<?xml version="1.0" encoding="utf-8" standalone="no"?>
<!DOCTYPE article SYSTEM "http://www.hydrol-earth-syst-sci.net/inc/hess/copernicus.dtd">
<article language="en">
	<journal>
		<journal_title>Hydrology and Earth System Sciences</journal_title>
		<journal_url>www.hydrol-earth-syst-sci.net</journal_url>
		<issn>1027-5606</issn>
		<eissn>1607-7938</eissn>
		<volume_number>12</volume_number>
		<issue_number>4</issue_number>
		<publication_year>2008</publication_year>
	</journal>
	<doi>10.5194/hess-12-989-2008</doi>
	<article_url>http://www.hydrol-earth-syst-sci.net/12/989/2008/</article_url>
	<abstract_html>http://www.hydrol-earth-syst-sci.net/12/989/2008/hess-12-989-2008.html</abstract_html>
	<fulltext_pdf>http://www.hydrol-earth-syst-sci.net/12/989/2008/hess-12-989-2008.pdf</fulltext_pdf>
	<start_page>989</start_page>
	<end_page>1006</end_page>
	<publication_date>2008-07-28</publication_date>
	<article_title content_type="html">HYDROGEIOS: a semi-distributed GIS-based hydrological model for modified river basins</article_title>
	<authors>
		<author numeration="1" affiliations="1">
			<name>A. Efstratiadis</name>
			<email>andreas@itia.ntua.gr</email>
		</author>
		<author numeration="2" affiliations="2">
			<name>I. Nalbantis</name>
		</author>
		<author numeration="3" affiliations="1">
			<name>A. Koukouvinos</name>
		</author>
		<author numeration="4" affiliations="1">
			<name>E. Rozos</name>
		</author>
		<author numeration="5" affiliations="1">
			<name>D. Koutsoyiannis</name>
		</author>
	</authors>
	<affiliations>
		<affiliation numeration="1" content_type="html">Department of Water Resources and Environment, School of Civil Engineering, National Technical Univ. of Athens, Greece</affiliation>
		<affiliation numeration="2" content_type="html">Laboratory of Reclamation Works and Water Resources Management, School of Rural and Surveying Engineering, National Technical Univ. of Athens, Greece</affiliation>
	</affiliations>
	<abstract content_type="html">The HYDROGEIOS modelling framework represents the main processes of the
hydrological cycle in heavily modified catchments, with decision-depended
abstractions and interactions between surface and groundwater flows. A
semi-distributed approach and a monthly simulation time step are adopted,
which are sufficient for water resources management studies. The modelling
philosophy aims to ensure consistency with the physical characteristics of
the system, while keeping the number of parameters as low as possible.
Therefore, multiple levels of schematization and parameterization are
adopted, by combining multiple levels of geographical data. To optimally
allocate human abstractions from the hydrosystem during a planning horizon
or even to mimic the allocation occurred in a past period (e.g. the
calibration period), in the absence of measured data, a linear programming
problem is formulated and solved within each time step. With this technique
the fluxes across the hydrosystem are estimated, and the satisfaction of
physical and operational constraints is ensured. The model framework
includes a parameter estimation module that involves various goodness-of-fit
measures and state-of-the-art evolutionary algorithms for global and
multiobjective optimization. By means of a challenging case study, the paper
discusses appropriate modelling strategies which take advantage of the above
framework, with the purpose to ensure a robust calibration and reproduce
natural and human induced processes in the catchment as faithfully as
possible.</abstract>
	<references>
		<reference numeration="1" content_type="text"> Ajami, N. K., Gupta, H., Wagener, T., and Sorooshian, S.: Calibration of a semi-distributed hydrologic model for streamflow estimation along a river system, J. Hydrol., 298, 112–135, 2004. </reference>
