Estimating design floods in Norway considering distinct flood generation mechanisms

2020 ◽  
Author(s):  
Lei Yan ◽  
Lihua Xiong ◽  
Lingqi Li ◽  
Gusong Ruan ◽  
Chong-Yu Xu ◽  
...  
Keyword(s):  
Frequency Analysis ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Weighting Coefficient ◽  
Mixture Distributions ◽  
Flood Events ◽  
Component Mixture ◽  
Design Floods ◽  
Flood Generation ◽  
Generation Mechanisms

<p>In the traditional flood frequency analysis, researchers typically assume the flood events result from a homogeneous flood population. However, actually flood events are likely to be generated by distinct flood generation mechanisms (FGMs), such as snowmelt-induced floods and rainfall-induced floods. To address this problem in flood frequency analysis, currently, the most popular practice for mixture modeling of flood events is to use two-component mixture distributions (TCMD) without a priori classification of distict FGMs, which could result in component distributions without physical reality or lead to a larger standard error of the estimated quantiles. To improve the mixture distribution modeling in Norway, we firstly classify the flood series of 34 watersheds into snowmelt-induced long-duration floods and rainfall-induced short-duration floods based on an index named flood timescale (FT), defined as the ratio of the flood volume to peak value. A total of ten types of mixture distributions are considered in the application of FT-based TCMD to model the flood events in Norway. The results indicate that the FT-based TCMD model can reduce the uncertainty in the estimation of design floods. The improved predictive ability of the FT-based TCMD model is largely due to its explicit recognition of distinct FGMs, enabling the determination of the weighting coefficient without optimization.</p>

scholarly journals Reassessing flood frequency For the Sussex Ouse, Lewes: the Inclusion of historical Flood Information since AD 1650

2013 ◽  
Vol 1 (6) ◽  
pp. 7615-7646 ◽  
Author(s):  
N. Macdonald ◽  
T. R. Kjeldsen ◽  
I. Prosdocimi ◽  
H. Sangster
Keyword(s):  
Sensitivity Analysis ◽  
Frequency Analysis ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Flood Events ◽  
Combined Analysis ◽  
Historical Information ◽  
Site Analysis ◽  
Flood Level ◽  
Severe Flooding

Abstract. The application of historical flood information as a tool for augmenting instrumental flood data is increasingly recognised as a valuable tool; most previous studies have focused on large catchments with historic settlements, this paper applies the approach to the smaller lowland system of the Sussex Ouse in Southeast England. The reassessment of flood risk on the Sussex Ouse is pertinent in light of severe flooding in October 2000 and heightened concerns of a perceived increase in flooding nationally. Systematic flood level readings from 1960 and accounts detailing past flood events within the catchment are compiled back to c.1750. This extended flood record provides an opportunity to reassess estimates of flood frequency over a timescale not normally possible within flood frequency analysis. This paper re-evaluates flood frequency at Lewes on the Sussex Ouse downstream of the confluence of the Sussex Ouse and River Uck. The paper considers the strengths and weaknesses in estimates resulting from contrasting methods of analysis and their corresponding data: (i) single site analysis of gauged annual maxima; (ii) combined analysis of systematic annual maxima augmented with historical peaks of estimated magnitude; (iii) combined analysis of systematic annual maxima augmented with historical peaks of estimated magnitude exceeding a known threshold, and (iv) sensitivity analysis including only the very largest historical flood events. Use of the historical information was found to yield much tighter confidence intervals of risk estimates, with uncertainty reduced by up to 40% for the 100 yr return frequency event when historical information was added to the gauged data.

Flood frequency from maximum daily vs. instantaneous peak flows

2021 ◽  
Author(s):  
Anne Fangmann ◽  
Uwe Haberlandt
Keyword(s):  
High Resolution ◽  
Frequency Analysis ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Error Assessment ◽  
Annual Maximum ◽  
High Resolution Data ◽  
Flood Peaks ◽  
Design Floods ◽  
Resolution Data

<p>Flood frequency analysis (FFA) has long been the standard procedure for obtaining design floods for all kinds of purposes. Ideally, the data at the basis of the statistical operations have a high temporal resolution, in order to facilitate a full account of the observed flood peaks and hence a precise model fitting and flood quantile estimation.</p><p>Unfortunately, high-resolution flows are rarely disposable. Often, average daily flows pose the only available/sufficiently long base for flood frequency analysis. This averaging naturally causes a significant smoothing of the flood wave, such that the “instantaneous” peak can no longer be observed. As a possible consequence, design floods derived from these data may be severely underrated.</p><p>How strongly the original peaks are flattened and how this influences the design flood estimation depends on a variety of factors and varies from gauge to gauge. In this study we are looking at a range of errors arising from the use of daily instead of instantaneous flow data. These include differences in the observed individual flood peaks and mean annual maximum floods, as well as the estimated distribution parameters and flood quantiles. The aim is to identify catchment specific factors that influence the magnitude of these errors, and ultimately to provide a means for error assessment on the mere basis of local hydrological conditions, specifically where no high-resolution data is available.</p><p>The analyses are carried out on an all-German dataset of discharge gauges, for which high-resolution data is available for at least 30 years. The classical FFA approach of fitting distributions to annual maximum series is utilized for error assessment. For identification of influencing factors, both the discharge series themselves and a catalogue of climatic and physiographic catchment descriptors are screened.</p>

scholarly journals Modeling the spatial dependence of floods using the Fisher copula

