scholarly journals On the asymptotic behavior of flood peak distributions

2006 ◽  
Vol 10 (2) ◽  
pp. 233-243 ◽  
Author(s):  
E. Gaume
Keyword(s):  
Frequency Distribution ◽  
Return Period ◽  
Asymptotic Properties ◽  
Flood Frequency ◽  
Point Of View ◽  
Rainfall Runoff ◽  
New Approach ◽  
Flood Peak ◽  
Flood Quantiles ◽  
The Impact

Abstract. This paper presents some analytical results and numerical illustrations on the asymptotic properties of flood peak distributions obtained through derived flood frequency approaches. It confirms and extends the results of previous works: i.e. the shape of the flood peak distributions are asymptotically controlled by the rainfall statistical properties, given limited and reasonable assumptions concerning the rainfall-runoff process. This result is partial so far: the impact of the rainfall spatial heterogeneity has not been studied for instance. From a practical point of view, it provides a general framework for analysis of the outcomes of previous works based on derived flood frequency approaches and leads to some proposals for the estimation of very large return-period flood quantiles. This paper, focussed on asymptotic distribution properties, does not propose any new approach for the extrapolation of flood frequency distribution to estimate intermediate return period flood quantiles. Nevertheless, the large distance between frequent flood peak values and the asymptotic values as well as the simulations conducted in this paper help quantifying the ill condition of the problem of flood frequency distribution extrapolation: it illustrates how large the range of possibilities for the shapes of flood peak distributions is.

scholarly journals On the asymptotic behavior of flood peak distributions – theoretical results

2005 ◽  
Vol 2 (5) ◽  
pp. 1835-1864 ◽  
Author(s):  
E. Gaume
Keyword(s):  
Frequency Distribution ◽  
Return Period ◽  
Asymptotic Properties ◽  
Flood Frequency ◽  
Point Of View ◽  
Rainfall Runoff ◽  
New Approach ◽  
Flood Peak ◽  
Flood Quantiles ◽  
The Impact

Abstract. This paper presents some analytical results and numerical illustrations on the asymptotic properties of flood peak distributions obtained through derived flood frequency approaches. It confirms and extends the results of previous works: i.e. the shape of the flood peak distributions are asymptotically controlled by the rainfall statistical properties, given limited and reasonable assumptions concerning the rainfall-runoff process. This previous result is partial so far: only two types of rainfall intensity distributions have been considered (extreme value distributions of types I and II), and the impact of the rainfall spatial heterogeneity has not been studied. From a practical point of view, it provides a general framework for analysis of the outcomes of previous works based on derived flood frequency approaches and leads to some proposals for the estimation of very large return-period flood quantiles. This paper, focussed on asymptotic distribution properties, does not propose any new approach for the extrapolation of flood frequency distribution to estimate intermediate return period flood quantiles. Nevertheless, the large distance between frequent flood peak values and the asymptotic values as well as the simulations conducted in this paper help quantifying the ill condition of the problem of flood frequency distribution extrapolation: it illustrates how large the range of possibilities for the shapes of flood peak distributions is.

scholarly journals Benefits and limitations of data assimilation for discharge forecasting using an event-based rainfall–runoff model

2013 ◽  
Vol 13 (3) ◽  
pp. 583-596 ◽  
Author(s):  
M. Coustau ◽  
S. Ricci ◽  
V. Borrell-Estupina ◽  
C. Bouvier ◽  
O. Thual
Keyword(s):  
Data Assimilation ◽  
Hydrological Model ◽  
Peak Discharge ◽  
State Variables ◽  
Rainfall Runoff ◽  
Discharge Data ◽  
Southern France ◽  
Flood Peak ◽  
Event Based ◽  
The Impact

Abstract. Mediterranean catchments in southern France are threatened by potentially devastating fast floods which are difficult to anticipate. In order to improve the skill of rainfall-runoff models in predicting such flash floods, hydrologists use data assimilation techniques to provide real-time updates of the model using observational data. This approach seeks to reduce the uncertainties present in different components of the hydrological model (forcing, parameters or state variables) in order to minimize the error in simulated discharges. This article presents a data assimilation procedure, the best linear unbiased estimator (BLUE), used with the goal of improving the peak discharge predictions generated by an event-based hydrological model Soil Conservation Service lag and route (SCS-LR). For a given prediction date, selected model inputs are corrected by assimilating discharge data observed at the basin outlet. This study is conducted on the Lez Mediterranean basin in southern France. The key objectives of this article are (i) to select the parameter(s) which allow for the most efficient and reliable correction of the simulated discharges, (ii) to demonstrate the impact of the correction of the initial condition upon simulated discharges, and (iii) to identify and understand conditions in which this technique fails to improve the forecast skill. The correction of the initial moisture deficit of the soil reservoir proves to be the most efficient control parameter for adjusting the peak discharge. Using data assimilation, this correction leads to an average of 12% improvement in the flood peak magnitude forecast in 75% of cases. The investigation of the other 25% of cases points out a number of precautions for the appropriate use of this data assimilation procedure.