		<reference numeration="2" content_type="text"> Andréassian, V., Perrin, C., Michel, C., Usart-Sanchez, I., and Lavabre, J.: Impact of imperfect rainfall knowledge on the efficiency and the parameters of watershed models, J. Hydrol., 250, 206–223, 2001. </reference>
		<reference numeration="3" content_type="text"> Beldring, S.: Multi-criteria validation of a precipitation-runoff model, J. Hydrol., 257, 189–211, 2002. </reference>
		<reference numeration="4" content_type="text"> Beven, K. J. and Binley, A. M.: The future of distributed models: model calibration and uncertainty prediction, Hydrol. Processes, 6, 279–298, 1992. </reference>
		<reference numeration="5" content_type="text"> Beven, K. J.: Changing ideas in hydrology – The case of physically-based models, J. Hydrol., 105, 157–172, 1989. </reference>
		<reference numeration="6" content_type="text"> Beven, K. J.: How far can we go in distributed hydrological modelling?, Hydrol. Earth Syst. Sci., 5, 1–12, 2001. </reference>
		<reference numeration="7" content_type="text"> Beven, K. J.: Uniqueness of place and process representations in hydrological modelling, Hydrol. Earth Syst. Sci., 4, 203–213, 2000. </reference>
		<reference numeration="8" content_type="text"> 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(12), 3663–3674, 2000. </reference>
		<reference numeration="9" content_type="text"> Boyle, D. P., Gupta, H. V., Sorooshian, S., Koren, V., Zhang, Z., and Smith, M.: Toward improved streamflow forecasts: value of semidistributed modeling, Water Resour. Res., 37(11), 2749–2760, 2001. </reference>
		<reference numeration="10" content_type="text"> Brath, A., Montanari, A., and Moretti, G.: Assessing the effect on flood frequency of land use change via hydrological simulation (with uncertainty), J. Hydrol., 324(1–4), 141–153, 2006. </reference>
		<reference numeration="11" content_type="text"> Butts, M. B., Payne, J. T., Kristensen, M., and Madsen, H.: An evaluation of the impact of model structure on hydrological uncertainty for streamflow simulation, J. Hydrol., 298, 242–266, 2004. </reference>
		<reference numeration="12" content_type="text"> Chaubey, I., Haan, C. T., Grunwald, S., and Salisbury, J. M.: Uncertainty in the model parameters due to spatial variability of rainfall, J. Hydrol., 220, 48–61, 1999. </reference>
		<reference numeration="13" content_type="text"> Dai, T. and Labadie, J. W.: River basin network model for integrated water quantity/quality management, J. Water Res. Pl.-ASCE, 127(5), 295–305, 2001. </reference>
		<reference numeration="14" content_type="text"> Duan, Q., Sorooshian, S., and Gupta, V.: Effective and efficient global optimization for conceptual rainfall-runoff models, Water Resour. Res., 28(4), 1015–1031, 1992. </reference>
		<reference numeration="15" content_type="text"> E.U.: Directive 2000/60/EC establishing a framework for Community action in the field of water policy, Official Journal of the European Communities, L 327, 2000. </reference>
		<reference numeration="16" content_type="text"> Eckhardt, K. and Arnold, J. G.: Automatic calibration of a distributed catchment model, J. Hydrol., 251, 103–109, 2001. </reference>
		<reference numeration="17" content_type="text"> Efstratiadis, A. and Koutsoyiannis, D.: An evolutionary annealing-simplex algorithm for global optimisation of water resource systems, Proc. Fifth Intern. Conf. on Hydroinformatics, Cardiff, UK, IWA, 1423–1428, 2002. </reference>
		<reference numeration="18" content_type="text"> Efstratiadis, A. and Koutsoyiannis, D.: Fitting hydrological models on multiple responses using the multiobjective evolutionary annealing-simplex approach, in: Practical Hydroinformatics: Computational Intelligence and Technological Developments in Water Applications, edited by: Abrahart, R. J., See, L. M., and Solomatine, D. P., Springer DE, Water Science and Technology Library, 259–273, 2008. </reference>
