Frequency Analysis of Rainfall Data Using Probability Distribution Models

Abstract Urban runoff is a major cause of urban flooding and is difficult to monitor in the long term. In contrast, long term continuous rainfall data are generally available for any given region. As a result, it has become customary to use design rainfall depth as a proxy for runoff in urban hydrological analyses, with an assumption of the same frequency for runoff and rainfall. However, this approach has lack of overall coordination and cannot fully reflect the variability of rainfall characteristics. To address this issue, this study presents a three-dimensional copula-based multivariate frequency analysis of rainfall characteristics based on a long term (1961–2012) rainfall data from Guangzhou, China. Firstly, continuous rainfall data were divided into individual rainfall events using the rainfall intensity method. Then the characteristic variables of rainfall (design rainfall depth, DRD; total rainfall depth, TRD; peak rainfall depth, PRD) were sampled using the annual maximum method. Finally, a copula method was used to develop the multivariate joint probability distribution and the conditional probability distribution of rainfall characteristics. The results showed that the copula-based method is easy to implement and can better reflect urban rainstorm characteristics. It can serve a scientific reference for urban flood control and drainage planning.

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Flood Frequency Analysis Considering Probability Distribution and Return Period under Non-stationary Condition

Journal of Korea Water Resources Association ◽

10.3741/jkwra.2015.48.7.567 ◽

2015 ◽

Vol 48 (7) ◽

pp. 567-579 ◽

Cited By ~ 2

Author(s):

Sang Ug Kim ◽

◽

Yeong Seob Lee

Keyword(s):

Probability Distribution ◽

Frequency Analysis ◽

Return Period ◽

Flood Frequency ◽

Flood Frequency Analysis ◽

Stationary Condition

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

Hydrology and Earth System Sciences ◽

10.5194/hess-18-353-2014 ◽

2014 ◽

Vol 18 (1) ◽

pp. 353-365 ◽

Cited By ~ 35

Author(s):

U. Haberlandt ◽

I. Radtke

Keyword(s):

Time Series ◽

Frequency Analysis ◽

Model Calibration ◽

Hydrological Model ◽

Probability Distributions ◽

Flood Frequency ◽

Rainfall Data ◽

Flood Frequency Analysis ◽

Peak Flows ◽

Hourly Rainfall

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.

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