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T. Roy; H. V. Gupta; A. Serrat-Capdevila; J. B. Valdes (2017)
Publisher: Copernicus Publications
Journal: Hydrology and Earth System Sciences
Languages: English
Types: 0038
Subjects: T, G, GE1-350, Geography. Anthropology. Recreation, Environmental technology. Sanitary engineering, Environmental sciences, Technology, TD1-1066
Daily, quasi-global (50° N–S and 180° W–E), satellite-based estimates of actual evapotranspiration at 0.25° spatial resolution have recently become available, generated by the Global Land Evaporation Amsterdam Model (GLEAM). We investigate the use of these data to improve the performance of a simple lumped catchment-scale hydrologic model driven by satellite-based precipitation estimates to generate streamflow simulations for a poorly gauged basin in Africa. In one approach, we use GLEAM to constrain the evapotranspiration estimates generated by the model, thereby modifying daily water balance and improving model performance. In an alternative approach, we instead change the structure of the model to improve its ability to simulate actual evapotranspiration (as estimated by GLEAM). Finally, we test whether the GLEAM product is able to further improve the performance of the structurally modified model. Results indicate that while both approaches can provide improved simulations of streamflow, the second approach also improves the simulation of actual evapotranspiration significantly, which substantiates the importance of making diagnostic structural improvements to hydrologic models whenever possible.
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    • Allen, R. G., Pereira, L. S., Raes, D., and Smith, M.: Crop evapotranspiration - Guidelines for computing crop water requirements, FAO Irrigation and Drainage Paper 56, Food and Agriculture Organization of the United Nations, Rome, 1998.
    • Arboleda, A., Ghilain, N., and Gellens-Meulenberghs, F.: The LSASAF evapotranspiration product - first results with MSG, in Proceedings of the 2005 EUMETSAT meteorological satellite data user's conference, Dubrovnik, Croatia, 2005.
    • Bahremand, A.: HESS Opinions: Advocating process modeling and de-emphasizing parameter estimation, Hydrol. Earth Syst. Sci., 20, 1433-1445, doi:10.5194/hess-20-1433-2016, 2016.
    • Bastiaanssen, W. G. M., Pelgrum, H., Wang, J., Ma, Y., Moreno, J. F., Roerink, G. J., and van der Wal, T.: A remote sensing surface energy balance algorithm for land (SEBAL), J. Hydrol., 212- 213, 213-229, doi:10.1016/S0022-1694(98)00254-6, 1998.
    • Box, G. E. P. and Cox, D. R.: An Analysis of Transformations, J. R. Stat. Soc. Ser. B, 26, 211-252, 1964.
    • Boyle, D. P., Gupta, H. V., and Sorooshian, S.: Toward improved calibration of hydrologic models: Combining the strengths of manual and automatic methods, Water Resour. Res., 36, 3663- 3674, doi:10.1029/2000WR900207, 2000.
    • Bruton, J. M., McClendon, R. W., and Hoogenboom, G.: Estimating Daily Pan Evaporation with Artificial Neural Networks, Trans. ASAE, 43, 491-496, doi:10.13031/2013.2730, 2000.
    • Bulygina, N. and Gupta, H.: Estimating the uncertain mathematical structure of a water balance model via Bayesian data assimilation, Water Resour. Res., 45, W00B13, doi:10.1029/2007WR006749, 2009.
    • Bulygina, N. and Gupta, H.: How Bayesian data assimilation can be used to estimate the mathematical structure of a model, Stoch. Environ. Res. Risk A., 24, 925-937, doi:10.1007/s00477-010- 0387-y, 2010.
    • Bulygina, N. and Gupta, H.: Correcting the mathematical structure of a hydrological model via Bayesian data assimilation, Water Resour. Res., 47, W05514, doi:10.1029/2010WR009614, 2011.
    • Camberlin, P., Moron, V., Okoola, R., Philippon, N., and Gitau, W.: Components of rainy seasons' variability in Equatorial East Africa: onset, cessation, rainfall frequency and intensity, Theor. Appl. Climatol., 98, 237-249, doi:10.1007/s00704-009-0113-1, 2009.
