Towards the use of catchment geomorphology in flood frequency predictions

A further development of a topography-based model of catchment hydrology (TOPMODEL) is described and applied to the problem of predicting flood frequency characteristics. The model can simulate infiltration excess, saturation excess, and subsurface runoff contributions to peak flows. Catchment geomorphology plays a central role in predicting the nature of the hydrological response. Using stochastic rainfall and initial condition inputs based on measured data, the model satisfactorily reproduces the mean hourly flow flood frequency growth curve for the Wye catchment, but not the mean number of peaks greater than 3mm h^?1 each year. Suggestions for further improvements are made.     

Bayesian flood frequency analysis in the light of model and parameter uncertainties

The specific objective of the paper is to propose a new flood frequency analysis method considering uncertainty of both probability distribution selection (model uncertainty) and uncertainty of parameter estimation (parameter uncertainty). Based on Bayesian theory sampling distribution of quantiles or design floods coupling these two kinds of uncertainties is derived, not only point estimator but also confidence interval of the quantiles can be provided. Markov Chain Monte Carlo is adopted in order to overcome difficulties to compute the integrals in estimating the sampling distribution. As an example, the proposed method is applied for flood frequency analysis at a gauge in Huai River, China. It has been shown that the approach considering only model uncertainty or parameter uncertainty could not fully account for uncertainties in quantile estimations, instead, method coupling these two uncertainties should be employed. Furthermore, the proposed Bayesian-based method provides not only various quantile estimators, but also quantitative assessment on uncertainties of flood frequency analysis.     

A bivariate gamma distribution for use in multivariate flood frequency analysis

A gamma distribution is one of the most frequently selected distribution types for hydrological frequency analysis. The bivariate gamma distribution with gamma marginals may be useful for analysing multivariate hydrological events. This study investigates the applicability of a bivariate gamma model with five parameters for describing the joint probability behavior of multivariate flood events. The parameters are proposed to be estimated from the marginal distributions by the method of moments. The joint distribution, the conditional distribution, and the associated return periods are derived from marginals. The usefulness of the model is demonstrated by representing the joint probabilistic behaviour between correlated flood peak and flood volume and between correlated flood volume and flood duration in the Madawask River basin in the province of Quebec, Canada. Copyright © 2001 John Wiley & Sons, Ltd.     

The influence of hydroclimatic variability on flood frequency in the Lower Rhine

Climate change is expected to significantly affect flooding regimes of river systems in the future. For Western Europe, flood risk assessments generally assume an increase in extreme events and flood risk, and as a result major investments are planned to reduce their impacts. However, flood risk assessments for the present day and the near future suffer from uncertainty, coming from short measurements series, limited precision of input data, arbitrary choices for particular statistical and modelling approaches, and climatic non‐stationarities. This study demonstrates how historical and sedimentary information can extend data records, adds important information on extremes, and generally improves flood risk assessments. The collection of specific data on the occurrence and magnitude of extremes and the natural variability of the floods is shown to be of paramount importance to reduce uncertainty in our understanding of flooding regime changes in a changing climate. For the Lower Rhine (the Netherlands and Germany) estimated recurrence times and peak discharges associated with the current protection levels correlate poorly with historical and sedimentary information and seem biased towards the recent multi‐decadal period of increased flood activity. Multi‐decadal and centennial variability in flood activity is recorded in extended series of discharge data, historical information and sedimentary records. Over the last six centuries that variability correlates with components of the Atlantic climate system such as the North Atlantic Oscillation (NAO) and Atlantic Multi‐decadal Oscillation (AMO). These climatic non‐stationarities importantly influence flood activity and the outcomes of flood risk assessments based on relatively short measurement series. Copyright © 2016 John Wiley & Sons, Ltd.     

