scholarly journals A unified statistical model for hydrological variables including the selection of threshold for the peak over threshold method

2012 ◽  
Vol 48 (10) ◽  
Author(s):  
S. Solari ◽  
M. A. Losada
Keyword(s):  
Statistical Model ◽  
Threshold Method ◽  
Peak Over Threshold ◽  
Peak Over Threshold Method ◽  
Hydrological Variables ◽  
Selection Of

scholarly journals Study on reservoir time-varying design flood of inflow based on Poisson process with time-dependent parameters

2018 ◽  
Vol 379 ◽  
pp. 119-123
Author(s):  
Jiqing Li ◽  
Jing Huang ◽  
Jianchang Li
Keyword(s):  
Poisson Process ◽  
Flood Control ◽  
Time Dependent ◽  
Model Parameters ◽  
Time Varying ◽  
Design Flood ◽  
Threshold Method ◽  
Peak Over Threshold ◽  
Peak Over Threshold Method ◽  
The Relationship

Abstract. The time-varying design flood can make full use of the measured data, which can provide the reservoir with the basis of both flood control and operation scheduling. This paper adopts peak over threshold method for flood sampling in unit periods and Poisson process with time-dependent parameters model for simulation of reservoirs time-varying design flood. Considering the relationship between the model parameters and hypothesis, this paper presents the over-threshold intensity, the fitting degree of Poisson distribution and the design flood parameters are the time-varying design flood unit period and threshold discriminant basis, deduced Longyangxia reservoir time-varying design flood process at 9 kinds of design frequencies. The time-varying design flood of inflow is closer to the reservoir actual inflow conditions, which can be used to adjust the operating water level in flood season and make plans for resource utilization of flood in the basin.

scholarly journals Analysis of extreme hydrological Events on THE danube using the Peak Over Threshold method

2010 ◽  
Vol 58 (2) ◽  
pp. 88-101 ◽  
Author(s):  
Veronika Bačová-Mitková ◽  
Milan Onderka
Keyword(s):  
Danube River ◽  
Data Series ◽  
Time Data ◽  
Threshold Method ◽  
Series Length ◽  
Discharge Data ◽  
Peak Over Threshold ◽  
Peak Over Threshold Method ◽  
Daily Discharge ◽  
Extreme Hydrological Events

Analysis of extreme hydrological Events on THE danube using the Peak Over Threshold methodThe Peak Over Threshold Method (POT) was used as an alternative technique to the traditional analysis of annual discharge maxima of the Danube River. The POT method was applied to a time-series of daily discharge values covering a period of 60 years (1931-1990) at the following gauge stations: Achleiten, Kienstock, Wien, Bratislava and Nagymaros. The first part of the paper presents the use of the POT method and how it was applied to daily discharges. All mean daily discharges exceeding a defined threshold were considered in the POT analysis. Based on the POT waves independence criteria the maximum daily discharge data were selected. Two theoretical log-normal (LN) and Log-Pearson III (LP3) distributions were used to calculate the probability of exceeding annual maximum discharges. Performance of the POT method was compared to the theoretical distributions (LN, LP3). The influence of the data series length on the estimation of theN-year discharges by POT method was carried out too. Therefore, with regard to later regulations along the Danube channel bank the 40, 20 and 10-year time data series were chosen in early of the 60-year period and second analysed time data series were selected from the end of the 60-year period. Our results suggest that the POT method can provide adequate and comparable estimates ofN-year discharges for more stations with short temporal coverage.

Predictions of Extreme Values of Significant Wave Height

2003 ◽  
Author(s):  
C. Guedes Soares ◽  
R. G. Ferreira ◽  
Manuel G. Scotto
Keyword(s):  
Extreme Value Theory ◽  
Significant Wave Height ◽  
Extreme Values ◽  
Value Theory ◽  
Environmental Data ◽  
Threshold Method ◽  
Peak Over Threshold ◽  
Peak Over Threshold Method ◽  
Low Probability ◽  
Quantile Estimates

This paper provides an overview of different methods of extrapolating environmental data to low probability levels based on the extreme value theory. It discusses the Annual Maxima method and the Peak Over Threshold method, using unified terminology and notation. Furthermore, it describes a method based on the r largest order statistics that has the advantage of providing more accurate parameters and quantile estimates than the Annual Maxima method. Several examples illustrate the methodology and reveal strengths and weaknesses of the various approaches.