    • Clark, M. P., Slater, A. G., Rupp, D. E., Woods, R. A., Vrugt, J. A., Gupta, H. V., Wagener, T., and Hay, L. E.: Framework for Understanding Structural Errors (FUSE): A modular framework to diagnose differences between hydrological models, Water Resour. Res., 44, W00B02, doi:10.1029/2007WR006735, 2008.
    • Clark, M. P., Lundquist, J. D., Rupp, D. E., Woods, R. A., Freer, J. E., Gutmann, E. D., Wood, A. W., Brekke, L. D., Arnold, J. R., Gochis, D. J., Rasmussen, R. M., Tarboton, D. G., and Marks, D. G.: The structure for unifying multiple modeling alternatives (SUMMA), Version 1.0: Technical Description, NCAR Technical Notes (NCAR/TN 514CSTR), National Center for Atmospheric Research, Boulder, Colorado, USA, 2015.
    • Demaria, E. and Serrat-Capdevila, A.: Validation of remote sensingestimated hydrometeorological variables, in: Earth Observation for Water Resources Management: Current Use and Future Opportunities for the Water Sector, edited by: García, L. E., Rodríguez, D. J., Wijnen, M., and Pakulski, I., The World Bank, Washington, D.C., 167-194, doi:10.1596/978-1-4648-0475-5, 2016.
    • Dessu, S. B. and Melesse, A. M.: Impact and uncertainties of climate change on the hydrology of the Mara River basin, Kenya/Tanzania, Hydrol. Process., 27, 2973-2986, doi:10.1002/hyp.9434, 2012.
    • Duan, Q., Sorooshian, S., and Gupta, V.: Effective and efficient global optimization for conceptual rainfall-runoff models, Water Resour. Res., 28, 1015-1031, doi:10.1029/91WR02985, 1992.
    • Duan, Q., Ajami, N. K., Gao, X., and Sorooshian, S.: Multimodel ensemble hydrologic prediction using Bayesian model averaging, Adv. Water Resour., 30, 1371-1386, doi:10.1016/j.advwatres.2006.11.014, 2007.
    • Fenicia, F., McDonnell, J. J., and Savenije, H. H. G.: Learning from model improvement: On the contribution of complementary data to process understanding, Water Resour. Res., 44, W06419, doi:10.1029/2007WR006386, 2008a.
    • Fenicia, F., Savenije, H. H. G., Matgen, P., and Pfister, L.: Understanding catchment behavior through stepwise model concept improvement, Water Resour. Res., 44, W01402, doi:10.1029/2006WR005563, 2008b.
    • Fenicia, F., Kavetski, D. and Savenije, H. H. G.: Elements of a flexible approach for conceptual hydrological modeling: 1. Motivation and theoretical development, Water Resour. Res., 47, W11510, doi:10.1029/2010WR010174, 2011.
    • Funk, C., Peterson, P., Landsfeld, M., Pedreros, D., Verdin, J., Shukla, S., Husak, G., Rowland, J., Harrison, L., Hoell, A., and Michaelsen, J.: The climate hazards infrared precipitation with stations - a new environmental record for monitoring extremes, Sci. Data, 2, 150066, doi:10.1038/sdata.2015.66, 2015.
    • Funk, C. C., Peterson, P. J., Landsfeld, M. F., Pedreros, D. H., Verdin, J. P., Rowland, J. D., Romero, B. E., Husak, G. J., Michaelsen, J. C. and Verdin, A. P.: A Quasi-Global Precipitation Time Series for Drought Monitoring, Geological Survey Data Series 832, p. 4, 2014.
    • Gash, J. H. C.: An analytical model of rainfall interception by forests, Q. J. Roy. Meteorol. Soc., 105, 43-55, doi:10.1002/qj.49710544304, 1979.