The formation of groups for regional flood frequency analysis

Abstract

A new technique is developed for identifying groups for regional flood frequency analysis. The technique uses a clustering algorithm as a starting point for partitioning the collection of catchments. The groups formed using the clustering algorithm are subsequently revised to improve the regional characteristics based on three requirements that are defined for effective groups. The result is overlapping groups that can be used to estimate extreme flow quantiles for gauged or ungauged catchments. The technique is applied to a collection of catchments from India and the results indicate that regions with the desired characteristics can be identified using the technique. The use of the groups for estimating extreme flow quantiles is demonstrated for three example sites.     

Maps of flood statistics for regional flood frequency analysis in New Zealand

Abstract

The two-parameter EV1 distribution adequately describes New Zealand's flood series. Contour maps of [Qbar]/A^0.8 and Q₁₀₀[Qbar] are presented, where [Qbar] is the mean annual flood, A is the basin area and Q₁₀₀ is the 1% annual exceedance probability flood. The maps are based directly on measured discharge series from a large sample of river recording stations. Thus when basins are ungauged, or have just a short record, an estimate of a design flood Q_T with specified annual exceedance probability (1/T) can be obtained using map estimates of [Qbar]/A^0.8 and Q₁₀₀[Qbar], without having first to estimate rainfall statistics for the basin, a particularly difficult task in sparsely instrumented mountainous areas. These maps succinctly summarize a great deal of hydrological information and permit improved flood frequency estimates.     

Assessment of change in design flood frequency under climate change using a multivariate downscaling model and a precipitation-runoff model

Precipitation and runoff are key elements in the hydrologic cycle because of their important roles in water supply, flood prevention, river restoration, and ecosystem management. Global climate change, widely accepted to be happening, is anticipated to have enormous consequences on future hydrologic patterns. Studies on the potential changes in global, regional, and local hydrologic patterns under global climate change scenarios have been an intense area of research in recent years. The present study contributes to this research topic through evaluation of design flood under climate change. The study utilizes a weather state-based, stochastic multivariate model as a conditional probability model for simulating the precipitation field. An important premise of this study is that large-scale climatic patterns serve as a major driver of persistent year-to-year changes in precipitation probabilities. Since uncertainty estimation in the study of climate change is needed to examine the reliability of the outcomes, this study also applies a Bayesian Markov chain Monte Carlo scheme to the widely used SAC-SMA (Sacramento soil moisture accounting) precipitation-runoff model. A case study is also performed with the Soyang Dam watershed in South Korea as the study basin. Finally, a comprehensive discussion on design flood under climate change is made.     

Impulse response of a linear diffusion analogy model as a flood frequency probability density function

Abstract

The impulse response of a linear convective-diffusion analogy (LD) model used for flow routing in open channels is proposed as a probability distribution for flood frequency analysis. The flood frequency model has two parameters, which are derived using the methods of moments and maximum likelihood. Also derived are errors in quantiles for these parameter estimation methods. The distribution shows that the two methods are equivalent in terms of producing mean values—the important property in case of unknown true distribution function. The flood frequency model is tested using annual peak discharges for the gauging sections of 39 Polish rivers where the average value of the ratio of the coefficient of skewness to the coefficient of variation equals about 2.52, a value closer to the ratio of the LD model than to the gamma or the lognormal model. The likelihood ratio indicates the preference of the LD over the lognormal for 27 out of 39 cases. It is found that the proposed flood frequency model represents flood frequency characteristics well (measured by the moment ratio) when the LD flood routing model is likely to be the best of all linear flow routing models.     

Towards an optimum model for the response of reinforced concrete beams to cyclic loads

The paper describes the development of a mathematical model to predict the flexural response of reinforced concrete beams to severe cyclic loading. The model is constructed with the aid of System Identification, which permits the coefficients in it to be so tuned as to replicate with the maximum possible accuracy the results of physical experiments. The uniaxial constitutive behaviour of the concrete and steel are considered separately, and they are then combined through a bond-slip relationship to form a global model for the composite material. The cyclic stress-strain law for the steel proves to be critical to the overall accuracy, and a suitable formulation is developed for it. Comparisons between observed and predicted behaviour are presented, both for the steel alone and for the composite material.     