		<reference numeration="19" content_type="text"> Efstratiadis, A.: Non-linear methods in multiobjective water resource optimisation problems, with emphasis on the calibration of hydrological models, PhD thesis, National Technical University of Athens, 391 pp., www.itia.ntua.gr/en/docinfo/838, 2008. </reference>
		<reference numeration="20" content_type="text"> Efstratiadis, A., Koutsoyiannis, D., and Xenos, D.: Minimising water cost in the water resource management of Athens, Urban Water J., 1(1), 3–15, 2004. </reference>
		<reference numeration="21" content_type="text"> Ewen, J., O&apos;Donnell, G., Burton, A., and O&apos;Connell, E.: Errors and uncertainty in physically-based rainfall-runoff modelling of catchment change effects, J. Hydrol., 330(3–4), 641–650, 2006. </reference>
		<reference numeration="22" content_type="text"> Flügel, W.-A.: Delineating Hydrological Response Units (HRU&apos;s) by GIS analysis for regional hydrological modelling using PRMS/MMS in the drainage basin of the River Bröl, Germany, Hydrol. Processes, 9, 423–436, 1995. </reference>
		<reference numeration="23" content_type="text"> Fredericks, J., Labadie, J., and Altenhofen, J.: Decision support system for conjunctive stream-aquifer management, J. Water Res. Pl.-ASCE, 124(2), 69–78, 1998. </reference>
		<reference numeration="24" content_type="text"> Freer, J., Beven, K. J., and Ambroise, B.: Bayesian estimation of uncertainty in runoff prediction and the value of data: an application of the GLUE approach, Water Resour. Res., 32(7), 2161–2173, 1996. </reference>
		<reference numeration="25" content_type="text"> Gan, T. Y., Dlamini, E. M., and Biftu, G. F.: Effects of model complexity and structure, data quality, and objective functions on hydrologic modelling, J. Hydrol., 192, 81–103, 1997. </reference>
		<reference numeration="26" content_type="text"> Graham, L. P., Labadie, J. W., Hutchison, I. P. G., and Ferguson, K. A.: Allocation of augmented water supply under a priority water rights system, Water Resour. Res., 22(7), 1083–1094, 1986. </reference>
		<reference numeration="27" content_type="text"> Gupta, H. V., Sorooshian, S., and Yapo, P. O.: Toward improved calibration of hydrologic models: multiple and non-commensurable measures of information, Water Resour. Res., 34(4), 751–763, 1998. </reference>
		<reference numeration="28" content_type="text"> Jakeman A. J., and Hornberger, G. M.: How much complexity is warranted in a rainfall-runoff model?, Water Resour. Res., 29, 2637–2649, 1993. </reference>
		<reference numeration="29" content_type="text"> Kitanidis, P. K. and R. L. Bras: Real-time forecasting with a conceptual hydrologic model: analysis of uncertainty, Water Resour. Res., 16(6), 1025–1033, 1980. </reference>
		<reference numeration="30" content_type="text"> Kottegoda, N. T.: Stochastic water resources technology, McMillan Press, Honk Kong, 1980. </reference>
		<reference numeration="31" content_type="text"> Koutsoyiannis, D., Efstratiadis, A., and Georgakakos, K.: Uncertainty assessment of future hydroclimatic predictions: A comparison of probabilistic and scenario-based approaches, J. Hydrometeorol., 8(3), 261–281, 2007. </reference>
		<reference numeration="32" content_type="text"> Koutsoyiannis, D., Efstratiadis, A., and Karavokiros, G.: A decision support tool for the management of multi-reservoir systems, J. Am. Water Resour. As., 38(4), 945–958, 2002. </reference>
		<reference numeration="33" content_type="text"> Koutsoyiannis, D., Karavokiros, G., Efstratiadis, A., Mamassis, N., Koukouvinos, A., and Christofides, A.: A decision support system for the management of the water resource system of Athens, Phys. Chem. Earth, 28(14–15), 599–609, 2003. </reference>
		<reference numeration="34" content_type="text"> Kuczera, G. and Parent, E.: Monte Carlo assessment of parameter uncertainty in conceptual catchment models: the Metropolis algorithm, J. Hydrol., 211, 69–85, 1998. </reference>
