Hydrological model calibration for derived flood frequency analysis using stochastic rainfall and probability distributions of peak flows
Creators: name: Haberlandt, Uwe
name: Radtke, I.
Publisher: name: Göttingen : Copernicus GmbH
Year: 2014
Subjects: subject: Continuous simulation
subject: Derived flood frequency analysis
subject: Derived flood frequency distributions
subject: Hydrological model calibrations
subject: Hydrological modeling
subject: Mesoscale catchments
subject: Model parameter sets
subject: Stochastic rainfalls
subject: Calibration
subject: Catchments
subject: Climate models
subject: Computer simulation
subject: Flood control
subject: Floods
subject: Probability distributions
subject: Stochastic models
subject: Stochastic systems
subject: Time series
subject: Rain
subject: calibration
subject: catchment
subject: data set
subject: discharge
subject: flood
subject: flood control
subject: frequency analysis
subject: hydrological modeling
subject: numerical model
subject: peak flow
subject: precipitation (climatology)
subject: rainfall
subject: stochasticity
subject: Germany
subject: Dewey Decimal Classification::500 | Naturwissenschaften::550 | Geowissenschaften
subject: Dewey Decimal Classification::500 | Naturwissenschaften::551 | Geologie, Hydrologie, Meteorologie

description: Derived flood frequency analysis allows the estimation of design floods with hydrological modeling for poorly observed basins considering change and taking into account flood protection measures. There are several possible choices regarding precipitation input, discharge output and consequently the calibration of the model. The objective of this study is to compare different calibration strategies for a hydrological model considering various types of rainfall input and runoff output data sets and to propose the most suitable approach. Event based and continuous, observed hourly rainfall data as well as disaggregated daily rainfall and stochastically generated hourly rainfall data are used as input for the model. As output, short hourly and longer daily continuous flow time series as well as probability distributions of annual maximum peak flow series are employed. The performance of the strategies is evaluated using the obtained different model parameter sets for continuous simulation of discharge in an independent validation period and by comparing the model derived flood frequency distributions with the observed one. The investigations are carried out for three mesoscale catchments in northern Germany with the hydrological model HEC-HMS (Hydrologic Engineering Center's Hydrologic Modeling System). The results show that (I) the same type of precipitation input data should be used for calibration and application of the hydrological model, (II) a model calibrated using a small sample of extreme values works quite well for the simulation of continuous time series with moderate length but not vice versa, and (III) the best performance with small uncertainty is obtained when stochastic precipitation data and the observed probability distribution of peak flows are used for model calibration. This outcome suggests to calibrate a hydrological model directly on probability distributions of observed peak flows using stochastic rainfall as input if its purpose is the application for derived flood frequency analysis.