The use of historical flood information in the English Midlands to improve risk assessment

Abstract

The Easter 1998 flood was the largest flood event in the gauged record of many basins of the English Midlands. Flood frequency analysis, using such gauged records only, placed the 1998 event at a return period of over 100 years on several basins. However a review of historical (pre-gauged) flooding on some rivers gives a different perspective. Examples are given of the use of historical flood information on the River Leam, the River Wreake at Melton Mowbray, the River Sence (tributary to the River Soar) and the River Frome at Stroud. The cost of acquiring such historical flood data is trivial in comparison to gauged data, but the benefits are demonstrated as significant. In particular, historical flood data provide a better basis for risk assessment and planning on flood plains through revised estimates of flood discharge and depth.     

An improved nonstationary model for flood frequency analysis and its implication for the Three Gorges Dam,China

This study proposes an improved nonstationary model for flood frequency analysis by investigating the relationship between flood peak and flood volume, using the Three Gorges Dam (TGD), China, for verification. First, the generalized additive model for location, scale and shape (GAMLSS) is used as the prior distribution. Then, under Bayesian theory, the prior distribution is updated using the conditional distribution, which is derived from the copula function. The results show that the improvement of the proposed model is significant compared with the GAMLSS-based prior distribution. Meanwhile, selection of a suitable prior distribution has a significant effect on the results of the improvement. For applications to the TGD, the nonstationary model can obviously increase the engineering management benefits and reduce the perceived risks of large floods. This study provides guidance for the dynamic management of hydraulic engineering under nonstationary conditions.     

Localized identification of MDOF structures in the frequency domain

For the identification of multi-degree-of-freedom structures, it is not practical to identify all of the parameters included in the structures because enormous computation time is required and because identifiability may not be possible. In this paper, a localized identification approach through substructuring is formulated in the frequency domain. A technique of spectral smoothing is incorporated in the approach to deal with noise-corrupted data. The proposed approach can be used to identify the structural parameters in any part of interest in a structure. The numerical investigations for a lumped mass-spring-dashpot system indicate that faster convergence and higher accuracy are achieved and the noise influences on the identified results are reduced greatly by spectral smoothing. The approach also applies to whole-structure identification if the required records available and the numerical example shows that higher accuracy results are obtained with less cpu time and more poorly guessed initial values as compared with the general complete-structure identification.     

The data‐driven approach as an operational real‐time flood forecasting model

Accurate water level forecasts are essential for flood warning. This study adopts a data‐driven approach based on the adaptive network–based fuzzy inference system (ANFIS) to forecast the daily water levels of the Lower Mekong River at Pakse, Lao People's Democratic Republic. ANFIS is a hybrid system combining fuzzy inference system and artificial neural networks. Five ANFIS models were developed to provide water level forecasts from 1 to 5 days ahead, respectively. The results show that although ANFIS forecasts of water levels up to three lead days satisfied the benchmark, four‐ and five‐lead‐day forecasts were only slightly better in performance compared with the currently adopted operational model. This limitation is imposed by the auto‐ and cross‐correlations of the water level time series. Output updating procedures based on the autoregressive (AR) and recursive AR (RAR) models were used to enhance ANFIS model outputs. The RAR model performed better than the AR model. In addition, a partial recursive procedure that reduced the number of recursive steps when applying the AR or the RAR model for multi‐step‐ahead error prediction was superior to the fully recursive procedure. The RAR‐based partial recursive updating procedure significantly improved three‐, four‐ and five‐lead‐day forecasts. Our study further shows that for long lead times, ANFIS model errors are dominated by lag time errors. Although the ANFIS model with the RAR‐based partial recursive updating procedure provided the best results, this method was able to reduce the lag time errors significantly for the falling limbs only. Improvements for the rising limbs were modest. Copyright © 2011 John Wiley & Sons, Ltd.     

