Allen, R. G., Perista, L. S., Raes, D., and Smith, M.: Crop
Evapotranspiration – Guidelines for Computing Crop Water Requirements; FAO
Irrigation and Drainage papers 56, FAO – Food and Agriculture Organization of the United Nations, Rome, 1998.

Alves, I. and Pereira, L. S.: Modeling surface resistance from climatic
variables?, Agr. Water Manage., 42, 371–385, 2000.

Aubinet, M., Grelle, A., Ibrom, A., Rannik, Ü., Moncrieff, J., and
Foken, T.: Estimates of the annual net carbon and water exchange of forests:
the euroflux methodology, Adv. Ecol. Res., 30, 113–175, 2000.

Baldocchi, D. D.: Assessing the eddy covariance technique for evaluating
carbon dioxide exchange rates of ecosystems: past, present and future, Global
Change. Biol., 9, 479–492, 2003.

Bardossy, A. and Das, T.: Influence of rainfall observation network on model calibration and application, Hydrol. Earth Syst. Sci., 12, 77–89,
https://doi.org/10.5194/hess-12-77-2008, 2008.

Barton, I. J.: A Parameterization of the Evaporation from Nonsaturated
Surfaces, J. Appl. Meteorol., 18, 43–47, 1979.

Beyrich, F., Richter, S. H., Weisensee, U., Kohsiek, W., Lohse, H., de Bruin, H. A. R., Foken, T., Göckede, M., Berger, F., Vogt, R., and Batchvarova, E.: Experimental determination of turbulent fluxes over the heterogeneous litfass area: selected results from the litfass-98 experiment, Theor. Appl. Climatol., 73, 19–34, https://doi.org/10.1007/s00704-002-0691-7, 2002.

Bohn, T. J. and Vivoni, E. R.: Process-based characterization of
evapotranspiration sources over the North American monsoon region, Water
Resour. Res., 52, 358–384, https://doi.org/10.1002/2015WR017934, 2016.

Bonan, G.: Ecological climatology: concepts and applications, Cambridge
University Press, Cambridge, 2008.

Braswell, B. H., Sacks, W. J., Linder, E., and Schimel, D. S.: Estimating
diurnal to annual ecosystem parameters by synthesis of a carbon flux model
with eddy covariance net ecosystem exchange observations, Global Change
Biol., 11, 335–355, 2005.

Brutsaert, W.: Hydrology: An Introduction, Cambridge University Press,
Cambridge, 2005.

Brutsaert, W. and Stricker, H.: An advection-aridity approach to estimate actual regional evapotranspiration, Water Resour. Res., 15, 443–450, 1979.

Chen, D. Y., Wang, X., Liu, S. Y., Wang, Y. K., Gao, Z. Y., Zhang, L. L.,
Wei, X. G., and Wei, X. D.: Using Bayesian analysis to compare the
performance of three evapotranspiration models for rainfed jujube (Ziziphus
jujuba Mill.) plantations in the Loess Plateau, Agr. Water. Manage., 159,
341–357, 2015.

Elshall, A. S., Ye, M., Pei, Y., Zhang, F., Niu, G. Y., and Barron-Gafford,
G. A.: Relative model score: A scoring rule for evaluating ensemble
simulations with application to microbial soil respiration modeling, Stoch.
Environ. Res. A., https://doi.org/10.1007/s00477-018-1592-3, in press, 2018.

Ershadi, A., Mccabe, M. F., Evans, J. P., Chaney, N. W., and Wood, E. F.:
Multi-site evaluation of terrestrial evaporation models using fluxnet data,
Agr. Forest Meteorol., 187, 46–61, 2014.

Ershadi, A., McCabe, M .F., Evans, J. P., and Wood, E. F.: Impact of model
structure and parameterization on Penman–Monteith type evaporation models,
J. Hydrol., 525, 521–535, 2015.

Fisher, J. B., DeBiase, T. A., Qi, Y., Xu, M., and Goldstein, A. H.:
Evapotranspiration models compared on a Sierra Nevada forest ecosystem,
Environ. Model. Softw., 20, 783–796, 2005.