2019 ◽  
Vol 23 (1) ◽  
pp. 107-124 ◽  
Author(s):  
Manuela I. Brunner ◽  
Reinhard Furrer ◽  
Anne-Catherine Favre
Keyword(s):  
Frequency Analysis ◽  
Spatial Dependence ◽  
Regional Scale ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Flood Event ◽  
Dependence Structure ◽  
Suitable Model ◽  
Flood Events ◽  
Extreme Flood

Abstract. Floods often affect not only a single location, but also a whole region. Flood frequency analysis should therefore be undertaken at a regional scale which requires the considerations of the dependence of events at different locations. This dependence is often neglected even though its consideration is essential to derive reliable flood estimates. A model used in regional multivariate frequency analysis should ideally consider the dependence of events at multiple sites which might show dependence in the lower and/or upper tail of the distribution. We here seek to propose a simple model that on the one hand considers this dependence with respect to the network structure of the region and on the other hand allows for the simulation of stochastic event sets at both gauged and ungauged locations. The new Fisher copula model is used for representing the spatial dependence of flood events in the nested Thur catchment in Switzerland. Flood event samples generated for the gauged stations using the Fisher copula are compared to samples generated by other dependence models allowing for modeling of multivariate data including elliptical copulas, R-vine copulas, and max-stable models. The comparison of the dependence structures of the generated samples shows that the Fisher copula is a suitable model for capturing the spatial dependence in the data. We therefore use the copula in a way such that it can be used in an interpolation context to simulate event sets comprising gauged and ungauged locations. The spatial event sets generated using the Fisher copula well capture the general dependence structure in the data and the upper tail dependence, which is of particular interest when looking at extreme flood events and when extrapolating to higher return periods. The Fisher copula was for a medium-sized catchment found to be a suitable model for the stochastic simulation of flood event sets at multiple gauged and ungauged locations.

scholarly journals Reassessing flood frequency for the Sussex Ouse, Lewes: the inclusion of historical flood information since AD 1650

2014 ◽  
Vol 14 (10) ◽  
pp. 2817-2828 ◽  
Author(s):  
N. Macdonald ◽  
T. R. Kjeldsen ◽  
I. Prosdocimi ◽  
H. Sangster
Keyword(s):  
Sensitivity Analysis ◽  
Frequency Analysis ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Flood Events ◽  
Combined Analysis ◽  
Historical Information ◽  
Site Analysis ◽  
Flood Level ◽  
Severe Flooding

Abstract. The application of historical flood information as a tool for augmenting instrumental flood data is increasingly recognised as a valuable tool. Most previous studies have focused on large catchments with historic settlements, this paper applies the approach to the smaller lowland system of the Sussex Ouse in southeast England. The reassessment of flood risk on the Sussex Ouse is pertinent in light of the severe flooding in October 2000 and heightened concerns of a perceived increase in flooding nationally. Systematic flood level readings from 1960 and accounts detailing past flood events within the catchment are compiled back to ca. 1750. This extended flood record provides an opportunity to reassess estimates of flood frequency over a timescale not normally possible within flood frequency analysis. This paper re-evaluates flood frequency at Lewes on the Sussex Ouse downstream of the confluence of the Sussex Ouse and River Uck. The paper considers the strengths and weaknesses in estimates resulting from contrasting methods of analysis and their corresponding data: (i) single site analysis of gauged annual maxima; (ii) combined analysis of systematic annual maxima augmented with historical peaks of estimated magnitude; (iii) combined analysis of systematic annual maxima augmented with historical peaks of estimated magnitude exceeding a known threshold, and (iv) sensitivity analysis including only the very largest historical flood events. Use of the historical information was found to yield much tighter confidence intervals of risk estimates, with uncertainty reduced by up to 40% for the 100-year return frequency event when historical information was added to the gauged data.

scholarly journals Probabilistic interval estimation of design floods under non-stationary conditions by an integrated approach

2021 ◽  
Author(s):  
Yanlai Zhou ◽  
Shenglian Guo ◽  
Chong-Yu Xu ◽  
Lihua Xiong ◽  
Hua Chen ◽  
...  
Keyword(s):  
Frequency Analysis ◽  
Additive Model ◽  
Interval Estimation ◽  
Integrated Approach ◽  
Copula Function ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Time Varying ◽  
Joint Distributions ◽  
Design Floods