    • Gharari, S., Hrachowitz, M., Fenicia, F., Gao, H., and Savenije, H. H. G.: Using expert knowledge to increase realism in environmental system models can dramatically reduce the need for calibration, Hydrol. Earth Syst. Sci., 18, 4839-4859, doi:10.5194/hess-18-4839-2014, 2014.
    • Gupta, H. V. and Nearing, G. S.: Debates-the future of hydrological sciences: A (common) path forward? Using models and data to learn: A systems theoretic perspective on the future of hydrological science, Water Resour. Res., 50, 5351-5359, doi:10.1002/2013WR015096, 2014.
    • Gupta, H. V., Wagener, T., and Liu, Y.: Reconciling theory with observations: elements of a diagnostic approach to model evaluation, Hydrol. Process., 22, 3802-3813, doi:10.1002/hyp.6989, 2008.
    • Gupta, H. V., Kling, H., Yilmaz, K. K., and Martinez, G. F.: Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling, J. Hydrol., 377, 80-91, doi:10.1016/j.jhydrol.2009.08.003, 2009.
    • Gupta, H. V., Clark, M. P., Vrugt, J. A., Abramowitz, G., and Ye, M.: Towards a comprehensive assessment of model structural adequacy, Water Resour. Res., 48, W08301, doi:10.1029/2011WR011044, 2012.
    • Hargreaves, G. H. and Samani, Z. A.: Reference crop evapotranspiration from temperature, Appl. Eng. Agric., 1(2), 96-99, 1985.
    • Houser, P. R., Shuttleworth, W. J., Famiglietti, J. S., Gupta, H. V., Syed, K. H., and Goodrich, D. C.: Integration of soil moisture remote sensing and hydrologic modeling using data assimilation, Water Resour. Res., 34, 3405-3420, doi:10.1029/1998WR900001, 1998.
    • Hsu, K., Gao, X., Sorooshian, S. and Gupta, H. V.: Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks, J. Appl. Meteorol., 36, 1176-1190, doi:10.1175/1520-0450(1997)036<1176:PEFRSI>2.0.CO;2, 1997.
    • Huffman, G. J., Bolvin, D. T., Nelkin, E. J., Wolff, D. B., Adler, R. F., Gu, G., Hong, Y., Bowman, K. P., and Stocker, E. F.: The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-Global, Multiyear, Combined-Sensor Precipitation Estimates at Fine Scales, J. Hydrometeorol., 8, 38-55, doi:10.1175/JHM560.1, 2007.
    • Jain, A. and Roy, T.: Evaporation modeling using neural networks for assessing the self-sustainability of a water body, Lakes Reserv. Res. Manage., in review, 2017.
    • Joyce, R. J., Janowiak, J. E., Arkin, P. A., and Xie, P.: CMORPH: A Method that Produces Global Precipitation Estimates from Passive Microwave and Infrared Data at High Spatial and Temporal Resolution, J. Hydrometeorol., 5, 487-503, doi:10.1175/1525- 7541(2004)005<0487:CAMTPG>2.0.CO;2, 2004.
    • Kumar, R., Samaniego, L., and Attinger, S.: Implications of distributed hydrologic model parameterization on water fluxes at multiple scales and locations, Water Resour. Res., 49, 360-379, doi:10.1029/2012WR012195, 2013.
    • Marshall, L., Sharma, A., and Nott, D.: Modeling the catchment via mixtures: Issues of model specification and validation, Water Resour. Res., 42, W11409, doi:10.1029/2005WR004613, 2006.
    • Martens, B., Miralles, D. G., Lievens, H., van der Schalie, R., de Jeu, R. A. M., Férnandez-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. Discuss., doi:10.5194/gmd-2016-162, in review, 2016.
    • Martinez, G. F. and Gupta, H. V.: Hydrologic consistency as a basis for assessing complexity of monthly water balance models for the continental United States, Water Resour. Res., 47, W12540, doi:10.1029/2011WR011229, 2011.
