<?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>6</issue_number>
		<publication_year>2008</publication_year>
	</journal>
	<doi>10.5194/hess-12-1353-2008</doi>
	<article_url>http://www.hydrol-earth-syst-sci.net/12/1353/2008/</article_url>
	<abstract_html>http://www.hydrol-earth-syst-sci.net/12/1353/2008/hess-12-1353-2008.html</abstract_html>
	<fulltext_pdf>http://www.hydrol-earth-syst-sci.net/12/1353/2008/hess-12-1353-2008.pdf</fulltext_pdf>
	<start_page>1353</start_page>
	<end_page>1367</end_page>
	<publication_date>2008-12-15</publication_date>
	<article_title content_type="html">A space-time hybrid hourly rainfall model for derived flood frequency analysis</article_title>
	<authors>
		<author numeration="1" affiliations="1">
			<name>U. Haberlandt</name>
			<email>haberlandt@iww.uni-hannover.de</email>
		</author>
		<author numeration="2" affiliations="2">
			<name>A.-D. Ebner von Eschenbach</name>
		</author>
		<author numeration="3" affiliations="1">
			<name>I. Buchwald</name>
		</author>
	</authors>
	<affiliations>
		<affiliation numeration="1" content_type="html">Institute of Water Resources Management, Hydrology and Agricultural Hydraulic Engineering, Leibniz University of Hannover, Hannover, Germany</affiliation>
		<affiliation numeration="2" content_type="html">Federal Institute of Hydrology, Koblenz, Germany</affiliation>
	</affiliations>
	<abstract content_type="html">For derived flood frequency analysis based on hydrological modelling long
continuous precipitation time series with high temporal resolution are
needed. Often, the observation network with recording rainfall gauges is
poor, especially regarding the limited length of the available rainfall time
series. Stochastic precipitation synthesis is a good alternative either to
extend or to regionalise rainfall series to provide adequate input for
long-term rainfall-runoff modelling with subsequent estimation of design
floods. Here, a new two step procedure for stochastic synthesis of
continuous hourly space-time rainfall is proposed and tested for the
extension of short observed precipitation time series.
&lt;br&gt;&lt;br&gt;
First, a single-site alternating renewal model is presented to simulate
independent hourly precipitation time series for several locations. The
alternating renewal model describes wet spell durations, dry spell durations
and wet spell intensities using univariate frequency distributions
separately for two seasons. The dependence between wet spell intensity and
duration is accounted for by 2-copulas. For disaggregation of the wet spells
into hourly intensities a predefined profile is used. In the second step a
multi-site resampling procedure is applied on the synthetic point rainfall
event series to reproduce the spatial dependence structure of rainfall.
Resampling is carried out successively on all synthetic event series using
simulated annealing with an objective function considering three bivariate
spatial rainfall characteristics. In a case study synthetic precipitation is
generated for some locations with short observation records in two mesoscale
catchments of the Bode river basin located in northern Germany. The
synthetic rainfall data are then applied for derived flood frequency
analysis using the hydrological model HEC-HMS. The results show good
performance in reproducing average and extreme rainfall characteristics as
well as in reproducing observed flood frequencies. The presented model has
the potential to be used for ungauged locations through regionalisation of
the model parameters.</abstract>
	<references>
		<reference numeration="1" content_type="text"> Aarts, E. and Korst, J.: Simulated Annealing and Boltzmann Machines: A Stochastic Approach to Combinatorial Optimization and Neural Computing, John Wiley, New York, 284 pp., 1989. </reference>
		<reference numeration="2" content_type="text"> Acreman, M. C.: Simple Stochastic Model of Hourly Rainfall for Farnborough, England, Hydrol. Sci. J., 35, 119–148, 1990. </reference>
