scholarly journals The Influence of Hindcast Modeling Uncertainty on the Prediction of High Return Period Wave Conditions

2004 ◽  
Author(s):  
Daniel C. Brooker ◽  
Geoffrey K. Cole ◽  
Jason D. McConochie
Keyword(s):  
Return Period ◽  
Extreme Value Distribution ◽  
Likelihood Estimation ◽  
Extreme Value ◽  
Extreme Value Analysis ◽  
Random Errors ◽  
Value Analysis ◽  
Simulation Techniques ◽  
Modeling Uncertainties ◽  
Wave Conditions

Extreme value analysis for the prediction of long return period met-ocean conditions is often based upon hindcast studies of wind and wave conditions. The random errors associated with hindcast modeling are not usually incorporated when fitting an extreme value distribution to hindcast data. In this paper, a modified probability distribution function is derived so that modeling uncertainties can be explicitly included in extreme value analysis. Maximum likelihood estimation is then used to incorporate hindcast uncertainty into return value estimates and confidence intervals. The method presented here is compared against simulation techniques for accounting for hindcast errors. The influence of random errors within modeled datasets on predicted 100 year return wave estimates is discussed.

scholarly journals Deriving the 100-Year Total Water Level around the Coast of Corsica by Combining Trivariate Extreme Value Analysis and Coastal Hydrodynamic Models

2021 ◽  
Vol 9 (12) ◽  
pp. 1347
Author(s):  
Jessie Louisor ◽  
Jérémy Rohmer ◽  
Thomas Bulteau ◽  
Faïza Boulahya ◽  
Rodrigo Pedreros ◽  
...  
Keyword(s):  
Water Level ◽  
Return Period ◽  
Extreme Value ◽  
Coastal Areas ◽  
Extreme Value Analysis ◽  
Total Water ◽  
Hydrodynamic Models ◽  
Value Analysis ◽  
Multivariate Extreme Value ◽  
Total Water Level

As low-lying coastal areas can be impacted by flooding caused by dynamic components that are dependent on each other (wind, waves, water levels—tide, atmospheric surge, currents), the analysis of the return period of a single component is not representative of the return period of the total water level at the coast. It is important to assess a joint return period of all the components. Based on a semiparametric multivariate extreme value analysis, we determined the joint probabilities that significant wave heights (Hs), wind intensity at 10 m above the ground (U), and still water level (SWL) exceeded jointly imposed thresholds all along the Corsica Island coasts (Mediterranean Sea). We also considered the covariate peak direction (Dp), the peak period (Tp), and the wind direction (Du). Here, we focus on providing extreme scenarios to populate coastal hydrodynamic models, SWAN and SWASH-2DH, in order to compute the 100-year total water level (100y-TWL) all along the coasts. We show how the proposed multivariate extreme value analysis can help to more accurately define low-lying zones potentially exposed to coastal flooding, especially in Corsica where a unique value of 2 m was taken into account in previous studies. The computed 100y-TWL values are between 1 m along the eastern coasts and a maximum of 1.8 m on the western coast. The calculated values are also below the 2.4 m threshold recommended when considering the sea level rise (SLR). This highlights the added value of performing a full integration of extreme offshore conditions, together with their dependence on hydrodynamic simulations for screening out the coastal areas potentially exposed to flooding.

scholarly journals Assessing the features of extreme smog in China and the differentiated treatment strategy

2018 ◽  
Vol 474 (2209) ◽  
pp. 20170511 ◽  
Author(s):  
Lu Deng ◽  
Zhengjun Zhang
Keyword(s):  
Control Strategies ◽  
Hazard Analysis ◽  
Extreme Value Distribution ◽  
Extreme Value ◽  
Return Level ◽  
Extreme Value Analysis ◽  
Value Analysis ◽  
Mean Values ◽  
And Control ◽  
Parameter Values

Extreme smog can have potentially harmful effects on human health, the economy and daily life. However, the average (mean) values do not provide strategically useful information on the hazard analysis and control of extreme smog. This article investigates China's smog extremes by applying extreme value analysis to hourly PM2.5 data from 2014 to 2016 obtained from monitoring stations across China. By fitting a generalized extreme value (GEV) distribution to exceedances over a station-specific extreme smog level at each monitoring location, all study stations are grouped into eight different categories based on the estimated mean and shape parameter values of fitted GEV distributions. The extreme features characterized by the mean of the fitted extreme value distribution, the maximum frequency and the tail index of extreme smog at each location are analysed. These features can provide useful information for central/local government to conduct differentiated treatments in cities within different categories and conduct similar prevention goals and control strategies among those cities belonging to the same category in a range of areas. Furthermore, hazardous hours, breaking probability and the 1-year return level of each station are demonstrated by category, based on which the future control and reduction targets of extreme smog are proposed for the cities of Beijing, Tianjin and Hebei as an example.