    • McCabe, M. F., Ershadi, A., Jimenez, C., Miralles, D. G., Michel, D., and Wood, E. F.: The GEWEX LandFlux project: evaluation of model evaporation using tower-based and globally gridded forcing data, Geosci. Model Dev., 9, 283-305, doi:10.5194/gmd9-283-2016, 2016.
    • Michel, D., Jiménez, C., Miralles, D. G., Jung, M., Hirschi, M., Ershadi, A., Martens, B., McCabe, M. F., Fisher, J. B., Mu, Q., Seneviratne, S. I., Wood, E. F., and Fernández-Prieto, D.: The WACMOS-ET project - Part 1: Tower-scale evaluation of four remote-sensing-based evapotranspiration algorithms, Hydrol. Earth Syst. Sci., 20, 803-822, doi:10.5194/hess-20-803- 2016, 2016.
    • 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, doi: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, doi:10.5194/hess-20-823-2016, 2016.
    • Monteith, J. L.: Evaporation and environment, in: Proceedings of the 19th Symposium of the Society for Experimental Biology, edited by: Fogg, G. E., Cambridge University Press, New York, USA, 205-234, 1965.
    • Moore, R. J.: The probability-distributed principle and runoff production at point and basin scales, Hydrolog. Sci. J., 30, 273-297, doi:10.1080/02626668509490989, 1985.
    • Moradkhani, H., Hsu, K.-L., Gupta, H., and Sorooshian, S.: Uncertainty assessment of hydrologic model states and parameters: Sequential data assimilation using the particle filter, Water Resour. Res., 41, W05012, doi:10.1029/2004WR003604, 2005.
    • Nearing, G. S.: Diagnostics And Generalizations Of Parametric State Estimation, The University of Arizona, Tucson, USA, 2013.
    • Nearing, G. S. and Gupta, H. V.: The quantity and quality of information in hydrologic models, Water Resour. Res., 51, 524-538, doi:10.1002/2014WR015895, 2015.
    • Nearing, G. S., Gupta, H. V., Crow, W. T., and Gong, W.: An approach to quantifying the efficiency of a Bayesian filter, Water Resour. Res., 49, 2164-2173, doi:10.1002/wrcr.20177, 2013a.
    • Nearing, G. S., Gupta, H. V., and Crow, W. T.: Information loss in approximately Bayesian estimation techniques: A comparison of generative and discriminative approaches to estimating agricultural productivity, J. Hydrol., 507, 163-173, doi:10.1016/j.jhydrol.2013.10.029, 2013b.
    • Penman, H. L.: Natural evaporation from open water, bare soil and grass, P. Roy. Soc. Lond. A, 193, 120-145, 1948.
    • Pokhrel, P., Gupta, H. V., and Wagener, T.: A spatial regularization approach to parameter estimation for a distributed watershed model, Water Resour. Res., 44, W12419, doi:10.1029/2007WR006615, 2008.
    • Pokhrel, P., Yilmaz, K. K., and Gupta, H. V.: Multiple-criteria calibration of a distributed watershed model using spatial regularization and response signatures, J. Hydrol., 418-419, 49-60, doi:10.1016/j.jhydrol.2008.12.004, 2012.
    • 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, doi:10.1175/1520- 0493(1972)100<0081:OTAOSH>2.3.CO;2, 1972.
    • Razavi, S. and Gupta, H. V.: A new framework for comprehensive, robust, and efficient global sensitivity analysis: 2. Application, Water Resour. Res., 52, 440-455, doi:10.1002/2015WR017559, 2016.
    • Roy, T., Serrat-Capdevila, A., Gupta, H., and Valdes, J.: A platform for probabilistic Multimodel and Multiproduct Streamflow Forecasting, Water Resour. Res., 53, doi:10.1002/2016WR019752, 2017a.
    • Roy, T., Serrat-Capdevila, A., Valdes, J. B., Durcik, M. and Gupta, H. V.: Design and implementation of an operational multimodel multiproduct real-time probabilistic streamflow forecasting platform, in review, 2017b.
