scholarly journals Stochastic generation of spatially coherent river discharge peaks for continental event-based flood risk assessment

2019 ◽  
Vol 19 (5) ◽  
pp. 1041-1053 ◽  
Author(s):  
Dirk Diederen ◽  
Ye Liu ◽  
Ben Gouldby ◽  
Ferdinand Diermanse ◽  
Sergiy Vorogushyn
Keyword(s):  
Risk Assessment ◽  
Flood Risk ◽  
River Discharge ◽  
Time Series Data ◽  
Statistical Dependence ◽  
Series Data ◽  
Model Parameters ◽  
Large Set ◽  
Flood Risk Assessment ◽  
Event Based

Abstract. We present a new method to generate spatially coherent river discharge peaks over multiple river basins, which can be used for continental event-based probabilistic flood risk assessment. We first extract extreme events from river discharge time series data over a large set of locations by applying new peak identification and peak-matching methods. Then we describe these events using the discharge peak at each location while accounting for the fact that the events do not affect all locations. Lastly we fit the state-of-the-art multivariate extreme value distribution to the discharge peaks and generate from the fitted model a large catalogue of spatially coherent synthetic event descriptors. We demonstrate the capability of this approach in capturing the statistical dependence over all considered locations. We also discuss the limitations of this approach and investigate the sensitivity of the outcome to various model parameters.

Event-based approach for probabilistic flood risk assessment

2013 ◽  
Vol 12 (4) ◽  
pp. 377-389 ◽  
Author(s):  
Marco A. Torres ◽  
Miguel A. Jaimes ◽  
Eduardo Reinoso ◽  
Mario Ordaz
Keyword(s):  
Risk Assessment ◽  
Flood Risk ◽  
Flood Risk Assessment ◽  
Event Based

scholarly journals Maximizing the usefulness of flood risk assessment for the River Vistula in Warsaw

2013 ◽  
Vol 1 (3) ◽  
pp. 2695-2730
Author(s):  
A. Kiczko ◽  
R. J. Romanowicz ◽  
M. Osuch ◽  
E. Karamuz
Keyword(s):  
Risk Assessment ◽  
Wave Propagation ◽  
Return Period ◽  
Flood Risk ◽  
Model Parameters ◽  
Flood Risk Assessment ◽  
Flood Wave ◽  
Flow Routing ◽  
Risk Maps ◽  
Inundation Extent

Abstract. The derivation of flood risk maps requires an estimation of maximum inundation extent for a flood with a given return period, e.g. 100 or 500 yr. The results of numerical simulations of flood wave propagation are used to overcome the lack of relevant observations. In practice, deterministic 1-D models are used for flow routing, giving a simplified image of flood wave propagation. The solution of a 1-D model depends on the initial and boundary conditions and estimates of model parameters which are usually identified using the inverse problem based on the available noisy observations. Therefore, there is a large uncertainty involved in the derivation of flood risk maps. Bayesian conditioning based on multiple model simulations can be used to quantify this uncertainty; however, it is too computer-time demanding to be applied in flood risk assessment in practice, without further flow routing model simplifications. In order to speed up the computation times the assumption of a gradually varied flow and the application of a steady state flow routing model may be introduced. The aim of this work is an analysis of the influence of those simplifying model assumptions and uncertainty of observations and modelling errors on flood inundation mapping and a quantitative comparison with deterministic flood extent maps. Apart from the uncertainty related to the model structure and its parameters, the uncertainty of the estimated flood wave with a specified probability of return period (so-called 1-in-10 yr, or 1-in-100 yr flood) is also taken into account. In order to derive the uncertainty of inundation extent conditioned on the design flood wave, the probabilities related to the design wave and flow model uncertainties are integrated. In the present paper we take into account the dependence of roughness coefficients on discharge. The roughness is parameterised based on the available observed historical flood waves. The approach presented allows for the relationship between flood extent and flow values to be derived thus giving a cumulative assessment of flood risk. The methods are illustrated using the Warsaw reach of the River Vistula as a case study. The results indicate that the uncertainties have a substantial influence on flood risk assessment.

scholarly journals Stochastic generation of spatially coherent river discharge peaks for large-scale, event-based flood risk assessment

2018 ◽  
Author(s):  
Dirk Diederen ◽  
Ye Liu ◽  
Ben Gouldby ◽  
Ferdinand Diermanse ◽  
Sergiy Vorogushyn
Keyword(s):  
Risk Assessment ◽  
Flood Risk ◽  
River Discharge ◽  
Large Scale ◽  
Time Window ◽  
Insurance Industry ◽  
Flood Risk Assessment ◽  
Data Set ◽  
Discharge Data ◽  
Short Time Window

Abstract. Flood risk assessments are required for long-term planning, e.g. for investments in infrastructure and other urban capital. Vorogushyn et al. (2018) call for new methods in large-scale Flood Risk Assessment (FRA) to enable the capturing of system interactions and feedbacks. With the increase of computational power, large-scale, continental FRAs have recently become feasible (Ward et al., 2013; Alfieri et al., 2014; Dottori et al., 2016; Vousdoukas, 2016; Winsemius et al., 2016; Paprotny et al., 2017). Flood events cause large damages worldwide (Desai et al., 2015). Moreover, widespread flooding can potentially cause large damage in a short time window. Therefore, large-scale (e.g. pan-European) events and for instance maximum probable damages are of interest, in particular for the (re)insurance industry, because they want to know the chance of their widespread portfolio of assets getting affected by large-scale events (Jongman et al., 2014). Using a pan-European data set of modelled, gridded river discharge data, we tracked discharge waves in all major European river basins. We synthetically generated a large catalogue of synthetic, pan-European events, consisting of spatially coherent discharge peak sets.

Flood Risk Assessment and Forecasting for the Ganges-Brahmaputra-Meghna River Basins

10.1596/28574 ◽  
2017 ◽  
Author(s):  
Satya Priya ◽  
William Young ◽  
Thomas Hopson ◽  
Ankit Avasthi
Keyword(s):  
Risk Assessment ◽  
Flood Risk ◽  
River Basins ◽  
Flood Risk Assessment ◽  
The Ganges

Neural networks approached for modelling river suspended sediment concentration due to tropical storms

2013 ◽  
Vol 11 (4) ◽  
pp. 457-466
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Suspended Sediment ◽  
Suspended Sediment Concentration ◽  
Time Series Data ◽  
Water Discharge ◽  
Sediment Concentration ◽  
Series Data ◽  
Generalized Regression Neural Network ◽  
Event Based

Artificial neural networks are one of the advanced technologies employed in hydrology modelling. This paper investigates the potential of two algorithm networks, the feed forward backpropagation (BP) and generalized regression neural network (GRNN) in comparison with the classical regression for modelling the event-based suspended sediment concentration at Jiasian diversion weir in Southern Taiwan. For this study, the hourly time series data comprised of water discharge, turbidity and suspended sediment concentration during the storm events in the year of 2002 are taken into account in the models. The statistical performances comparison showed that both BP and GRNN are superior to the classical regression in the weir sediment modelling. Additionally, the turbidity was found to be a dominant input variable over the water discharge for suspended sediment concentration estimation. Statistically, both neural network models can be successfully applied for the event-based suspended sediment concentration modelling in the weir studied herein when few data are available.

Sign in / Sign up

Export Citation Format

Share Document