Hydrological model calibration for derived flood frequency analysis using stochastic rainfall and probability distributions of peak flows

Abstract

Abstract. 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.

Rainfall disaggregation for hydrological modeling: Is there a need for spatial consistence?

“…The second method, resulting in rainfall data set V3, is a more pragmatic solution and was introduced by Haberlandt and Radtke (2014). For each day, the station with the highest rainfall amount is identified.…”

“…Hence, the temporal connection between rainfall and runoff is missing. An alternative strategy is the calibration on runoff statistics and has been applied before by amongst others 20 Yu and Yang (2000), Westerberg et al (2011), Haberlandt and Radtke (2014), Wallner and Haberlandt (2015) and Ding et al (2015). Runoff statistics have no connection to time, but contain useful information about the hydrograph and hence about the hydrological regime and its characteristics.…”

“…The hypothesis of this study is that 10 rainfall data sets with different degrees of spatial consistence will result in different areal rainfall intensities and hence influence runoff statistics derived from simulated runoff time series. Therefore, three different rainfall data sets are used as input for rainfall runoff modelling: disaggregated time series with (Müller and Haberlandt, 2015) and without (Ding et al, 2016) implemented spatial consistence, and thirdly time series with an "overestimated spatial consistence" by parallelization (Haberlandt and Radtke, 2014). A systematic comparison is carried out including rainfall-runoff simulations with and 15 without calibration, differing station densities and different rainfall-runoff models.…”

“…CC BY 4.0 License. Haberlandt and Radtke (2014) overcame the lack of spatial consistence using a parallelization approach. For each day they chose the relative diurnal cycle from the disaggregation time series of the station with the highest daily rainfall amount and then adopted this diurnal pattern for all other time series.…”

Application of HEC-HMS in a Cold Region Watershed and Use of RADARSAT-2 Soil Moisture in Initializing the Model

“…The model can be used for both continuous and event-based modelling. Many researchers have successfully used HEC-HMS [26][27][28] for flood event modelling. The soil moisture accounting (SMA) algorithm has been successfully used in continuous simulation of the model [29][30][31].…”