		<reference numeration="3" content_type="text"> Ahmad, M. I., Sinclair, C. D., and Spurr, B. D.: Assessment of Flood Frequency Models Using Empirical Distribution Function Statistics, Water Resour. Res., 24, 1323–1328, 1988. </reference>
		<reference numeration="4" content_type="text"> Aronica, G. T. and Candela, A.: Derivation of flood frequency curves in poorly gauged Mediterranean catchments using a simple stochastic hydrological rainfall-runoff model, J. Hydrol., 347, 132–142, 2007. </reference>
		<reference numeration="5" content_type="text"> Bárdossy, A.: Generating precipitation time series using simulated annealing, Water Resour. Res., 34, 1737–1744, 1998. </reference>
		<reference numeration="6" content_type="text"> Bárdossy, A. and Das, T.: Influence of rainfall observation network on model calibration and application, Hydrol. Earth Syst. Sci., 12, 77–89, 2008. </reference>
		<reference numeration="7" content_type="text"> Beven, K. and Freer, J.: Equifinality, data assimilation, and uncertainty estimation in mechanistic modelling of complex environmental systems using the GLUE methodology, J. Hydrol., 249, 11–29, 2001. </reference>
		<reference numeration="8" content_type="text"> Blazkova, S. and Beven, K.: Flood frequency estimation by continuous simulation of subcatchment rainfalls and discharges with the aim of improving dam safety assessment in a large basin in the Czech Republic, J. Hydrol., 292, 153–172, 2004. </reference>
		<reference numeration="9" content_type="text"> Cameron, D. S., Beven, K. J., Tawn, J., Blazkova, S., and Naden, P.: Flood frequency estimation by continuous simulation for a gauged upland catchment (with uncertainty), J. Hydrol., 219, 169–187, 1999. </reference>
		<reference numeration="10" content_type="text"> Cowpertwait, P. S. P.: A spatial-temporal point process model of rainfall for the Thames catchment, UK, J. Hydrol., 330, 586–595, 2006. </reference>
		<reference numeration="11" content_type="text"> Cunderlik, J. M. and Simonovic, S. P.: Hydrological extremes in a southwestern Ontario river basin under future climate conditions, Hydrol. Sci. J., 50, 631–654, 2005. </reference>
		<reference numeration="12" content_type="text"> De Michele, C. and Salvadori, G.: A Generalized Pareto intensity-duration model of storm rainfall exploiting 2-Copulas, J. Geophys. Res., 108(D2), 4067, doi:4010.1029/2002JD002534, 2003. </reference>
		<reference numeration="13" content_type="text"> Evin, G. and Favre, A.-C.: A new rainfall model based on the Neyman-Scott process using cubic copulas, Wat. Resour. Res., 44, W03433, doi:03410.01029/02007WR006054, 2008. </reference>
		<reference numeration="14" content_type="text"> Fenicia, F., Savenije, H. H. G., Matgen, P., and Pfister, L.: Understanding catchment behavior through stepwise model concept improvement, Wat. Resour. Res., 44, W01402, doi:01410.01029/02006WR005563, 2008. </reference>
		<reference numeration="15" content_type="text"> Fleming, M. and Neary, V.: Continuous Hydrologic Modeling Study with the Hydrologic Modeling System, J. Hydrologic Eng., 9, 175–183, 2004. </reference>
		<reference numeration="16" content_type="text"> Götzinger, J. and Bárdossy, A.: Generic error model for calibration and uncertainty estimation of hydrological models, Wat. Resour. Res., 44, W00B07, doi:10.1029/2007WR006691, 2008. </reference>
		<reference numeration="17" content_type="text"> Grace, R. A. and Eagleson, P. S.: The Synthesis of Short-Time-Increment Rainfall Sequences, Hydrodynamics Laboratory, Massachusetts Institute of Technology, Cambridge, USA, Report No. 91, 1966. </reference>
		<reference numeration="18" content_type="text"> Haan, C. T., Allen, D. M., and Street, J. O.: A Markov Chain Model of Daily Rainfall, Water Resour. Res., 12, 443–449, 1976. </reference>
		<reference numeration="19" content_type="text"> Haberlandt, U.: Stochastic rainfall synthesis using regionalized model parameters, J. Hydrol. Eng., 3, 160–168, 1998. </reference>
		<reference numeration="20" content_type="text"> Hosking, J. R. M.: The four parameter kappa distribution, IBM J. Res. Develop., 38, 251–258, 1994. </reference>