		<reference numeration="35" content_type="text"> Kuczera, G.: Fast multireservoir multiperiod linear programming models, Water Resour. Res., 25(2), 169–176, 1989. </reference>
		<reference numeration="36" content_type="text"> Kuczera, G.: On the relationship of the reliability of parameter estimates and hydrologic time series data used in calibration, Water Resour. Res., 18, 146–154, 1982. </reference>
		<reference numeration="37" content_type="text"> Madsen, H.: Automatic calibration of a conceptual rainfall-runoff model using multiple objectives, J. Hydrol., 235, 276–288, 2000. </reference>
		<reference numeration="38" content_type="text"> Madsen, H.: Parameter estimation in distributed hydrological catchment modelling using automatic calibration with multiple objectives, Adv. Water Resour., 26, 205–216, 2003. </reference>
		<reference numeration="39" content_type="text"> Mazi, A., Koussis, A. D., Restrepo, P. J., and Koutsoyiannis, D.: A groundwater-based, objective-heuristic parameter optimisation method for a precipitation-runoff model and its application to a semi-arid basin, J. Hydrol., 290, 243–258, 2004. </reference>
		<reference numeration="40" content_type="text"> Muleta, M. K. and Nicklow, J. W.: Sensitivity and uncertainty analysis coupled with automatic calibration for a distributed watershed model, J. Hydrol., 306, 127–145, 2005. </reference>
		<reference numeration="41" content_type="text"> Nalbantis, I., Rozos, E., Tentes, G., Efstratiadis, A., and Koutsoyiannis, D.: Integrating groundwater models within a decision support system, Proc. 5th Intern. Conf. of European Water Resources Association: &quot;Water Resources Management in the Era of Transition&quot;, edited by: Tsakiris, G., Athens, 4–8 September 2002, EWRA, IAHR, 279–286, 2002. </reference>
		<reference numeration="42" content_type="text"> Nandakumar, N. and Mein, R. G.: Uncertainty in rainfall-runoff model simulations and the implications for predicting the hydrologic effects of land-use change, J. Hydrol., 192(1–4), 211–232, 1997. </reference>
		<reference numeration="43" content_type="text"> Nash, J. E. and Sutcliffe, J. V.: River flow forecasting through conceptual models, I, A discussion of principles, J. Hydrol., 10(3), 282–290, 1970. </reference>
		<reference numeration="44" content_type="text"> Nelder, J. A. and Mead, R.: A simplex method for function minimization, Comput. J., 7(4), 308–313, 1965. </reference>
		<reference numeration="45" content_type="text"> Panday, S. and Huyakorn, P. S.: A fully coupled physically-based spatially distributed model for evaluating surface/subsurface flow, Adv. Water. Resour., 27, 361–382, 2004. </reference>
		<reference numeration="46" content_type="text"> Pappenberger, F. and Beven, K. J.: Ignorance is bliss: or seven reasons not to use uncertainty analysis, Water Resour. Res., 42, W05302, doi:10.1029/2005WR004820, 2006. </reference>
		<reference numeration="47" content_type="text"> Paturel, J. E., Servat, E., and Vassiliadis, A.: Sensitivity of conceptual rainfall-runoff algorithms to errors in input data – case of the GR2M model, J. Hydrol., 168, 111–125, 1995. </reference>
		<reference numeration="48" content_type="text"> Refsgaard, J. C.: Parameterisation, calibration and validation of distributed hydrological models, J. Hydrol., 198, 69–97, 1997. </reference>
		<reference numeration="49" content_type="text"> Rozos, E. and Koutsoyiannis, D.: A multicell karstic aquifer model with alternative flow equations, J. Hydrol., 325(1–4), 340–355, 2006. </reference>
		<reference numeration="50" content_type="text"> Rozos, E. and Koutsoyiannis, D.: Application of the Integrated Finite Difference Method in groundwater flow, EGU General Assembly 2005, Geophys. Res. Abstr., 7, 00579, 2005. </reference>
		<reference numeration="51" content_type="text"> Rozos, E., Efstratiadis, A., Nalbantis, I., and Koutsoyiannis, D.: Calibration of a semi-distributed model for conjunctive simulation of surface and groundwater flows, Hydrol. Sci. J., 49(5), 819–842, 2004. </reference>
