scholarly journals Hydro-meteorological characteristics and occurrence probability of extreme flood events in Moroccan High Atlas

2020 ◽  
Vol 11 (S1) ◽  
pp. 310-321 ◽  
Author(s):  
Mohamed El Mehdi Saidi ◽  
Tarik Saouabe ◽  
Abdelhafid El Alaoui El Fels ◽  
El Mahdi El Khalki ◽  
Abdessamad Hadri
Keyword(s):  
Return Period ◽  
Peak Flow ◽  
Extreme Event ◽  
Rare Event ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
High Atlas ◽  
Flood Water ◽  
Extreme Flood ◽  
Water Volumes

Abstract Flood frequency analysis could be a tool to help decision-makers to size hydraulic structures. To this end, this article aims to compare two analysis methods to see how rare an extreme hydrometeorological event is, and what could be its return period. This event caused many deadly floods in southwestern Morocco. It was the result of unusual atmospheric conditions, characterized by a very low atmospheric pressure off the Moroccan coast and the passage of the jet stream further south. Assessment of frequency and return period of this extreme event is performed in a High Atlas watershed (the Ghdat Wadi) using historical floods. We took into account, on the one hand, flood peak flows and, on the other hand, flood water volumes. Statistically, both parameters are better adjusted respectively to Gamma and Log Normal distributions. However, the peak flow approach underestimates the return period of long-duration hydrographs that do not have a high peak flow, like the 2014 event. The latter is indeed better evaluated, as a rare event, by taking into account the flood water volumes. Therefore, this parameter should not be omitted in the calculation of flood probabilities for watershed management and the sizing of flood protection infrastructure.

scholarly journals Flood frequency analysis supported by the largest historical flood

2014 ◽  
Vol 14 (6) ◽  
pp. 1543-1551 ◽  
Author(s):  
W. G. Strupczewski ◽  
K. Kochanek ◽  
E. Bogdanowicz
Keyword(s):  
Return Period ◽  
Historical Record ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Historical Period ◽  
Time Period ◽  
Extreme Flood ◽  
Discrete Uniform Distribution ◽  
L Value ◽  
Discrete Uniform

Abstract. The use of non-systematic flood data for statistical purposes depends on the reliability of the assessment of both flood magnitudes and their return period. The earliest known extreme flood year is usually the beginning of the historical record. Even if one properly assesses the magnitudes of historic floods, the problem of their return periods remains unsolved. The matter at hand is that only the largest flood (XM) is known during whole historical period and its occurrence marks the beginning of the historical period and defines its length (L). It is common practice to use the earliest known flood year as the beginning of the record. It means that the L value selected is an empirical estimate of the lower bound on the effective historical length M. The estimation of the return period of XM based on its occurrence (L), i.e. ^M = L, gives a severe upward bias. The problem arises that to estimate the time period (M) representative of the largest observed flood XM. From the discrete uniform distribution with support 1, 2, ... , M of the probability of the L position of XM, one gets ^L = M/2. Therefore ^M = 2L has been taken as the return period of XM and as the effective historical record length as well this time. As in the systematic period (N) all its elements are smaller than XM, one can get ^M = 2t( L+N). The efficiency of using the largest historical flood (XM) for large quantile estimation (i.e. one with return period T = 100 years) has been assessed using the maximum likelihood (ML) method with various length of systematic record (N) and various estimates of the historical period length ^M comparing accuracy with the case when systematic records alone (N) are used only. The simulation procedure used for the purpose incorporates N systematic record and the largest historic flood (XMi) in the period M, which appeared in the Li year of the historical period. The simulation results for selected two-parameter distributions, values of their parameters, different N and M values are presented in terms of bias and root mean square error RMSEs of the quantile of interest are more widely discussed.

scholarly journals Flood Frequency Analysis supported by the largest historical flood

2013 ◽  
Vol 1 (6) ◽  
pp. 6133-6153 ◽  
Author(s):  
W. G. Strupczewski ◽  
K. Kochanek ◽  
E. Bogdanowicz
Keyword(s):  
Return Period ◽  
Historical Record ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Historical Period ◽  
Time Period ◽  
Extreme Flood ◽  
Discrete Uniform Distribution ◽  
L Value ◽  
Discrete Uniform