scholarly journals Climate and hydrological variability: the catchment filtering role

2015 ◽  
Vol 19 (1) ◽  
pp. 379-387 ◽  
Author(s):  
I. Andrés-Doménech ◽  
R. García-Bartual ◽  
A. Montanari ◽  
J. B. Marco
Keyword(s):  
Climate Change ◽  
Climate Variability ◽  
Peak Flow ◽  
Flood Frequency ◽  
Annual Maximum ◽  
Return Periods ◽  
Rainfall Runoff ◽  
Maximum Peak ◽  
Hydrological Variability ◽  
The Impact

Abstract. Measuring the impact of climate change on flood frequency is a complex and controversial task. Identifying hydrological changes is difficult given the factors, other than climate variability, which lead to significant variations in runoff series. The catchment filtering role is often overlooked and thus may hinder the correct identification of climate variability signatures on hydrological processes. Does climate variability necessarily imply hydrological variability? This research aims to analytically derive the flood frequency distribution based on realistic hypotheses about the rainfall process and the rainfall–runoff transformation. The annual maximum peak flow probability distribution is analytically derived to quantify the filtering effect of the rainfall–runoff process on climate change. A sensitivity analysis is performed according to typical semi-arid Mediterranean climatic and hydrological conditions, assuming a simple but common scheme for the rainfall–runoff transformation in small-size ungauged catchments, i.e. the CN-SCS model. Variability in annual maximum peak flows and its statistical significance are analysed when changes in the climatic input are introduced. Results show that depending on changes in the annual number of rainfall events, the catchment filtering role is particularly significant, especially when the event rainfall volume distribution is not strongly skewed. Results largely depend on the return period: for large return periods, peak flow variability is significantly affected by the climatic input, while for lower return periods, infiltration processes smooth out the impact of climate change.

Data-based attribution of changes in flood quantiles across Europe between 1960 and 2010

2021 ◽  
Author(s):  
Miriam Bertola ◽  
Alberto Viglione ◽  
Sergiy Vorogushyn ◽  
David Lun ◽  
Bruno Merz ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Return Period ◽  
Extreme Precipitation ◽  
Western Europe ◽  
Expert Knowledge ◽  
Flood Frequency ◽  
Antecedent Soil Moisture ◽  
Flood Quantiles ◽  
Comparable Magnitude ◽  
Large Floods

<p>Changes in European floods during past decades have been analysed and detected by several studies. These studies typically focused on the mean flood behaviour, without distinguishing small and large floods. In this work, we investigate the causes of the detected flood trends across Europe over five decades (1960-2010), as a function of the return period. We adopt a regional non-stationary flood frequency approach to attribute observed flood changes to potential drivers, used as covariates of the parameters of the regional probability distribution of floods. The elasticities of floods with respect to the drivers and the regional contributions of the drivers to changes in flood quantiles associated with small and large return periods (i.e. 2-year and 100-year floods, respectively) are estimated by Bayesian inference, with prior information on the elasticity parameters obtained from expert knowledge and the literature. The data-based attribution approach is applied to annual maximum flood discharge seires from 2370 hydrometric stations in Europe. Extreme precipitation, antecedent soil moisture and snowmelt are the potential drivers considered. Results show that extreme precipitation mainly contributes to positive flood changes in North-western Europe. Both antecedent soil moisture and extreme precipitation contribute to negative flood changes in Southern Europe, with relative contributions varying with the return period. Antecedent soil moisture contributes the most to changes in small floods (i.e. T=2-10 years), while the two drivers contribute with comparable magnitude to changes in more extreme events. In eastern Europe, snowmelt clearly drives negative changes in both small and large floods.</p>

scholarly journals Analysis of Problems Related to the Calculation of Flood Frequency Using Rainfall-Runoff Models: A Case Study in Poland

2020 ◽  
Vol 12 (17) ◽  
pp. 7187
Author(s):  
Dariusz Młyński
Keyword(s):  
Sensitivity Analysis ◽  
Return Period ◽  
Maximum Flow ◽  
High Sensitivity ◽  
Flood Frequency ◽  
Rainfall Runoff ◽  
Model Quality ◽  
Retention Capacity