Flint A. L. and Childs, S. W.: Use of the Priestley–Taylor evaporation
equation for soil water limited conditions in a small forest clearcut,
Agr. Forest Meteorol., 56, 247–260, 1991.

Foken, T., Mauder, M., Liebethal, C., Wimmer, F., Beyrich, F., Leps, J. P.,
Raasch, S., DeBruin, H. A. R., Meijninger, W. M. L., and Bange, J.: Energy
balance closure for the LITFASS-2003 experiment, Theor. Appl. Climatol.,
101, 149–160, https://doi.org/10.1007/s00704-009-0216-8, 2010.

Franssen, H. J. H., Stöckli, R., Lehner, I., Rotenberg, E., and
Seneviratne S. I.: Energy balance closure of eddy-covariance data: A
multisite analysis for European FLUXNET stations, Agr. Forest Meteorol.,
150, 1553–1567, https://doi.org/10.1016/j.agrformet.2010.08.005, 2010.

Gelman, A. and Meng, X. L.: Simulating normalizing constants: From importance sampling to bridge sampling to path sampling, Stat. Sci., 13, 163–185, 1998.

Gelman, A. and Rubin, D. B.: Inference from iterative simulation using
multiple sequences, Stat. Sci., 7, 457–472, 1992.

Giudice, D., Albert, C., Rieckermann, J., and Reichert, P.: Describing the
catchment-averaged precipitation as a stochastic process improves parameter
and input estimation, Water Resour. Res., 52, 3162–3186,
https://doi.org/10.1002/2015WR017871, 2016.

Höge, M., Wöhling, T., and Nowak, W.: A primer for model selection:
The decisive role of model complexity, Water Resour. Res., 54, 1688–1715,
https://doi.org/10.1002/2017WR021902, 2018.

Jefferys, W. H. and Berger, J. O.: Sharpening Ockham's razor on a Bayesian
strop, Am. Sci., 89, 64–72, 1992.

Kashyap, R. L.: Optimal choice of AR and MA parts in autoregressive moving
average models, IEEE T. Pattern Anal. Mach. Intell., 4, 99–104, 1982.

Katerji, N. and Rana, G.: Modelling evapotranspiration of six irrigated crops under Mediterranean climate conditions, Agr. Forest Meteorol., 138, 142–155, 2006.

Katerji, N., Rana, G., and Fahed, S.: Parameterizing canopy resistance using
mechanistic and semi-empirical estimates of hourly evapotranspiration:
critical evaluation for irrigated crops in the Mediterranean, Hydrol. Process., 25, 117–129, 2011.

Kato, T., Kimura, R., and Kamichika, M.: Estimation of evapotranspiration,
transpiration ratio and water-use efficiency from a sparse canopy using a
compartment model, Agr. Water Manage., 65, 173–191, 2004.

Kelliher, F. M., Leunig, R., Raupach, M. R., and Schulze, E. D.: Maximum conductances for evaporation from global vegetation types, Agr. Forest Meteorol., 73, 1–16, 1995.

Kessler, E. and Neas, B.: On correlation, with applications to the radar
and raingage measurement of rainfall, Atmos. Res., 34, 217–229, 1994.

Laloy, E., Linde, N., Jacques, D., and Vrugt, J. A.: Probabilistic inference
of multi-Gaussian fields from indirect hydrological data using circulant
embedding and dimensionality reduction, Water Resour. Res., 51, 4224–4243,
https://doi.org/10.1002/2014WR016395, 2015.

Lartillot, N. and Philippe, H.: Computing Bayes factors using thermodynamic
integration, Syst. Biol., 55, 195–207, 2006.

Leeb, H. and Pötscher, B. M.: Model selection, Springer, Berlin, Germany,
889–925, https://doi.org/10.1007/978-3-540-71297-8-39, 2009.

Legates, D. R. and McCabe, G. J.: Evaluating the use of “goodnessof-fit”
measures in hydrologic and hydroclimatic model validation, Water Resour.
Res., 35, 233–241, 1999.

Leuning, R., Zhang, Y. Q., Rajaud, A., Cleugh, H., and Tu, K.: A simple
surface conductance model to estimate regional evaporation using MODIS leaf
area index and the Penman–Monteith equation, Water Resour. Res., 44, W10419, https://doi.org/10.1029/2007WR006562, 2008.