Long-Term Statistical Analysis of Typhoon Wave Heights With Poisson-Maximum Entropy Distribution

2009 ◽  
Author(s):  
Sheng Dong ◽  
Wei Liu ◽  
Lizhen Zhang ◽  
C. Guedes Soares
Keyword(s):  
Maximum Entropy ◽  
Wave Height ◽  
Estimation Accuracy ◽  
Threshold Method ◽  
Compound Distribution ◽  
Typhoon Wave ◽  
Peak Over Threshold ◽  
Peak Over Threshold Method ◽  
Maximum Entropy Distribution

Using the maximum typhoon wave height series observed at Nakagusukuwan Observation Station in Japan, a novel compound distribution, Poisson-maximum entropy distribution, is proposed to calculate typhoon wave height return values. In this paper, both the Annual Maximum method and Peak Over Threshold method are adopted for long-term wave height analysis. Calculation results by Peak Over Threshold method show that the choice of threshold slightly affects the return values of wave height under the same long statistical series. For a relatively short sample by the Peak Over Threshold method, the estimation accuracy is still higher under the condition that the maximum typhoon wave height is included in the statistical sample.

Extreme value statistics for river quality simulations

2010 ◽  
Vol 61 (2) ◽  
pp. 397-406 ◽  
Author(s):  
N. Schindler ◽  
J. Tränckner ◽  
P. Krebs
Keyword(s):  
Urban Areas ◽  
Concentration Level ◽  
Extreme Value ◽  
Extreme Value Statistics ◽  
Threshold Method ◽  
Peak Over Threshold ◽  
Peak Over Threshold Method ◽  
The Impact ◽  
Posteriori Estimation

Various methods have been proposed to assess intermittent pollution loads and impacts on rivers in urban areas. Although the variables to describe the impact are mainly the same, the standards show significant differences in the assessment of permitted concentration level, duration and return period. The probability of an event is derived using either frequencies of occurrence or predefined extreme value distributions. Both methods have drawbacks. To bypass these, an a posteriori estimation of the statistical distribution of data based on the peak-over-threshold method is proposed. The method is exemplarily demonstrated using a semi-virtual case study.

Assessment of Load Extrapolation Methods for Wind Turbines

2011 ◽  
Vol 133 (2) ◽  
Author(s):  
Henrik Stensgaard Toft ◽  
John Dalsgaard Sørensen ◽  
Dick Veldkamp
Keyword(s):  
Analytical Solution ◽  
Gaussian Process ◽  
Wind Turbine ◽  
Process Model ◽  
Goodness Of Fit ◽  
Bending Moment ◽  
Threshold Value ◽  
Threshold Method ◽  
Peak Over Threshold ◽  
Peak Over Threshold Method

In the present paper, methods for statistical load extrapolation of wind-turbine response are studied using a stationary Gaussian process model, which has approximately the same spectral properties as the response for the out-of-plane bending moment of a wind-turbine blade. For a Gaussian process, an approximate analytical solution for the distribution of the peaks is given by Rice. In the present paper, three different methods for statistical load extrapolation are compared with the analytical solution for one mean wind speed. The methods considered are global maxima, block maxima, and the peak over threshold method with two different threshold values. The comparisons show that the goodness of fit for the local distribution has a significant influence on the results, but the peak over threshold method with a threshold value on the mean plus 1.4 standard deviations generally gives the best results. By considering Gaussian processes for 12 mean wind speeds, the “fitting before aggregation” and “aggregation before fitting” approaches are studied. The results show that the fitting before aggregation approach gives the best results.

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