    • Savenije, H. H. G.: HESS Opinions “Topography driven conceptual modelling (FLEX-Topo)”, Hydrol. Earth Syst. Sci., 14, 2681- 2692, doi:10.5194/hess-14-2681-2010, 2010.
    • Schaefli, B., Harman, C. J., Sivapalan, M., and Schymanski, S. J.: HESS Opinions: Hydrologic predictions in a changing environment: behavioral modeling, Hydrol. Earth Syst. Sci., 15, 635- 646, doi:10.5194/hess-15-635-2011, 2011.
    • Shuttleworth, W. J.: Evaporation, in: chap. 4, Handbook of Hydrology, edited by: Maidment, D. R., McGraw-Hill Inc., New York, 1992.
    • Sudheer, K. P., Gosain, A. K., Mohana Rangan, D., and Saheb, S. M.: Modelling evaporation using an artificial neural network algorithm, Hydrol. Process., 16, 3189-3202, doi:10.1002/hyp.1096, 2002.
    • Thornthwaite, C. W.: An approach toward a rational classification of climate, Geogr. Rev., 38, 55-94, 1948.
    • Trambauer, P., Dutra, E., Maskey, S., Werner, M., Pappenberger, F., van Beek, L. P. H., and Uhlenbrook, S.: Comparison of different evaporation estimates over the African continent, Hydrol. Earth Syst. Sci., 18, 193-212, doi:10.5194/hess-18-193-2014, 2014.
    • Troch, P. A., Lahmers, T., Meira, A., Mukherjee, R., Pedersen, J. W., Roy, T., and Valdés-Pineda, R.: Catchment coevolution: A useful framework for improving predictions of hydrological change?, Water Resour. Res., 51, 4903-4922, doi:10.1002/2015WR017032, 2015.
    • van Emmerik, T., Mulder, G., Eilander, D., Piet, M., and Savenije, H.: Predicting the ungauged basin: model validation and realism assessment, Front. Earth Sci., 3, 1-11, doi:10.3389/feart.2015.00062, 2015.
    • Vrugt, J. A., Gupta, H. V., Bouten, W., and Sorooshian, S.: A Shuffled Complex Evolution Metropolis algorithm for optimization and uncertainty assessment of hydrologic model parameters, Water Resour. Res., 39, 1201, doi:10.1029/2002WR001642, 2003.
    • Vrugt, J. A., ter Braak, C. J. F., Gupta, H. V., and Robinson, B. A.: Equifinality of formal (DREAM) and informal (GLUE) Bayesian approaches in hydrologic modeling?, Stoch. Environ. Res. Risk A., 23, 1011-1026, doi:10.1007/s00477-008-0274-y, 2009.
    • Wagener, T., Boyle, D. P., Lees, M. J., Wheater, H. S., Gupta, H. V., and Sorooshian, S.: A framework for development and application of hydrological models, Hydrol. Earth Syst. Sci., 5, 13-26, doi:10.5194/hess-5-13-2001, 2001.
    • Wang, D., Chen, Y., and Cai, X.: State and parameter estimation of hydrologic models using the constrained ensemble Kalman filter, Water Resour. Res., 45, W11416, doi:10.1029/2008WR007401, 2009.
    • Winsemius, H. C., Savenije, H. H. G., and Bastiaanssen, W. G. M.: Constraining model parameters on remotely sensed evaporation: justification for distribution in ungauged basins?, Hydrol. Earth Syst. Sci., 12, 1403-1413, doi:10.5194/hess-12-1403- 2008, 2008.
    • WREM: Mara River Basin Monograph, available at: http: //nileis.nilebasin.org/system/files/MaraMonograph.pdf (last access: 10 February 2017), 2008.
    • Zhang, Y., Chiew, F. H. S., Zhang, L., and Li, H.: Use of Remotely Sensed Actual Evapotranspiration to Improve Rainfall-Runoff Modeling in Southeast Australia, J. Hydrometeorol., 10, 969- 980, doi:10.1175/2009JHM1061.1, 2009.
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