lorenzo A. , Revuelto J. , López-Moreno J. I. , González-Hidalgo J. C. , Moran-Tejeda E. , and Espejo F. , 2014: Reference evapotranspiration variability and trends in Spain, 1961–2011. Global Planet. Change, 121, 2640, doi:10.1016/j.gloplacha.2014.06.005.

    • Search Google Scholar
    • Export Citation
  • Vörösmarty, C. J., Federer C. A. , and Schloss A. L. , 1998: Potential evaporation functions compared on US watersheds: Possible implications for global-scale water balance and terrestrial ecosystem modeling. J. Hydrol., 207, 147169, doi:10.1016/S0022-1694(98)00109-7.

    • Search Google Scholar
    • Export Citation
  • Wang, G., and Zhang J. , 2005: Study on SWBM and its application in semi-arid basins (in Chinese). Hydrology, 25, 710.

  • Wang, G. Q., and Coauthors, 2013: Simulating the impact of climate change on runoff in a typical river catchment of the loess plateau, China. J. Hydrometeor., 14, 15531561, doi:10.1175/JHM-D-12-081.1.

    • Search Google Scholar
    • Export Citation
  • Wang, G. Q., and Coauthors, 2014: Regional calibration of a water balance model for estimating stream flow in ungauged areas of the Yellow River basin. Quat. Int., 336, 6572, doi:10.1016/j.quaint.2013.08.051.

    • Search Google Scholar
    • Export Citation
  • Wang, Q., 1991: The genetic algorithm and its application to calibrating conceptual rainfall–runoff models. Water Resour. Res., 27, 24672471, doi:10.1029/91WR01305.

    • Search Google Scholar
    • Export Citation
  • Wang, Q., 1997: Using genetic algorithms to optimise model parameters. Environ. Modell. Software, 12, 2734, doi:10.1016/S1364-8152(96)00030-8.

    • Search Google Scholar
    • Export Citation
  • Wang, W., Xing W. , and Shao Q. , 2015: How large are uncertainties in future projection of reference evapotranspiration through different approaches? J. Hydrol., 524, 696700, doi:10.1016/j.jhydrol.2015.03.033.

    • Search Google Scholar
    • Export Citation
  • Wang, X., Melesse A. , and Yang W. , 2006: Influences of potential evapotranspiration estimation methods on SWAT’s hydrologic simulation in a northwestern Minnesota watershed. Trans. ASABE, 49, 17551771, doi:10.13031/2013.22297.

    • Search Google Scholar
    • Export Citation
  • Wu, Z. Y., Lu G. H. , Wen L. , and Lin C. A. , 2011: Reconstructing and analyzing China’s fifty-nine year (1951–2009) drought history using hydrological model simulation. Hydrol. Earth Syst. Sci., 15, 28812894, doi:10.5194/hess-15-2881-2011.

    • Search Google Scholar
    • Export Citation
  • Xu, C.-Y., and Singh V. P. , 2000: Evaluation and generalization of radiation-based methods for calculating evaporation. Hydrol. Processes, 14, 339349, doi:10.1002/(SICI)1099-1085(20000215)14:2<339::AID-HYP928>3.0.CO;2-O.

    • Search Google Scholar
    • Export Citation
  • Xu, C.-Y., and Singh V. P. , 2002: Cross comparison of empirical equations for calculating potential evapotranspiration with data from Switzerland. Water Resour. Manage., 16, 197219, doi:10.1023/A:1020282515975.

    • Search Google Scholar
    • Export Citation
  • Xu, C.-Y., and Singh V. P. , 2005: Evaluation of three complementary relationship evapotranspiration models by water balance approach to estimate actual regional evapotranspiration in different climatic regions. J. Hydrol., 308, 105121, doi:10.1016/j.jhydrol.2004.10.024.

    • Search Google Scholar
    • Export Citation
  • Yang, T., Gao X. , Sellars S. L. , and Sorooshian S. , 2015: Improving the multi-objective evolutionary optimization algorithm for hydropower reservoir operations in the California Oroville–Thermalito complex. Environ. Modell. Software, 69, 262279, doi:10.1016/j.envsoft.2014.11.016.

    • Search Google Scholar
    • Export Citation
  • Yuan, S., and Quiring S. M. , 2014: Drought in the U.S. Great Plains (1980–2012): A sensitivity study using different methods for estimating potential evapotranspiration in the Palmer drought severity index. J. Geophys. Res. Atmos., 119, 10 99611 010, doi:10.1002/2014JD021970.