scholarly journals Characterization of Extreme Wave Conditions for Wave Energy Converter Design and Project Risk Assessment

2020 ◽  
Vol 8 (4) ◽  
pp. 289 ◽  
Author(s):  
Vincent S. Neary ◽  
Seongho Ahn ◽  
Bibiana E. Seng ◽  
Mohammad Nabi Allahdadi ◽  
Taiping Wang ◽  
...  
Keyword(s):  
Return Period ◽  
Wave Energy ◽  
Wave Energy Converter ◽  
Extreme Value Analysis ◽  
Extreme Wave ◽  
Energy Converter ◽  
Sea State ◽  
Value Analysis ◽  
Wave Conditions ◽  
Wave Models

Best practices and international standards for determining n-year return period extreme wave (sea states) conditions allow wave energy converter designers and project developers the option to apply simple univariate or more complex bivariate extreme value analysis methods. The present study compares extreme sea state estimates derived from univariate and bivariate methods and investigates the performance of spectral wave models for predicting extreme sea states at buoy locations within several regional wave climates along the US East and West Coasts. Two common third-generation spectral wave models are evaluated, a WAVEWATCH III® model with a grid resolution of 4 arc-minutes (6–7 km), and a Simulating WAves Nearshore model, with a coastal resolution of 200–300 m. Both models are used to generate multi-year hindcasts, from which extreme sea state statistics used for wave conditions characterization can be derived and compared to those based on in-situ observations at National Data Buoy Center stations. Comparison of results using different univariate and bivariate methods from the same data source indicates reasonable agreement on average. Discrepancies are predominantly random. Large discrepancies are common and increase with return period. There is a systematic underbias for extreme significant wave heights derived from model hindcasts compared to those derived from buoy measurements. This underbias is dependent on model spatial resolution. However, simple linear corrections can effectively compensate for this bias. A similar approach is not possible for correcting model-derived environmental contours, but other methods, e.g., machine learning, should be explored.

Analysing the Return Period and Risk under the Influence of Physical Covariates on Hydrological Extreme

2021 ◽  
Author(s):  
Jew Das ◽  
Nanduri Umamahesh ◽  
Srinidhi Jha
Keyword(s):  
Water Resources ◽  
Return Period ◽  
Large Scale ◽  
Location Parameter ◽  
Extreme Value ◽  
Extreme Value Analysis ◽  
Change Scenario ◽  
Stationary Condition ◽  
Value Analysis ◽  
Monsoon Index

<p>For sustainable water resources planning and management, it is necessary to redefine the concept of return period, risk, and reliability of hydrologic extreme under non-stationary condition. Thus, the present study aims to examine the return period, risk introducing physical based covariates in the location parameter of the generalised extreme value (GEV) distribution. The study is performed over the Godavari River basin, India. The expected waiting time (EWT) approach is used to make comparison of return period, risk between stationary and non-stationary approaches. From the analysis, it is found that 50% of the gauging stations are impacted by large scale modes/oscillations and regional hydrological variability, primarily by Indian Summer Monsoon Index (ISMI) and precipitation. The EWT interpretation estimates that the non-stationary return period, risk, and reliability are significantly different from stationary condition. Hence, it is concluded that return period analysis and risk assessment using non-stationary approach can be beneficial to water managers and policy makers in order to devise sustainable and resilient water resources infrastructure under climate change scenario.</p><p><strong>Keywords:</strong> Extreme value analysis; Return period; Risk; Non-stationarity; Uncertainty</p>

scholarly journals The transformed-stationary approach: a generic and simplified methodology for non-stationary extreme value analysis

2016 ◽  
Vol 20 (9) ◽  
pp. 3527-3547 ◽  
Author(s):  
Lorenzo Mentaschi ◽  
Michalis Vousdoukas ◽  
Evangelos Voukouvalas ◽  
Ludovica Sartini ◽  
Luc Feyen ◽  
...  
Keyword(s):  
Time Series ◽  
Extreme Value Distribution ◽  
Extreme Value ◽  
Value Distribution ◽  
Stationary Time Series ◽  
Alternative Methods ◽  
Extreme Value Analysis ◽  
Accurate Estimation ◽  
Residual Water ◽  
Value Analysis

