scholarly journals Flood Frequency Analysis for the Annual Peak Flows Simulated by an Event-Based Rainfall-Runoff Model in an Urban Drainage Basin

Water ◽  
2014 ◽  
Vol 6 (12) ◽  
pp. 3841-3863 ◽  
Author(s):  
Jeonghwan Ahn ◽  
Woncheol Cho ◽  
Taereem Kim ◽  
Hongjoon Shin ◽  
Jun-Haeng Heo
Keyword(s):  
Frequency Analysis ◽  
Drainage Basin ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Urban Drainage ◽  
Rainfall Runoff ◽  
Annual Peak ◽  
Rainfall Runoff Model ◽  
Event Based ◽  
Runoff Model

Flood frequency analysis for nonstationary annual peak records in an urban drainage basin

2009 ◽  
Vol 32 (8) ◽  
pp. 1255-1266 ◽  
Author(s):  
Gabriele Villarini ◽  
James A. Smith ◽  
Francesco Serinaldi ◽  
Jerad Bales ◽  
Paul D. Bates ◽  
...  
Keyword(s):  
Frequency Analysis ◽  
Drainage Basin ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Urban Drainage ◽  
Annual Peak

scholarly journals An integrated simulation method for flash-flood risk assessment: 1. Frequency predictions in the Bisagno River by combining stochastic and deterministic methods

2002 ◽  
Vol 6 (2) ◽  
pp. 267-284 ◽  
Author(s):  
M.C. Rulli ◽  
R. Rosso
Keyword(s):  
River Basin ◽  
Frequency Analysis ◽  
Extreme Values ◽  
Flash Flood ◽  
Simulation Method ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Rainfall Runoff ◽  
Rainfall Runoff Model ◽  
Runoff Model

Abstract. A stochastic rainfall generator and a deterministic rainfall-runoff model, both distributed in space and time, are combined to provide accurate flood frequency prediction in the Bisagno River basin (Thyrrenian Liguria, N.W. Italy). The inadequacy of streamflow records with respect to the return period of the required flow discharges makes the stochastic simulation methodology a useful operational alternative to a regionalisation procedure for flood frequency analysis and derived distribution techniques. The rainfall generator is the Generalized Neyman-Scott Rectangular Pulses (GNSRP) model. The rainfall-runoff model is the FEST98 model. The GNSRP generator was calibrated using a continuous 7-years' record of hourly precipitation measurements at five raingauges scattered over the Bisagno basin. The calibrated rainfall model was then used to generate a 1000 years' series of continuous rainfall data at the gauging sites and a flood-oriented model validation procedure was developed to evaluate the agreement between observed and simulated extreme values of rainfall at different scales of temporal aggregation. The synthetic precipitation series were input to the FEST98 model to provide flood hydrographs at selected cross-sections across the river network. Flood frequency analysis of the annual flood series (AFS) obtained from these simulations was undertaken using L-moment estimations of Generalized Extreme Value (GEV) distributions. The results are compared with those determined by applying a regional flood analysis in Thyrrhenian Liguria and the derived distribution techniques to the Bisagno river basin. This approach is also useful to assess the effects of changes in land use on flood frequency regime (see Rosso and Rulli, 2002). Keywords: flood frequency, stochastic rainfall generator, distributed rainfall runoff model, derived distribution

Spatial representation of stochastically generated rainfall for derived flood frequency analysis

2021 ◽  
Author(s):  
Luisa-Bianca Thiele ◽  
Ross Pidoto ◽  
Uwe Haberlandt
Keyword(s):  
Time Series ◽  
Spatial Representation ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Multiple Point ◽  
Rainfall Time Series ◽  
Rainfall Runoff ◽  
Areal Rainfall ◽  
Rainfall Runoff Model ◽  
Runoff Model

<p>For derived flood frequency analyses, stochastic rainfall models can be linked with rainfall-runoff models to improve the accuracy of design flood estimations when the length of observed rainfall and runoff data is not sufficient. The stochastic rainfall time series, which are used as input for the rainfall-runoff model, can be generated with different spatial resolution: (a) Point rainfall, which is stochastically generated rainfall at a single site. (b) Areal rainfall, which is catchment rainfall averaged over multiple sites before using the single-site stochastic rainfall model. (c) Multiple point rainfall, which is stochastically generated at multiple sites with spatial correlation before averaging to catchment rainfall. To find the most applicable spatial representation of stochastically generated rainfall for derived flood frequency analysis, simulated and observed runoff time series will be compared based on runoff statistics. The simulated runoff time series are generated utilizing the rainfall-runoff model HBV-IWW with an hourly time step. The rainfall-runoff model is driven with point, areal and multiple point stochastic rainfall time series generated by an Alternating Renewal rainfall model (ARM). In order to take into account the influence of catchment size on the results, catchments of different sizes within Germany are considered in this study.  While point rainfall may be applicable for small catchments, it is expected that above a certain catchment size a more detailed spatial representation of stochastically generated rainfall is necessary. Here, it would be advantageous if the results based on areal rainfall are comparable to those of the multiple point rainfall. The stochastically generation of areal rainfall is less complex compared to the stochastically generation of multiple point rainfall and extremes at the catchment scale may also be better represented by areal rainfall.    </p>

scholarly journals Regional Flood Frequency Analysis for a Poorly Gauged Basin Using the Simulated Flood Data and L-Moment Method

Water ◽  
2019 ◽  
Vol 11 (8) ◽  
pp. 1717 ◽  
Author(s):  
Do-Hun Lee ◽  
Nam Won Kim
Keyword(s):  
Estimation Method ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Rainfall Runoff ◽  
Regional Flood Frequency Analysis ◽  
Flood Estimation ◽  
Rainfall Runoff Model ◽  
Quantile Estimates ◽  
Runoff Model ◽  
Regional Flood Frequency