		<reference numeration="21" content_type="text"> Hosking, J. R. M. and Wallis, J. R.: Regional frequency analysis: an approach based on L-moments, Cambridge University Press, New York, USA, 1997. </reference>
		<reference numeration="22" content_type="text"> Koutsoyiannis, D., Onof, C., and Wheater, H. S.: Multivariate rainfall disaggregation at a fine timescale, Water Resour. Res., 39, 1173, doi:1110.1029/2002WR001600, 2003. </reference>
		<reference numeration="23" content_type="text"> Lall, U. and Sharma, A.: A nearest neighbor bootstrap for resampling hydrological time series, Water Resour. Res., 32, 679–693, 1996. </reference>
		<reference numeration="24" content_type="text"> LAU: Das Frühjahrshochwasser vom April 1994 in den Flusseinzugsgebieten der Saale und Bode in Sachsen-Anhalt, Berichte des Landesamtes für Umweltschutz Sachsen-Anhalt, Germany, 1995. </reference>
		<reference numeration="25" content_type="text"> Lu, M. and Yamamoto, T.: Application of a Random Cascade Model to Estimation of Design Flood from Rainfall Data, J. Hydrol. Eng., 13, 385–391, 2008. </reference>
		<reference numeration="26" content_type="text"> Maskey, S., Guinot, V., and Price, R. K.: Treatment of precipitation uncertainty in rainfall-runoff modelling: a fuzzy set approach, Adv. Water Res., 27, 889–898, 2004. </reference>
		<reference numeration="27" content_type="text"> Moretti, G. and Montanari, A.: Inferring the flood frequency distribution for an ungauged basin using a spatially distributed rainfall-runoff model, Hydrol. Earth Syst. Sci., 12, 1141–1152, 2008. </reference>
		<reference numeration="28" content_type="text"> Neary, V. S., Habib, E., and Fleming, M.: Hydrologic Modeling with NEXRAD Precipitation in Middle Tennessee, J. Hydrol. Eng., 9, 339–349, 2004. </reference>
		<reference numeration="29" content_type="text"> Nelsen, R. B.: An Introduction to Copulas, 2nd ed., Springer, New York, USA, 2006. </reference>
		<reference numeration="30" content_type="text"> Olsson, J.: Evaluation of a scaling cascade model for temporal rain- fall disaggregation, Hydrol. Earth Syst. Sci., 2, 19–30, 1998. </reference>
		<reference numeration="31" content_type="text"> Onof, C., Chandler, R. E., Kakou, A., Northrop, P., Wheater, H. S., and Isham, V.: Rainfall modelling using Poisson-cluster processes: A review of developments, Stochastic Environ. Res. Risk Assess., 14, 384–411, 2000. </reference>
		<reference numeration="32" content_type="text"> Pegram, G. G. S. and Clothier, A. N.: High resolution space–time modelling of rainfall: the &quot;String of Beads&quot; model, J. Hydrol., 241, 26–41, 2001. </reference>
		<reference numeration="33" content_type="text"> Rodríguez-Iturbe, I., Febres de Power, B., and Valdés, J. B.: Rectangular Pulses Point Process Models for Rainfall: Analysis of Empirical Data, J. Geophys. Res., 92(D8), 9645–9656, 1987. </reference>
		<reference numeration="34" content_type="text"> Scharffenberg, W. A. and Fleming, M. J.: Hydrologic modelling system, HEC-HMS, User&apos;s Manual, 248 pp., 2005. </reference>
		<reference numeration="35" content_type="text"> Stedinger, J. R., Vogel, R. M., and Foufoula-Georgiou, E.: Frequency analysis of extreme events, in: Handbook of hydrology, edited by: Maidment, D. R., MacGRAW-HILL, New York, USA, 18.11–18.66, 1993. </reference>
		<reference numeration="36" content_type="text"> USACE: HEC-1 flood hydrograph package. User&apos;s manual, US Army Corps of Engineers, Hydrologic Engineering Center, Davis, CA, USA, 1998. </reference>
		<reference numeration="37" content_type="text"> Wendling, U., Schellin, H.-G., and Thomä, M.: Bereitstellung von täglichen Informationen zum Wasserhaushalt des Bodens für die Zwecke der agrarmeteorologischen Beratung, Z. Meteorologie, 41, 468–474, 1991. </reference>
		<reference numeration="38" content_type="text"> Wilks, D. S.: Multisite generalization of a daily stochastic precipitation generation model, J. Hydrol., 210, 178–191, 1998. </reference>
	</references>
</article>