Abstract. The use of non-systematic flood data for statistical purposes depends on reliability of assessment both flood magnitudes and their return period. The earliest known extreme flood year is usually the beginning of the historical record. Even if one properly assess the magnitudes of historic floods, the problem of their return periods remains unsolved. The matter in hand is that the only largest flood (XM) is known during whole historical period and its occurrence marks the beginning of the historical period and defines its length (L). It is the common practice of using the earliest known flood year as the beginning of the record. It means that the L value selected is an empirical estimate of the lower bound on the effective historical length M. The estimation of the return period of XM based on its occurrence (L), i.e. ∧ M = L, gives the severe upward bias. Problem arises to estimate the time period (M) representative of the largest observed flood XM. From the discrete uniform distribution with support 1,2, ... , M of the probability of the L position of XM one gets ∧ L = M/2. Therefore ∧ M = 2L has been taken as the return period of XM and as the effective historical record length as well this time. As in the systematic period (N) all its elements are smaller than XM, one can get ∧ M =2(L+N). The efficiency of using the largest historical flood (XM) for large quantile estimation (i.e. one with return period T = 100 yr has been assessed using ML method with various length of systematic record (N) and various estimates of historical period length ∧ M comparing accuracy with the case when systematic records alone (N) are used only. The simulation procedure used for the purpose incorporates N systematic record and one largest historic flood (XMi) in the period M which appeared in the Li year backward from the end of historical period. The simulation result for selected distributions, values of their parameters, different N and M values are presented in terms of bias and RMSE of the quantile of interest and widely discussed.

scholarly journals Stochastic semi-continuous simulation for extreme flood estimation in catchments with combined rainfall–snowmelt flood regimes

2014 ◽  
Vol 14 (5) ◽  
pp. 1283-1298 ◽  
Author(s):  
D. Lawrence ◽  
E. Paquet ◽  
J. Gailhard ◽  
A. K. Fleig
Keyword(s):  
Frequency Analysis ◽  
Probabilistic Model ◽  
Extreme Precipitation ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Continuous Simulation ◽  
Weather Patterns ◽  
Extreme Flood ◽  
Flood Estimation

Abstract. Simulation methods for extreme flood estimation represent an important complement to statistical flood frequency analysis because a spectrum of catchment conditions potentially leading to extreme flows can be assessed. In this paper, stochastic, semi-continuous simulation is used to estimate extreme floods in three catchments located in Norway, all of which are characterised by flood regimes in which snowmelt often has a significant role. The simulations are based on SCHADEX, which couples a precipitation probabilistic model with a hydrological simulation such that an exhaustive set of catchment conditions and responses is simulated. The precipitation probabilistic model is conditioned by regional weather patterns, and a bottom–up classification procedure was used to define a set of weather patterns producing extreme precipitation in Norway. SCHADEX estimates for the 1000-year (Q1000) discharge are compared with those of several standard methods, including event-based and long-term simulations which use a single extreme precipitation sequence as input to a hydrological model, statistical flood frequency analysis based on the annual maximum series, and the GRADEX method. The comparison suggests that the combination of a precipitation probabilistic model with a long-term simulation of catchment conditions, including snowmelt, produces estimates for given return periods which are more in line with those based on statistical flood frequency analysis, as compared with the standard simulation methods, in two of the catchments. In the third case, the SCHADEX method gives higher estimates than statistical flood frequency analysis and further suggests that the seasonality of the most likely Q1000 events differs from that of the annual maximum flows. The semi-continuous stochastic simulation method highlights the importance of considering the joint probability of extreme precipitation, snowmelt rates and catchment saturation states when assigning return periods to floods estimated by precipitation-runoff methods. The SCHADEX methodology, as applied here, is dependent on observed discharge data for calibration of a hydrological model, and further study to extend its application to ungauged catchments would significantly enhance its versatility.

scholarly journals Regional Flood Frequency Analysis using Dimensionless Index Flood Method

