scholarly journals UNCERTAINTY AND BIAS IN EXTREME VALUE ANALYSIS OF RECORDS OF STORM SURGES FOR COASTAL PROTECTION PLANS

2018 ◽  
pp. 47
Author(s):  
Toshikazu Kitano
Keyword(s):  
Confidence Interval ◽  
Storm Surge ◽  
Return Period ◽  
Prediction Interval ◽  
Storm Surges ◽  
Extreme Value ◽  
Return Level ◽  
Extreme Value Analysis ◽  
Coastal Protection ◽  
Value Analysis

There are several arguments to be discussed for the probability of hazards due to the storm surge. One is a common point of describing the uncertainty of extreme events, and another point is for the special case due to the storm surge. 1) Return level is one of the important results by extreme value analysis, and the confidence interval also serves us an useful and desirable information for uncertainty. Is it true? The answer is negative. Return period is right, and important. But the confidence interval, in this case, is shown for return level which is the constant value that is significant after the repeating encounters of the exceedance levels over very long period. But it is of our interest to know which value is the successively occurring level in the future return period, which is a stochastic variable. It is not a constant value but unknown even for the God. The prediction interval should be employed for the next realized value of our interest.

Spatial stochastic simulation to aid local extreme value analysis of cyclone-induced wave heights when numerical hydrodynamic simulations are scarce

2021 ◽  
Author(s):  
Jeremy Rohmer ◽  
Rodrigo Pedreros ◽  
Yann Krien
Keyword(s):  
Confidence Interval ◽  
Stochastic Simulation ◽  
Extreme Value ◽  
Return Level ◽  
Extreme Value Analysis ◽  
Percentage Error ◽  
Value Analysis ◽  
Return Levels ◽  
Wave Heights ◽  
Local Extreme

<p>To estimate return levels of wave heights (Hs) induced by tropical cyclones at the coast, a commonly-used approach is to (1) randomly generate a large number of synthetic cyclone events (typically >1,000); (2) numerically simulate the corresponding Hs over the whole domain of interest; (3) extract the Hs values at the desired location at the coast and (4) perform the local extreme value analysis (EVA) to derive the corresponding return level. Step 2 is however very constraining because it often involves a numerical hydrodynamic simulator that can be prohibitive to run: this might limit the number of results to perform the local EVA (typically to several hundreds). In this communication, we propose a spatial stochastic simulation procedure to increase the database size of numerical results with synthetic maps of Hs that are stochastically generated. To do so, we propose to rely on a data-driven dimensionality-reduction method, either unsupervised (Principal Component Analysis) or supervised (Partial Least Squares Regression), that is trained with a limited number of pre-existing numerically simulated Hs maps. The procedure is applied to the Guadeloupe island and results are compared to the commonly-used approach applied to a large database of Hs values computed for nearly 2,000 synthetic cyclones (representative of 3,200 years – Krien et al., NHESS, 2015). When using only a hundred of cyclones, we show that the estimates of the 100-year return levels can be achieved with a mean absolute percentage error (derived from a bootstrap-based procedure) ranging between 5 and 15% around the coasts while keeping the width of the 95% confidence interval of the same order of magnitude than the one using the full database. Without synthetic Hs maps augmentation, the error and confidence interval width are both increased by nearly 100%. A careful attention is paid to the tuning of the approach by testing the sensitivity to the spatial domain size, the information loss due to data compression, and the number of cyclones. This study has been carried within the Carib-Coast INTERREG project (https://www.interreg-caraibes.fr/carib-coast).</p>

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.

Seasonal shift in storm surges at Brest revealed by extreme value analysis

2021 ◽  
Author(s):  
Markus Reinert ◽  
Lucia Pineau‐Guillou ◽  
Nicolas Raillard ◽  
Bertrand Chapron
Keyword(s):  
Storm Surges ◽  
Extreme Value ◽  
Extreme Value Analysis ◽  
Value Analysis ◽  
Seasonal Shift

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 Regional frequency analysis of extreme rainfall in Belgium based on radar estimates

2017 ◽  
Vol 21 (10) ◽  
pp. 5385-5399 ◽  
Author(s):  
Edouard Goudenhoofdt ◽  
Laurent Delobbe ◽  
Patrick Willems
Keyword(s):  
Confidence Interval ◽  
Frequency Analysis ◽  
Extreme Rainfall ◽  
Extreme Value ◽  
Regional Frequency Analysis ◽  
Extreme Value Analysis ◽  
Value Analysis ◽  
Temporal Sampling ◽  
Rainfall Extremes ◽  
Regional Frequency

