Problems in the extreme value analysis

Makkonen, Lasse

doi:10.1016/j.strusafe.2006.12.001

Cited by 73 publications

(35 citation statements)

References 56 publications

(132 reference statements)

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“…, N thr ) by simply ordering the data from the smallest (x 1 ) to the largest (x N thr ), and calculating an empirical value of F(x k ) from the ranked position of x k (Palutikof et al 1999). These estimates are known as the plotting positions, which can be calculated (Gumbel 1958;Makkonen 2007) via…”

Section: Return Valuesmentioning

confidence: 99%

Determination of Precipitation Return Values in Complex Terrain and Their Evaluation

et al. 2010

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To determine return values at various return periods for extreme daily precipitation events over complex orography, an appropriate threshold value and distribution function are required. The return values are calculated using the peak-over-threshold approach in which only a reduced sample of precipitation events exceeding a predefined threshold is analyzed. To fit the distribution function to the sample, the L-moment method is used. It is found that the deviation between the fitted return values and the plotting positions of the ranked precipitation events is smaller for the kappa distribution than for the generalized Pareto distribution.As a second focus, the ability of regional climate models to realistically simulate extreme daily precipitation events is assessed. For this purpose the return values are derived using precipitation events exceeding the 90th percentile of the precipitation time series and a fit of a kappa distribution. The results of climate simulations with two different regional climate models are analyzed for the 30-yr period 1971-2000: the so-called consortium runs performed with the climate version of the Lokal Modell (referred to as the CLM-CR) at 18-km resolution and the Regional Model (REMO)-Umweltbundesamt (UBA) simulations at 10-km resolution. It was found that generally the return values are overestimated by both models. Averaged across the region the overestimation is higher for REMO-UBA compared to CLM-CR.

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Section: Return Valuesmentioning

confidence: 99%

Determination of Precipitation Return Values in Complex Terrain and Their Evaluation

et al. 2010

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“…This was because the hydrological models did not adequately highlight the most extreme events that were experienced. This might be a consequence of the use of too narrow selection of global or regional climate models, as Veijalainen et al [27] present or the use of insufficient methods to estimate the probability of extreme weather events [39]. Nevertheless, it seems that the main risks can be best recognized by analysing the extreme weather events of past decades and years, together with studying the possible climate change impacts on seasonal variations.…”

Section: Discussionmentioning

confidence: 99%

“…The second assessment was performed to find out if any of the climate induced risks analyzed in 2008 had already emerged or showed any signs of change. The motivation to update the risk assessment arose from a research point of view when it was noticed that, since the first risk assessment (only 7 years ago), the yearly mean temperature has risen compared to the last three decades [39]. Another driver was that the persons who participated in the first risk assessment were about to retire, and there was a desire to utilize their vast and long-term experience of river behavior and risk management.…”

Section: Description Of the Risk Analysis Methods Utilizedmentioning

confidence: 99%

Combining Climate Scenarios and Risk Management Approach—A Finnish Case Study

Molarius

Keränen

2015

Climate

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Climate change impacts on nature and the environment have been widely discussed and studied. Traditionally, a company's continuity management is based on risk analysis. There are also attempts to implement scenario-based methods in the risk management procedures of companies. For industrial decision makers, it is vital to acknowledge the impacts of climate change with regards to their adaptation strategies. However, a scenario-based approach is not always the most effective way to analyze these risks. This paper investigates the integration of scenario and risk-based methods for a company's adaptation planning. It considers the uncertainties of the climate change scenarios and the recognized risks as well as suitable adaptation strategies. The paper presents the results of climate risk analysis prepared for two Finnish hydropower plants. The introduced method was first piloted in 2008 and then again in 2015. The update of the analysis pointed out that at the company level, the climate risks and other risks originating from governmental or political decisions form an intertwined wholeness where the origin of the risk is difficult to outline. It seems that, from the business point of view, the main adaptation strategies suggested by the integrated risk and scenarios approach are those that support buying "safety margins" in new investments and reducing decision time horizons. Both of these adaptation strategies provide an advantage in the circumstances where also political decisions and societal changes have a great effect on decision making.

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“…As confirmed by the renewed interest appeared in the recent literature (Rigdon and Basu 1989;Makkonen 2006Makkonen , 2008ade Haan 2007;Cook 2011Cook , 2012Kim et al 2012;Erto and Lepore 2013;Fuglem et al 2013;LozanoAguilera et al 2014) practitioners are used to exploiting modern software that adopts graphical estimation methods via probability papers, even if there is a variety of effective analytical methods available, such as Maximum Likelihood and Bayesian techniques. In fact, especially in critical applications, the graphical estimation gives the unique opportunity to share statistical information with non-statisticians (e.g., by allowing a visual check of the fit of the chosen model and by giving helpful understanding of the consequent conclusions).…”

Section: Introductionmentioning

confidence: 99%

Best unbiased graphical estimators of location-scale distribution parameters: application to the Pozzuoli’s bradyseism earthquake data

Erto

Lepore

2016

Environ Ecol Stat

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Graphical techniques are recommended for critical applications because they allow for visual analysis and helpful understanding of the results, also for nonstatisticians. However, "graphical" estimation methods are often criticized because they are less efficient with respect to "analytical" methods. This paper proposes a new general graphical method that leads to the best linear unbiased estimators of location-scale distribution parameters. Therefore, the reputation of graphical methods is raised and their strategic use is encouraged. An applicative example analyzes the earthquake magnitudes registered during the serious 1983-1984 bradyseismic crisis in Campi Flegrei, Italy. This graphical analysis will certainly work as a strategic reference picture to which the data, arising from the further bradyseismic crises expected in the next future, can be compared.

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Problems in the extreme value analysis

Cited by 73 publications

References 56 publications

Determination of Precipitation Return Values in Complex Terrain and Their Evaluation

Determination of Precipitation Return Values in Complex Terrain and Their Evaluation

Combining Climate Scenarios and Risk Management Approach—A Finnish Case Study

Best unbiased graphical estimators of location-scale distribution parameters: application to the Pozzuoli’s bradyseism earthquake data

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