Use of Akaike information criterion for selection of flood frequency distribution

1992 ◽  
Vol 19 (4) ◽  
pp. 616-626 ◽  
Author(s):  
K. C. Ander Chow ◽  
W. Edgar Watt
Keyword(s):  
Frequency Distribution ◽  
Goodness Of Fit ◽  
Information Criterion ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Goodness Of Fit Tests ◽  
Single Station ◽  
The Cost ◽  
Selection Of

When conducting a conventional single-station flood frequency analysis, an appropriate distribution must be selected. Typically, sample statistics, probability plots, goodness-of-fit tests, etc. are used to facilitate the decision process. For the predominate case of a relatively short record length of flood series, this standard approach leads to undue emphasis on goodness of fit and virtually no consideration of the uncertainty due to additional parameters. The information criterion suggested by Akaike (AIC) is a measure to evaluate the "benefit" of goodness of fit and the "cost" of parameter uncertainty. The criterion is tested for 42 long-term hydrometric stations across Canada and its applicability and limitations are demonstrated in eight samples. The AIC is recommended as an aid in selecting a flood frequency distribution. Key words: flood frequency, goodness of fit, single station, information criterion.

scholarly journals Selecting the best probability distribution for at-site flood frequency analysis; a study of Torne River

2019 ◽  
Vol 1 (12) ◽  
Author(s):  
Mahmood Ul Hassan ◽  
Omar Hayat ◽  
Zahra Noreen
Keyword(s):  
Probability Distribution ◽  
Frequency Analysis ◽  
Goodness Of Fit ◽  
Direct Method ◽  
Estimation Method ◽  
Likelihood Estimation ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Goodness Of Fit Tests ◽  
Pearson Type

AbstractAt-site flood frequency analysis is a direct method of estimation of flood frequency at a particular site. The appropriate selection of probability distribution and a parameter estimation method are important for at-site flood frequency analysis. Generalized extreme value, three-parameter log-normal, generalized logistic, Pearson type-III and Gumbel distributions have been considered to describe the annual maximum steam flow at five gauging sites of Torne River in Sweden. To estimate the parameters of distributions, maximum likelihood estimation and L-moments methods are used. The performance of these distributions is assessed based on goodness-of-fit tests and accuracy measures. At most sites, the best-fitted distributions are with LM estimation method. Finally, the most suitable distribution at each site is used to predict the maximum flood magnitude for different return periods.

Identification of suitable copulas for bivariate frequency analysis of flood peak and flood volume data

2011 ◽  
Vol 42 (2-3) ◽  
pp. 193-216 ◽  
Author(s):  
Hemant Chowdhary ◽  
Luis A. Escobar ◽  
Vijay P. Singh
Keyword(s):  
Frequency Analysis ◽  
Goodness Of Fit ◽  
Peak Flow ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Dependence Structure ◽  
Volume Data ◽  
Data Set ◽  
Flood Peak ◽  
Goodness Of Fit Tests

Multivariate flood frequency analysis, involving flood peak flow, volume and duration, has been traditionally accomplished by employing available functional bivariate and multivariate frequency distributions that have a restriction on the marginals to be from the same family of distributions. The copula concept overcomes this restriction by allowing a combination of arbitrarily chosen marginal types. It also provides a wider choice of admissible dependence structure as compared to the conventional approach. The availability of a vast variety of copula types makes the selection of an appropriate copula family for different hydrological applications a non-trivial task. Graphical and analytic goodness-of-fit tests for testing the suitability of copulas are beginning to evolve and are being developed; there is limited experience of their usage at present, especially in the hydrological field. This paper provides a step-wise procedure for copula selection and illustrates its application to bivariate flood frequency analysis, involving flood peak flow and volume data. Several graphical procedures, tail dependence characteristics, and formal goodness-of-fit tests involving a parametric bootstrap-based technique are considered while investigating the relative applicability of six copula families. The Clayton copula has been identified as a valid model for the particular flood peak flow and volume data set considered in the study.

scholarly journals Flood Frequency Analysis of River Niger, Shintaku Gauging Station, Kogi State, Nigeria

2020 ◽  
Vol 5 (2) ◽  
Author(s):  
Andy O Ibeje
Keyword(s):  
Frequency Distribution ◽  
Goodness Of Fit ◽  
Climatic Factors ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Optimal Model ◽  
Frequency Curve ◽  
Type Iii ◽  
Pearson Type ◽  
Niger River

The study outlines a frequency distribution study on the highest annual flood statistics in Niger River located at Shintaku hydrologic Station for period of 58years. In order to determine the optimal model for highest annual flood analysis Generalised extreme value, Log normal, Gumbel maximum, Generalised Pareto and Log Pearson III, were tested. Based on error produced by criteria of goodness of Fit tests, the optimal model was determined. Results obtained indicated that Log Pearson type III was best to model maximum flood magnitude of Niger River at Shintaku station. The flood frequency curve was therefore derived using Log Pearson type III frequency distribution. Flood frequency curve showed that return periods of 50 and 100 years with the probabilities of 2% and1% respectively, yielded discharges of 15300m3/s and 15600m3/s respectively. These results were strongly influenced by their topographical, geographical and climatic factors. The findings of this work will be useful for design engineers in deciding the dimension of hydraulic structures such as spillway, dams, canals, bridges and levees among others. Future studies are required to include flood forecasting in the development of inundation maps for Niger River.Keywords—Return period, Frequency Distribution, Flood, Niger River, Flood Modeling