Abstract Quantifying the uncertainty of non-stationary flood frequency analysis is very crucial and beneficial for planning and design of water engineering projects, which is fundamentally challenging especially in the presence of high climate variability and reservoir regulation. This study proposed an integrated approach that combined the Generalized Additive Model for Location, Scale and Shape parameters (GAMLSS) method, the Copula function and the Bayesian Uncertainty Processor (BUP) technique to make reliable probabilistic interval estimations of design floods. The reliability and applicability of the proposed approach were assessed by flood datasets collected from two hydrological monitoring stations located in the Hanjiang River of China. The precipitation and the reservoir index were selected as the explanatory variables for modeling the time-varying parameters of marginal and joint distributions using long-term (1954–2018) observed datasets. First, the GAMLSS method was employed to model and fit the time-varying characteristics of parameters in marginal and joint distributions. Second, the Copula function was employed to execute the point estimations of non-stationary design floods. Finally, the BUP technique was employed to perform the interval estimations of design floods based on the point estimations obtained from the Copula function. The results demonstrated that the proposed approach can provide reliable probabilistic interval estimations of design floods meanwhile reducing the uncertainty of non-stationary flood frequency analysis. Consequently, the integrated approach is a promising way to offer an indication on how design values can be estimated in a high-dimensional problem.

scholarly journals Assessing the performance of regional flood frequency analysis methods in South Africa

Water SA ◽  
2018 ◽  
Vol 44 (3 July) ◽  
Author(s):  
JJ Nathanael ◽  
JC Smithers ◽  
MJC Horan
Keyword(s):  
South Africa ◽  
Frequency Analysis ◽  
Relevant Literature ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Design Flood ◽  
Regional Flood Frequency Analysis ◽  
Design Floods ◽  
Regional Flood Frequency ◽  
Better Than

In engineering and flood hydrology, the estimation of a design flood associates the magnitude of a flood with a level of exceedance, or return period, for a given site. The use of a regional flood frequency analysis (RFFA) approach improves the accuracy and reliability of estimates of design floods. However, no RFFA method is currently widely used in South Africa, despite a number of RFFA studies having been undertaken in Africa and which include South Africa in their study areas. Hence, the performance of the current RFFA approaches needs to be assessed in order to determine the best approaches to use and to determine if a new RFFA approach needs to be developed for use in South Africa. Through a review of the relevant literature it was found that the Meigh et al. (1997) method, the Mkhandi et al. (2000) method, the Görgens (2007) Joint Peak-Volume (JPV) method and the Haile (2011) method are available for application in a nationwide study. The results of the study show that the Haile method generally performs better than the other RFFA methods; however, it also consistently underestimates design floods. Due to the poor overall performance of the RFFA methods assessed, it is recommended that a new RFFA method be developed for application in design flood practice in South Africa.

scholarly journals Modeling the spatial dependence of floods using the Fisher copula

2018 ◽  
Author(s):  
Manuela I. Brunner ◽  
Reinhard Furrer ◽  
Anne-Catherine Favre
Keyword(s):  
Frequency Analysis ◽  
Spatial Dependence ◽  
Regional Scale ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Flood Event ◽  
Dependence Structure ◽  
Suitable Model ◽  
Flood Events ◽  
Extreme Flood

Abstract. Floods do often not only affect a single location but a whole region. Flood frequency analysis should therefore be undertaken at a regional scale which requires the considerations of the dependence of events at different locations. This dependence is often neglected even though its consideration is essential to derive reliable flood estimates. A model used in regional multivariate frequency analysis should ideally consider the dependence of events at multiple sites which might show dependence in the lower and/or upper tail of the distribution. We here seek at proposing a simple model that on the one hand considers this dependence with respect to the network structure of the region and on the other hand, allows for the simulation of stochastic event sets at both gauged and ungauged locations. The new Fisher copula model is used for representing the spatial dependence of flood events in the nested Thur catchment in Switzerland. Flood event samples generated for the gauged stations using the Fisher copula are compared to samples generated by other dependence models allowing for modeling multivariate data including elliptical copulas, R-vine copulas, and max-stable models. The comparison of the dependence structures of the generated samples shows that the Fisher copula is a suitable model for capturing the spatial dependence in the data. We therefore use the copula in a way such that it can be used in an interpolation context to simulate event sets comprising gauged and ungauged locations. The spatial event sets generated using the Fisher copula well capture the general dependence structure in the data and the upper tail dependence, which is of particular interest when looking at extreme flood events and when extrapolating to higher return periods. The Fisher copula is therefore a suitable model for the stochastic simulation of flood event sets at multiple gauged and ungauged locations.

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