Real-time flood forecasting with the use of inadequate data

Abstract

A modelling scheme is developed for real-time flood forecasting. It is composed of (a) a rainfall forecasting model, (b) a conceptual rainfall-runoff model, and (c) a stochastic error model of the ARMA family for forecast error correction. Initialization of the rainfall-runoff model is based on running this model on a daily basis for a certain period prior to the flood onset while parameters of the error model are updated through the Recursive Least Squares algorithm. The scheme is suitable for the early stages of operation of flood forecasting systems in the presence of inadequate historical data. A validation framework is set up which simulates real-time flood forecasting conditions. Thus, the effects of the procedures for rainfall-runoff model initialization, forecast error correction and rainfall forecasting are assessed. Two well-known conceptual rainfall-runoff models (the Soil Moisture Accounting model of the US National Weather Service River Forecast Service—SMA-NWSRFS and TANK) together with data from a Greek basin are used for illustration purposes.     

Performance of flood frequency pooling analysis in a low CV context

Abstract

Pooling of flood data is widely used to provide a framework to estimate design floods by the Index Flood method. Design flood estimation with this approach involves derivation of a growth curve which shows the relationship between X_T and the return period T, where X_T ?=?Q_T /Q_I and Q_I is the index flood at the site of interest. An implicit assumption with the Index Flood procedure of pooling analysis is that the X_T –T relationship is the same at all sites in a homogeneous pooling group, although this assumption would generally be violated to some extent in practical cases, i.e. some degree of heterogeneity exists. In fact, in only some cases is the homogeneity criterion effectively satisfied for Irish conditions. In this paper, the performance of the index-flood pooling analysis is assessed in the Irish low CV (coefficient of variation) hydrology context considering that heterogeneity is taken into account. It is found that the performance of the pooling method is satisfactory provided there are at least 350 station years of data included. Also it is found that, in a highly heterogeneous group, it is more desirable to have many sites with short record lengths than a smaller number of sites with long record lengths. Increased heterogeneity decreases the advantage of pooling group-based estimation over at-site estimation. Only a heterogeneity measure (H1) less than 4.0 can render the pooled estimation preferable to that obtained for at-site estimation for the estimation of 100-year flood. In moderately to highly heterogeneous regions it is preferable to conduct at-site analysis for the estimation of 100-year flood if the record length at the site concerned exceeds 50.

Editor Z.W. Kundzewicz; Associate editor A. Carsteanu

Citation Das, S. and Cunnane, C., 2012. Performance of flood frequency pooling analysis in a low CV context. Hydrological Sciences Journal, 57 (3), 433–444.     

The potential use of pollen in the identification of suspended sediment sources

Pollen and spores form a significant part of the suspended organic load of a New Forest stream. Flood concentrations reach 230 grains ml^?1 while baseflow carries under one grain ml^?1. Hydrographs from different seasons show differing hysteretic loops for pollen and spore concentrations against discharge and suspended sediment. These variations reflect not only factors of production, but the type and distance of the contributing sources. This investigation suggests that variations in flood pollen and spore concentrations may be used to trace such suspended sediment sources as: eroding bedrock, channel banks, or hillslopes under specific vegetation covers.     

Structural identification in the frequency domain from earthquake records

A method has been developed to identify the parameters of the lower modes of a linear, time-invariant model of a structure from its recorded earthquake response. The identification is performed by selecting the parameters to obtain a least squares fit over a specified frequency band between the unsmoothed, complex-valued finite Fourier transform of the recorded acceleration response and the corresponding transform calculated from the response of the model. By including the effects of the initial and final conditions in the analysis, only a portion of the recorded excitation and response acceleration histories need be considered. The method is demonstrated first by an application to generated test data, and then to measured earthquake response.