Liang, J., Zhang, L., Cao, X., Wen, J., Wang, J., and Wang, G.: Energy
balance in the semiarid area of the Loess Plateau, China, J. Geophys. Res.-Atmos., 122, 2155–2168, https://doi.org/10.1002/2015JD024572, 2017.

Li, S., Kang, S., Zhang, L., Ortega-Farias, S., Li, F., Du, T., Tong, L.,
Wang, S., Ingman, M., and Guo, W.: Measuring and modeling maize
evapotranspiration under plastic film-mulching condition, J. Hydrol., 503,
153–168, 2013.

Li, S., Zhang, L., Kang, S., Tong, L., Du, T., Hao, X., and Zhao, P.: Comparison of several surface resistance models for estimating crop evapotranspiration over the entire growing season in arid regions, Agr. Forest Meteorol., 208, 1–15, 2015.

Li, X., Cheng, G. D., Liu, S. M., Xiao, Q., Ma, M. G., Jin, R., Che, T.,
Liu, Q. H., Wang, W. Z., Qi, Y., Wen, J. G., Li, H. Y., Zhu, G. F., Guo, J.
W., Ran, Y. H., Wang, S. G., Zhu, Z. L., Zhou, J., Hu, X. L., and Xu, Z. W.:
Heihe Watershed Allied Telemetry Experimental Research (HiWATER): Scientific
objectives and experimental design, B. Am. Meteorol. Soc., 94, 1145–1160,
2013.

Liu, G., Liu, Y., Hafeez, M., Xu, D., and Vote, C.: Comparison of two methods to derive time series of actual evapotranspiration using eddy covariance
measurements in the southeastern Australia, J. Hydrol., 454–455, 1–6, 2012.

Liu, P., Elshall, A. S., Ye, M., Beerli, P., Zeng, X., Lu, D., and Tao, Y.:
Evaluating marginal likelihood with thermodynamic integration method and
comparison with several other numerical methods, Water Resour. Res., 52,
734–758, https://doi.org/10.1002/2014WR016718, 2016.

Liu, S. M., Xu, Z. W.,Wang,W. Z., Jia, Z. Z., Zhu, M. J., Bai, J., and Wang,
J. M.: A comparison of eddy-covariance and large aperture scintillometer
measurements with respect to the energy balanceclosure problem, Hydrol. Earth Syst. Sci., 15, 1291–1306, https://doi.org/10.5194/hess-15-1291-2011, 2011.

Marshall, L., Nott, D., and Sharma, A.: Hydrological model selection: A
Bayesian alternative, Water Resour. Res., 41, 3092–3100, https://doi.org/10.1029/2004WR003719, 2005.

Matheny, A. M., Bohrer, G., Stoy, P. C., Baker, I. T., Black, A. T., Desai,
A. R., Dietze, M. C., Gough, C. M., Ivanov, V. Y., Jassal, R. S., Novick, K. A., Schäfer, K. V. R., and Verbeeck, H.: Characterizing the diurnal
patterns of errors in the prediction of evapotranspiration by several
land-surface models: An NACP analysis, J. Geophys. Res.-Biogeo., 119,
1458–1473, 2014.

Monteith, J. L.: Evaporation and environment, Symp. Soc. Exp. Biol., 19,
205–234, 1965.

Morison, J. I. L., Baker, N. R., Mullineaux, P. M., and Davies, W. J.:
Improving water use in crop production, Philos. T. Roy. Soc. B, 363,
639–658, 2008.

Neal, R. M.: Markov chain sampling methods for Dirichlet process mixture
models, J. Comput. Graph. Stat., 9, 249–265, 2000.

Oncley, S. P., Foken, T., Vogt, R., Kohsiek, W., DeBruin, H., Bernhofer, C.,
Christen, A., Van Gorsel, E., Grantz, D., and Feigenwinter, C.: The energy
balance experiment EBEX-2000. Part I: Overview and energy balance, Bound.-Lay. Meteorol., 123, 1–28, https://doi.org/10.1007/s10546-007-9161-1, 2007.