The design of hydraulic structures and the assessment of flood control measures require the estimation of flood quantiles. Since observed flood data are rarely available at the specific location, flood estimation in un-gauged or poorly gauged basins is a common problem in engineering hydrology. We investigated the flood estimation method in a poorly gauged basin. The flood estimation method applied the combination of rainfall-runoff model simulation and regional flood frequency analysis (RFFA). The L-moment based index flood method was performed using the annual maximum flood (AMF) data simulated by the rainfall-runoff model. The regional flood frequency distribution with 90% error bounds was derived in the Chungju dam basin of Korea, which has a drainage area of 6648 km2. The flood quantile estimates based on the simulated AMF data were consistent with the flood quantile estimates based on the observed AMF data. The widths of error bounds of regional flood frequency distribution increased sharply as the return period increased. The results suggest that the flood estimation approach applied in this study has the potential to estimate flood quantiles when the hourly rainfall measurements during major storms are widely available and the observed flood data are limited.

scholarly journals Using comparative analysis to teach about the nature of nonstationarity in future flood predictions

2012 ◽  
Vol 16 (5) ◽  
pp. 1269-1279 ◽  
Author(s):  
S. B. Shaw ◽  
M. T. Walter
Keyword(s):  
Comparative Analysis ◽  
Frequency Analysis ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Atmospheric Rivers ◽  
Changing Climate ◽  
Annual Peak ◽  
The Us ◽  
Hydrologic System ◽  
Daily Flows

Abstract. Comparative analysis has been a little used approach to the teaching of hydrology. Instead, hydrology is often taught by introducing fundamental principles with the assumption that they are sufficiently universal to apply across most any hydrologic system. In this paper, we illustrate the value of using comparative analysis to enhance students' insights into the degree and predictability of future non-stationarity in flood frequency analysis. Traditionally, flood frequency analysis is taught from a statistical perspective that can offer limited means of understanding the nature of non-stationarity. By visually comparing graphics of mean daily flows and annual peak discharges (plotted against Julian day) for watersheds in a variety of locales, distinct differences in the timing and nature of flooding in different regions of the US becomes readily apparent. Such differences highlight the dominant hydroclimatological drivers of different watersheds. When linked with information on the predictability of hydroclimatic drivers (hurricanes, atmospheric rivers, snowpack melt, convective events) in a changing climate, such comparative analysis provides students with an improved physical understanding of flood processes and a stronger foundation on which to make judgments about how to modify statistical techniques for making predictions in a changing climate. We envision that such comparative analysis could be incorporated into a number of other traditional hydrologic topics.

Urban drainage models – simplifying uncertainty analysis for practitioners

2013 ◽  
Vol 68 (10) ◽  
pp. 2136-2143 ◽  
Author(s):  
Luca Vezzaro ◽  
Peter Steen Mikkelsen ◽  
Ana Deletic ◽  
David McCarthy
Keyword(s):  
Uncertainty Analysis ◽  
Probability Distributions ◽  
Sensitivity Analyses ◽  
Optimum Number ◽  
Urban Drainage ◽  
Rainfall Runoff ◽  
Widespread Application ◽  
Uncertainty Analyses ◽  
Rainfall Runoff Model ◽  
Runoff Model

There is increasing awareness about uncertainties in the modelling of urban drainage systems and, as such, many new methods for uncertainty analyses have been developed. Despite this, all available methods have limitations which restrict their widespread application among practitioners. Here, a modified Monte-Carlo based method is presented that reduces the subjectivity inherent in typical uncertainty approaches (e.g. cut-off thresholds), while using tangible concepts and providing practical outcomes for practitioners. The method compares the model's uncertainty bands to the uncertainty inherent in each measured/observed datapoint; an issue that is commonly overlooked in the uncertainty analysis of urban drainage models. This comparison allows the user to intuitively estimate the optimum number of simulations required to conduct uncertainty analyses. The output of the method includes parameter probability distributions (often used for sensitivity analyses) and prediction intervals. To demonstrate the new method, it is applied to a conceptual rainfall-runoff model (MOPUS) using a dataset collected from Melbourne, Australia.

scholarly journals Investigation of the performance of a simple rainfall disaggregation scheme using semi-distributed hydrological modelling of the Lee catchment, UK

2010 ◽  
Vol 41 (2) ◽  
pp. 134-144
Author(s):  
Marie-Laure Segond ◽  
Howard S. Wheater ◽  
Christian Onof
Keyword(s):  
Daily Rainfall ◽  
Rainfall Runoff ◽  
Rain Gauges ◽  
Expected Performance ◽  
Hourly Data ◽  
Temporal Disaggregation ◽  
Rainfall Runoff Model ◽  
Event Based ◽  
Spatially Varying ◽  
Runoff Model

A simple and practical spatial–temporal disaggregation scheme to convert observed daily rainfall to hourly data is presented, in which the observed sub-daily temporal profile available at one gauge is applied linearly to all sites over the catchment to reproduce the spatially varying daily totals. The performance of the methodology is evaluated using an event-based, semi-distributed, nonlinear hydrological rainfall–runoff model to test the suitability of the disaggregation scheme for UK conditions for catchment sizes of 80–1,000 km2. The joint procedure is tested on the Lee catchment, UK, for five events from a 12 year period of data from 16 rain gauges and 12 flow stations. The disaggregation scheme generally performs extremely well in reproducing the simulated flow for the natural catchments, although, as expected, performance deteriorates for localized convective rainfall. However, some reduction in performance occurs when the catchments are artificially urbanised.

Sign in / Sign up

Export Citation Format

Share Document