Abstract. In Belgium, only rain gauge time series have been used so far to study extreme rainfall at a given location. In this paper, the potential of a 12-year quantitative precipitation estimation (QPE) from a single weather radar is evaluated. For the period 2005–2016, 1 and 24 h rainfall extremes from automatic rain gauges and collocated radar estimates are compared. The peak intensities are fitted to the exponential distribution using regression in Q-Q plots with a threshold rank which minimises the mean squared error. A basic radar product used as reference exhibits unrealistic high extremes and is not suitable for extreme value analysis. For 24 h rainfall extremes, which occur partly in winter, the radar-based QPE needs a bias correction. A few missing events are caused by the wind drift associated with convective cells and strong radar signal attenuation. Differences between radar and gauge rainfall values are caused by spatial and temporal sampling, gauge underestimations and radar errors. Nonetheless the fit to the QPE data is within the confidence interval of the gauge fit, which remains large due to the short study period. A regional frequency analysis for 1 h duration is performed at the locations of four gauges with 1965–2008 records using the spatially independent QPE data in a circle of 20 km. The confidence interval of the radar fit, which is small due to the sample size, contains the gauge fit for the two closest stations from the radar. In Brussels, the radar extremes are significantly higher than the gauge rainfall extremes, but similar to those observed by an automatic gauge during the same period. The extreme statistics exhibit slight variations related to topography. The radar-based extreme value analysis can be extended to other durations.

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 Bayesian inference for extreme value flood frequency analysis in Bangladesh using Hamiltonian Monte Carlo techniques

2019 ◽  
Vol 276 ◽  
pp. 04006
Author(s):  
Md Ashraful Alam ◽  
Craig Farnham ◽  
Kazuo Emura
Keyword(s):  
Monte Carlo ◽  
Flood Risk ◽  
Extreme Value ◽  
Return Level ◽  
Flood Frequency Analysis ◽  
Extreme Value Analysis ◽  
Risk Level ◽  
Mcmc Methods ◽  
Value Analysis ◽  
Hamiltonian Monte Carlo

In Bangladesh, major floods are frequent due to its unique geographic location. About one-fourth to one-third of the country is inundated by overflowing rivers during the monsoon season almost every year. Calculating the risk level of river discharge is important for making plans to protect the ecosystem and increasing crop and fish production. In recent years, several Bayesian Markov chain Monte Carlo (MCMC) methods have been proposed in extreme value analysis (EVA) for assessing the flood risk in a certain location. The Hamiltonian Monte Carlo (HMC) method was employed to obtain the approximations to the posterior marginal distribution of the Generalized Extreme Value (GEV) model by using annual maximum discharges in two major river basins in Bangladesh. The discharge records of the two largest branches of the Ganges-Brahmaputra-Meghna river system in Bangladesh for the past 42 years were analysed. To estimate flood risk, a return level with 95% confidence intervals (CI) has also been calculated. Results show that, the shape parameter of each station was greater than zero, which shows that heavy-tailed Frechet cases. One station, Bahadurabad, at Brahmaputra river basin estimated 141,387 m3s-1 with a 95% CI range of [112,636, 170,138] for 100-year return level and the 1000-year return level was 195,018 m3s-1 with a 95% CI of [122493, 267544]. The other station, Hardinge Bridge, at Ganges basin estimated 124,134 m3 s-1 with a 95% CI of [108,726, 139,543] for 100-year return level and the 1000-year return level was 170,537 m3s-1 with a 95% CI of [133,784, 207,289]. As Bangladesh is a flood prone country, the approach of Bayesian with HMC in EVA can help policy-makers to plan initiatives that could result in preventing damage to both lives and assets.

scholarly journals Characteristics of Extreme Value Statistics of Annual Maximum Monthly Precipitation in East Asia Calculated Using an Earth System Model of Intermediate Complexity

Atmosphere ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 1273
Author(s):  
Tosiyuki Nakaegawa ◽  
Takuro Kobashi ◽  
Hirotaka Kamahori
Keyword(s):  
Time Series ◽  
Extreme Precipitation ◽  
Extreme Value ◽  
Nonstationary Time Series ◽  
Return Level ◽  
Extreme Value Statistics ◽  
Extreme Value Analysis ◽  
Monthly Precipitation ◽  
Annual Maximum ◽  
Value Analysis

Extreme precipitation is no longer stationary under a changing climate due to the increase in greenhouse gas emissions. Nonstationarity must be considered when realistically estimating the amount of extreme precipitation for future prevention and mitigation. Extreme precipitation with a certain return level is usually estimated using extreme value analysis under a stationary climate assumption without evidence. In this study, the characteristics of extreme value statistics of annual maximum monthly precipitation in East Asia were evaluated using a nonstationary historical climate simulation with an Earth system model of intermediate complexity, capable of long-term integration over 12,000 years (i.e., the Holocene). The climatological means of the annual maximum monthly precipitation for each 100-year interval had nonstationary time series, and the ratios of the largest annual maximum monthly precipitation to the climatological mean had nonstationary time series with large spike variations. The extreme value analysis revealed that the annual maximum monthly precipitation with a return level of 100 years estimated for each 100-year interval also presented a nonstationary time series which was normally distributed and not autocorrelated, even with the preceding and following 100-year interval (lag 1). Wavelet analysis of this time series showed that significant periodicity was only detected in confined areas of the time–frequency space.

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>

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