Ortega-Farias, S., Olioso, A., Fuentes, S., and Valdes, H.: Latent heat flux
over a furrow-irrigated tomato crop using Penman–Monteith equation with a
variable surface canopy resistance, Agr. Water Manage., 82, 421–432, 2006.

Parlange, M. B. and Katul, G. G.: An advection-aridity evaporation model,
Water Resour. Res., 28, 127–132, 1992.

Poblete-Echeverria, C. and Ortega-Farias, S.: Estimation of actual
evapotranspiration for a drip-irrigated Merlot vineyard using a three-source
model, Irrig. Sci., 28, 65–78, 2009.

Priestley, C. H. B. and Taylor, R. J.: On the assessment of surface heat
flux and evaporation using large-scale parameters, Mon. Weather Rev., 100, 81–92, 1972.

Rana, G., Katerji, N., Ferrara, R. M., and Martinelli, N.: An operational
model to estimate hourly and daily crop evapotranspiration in hilly terrain:
validation on wheat and oat crops, Theor. Appl. Climatol., 103, 413–426, 2011.

Sadegh, M. and Vrugt J. A.: Approximate Bayesian Computation using Markov Chain Monte Carlo simulation: DREAM(ABC), Water Resour. Res., 50,
6767–6787, https://doi.org/10.1002/2014WR015386, 2014.

Samani, S., Ye, M., Zhang, F., Pei, Y. Z., Tang, G. P., Elshall, A. S., and
Moghaddam, A. A.: Impacts of prior parameter distributions on bayesian
evaluation of groundwater model complexity, Water Sci. Eng., 11, 89–100, https://doi.org/10.1016/j.wse.2018.06.001, 2018.

Schöniger, A., Wohling, T., Samaniego, L., and Nowak, W.: Model
selection on solid ground: Rigorous comparison of nine ways to evaluate
Bayesian model evidence, Water Resour. Res., 50, 9484–9513,
https://doi.org/10.1002/2014WR016062, 2014.

Schwarz, G.: Estimating the dimension of a model, Ann. Stat., 6, 461–464, https://doi.org/10.1214/aos/1176344136, 1978.

Sellers, P. J., Heiser, M. D., and Hall, F. G.: Relations between surface conductance and spectral vegetation indices at intermediate (100 m^{2} to 15 km^{2}) length scales, J. Geophys. Res., 97, 19033–19059, 1992.

Shao, J.: An asymptotic theory for linear model selection, Statist. Sin., 7, 221–242, 1997.

Shuttleworth, W. J. and Gurney, R. J.: The theoretical relationship between
foliage temperature and canopy resistance in sparse crops, Q. J. Roy.
Meteorol. Soc., 116, 497–519, 1990.

Stannard, D. I.: Comparison of Penman-Monteith, Shuttleworth–Wallace, and
modified Priestley-Taylor evapotranspiration models for wildland vegetation
in semiarid rangeland, Water Resour. Res., 29, 1379–1392, 1993.

Stull, R. B.: An introduction to boundary layer meteorology, Kluwer Academic
Publ., the Netherlands, 255 pp., 1988.

Sumner, D. M. and Jacobs, J. M.: Utility of Penman–Monteith
Priestley–Taylor reference evapotranspiration, and pan evaporation methods
to estimate pasture evapotranspiration, J. Hydrol., 308, 81–104, 2005.

Szilagyi, J. and Jozsa, J.: New findings about the complementary relationship based evaporation estimation methods, J. Hydrol., 354, 171–186, 2008.

Thomsen, J., Bohrer, G., Matheny, M. V., Ivanov, Y., He, L., Renninger, H.,
and Schäfer, K.: Contrasting hydraulic strategies during dry soil
conditions in Quercus rubra and Acer rubrum in a sandy site in Michigan,
Forests, 4, 1106–1120, 2013.

Tsvang, L., Fedorov, M., Kader, B., Zubkovskii, S., Foken, T., Richter, S.,
and Zeleny, Y.: Turbulent exchange over a surface with chessboardtype
inhomogeneities, Bound.-Lay. Meteorol., 55, 141–160, 1991.