Abstract. Statistical approaches to study extreme events require, by definition, long time series of data. In many scientific disciplines, these series are often subject to variations at different temporal scales that affect the frequency and intensity of their extremes. Therefore, the assumption of stationarity is violated and alternative methods to conventional stationary extreme value analysis (EVA) must be adopted. Using the example of environmental variables subject to climate change, in this study we introduce the transformed-stationary (TS) methodology for non-stationary EVA. This approach consists of (i) transforming a non-stationary time series into a stationary one, to which the stationary EVA theory can be applied, and (ii) reverse transforming the result into a non-stationary extreme value distribution. As a transformation, we propose and discuss a simple time-varying normalization of the signal and show that it enables a comprehensive formulation of non-stationary generalized extreme value (GEV) and generalized Pareto distribution (GPD) models with a constant shape parameter. A validation of the methodology is carried out on time series of significant wave height, residual water level, and river discharge, which show varying degrees of long-term and seasonal variability. The results from the proposed approach are comparable with the results from (a) a stationary EVA on quasi-stationary slices of non-stationary series and (b) the established method for non-stationary EVA. However, the proposed technique comes with advantages in both cases. For example, in contrast to (a), the proposed technique uses the whole time horizon of the series for the estimation of the extremes, allowing for a more accurate estimation of large return levels. Furthermore, with respect to (b), it decouples the detection of non-stationary patterns from the fitting of the extreme value distribution. As a result, the steps of the analysis are simplified and intermediate diagnostics are possible. In particular, the transformation can be carried out by means of simple statistical techniques such as low-pass filters based on the running mean and the standard deviation, and the fitting procedure is a stationary one with a few degrees of freedom and is easy to implement and control. An open-source MATLAB toolbox has been developed to cover this methodology, which is available at https://github.com/menta78/tsEva/ (Mentaschi et al., 2016).

scholarly journals A Climatology and Extreme Value Analysis of Large Hail in China

2020 ◽  
Vol 148 (4) ◽  
pp. 1431-1447
Author(s):  
Xiang Ni ◽  
Andreas Muehlbauer ◽  
John T. Allen ◽  
Qinghong Zhang ◽  
Jiwen Fan
Keyword(s):  
Spatial Distribution ◽  
Return Period ◽  
Eastern China ◽  
Northern China ◽  
Extreme Value ◽  
Western China ◽  
Extreme Value Analysis ◽  
Annual Maximum ◽  
Return Periods ◽  
Value Analysis

Abstract Hail size records are analyzed at 2254 stations in China and a hail size climatology is developed based on gridded hail observations for the period 1960–2015. It is found that the annual percentiles of hail size records changed sharply and national-wide after 1980, therefore two periods, 1960–79 and 1980–2015, are studied. There are some similarities between the two periods in terms of the characteristics of hail size such as the spatial distribution patterns of mean annual maximum hail size and occurrence week of annual maximum hail size. The 1980–2015 period had higher observation density than the 1960–79 period, but showed smaller mean annual maximum hail size, especially in northern China. In the majority of grid boxes, the annual maximum hail size experienced a decreasing trend during the 1980–2015 period. A Gumbel extreme value model is fitted to each grid box to estimate the return periods of maximum hail size. The scale and location parameter of the fitted Gumbel distributions are higher in eastern China than in western China, thereby reflecting a greater likelihood of large hail in eastern China. In southern China, the maximum hail size exceeds 127 mm for a 10-yr return period, whereas in northern China maximum hail size exceeds this threshold for a 50-yr return period. The Gumbel model is found to potentially underestimate the maximum hail size for certain return periods, but provides a more informed picture of the spatial distribution of extreme hail size and the regional differences.

scholarly journals Extreme wave analysis based on atmospheric pattern classification: an application along the Italian coast

2020 ◽  
Vol 20 (5) ◽  
pp. 1233-1246
Author(s):  
Francesco De Leo ◽  
Sebastián Solari ◽  
Giovanni Besio
Keyword(s):  
Extreme Value Distribution ◽  
Extreme Value ◽  
Value Distribution ◽  
Extreme Value Analysis ◽  
Extreme Wave ◽  
Wind Fields ◽  
Value Analysis ◽  
Atmospheric Circulation Patterns ◽  
Circulation Patterns ◽  
The Mediterranean