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 The economic and technological efciency of building producton in view of contemporary industrial building materials

2018 ◽  
Vol 196 ◽  
pp. 04074 ◽  
Author(s):  
Konstantin Tsapko
Keyword(s):  
Building Materials ◽  
Methodological Problem ◽  
Industrial Building ◽  
Risk And Uncertainty ◽  
Production Economic ◽  
Labor Resources ◽  
The Cost ◽  
Selection Of

Efficiency of construction production, economic category, expressing the achievement of construction and installation organizations the greatest result of production while minimizing the cost of material and labor resources. This publication examines the possibility of improving the efficiency of construction production through the use of innovative building materials and methods of their application. The methodological problem of the use of building materials, their choice depending on the long-term economic prospects. The questions of risk and uncertainty in the selection of effective building materials are considered.

scholarly journals Estimation of flood frequency using statistical method: Mahanadi River basin, India

2020 ◽  
Vol 3 (1) ◽  
pp. 189-207
Author(s):  
Sandeep Samantaray ◽  
Abinash Sahoo
Keyword(s):  
Stream Flow ◽  
Goodness Of Fit ◽  
Central India ◽  
Extreme Value ◽  
Flood Frequency ◽  
Goodness Of Fit Test ◽  
Mahanadi River ◽  
Goodness Of Fit Tests ◽  
Study Results ◽  
Anderson Darling

Abstract Estimating stream flow has a substantial financial influence, because this can be of assistance in water resources management and provides safety from scarcity of water and conceivable flood destruction. Four common statistical methods, namely, Normal, Gumbel max, Log-Pearson III (LP III), and Gen. extreme value method are employed for 10, 20, 30, 35, 40, 50, 60, 70, 75, 100, 150 years to forecast stream flow. Monthly flow data from four stations on Mahanadi River, in Eastern Central India, namely, Rampur, Sundargarh, Jondhra, and Basantpur, are used in the study. Results show that Gumbel max gives better flow discharge value than the Normal, LP III, and Gen. extreme value methods for all four gauge stations. Estimated flood values for Rampur, Sundargarh, Jondhra, and Basantpur stations are 372.361 m3/sec, 530.415 m3/sec, 2,133.888 m3/sec, and 3,836.22 m3/sec, respectively, considering Gumbel max. Goodness-of-fit tests for four statistical distribution techniques applied in the present study are also evaluated using Kolmogorov–Smirov, Anderson–Darling, Chi-squared tests at critical value 0.05 for the four proposed gauge stations. Goodness-of-fit test results show that Gen. extreme value gives best results at Rampur, Sundergarh, and Jondhra gauge stations followed by LP III, whereas LP III is the best fit for Basantpur, followed by Gen. extreme value.

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>

A knowledge-based expert system for flood frequency analysis

1990 ◽  
Vol 17 (4) ◽  
pp. 597-609 ◽  
Author(s):  
K. C. Ander Chow ◽  
W. E. Watt
Keyword(s):  
Expert System ◽  
Frequency Analysis ◽  
Sampling Error ◽  
Inductive Reasoning ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Design Flood ◽  
Knowledge Based ◽  
Single Station ◽  
Quantile Estimates

Single-station flood frequency analysis is an important element in hydrotechnical planning and design. In Canada, no single statistical distribution has been specified for floods; hence, the conventional approach is to select a distribution based on its fit to the observed sample. This selection is not straightforward owing to typically short record lengths and attendant sampling error, magnified influence of apparent outliers, and limited evidence of two populations. Nevertheless, experienced analysts confidently select a distribution for a station based only on a few heuristics. A knowledge-based expert system has been developed to emulate these expert heuristics. It can perform data analyses, suggest an appropriate distribution, detect outliers, and provide means to justify a design flood on physical grounds. If the sample is too small to give reliable quantile estimates, the system performs a Bayesian analysis to combine regional information with station-specific data. The system was calibrated and tested for 52 stations across Canada. Its performance was evaluated by comparing the distributions selected by experts with those given by the developed system. The results indicated that the system can perform at an expert level in the task of selecting distributions. Key words: flood frequency, expert system, single-station, fuzzy logic, inductive reasoning, production system.

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

2013 ◽  
Vol 1 (6) ◽  
pp. 6785-6828 ◽  
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 are simulated. The precipitation probabilistic model is conditioned by regional weather patterns, and a "bottom-up" classification procedure was used for defining a set of weather patterns producing extreme precipitation in Norway. SCHADEX estimates for the 1000 yr (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, with statistical flood frequency analysis based on the annual maximum series, and with 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.

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