Vinukollu R, K., Wood, E. F., Ferguson, C. R., and Fisher, J. B.: Global
estimates of evapotranspiration for climate studies using multi-sensor
remote sensing data: evaluation of three process-based approaches, Remote
Sens. Environ., 115, 801–823, 2011.

Vrugt, J. A., ter Braak, C. J. F., Clark, M. P. J., Hyman, M., and Robinson,
B. A.: Treatment of input uncertainty in hydrologic modeling: Doing
hydrology backward with Markov chain Monte Carlo simulation, Water Resour.
Res., 44, W00B09, https://doi.org/10.1029/2007WR006720, 2008.

Vrugt, J. A., ter Braak, C. J. F., Diks, C. G. H., Higdon, D., Robinson, B.
A., and Hyman, J. M.: Accelerating Markov chain Monte Carlo simulation by
differential evolution with self-adaptive randomized subspace sampling, Int.
J. Nonlin. Sci. Numer. Simul., 10, 273–290, 2009.

Webb, E. K., Pearman, G. I., and Leuning, R.: Correction of flux measurements for density effects due to heat and water-vapor transfer, Q. J. Roy. Meteorol. Soc., 106, 85–100, 1980.

Willmott, C. J.: On the validation of models, Phys. Geogr., 2, 184–194, 1981.

Xie, W., Lewis, P. O., Fan, Y., Kuo, L., and Chen, M. H.: Improving marginal
likelihood estimaton for Bayesian phylogenetic model selection, Syst. Biol.,
60, 150–160, 2011.

Xu, C. Y. and Singh, V. P.: A review on monthly water balance models for
water resources investigations, Water Resour. Manage., 12, 31–50, 1998.

Xu, Z. W., Liu, S. M., Li, X., Shi, S. J.,Wang, J. M., Zhu, Z. L., Xu, T.
R., Wang, W. Z., and Ma, M. G.: Intercomparison of surface energy flux
measurement systems used during the HiWATERUSOEXE, J. Geophys. Res., 118,
13140–13157, 2014.

Ye, M., Neuman, S. P., and Meyer, P. D.: Maximum likelihood Bayesian
averaging of spatial variability models in unsaturated fractured tuff, Water
Resour. Res., 40, W05113, https://doi.org/10.1029/2003WR002557, 2004.

Ye, M., Meyer, P. D., and Neuman, S. P.: On model selection criteria in
multimodel analysis, Water Resour. Res., 44, W03428, https://doi.org/10.1029/2008WR006803, 2008.

Zhang, B., Kang, S., Li, F.,and Zhang, L.: Comparison of three
evapotranspiration models to Bowen ratio-energy balance method for vineyard
in an arid desert region of northwest China, Agr. Forest Meteorol., 148,
1629–1640, 2008.

Zhang, K., Ma, J., Zhu, G., Ma, T., Han, T., and Feng, L. L.: Parameter
sensitivity analysis and optimization for a satellite-based
evapotranspiration model across multiple sites using moderate resolution
imaging spectroradiometer and flux data, J. Geophys. Res.-Atmos., 122, 230–245, 2017.

Zhang, X. Y., Liu, C. X., Hu, B. X., and Zhang, G. N.: Uncertainty analysis
of multi-rate kinetics of uranium desorption from sediments, J. Contam.
Hydrol., 156, 1–15, 2014.

Zhu, G. F., Su, Y. H., Li, X., Zhang, K., and Li, C. B.: Estimating actual
evapotranspiration from an alpine grassland on Qinghai–Tibetan plateau
using a two-source model and parameter uncertainty analysis by Bayesian
approach, J. Hydrol., 476, 42–51, 2013.

Zhu, G. F., Li, X., Su, Y. H., Zhang, K., Bai, Y., Ma, J. Z., Li, C. B., Hu, X. L., and He, J. H.: Simultaneously assimilating multivariate data sets into the two-source evapotranspiration model by Bayesian approach: application to spring maize in an arid region of northwestern China, Geosci. Model Dev., 7, 1467–1482, https://doi.org/10.5194/gmd-7-1467-2014, 2014.