Abstract. This paper provides a methodology for classifying samples of significant wave-height peaks in homogeneous subsets in terms of the atmospheric circulation patterns behind the observed extreme wave conditions. Then, a methodology is given for the computation of the overall extreme value distribution by starting from the distributions fitted to each single subset. To this end, the k-means clustering technique is used to classify the shape of the wind fields that occurred simultaneously to and prior to the occurrences of the extreme wave events. This results in a small number of characteristic circulation patterns related to as many subsets of extreme wave values. After fitting an extreme value distribution to each subset, bootstrapping is used to reconstruct the omni-circulation pattern's extreme value distribution. The methodology is applied to several locations along the Italian buoy network, and it is concluded from the obtained results that it yields a two-fold advantage: first, it is capable of identifying clearly differentiated subsets driven by homogeneous circulation patterns; second, it allows one to estimate high-return-period quantiles consistent with those resulting from the usual extreme value analysis. In particular, the circulation patterns highlighted are analyzed in the context of the Mediterranean Sea's atmospheric climatology and are shown to be due to well-known cyclonic systems typically crossing the Mediterranean basin.

Plotting positions for fitting distributions and extreme value analysis

2013 ◽  
Vol 40 (2) ◽  
pp. 130-139 ◽  
Author(s):  
M. Fuglem ◽  
G. Parr ◽  
I.J. Jordaan
Keyword(s):  
Probability Distributions ◽  
Extreme Value ◽  
Extreme Value Analysis ◽  
Random Data ◽  
Value Analysis ◽  
Correct Method ◽  
Simulation Techniques ◽  
Probability Paper ◽  
Distribution Parameters ◽  
The Impact

Random data are often examined on plotting paper to determine appropriate probability distributions and distribution parameters for extreme-value analyses and other applications. Engineers are not always aware of criteria that have been developed and published for selecting the type of probability paper, nor of the impact that the selection can have on results. Some of the advice available in the literature is inaccurate, so it is important that the basis for using the correct method be clearly demonstrated. In the present paper, it is confirmed using straightforward simulation techniques that use of plotting position methods specific to the distribution being considered is appropriate and that use of other plotting position methods can give inaccurate results.

scholarly journals AN ADVANCED STATISTICAL EXTREME VALUE MODEL FOR EVALUATING STORM SURGE HEIGHTS CONSIDERING SYSTEMATIC RECORDS AND SEA LEVEL RISE SCENARIO

2011 ◽  
Vol 1 (32) ◽  
pp. 23 ◽  
Author(s):  
Christoph Mudersbach ◽  
Juergen Jensen
Keyword(s):  
Sea Level Rise ◽  
Sea Level ◽  
Likelihood Estimation ◽  
Extreme Value ◽  
Water Levels ◽  
Extreme Value Analysis ◽  
Value Analysis ◽  
Additional Approach ◽  
Extreme Value Model ◽  
Value Model

In this paper, a non-stationary extreme value analysis approach is introduced in order to determine coastal design water levels for future time horizons. The non-stationary statistical approach is based on the Generalized Extreme Value (GEV) distribution and a L-Moment parameter estimation as well as a Maximum-Likelihood-estimation. An additional approach considers sea level rise scenarios in the non-stationary extreme value analysis. All the methods are applied to the annual maximum water levels from 1849-2007 at the German North Sea gauge at Cuxhaven. The results show, that the non-stationary GEV approach is suitable for determining coastal design water levels.

scholarly journals Statistics of Extreme Synoptic-Scale Wind Speeds in Ensemble Simulations of Current and Future Climate

2004 ◽  
Vol 17 (23) ◽  
pp. 4564-4574 ◽  
Author(s):  
H. W. van den Brink ◽  
G. P. Können ◽  
J. D. Opsteegh
Keyword(s):  
Climate Model ◽  
Extreme Value Distribution ◽  
Extreme Value ◽  
Extreme Value Analysis ◽  
Jet Stream ◽  
Return Periods ◽  
Value Analysis ◽  
Wind Speeds ◽  
Westerly Jet ◽  
Extreme Wind Speed

Abstract Statistical analysis of the wind speeds, generated by a climate model of intermediate complexity, indicates the existence of areas where the extreme value distribution of extratropical winds is double populated, the second population becoming dominant for return periods of order 103 yr. Meteorological analysis of the second population shows that it is caused when extratropical cyclones merge in an extremely strong westerly jet stream such that conditions are generated that are favorable for occurrence of strong diabatic feedbacks. Doubling of the greenhouse gas concentrations changes the areas of second population and increases its frequency. If these model results apply to the real world, then in the exit areas of the jet stream the extreme wind speed with centennial-to-millennial return periods is considerably larger than extreme value analysis of observational records implies.

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