This work aimed to quantify how the different parameters of the Snyder model influence the errors in design flows. The study was conducted for the Kamienica Nowojowska catchment (Poland). The analysis was carried out according to the following stages: determination of design precipitation, determination of design hyetograph, sensitivity analysis of the Snyder model, and quality assessment of the Snyder model. Based on the conducted research, it was found that the Snyder model did not show high sensitivity to the assumed precipitation distribution. The parameters depending on the retention capacity of the catchment had much greater impact on the obtained flow values. The verification of the model quality showed a significant disproportion in the calculated maximum flow values with the assumed return period.

scholarly journals Selection of Bias Correction Methods to Assess the Impact of Climate Change on Flood Frequency Curves

Water ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 2266 ◽  
Author(s):  
Enrique Soriano ◽  
Luis Mediero ◽  
Carlos Garijo
Keyword(s):  
Climate Change ◽  
Bias Correction ◽  
Climate Models ◽  
Climate Model ◽  
Control Period ◽  
Flood Frequency ◽  
Climate Projections ◽  
The Future ◽  
Flood Quantiles ◽  
The Impact

Climate projections provided by EURO-CORDEX predict changes in annual maximum series of daily rainfall in the future in some areas of Spain because of climate change. Precipitation and temperature projections supplied by climate models do not usually fit exactly the statistical properties of the observed time series in the control period. Bias correction methods are used to reduce such errors. This paper seeks to find the most adequate bias correction techniques for temperature and precipitation projections that minimizes the errors between observations and climate model simulations in the control period. Errors in flood quantiles are considered to identify the best bias correction techniques, as flood quantiles are used for hydraulic infrastructure design and safety assessment. In addition, this study aims to understand how the expected changes in precipitation extremes and temperature will affect the catchment response in flood events in the future. Hydrological modelling is required to characterize rainfall-runoff processes adequately in a changing climate, in order to estimate flood changes expected in the future. Four catchments located in the central-western part of Spain have been selected as case studies. The HBV hydrological model has been calibrated in the four catchments by using the observed precipitation, temperature and streamflow data available on a daily scale. Rainfall has been identified as the most significant input to the model, in terms of its influence on flood response. The quantile mapping polynomial correction has been found to be the best bias correction method for precipitation. A general reduction in flood quantiles is expected in the future, smoothing the increases identified in precipitation quantiles by the reduction of soil moisture content in catchments, due to the expected increase in temperature and decrease in mean annual precipitations.

scholarly journals Comparison Between Bivariate and Trivariate Flood Frequency Analysis Using the Archimedean Copula Functions, A Case Study of the Karun River in Iran

2021 ◽  
Author(s):  
Mohamad Haytham Klaho ◽  
Hamid R. Safavi ◽  
Mohamad H. Golmohammadi ◽  
Maamoun Alkntar
Keyword(s):  
Frequency Analysis ◽  
Return Period ◽  
Goodness Of Fit ◽  
Flood Frequency ◽  
Archimedean Copula ◽  
Flood Frequency Analysis ◽  
Cumulative Probability ◽  
Copula Functions ◽  
Flood Peak ◽  
Multiple Variables

Abstract Historically, severe floods have caused great human and financial losses. Therefore, the flood frequency analysis based on the flood multiple variables including flood peak, volume and duration poses more motivation for hydrologists to study. In this paper, the bivariate and trivariate flood frequency analysis and modeling using Archimedean copula functions is focused. For this purpose, the annual flood data over a 55-year historical period recorded at the Dez Dam hydrometric station were used. The results showed that based on goodness of fit criteria, the Frank function built upon the couple of the flood peak-volume and the couple of the flood peak-duration as well as the Clayton function built upon the flood volume-duration were identified to be the best copula families to be adopted. The trivariate analysis was conducted and the Clayton family was chosen as the best copula function. Thereafter, the common and conditional cumulative probability distribution functions were built and analyzed to determine the periodic "and", "or" and "conditional" bivariate and trivariate flood return periods. The results suggest that the bivariate conditional return period obtained for short-term periods is more reliable than the trivariate conditional return period. Additionally, the trivariate conditional return period calculated for long-term periods is more reliable than the bivariate conditional return period.

scholarly journals Inferring the flood frequency distribution for an ungauged basin using a spatially distributed rainfall-runoff model

2008 ◽  
Vol 5 (1) ◽  
pp. 1-26 ◽  
Author(s):  
G. Moretti ◽  
A. Montanari
Keyword(s):  
Frequency Distribution ◽  
River Flow ◽  
Spatial Scales ◽  
Flood Frequency ◽  
Rainfall Runoff ◽  
Ungauged Catchments ◽  
Wide Range ◽  
Spatially Distributed ◽  
Rainfall Runoff Model ◽  
Runoff Model