    • Search Google Scholar
    • Export Citation
  • Zhang, J., Sun F. , Xu J. , Chen Y. , Sang Y.-F. , and Liu C. , 2016: Dependence of trends in and sensitivity of drought over China (1961–2013) on potential evaporation model. Geophys. Res. Lett., 43, 206213, doi:10.1002/2015GL067473.

    • Search Google Scholar
    • Export Citation
  • Zhang, Y., Chiew F. , Zhang L. , Leuning R. , and Cleugh H. , 2008: Estimating catchment evaporation and runoff using MODIS leaf area index and the Penman–Monteith equation. Water Resour. Res., 44, W10420, doi:10.1029/2007WR006563.

    • Search Google Scholar
    • Export Citation
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Assessment of the Influences of Different Potential Evapotranspiration Inputs on the Performance of Monthly Hydrological Models under Different Climatic Conditions

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  • 1 Key Laboratory of Water Cycle and Related Land Surface Process, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
  • | 2 Department of Civil and Environmental Engineering, University of California, Irvine, Irvine, California
  • | 3 Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
  • | 4 College of Resource Environment and Tourism, Capital Normal University, Beijing, China
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Abstract

Potential evapotranspiration (PET), which determines the upper limit of actual evapotranspiration (AET), is a necessary input in monthly hydrological models. In this study, the sensitivities of monthly hydrological models to different PET inputs are investigated in 37 catchments under different climatic conditions. Four types of PET estimation methods (i.e., Penman–Monteith, Hargreaves–Samani, Jensen–Haise, and Hamon) give significantly different PET values in the 37 catchments. However, similar runoff simulations are produced based on different PET inputs in both nonhumid and humid regions. It is found that parameter calibration of the hydrological model can eliminate the influences of different PET inputs on runoff simulations in both nonhumid and humid regions. However, the influences of parameter calibration on the simulated water balance components, including AET and water storage change (WSC), are different in nonhumid and humid regions. In nonhumid regions, simulated runoff, AET, and WSC are similar using different PET inputs. In humid regions, simulated AET and WSC using different PET inputs are significantly different, and simulated runoff and the sums of AET and WSC are similar to each other. It is suggested that the choice of PET inputs for monthly hydrological models be based on the selected region and the relevant hydrological practices. In runoff modeling, different PET inputs can produce similar runoff simulations in both nonhumid and humid regions. However, when estimating AET and WSC in humid regions, different PET inputs will result in significantly different AET and WSC simulations, which should be noted by model users.

Corresponding author address: Xiaomang Liu, Key Laboratory of Water Cycle and Related Land Surface Process, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, 11A Datun Road, Chaoyang District, 100101 Beijing, China. E-mail: liuxm@igsnrr.ac.cn

Abstract

Potential evapotranspiration (PET), which determines the upper limit of actual evapotranspiration (AET), is a necessary input in monthly hydrological models. In this study, the sensitivities of monthly hydrological models to different PET inputs are investigated in 37 catchments under different climatic conditions. Four types of PET estimation methods (i.e., Penman–Monteith, Hargreaves–Samani, Jensen–Haise, and Hamon) give significantly different PET values in the 37 catchments. However, similar runoff simulations are produced based on different PET inputs in both nonhumid and humid regions. It is found that parameter calibration of the hydrological model can eliminate the influences of different PET inputs on runoff simulations in both nonhumid and humid regions. However, the influences of parameter calibration on the simulated water balance components, including AET and water storage change (WSC), are different in nonhumid and humid regions. In nonhumid regions, simulated runoff, AET, and WSC are similar using different PET inputs. In humid regions, simulated AET and WSC using different PET inputs are significantly different, and simulated runoff and the sums of AET and WSC are similar to each other. It is suggested that the choice of PET inputs for monthly hydrological models be based on the selected region and the relevant hydrological practices. In runoff modeling, different PET inputs can produce similar runoff simulations in both nonhumid and humid regions. However, when estimating AET and WSC in humid regions, different PET inputs will result in significantly different AET and WSC simulations, which should be noted by model users.

Corresponding author address: Xiaomang Liu, Key Laboratory of Water Cycle and Related Land Surface Process, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, 11A Datun Road, Chaoyang District, 100101 Beijing, China. E-mail: liuxm@igsnrr.ac.cn
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