2020 ◽  
Vol 6 (12) ◽  
pp. 2425-2436
Author(s):  
Andy Obinna Ibeje ◽  
Ben N. Ekwueme
Keyword(s):  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Accurate Estimation ◽  
Frequency Curve ◽  
Model Framework ◽  
Index Flood ◽  
Extreme Flood ◽  
Regional Flood Frequency Analysis ◽  
Spatially Distributed ◽  
Regional Flood Frequency

Hydrologic designs require accurate estimation of quartiles of extreme floods. But in many developing regions, records of flood data are seldom available. A model framework using the dimensionless index flood for the transfer of Flood Frequency Curve (FFC) among stream gauging sites in a hydrologically homogeneous region is proposed.  Key elements of the model framework include: (1) confirmation of the homogeneity of the region; (2) estimation of index flood-basin area relation; (3) derivation of the regional flood frequency curve (RFFC) and deduction of FFC of an ungauged catchment as a product of index flood and dimensionless RFFC. As an application, 1983 to 2004 annual extreme flood from six selected gauging sites located in Anambra-Imo River basin of southeast Nigeria, were used to demonstrate that the developed index flood model: , overestimated flood quartiles in an ungauged site of the basin.  It is recommended that, for wider application, the model results can be improved by the availability and use of over 100 years length of flood data spatially distributed at critical locations of the watershed. Doi: 10.28991/cej-2020-03091627 Full Text: PDF

scholarly journals Flood frequency analysis for annual maximum streamflow using a non-stationary GEV model

2019 ◽  
Vol 79 ◽  
pp. 03022
Author(s):  
Shangwen Jiang ◽  
Ling Kang
Keyword(s):  
Frequency Analysis ◽  
Return Period ◽  
Yangtze River ◽  
Flood Frequency ◽  
Additive Models ◽  
Flood Frequency Analysis ◽  
Annual Maximum ◽  
The Yangtze River ◽  
Stationary Model ◽  
Risk Of Failure

Under changing environment, the streamflow series in the Yangtze River have undergone great changes and it has raised widespread concerns. In this study, the annual maximum flow (AMF) series at the Yichang station were used for flood frequency analysis, in which a time varying model was constructed to account for non-stationarity. The generalized extreme value (GEV) distribution was adopted to fit the AMF series, and the Generalized Additive Models for Location, Scale and Shape (GAMLSS) framework was applied for parameter estimation. The non-stationary return period and risk of failure were calculated and compared for flood risk assessment between stationary and non-stationary models. The results demonstrated that the flow regime at the Yichang station has changed over time and a decreasing trend was detected in the AMF series. The design flood peak given a return period decreased in the non-stationary model, and the risk of failure is also smaller given a design life, which indicated a safer flood condition in the future compared with the stationary model. The conclusions in this study may contribute to long-term decision making in the Yangtze River basin under non-stationary conditions.

scholarly journals Flood Frequency Analysis at the Downstream of Sg. Perak River Basin using Annual Maximum Flow Discharge Data

2018 ◽  
Vol 7 (4.35) ◽  
pp. 709 ◽  
Author(s):  
Munir Snu ◽  
Sidek L.M ◽  
Haron Sh ◽  
Noh Ns.M ◽  
Basri H ◽  
...  
Keyword(s):  
Probability Distribution ◽  
River Basin ◽  
Frequency Analysis ◽  
Return Period ◽  
Maximum Flow ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Annual Maximum ◽  
Discharge Data ◽  
Flow Discharge