Abstract. The estimation of the peak river flow for ungauged river sections is a topical issue in applied hydrology. Spatially distributed rainfall-runoff models can be a useful tool to this end, since they are potentially able to simulate the river flow at any location of the watershed drainage network. However, it is not fully clear to what extent these models can provide reliable simulations over a wide range of spatial scales. This issue is investigated here by applying a spatially distributed, continuous simulation rainfall-runoff model to infer the flood frequency distribution of the Riarbero Torrent. This is an ungauged mountain creek located in northern Italy, whose drainage area is 17 km2. The results were checked by using estimates of the peak river flow obtained by applying a classical procedure based on hydrological similarity principles. The analysis highlights interesting perspectives for the application of spatially distributed models to ungauged catchments.

scholarly journals Impact of urbanization on rainfall-runoff processes: case study in the Liangshui River Basin in Beijing, China

2016 ◽  
Vol 373 ◽  
pp. 7-12 ◽  
Author(s):  
Zongxue Xu ◽  
Gang Zhao
Keyword(s):  
Land Use ◽  
Return Period ◽  
Overland Flow ◽  
Low Impact Development ◽  
Urban Land Use ◽  
Peak Time ◽  
Rainfall Runoff ◽  
Rapid Urbanization ◽  
Impervious Area ◽  
The Impact

Abstract. China is undergoing rapid urbanization during the past decades. For example, the proportion of urban population in Beijing has increased from 57.6 % in 1980 to 86.3 % in 2013. Rapid urbanization has an adverse impact on the urban rainfall-runoff processes, which may result in the increase of urban flood risk. In the present study, the major purpose is to investigate the impact of land use/cover change on hydrological processes. The intensive human activities, such as the increase of impervious area, changes of river network morphology, construction of drainage system and water transfer, were considered in this study. Landsat TM images were adopted to monitor urbanization process based on Urban Land-use Index (ULI). The SWMM model considering different urbanized scenarios and anthropogenic disturbance was developed. The measured streamflow data was used for model calibration and validation. Precipitation with different return periods was taken as model input to analyse the changes of flood characteristics under different urbanized scenarios. The results indicated that SWMM provided a good estimation for storms under different urbanized scenarios. The volume of surface runoff after urbanization was 3.5 times greater than that before urbanization; the coefficient of runoff changed from 0.12 to 0.41, and the ratio of infiltration decreased from 88 to 60 %. After urbanization, the time of overland flow concentration increased while the time of river concentration decreased; the peak time did not show much difference in this study. It was found that the peak flow of 20-year return-period after urbanization is greater than that of 100-year return-period before urbanization. The amplification effect of urbanization on flood is significant, resulting in an increase of the flooding risk. These effects are especially noticeable for extreme precipitation. The results in this study will provide technical support for the planning and management of urban storm water and the evaluation on Low Impact Development (LID) measures.

scholarly journals Evaluation of Return Period and Risk in Bivariate Non-Stationary Flood Frequency Analysis

Water ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 79 ◽  
Author(s):  
Ling Kang ◽  
Shangwen Jiang ◽  
Xiaoyong Hu ◽  
Changwen Li
Keyword(s):  
Return Period ◽  
Yangtze River ◽  
Flood Frequency Analysis ◽  
Time Varying ◽  
The Yangtze River ◽  
Stationary Model ◽  
Flood Peak ◽  
Flood Volume ◽  
Risk Of Failure ◽  
The Impact

The concept of a traditional return period has long been questioned in non-stationary studies, and the risk of failure was recommended to evaluate the design events in flood modeling. However, few studies have been done in terms of multivariate cases. To investigate the impact of non-stationarity on the streamflow series, the Yichang station in the Yangtze River was taken as a case study. A time varying copula model was constructed for bivariate modeling of flood peak and 7-day flood volume, and the non-stationary return period and risk of failure were applied to compare the results between stationary and non-stationary models. The results demonstrated that the streamflow series at the Yichang station showed significant non-stationary properties. The flood peak and volume series presented decreasing trends in their location parameters and the dependence structure between them also weakened over time. The conclusions of the bivariate non-stationary return period and risk of failure were different depending on the design flood event. In the event that both flood peak and volume are exceeding, the flood risk is smaller with the non-stationary model, which is a joint effect of the time varying marginal distribution and copula function. While in the event that either flood peak or volume exceed, the effect of non-stationary properties is almost negligible. As for the design values, the non-stationary model is characterized by a higher flood peak and lower flood volume. These conclusions may be helpful in long-term decision making in the Yangtze River basin under non-stationary conditions.

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