		<reference numeration="52" content_type="text"> Schoups, G., Addams, C. L., and Gorelick, S. M.: Multi-objective calibration of a surface water-groundwater flow model in an irrigated agricultural region: Yaqui Valley, Sonora, Mexico, Hydrol. Earth Syst. Sci., 9, 549–568, 2005. </reference>
		<reference numeration="53" content_type="text"> Seibert, J. and McDonnell, J. J.: On the dialog between experimentalist and modeler in catchment hydrology: use of soft data for multicriteria model calibration, Water Resour. Res., 38(11), 1241, doi:10.1029/2001WR000978, 2002. </reference>
		<reference numeration="54" content_type="text"> Seibert, J.: Multi-criteria calibration of a conceptual runoff model using a genetic algorithm, Hydrol. Earth Syst. Sci., 4, 215–224, 2000. </reference>
		<reference numeration="55" content_type="text"> Singh, V. and Bhallamudi, S. M.: Conjunctive surface-subsurface modeling of overland flow, Adv. Water Resour., 21, 567–579, 1998. </reference>
		<reference numeration="56" content_type="text"> Sorooshian, S. and Dracup, J. A.: Stochastic parameter estimation procedures for conceptual rainfall-runoff models: correlated and heteroscedastic error case, Water Resour. Res., 16(2), 430–442, 1980. </reference>
		<reference numeration="57" content_type="text"> Sorooshian, S., Gupta, V. K., and Fulton, J. L.: Evaluation of maximum likelihood parameter estimation techniques for conceptual rainfall-runoff models: influence of calibration data variability and length on model credibility, Water Resour. Res., 19(1), 251–259, 1983. </reference>
		<reference numeration="58" content_type="text"> Srinivasan, R., Muttiah, R. S., Dyke, P. T., Walker, C., and Arnold, J.: Hydrologic unit model for the United States (HUMUS), Texas Agricultural Experiment Station, Blackland Research Center, Temple, TX, 2000. </reference>
		<reference numeration="59" content_type="text"> Tang, Y., Reed, P., and Wagener, T., How effective and efficient are multiobjective evolutionary algorithms at hydrologic model calibration?, Hydrol. Earth Syst. Sci., 10, 289–307, 2006. </reference>
		<reference numeration="60" content_type="text"> Thiemann, M., Trosser, M., Gupta, H., and Sorooshian, S.: Bayesian recursive parameter estimation for hydrologic models, Water Resour. Res., 37(10), 2521–2536, 2001. </reference>
		<reference numeration="61" content_type="text"> Thornthwaite, C. W.: An approach toward a rational classification of climate, Geogr. Rev. ,38(1), 55–94, 1948. </reference>
		<reference numeration="62" content_type="text"> Vrugt, J. A., Bouten, W., Gupta, H. V., and Sorooshian, S.: Toward improved identifiability of hydrologic model parameters: the information content of experimental data, Water Resour. Res., 38(12), 1312, doi:10.1029/2001WR001118, 2002. </reference>
		<reference numeration="63" content_type="text"> Vrugt, J. A., Schoups, G., Hopmans, J. W., Young, C., Wallender, W. W., Harter, T., and Bouten, W.: Inverse modeling of large-scale spatially distributed vadose zone properties using global optimization, Water Resour. Res., 40, W06503, doi:10.1029/2003WR002706, 2004. </reference>
		<reference numeration="64" content_type="text"> 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, 2001. </reference>
		<reference numeration="65" content_type="text"> Waterloopkundig Laboratorium: RIBASIM User&apos;s Guide, 1991. </reference>
		<reference numeration="66" content_type="text"> Yapo, P. O., Gupta, H. V., and Sorooshian, S.: Multi-objective global optimization for hydrologic models, J. Hydrol., 204, 83–97, 1998. </reference>
		<reference numeration="67" content_type="text"> Yapo, P. O., Gupta, H. V., and Sorooshian, S.: Automatic calibration of conceptual rainfall-runoff models: sensitivity to calibration data, J. Hydrol., 181, 23–48, 1996. </reference>
	</references>
</article>