The recent flood event occurred in 2014 had caused disaster in Perak and Sungai Perak is the main river of Perak which is a major natural drainage system within the state. The aim of this paper is to determine the expected discharge to return period downstream for Sg. Perak River Basin in Perak by using annual maximum flow data. Flood frequency analysis is a technique to assume the flow values corresponding to specific return periods or probabilities along the river at a different site. The method involves the observed annual maximum flow discharge data to calculate statistical information such as standard deviations, mean, sum, skewness and recurrence intervals. The flood frequency analysis for Sg. Perak River Basin was used Log Pearson Type-III probability distribution method. The annual maximum peak flow series data varying over period 1961 to 2016. The probability distribution function was applied to return periods (T) where T values are 2years, 5years, 10years, 25years, 50years, and 100years generally used in flood forecasting. Flood frequency curves are plotted after the choosing the best fits probability distribution for annual peak maximum data. The results for flood frequency analysis shows that Sg. Perak at Jambatan Iskandar much higher inflow discharge  which is 3714.45m3/s at the 100years return period compare to Sg. Plus at Kg Lintang and Sg. Kinta at Weir G. With this, the 100years peak flow at Sg Perak river mouth is estimated to be in the range of 4,000 m3/s. Overall, the analysis relates the expected flow discharge to return period for all tributaries of Sg. Perak River Basin.

Assessment of overtopping reliability and benefits of a flood detention dam

2008 ◽  
Vol 35 (10) ◽  
pp. 1177-1182 ◽  
Author(s):  
A. Melih Yanmaz ◽  
M. Engin Gunindi
Keyword(s):  
Return Period ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Site Specific ◽  
Joint Return ◽  
Dam Site ◽  
Bivariate Gamma Distribution ◽  
Project Characteristics ◽  
Joint Return Period

There is a growing tendency to assess safety levels of existing dams and to design new dams using probabilistic approaches according to project characteristics and site-specific conditions. This study is a probabilistic assessment of the overtopping reliability of a dam, which will be designed for flood detention purpose, and will compute the benefits that can be gained as a result of the implementation of this dam. In a case study, a bivariate flood frequency analysis was carried out using a five-parameter bivariate gamma distribution. A family of joint return period curves relating the runoff peak discharges to the runoff volumes at the dam site was derived. A number of hydrographs were also obtained under a joint return period of 100 years to observe the variation of overtopping tendency. The maximum reservoir elevation and overtopping reliability were determined by performing a probabilistic reservoir routing based on Monte Carlo simulations.

May long-term historical hydrological data be misleading for flood frequency analysis in current conditions of climate change?

2020 ◽  
Author(s):  
Alexandra Fedorova ◽  
Nataliia Nesterova ◽  
Olga Makarieva ◽  
Andrey Shikhov
Keyword(s):  
Climate Change ◽  
Water Level ◽  
Frequency Analysis ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Economic Damage ◽  
Irkutsk Region ◽  
Extreme Flood ◽  
The Town

<p>In June 2019, the extreme flash flood was formed on the rivers of the Irkutsk region originating from the East Sayan mountains. This flood became the most hazardous one in the region in 80 years history of observations.</p><p>The greatest rise in water level was recorded at the Iya River in the town of Tulun (more than 9 m in three days). The recorded water level was more than 5 m above the dangerous mark of 850 cm and more than 2.5 m above the historical maximum water level which was observed in 1984.</p><p>The flood led to the catastrophic inundation of the town of Tulun, 25 people died and 8 went missing. According to preliminary assessment, economic damage from the flood in 2019 amounted up to half a billion Euro.</p><p>Among the reasons for the extreme flood in June 2019 that are discussed are heavy rains as a result of climate change, melting of snow and glaciers in the mountains of the East Sayan, deforestation of river basins due to clearings and fires, etc.</p><p>The aim of the study was to analyze the factors that led to the formation of a catastrophic flood in June 2019, as well as estimate the maximum discharge of at the Iya River. For calculations, the deterministic distributed hydrological model Hydrograph was applied. We used the observed data of meteorological stations and the forecast values ​​of the global weather forecast model ICON. The estimated discharge has exceeded previously observed one by about 50%.</p><p>The results of the study have shown that recent flood damage was caused mainly by unprepared infrastructure. The safety dam which was built in the town of Tulun just ten years ago was 2 meters lower than maximum observed water level in 2019. This case and many other cases in Russia suggest that the flood frequency analysis of even long-term historical data may mislead design engineers to significantly underestimate the probability and magnitude of flash floods. There are the evidences of observed precipitation regime transformations which directly contribute to the formation of dangerous hydrological phenomena. The details of the study for the Irkutsk region will be presented.</p>

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