scholarly journals Variability of geostrophic airflow over Poland, 1951-2014

2016 ◽  
Vol 10 (1) ◽  
pp. 5-18 ◽  
Author(s):  
Michał Marosz
Keyword(s):  
Return Period ◽  
Annual Cycle ◽  
Geostrophic Wind ◽  
Return Level ◽  
Statistical Characteristics ◽  
Maximum Speed ◽  
Wind Vector ◽  
Return Periods ◽  
Return Levels ◽  
Vector Module

Abstract The paper presents the analysis of the anemological conditions variability over Poland with the usage of geostrophic wind vector as an objective (and homogenous) information concerning the airflow over the area of research. The geostrophic wind vector components are calculated using SLP and air temperature (at sigma 995 level) at selected gridpoints which were subsequently interpolated to a central point thus describing the average flow over the research area. The data originated from NCEP/NCAR Reanalysis and its temporal range was 1951-2014. The analysis covers statistical characteristics of the overall annual cycle as well as trend analysis of the airflow features over Poland: geostrophic wind vector module (V), and its zonal (u) and meridional (v) components. Aside from general statistical characteristics for averages and extremes (quantiles 10% and 90%) GEV distribution was fitted to maximum annual/monthly geostrophic wind speed values which allowed the estimation of return levels for selected return periods. For the period 1951-2014 average geostrophic wind velocity over Poland equals 7.4 ms−1 and the 99% quantile exceeds 21 ms−1. Maximum speed ever recorded equalled 37.6 ms−1. Geostrophic wind vector module (V) and its components (u, v) exhibit clear annual cycle with the highest V values in winter. Positive (westerly) u values dominate in the colder part of the year. In spring the dominance of eastern advection appears and in summer the prevalence of westerly flow is only minimal. There exists a distinctive variability of decadal directional structure and this is clearly visible in the substantial increase in the share of western sector frequencies in 1981-1990 and following decade. Monthly V averages do not exhibit (except October) statistically significant trends whereas in spring and summer months as well as for annual averages of u component trend is significant. There are virtually no significant changes in the v values. GEV analysis allowed the year to be divided into two parts. Warm one with relatively low return levels – for many months not exceeding 20 ms−1 even for 50y return period. On the other hand winter months return level values exceed 30 ms−1 even for relatively short return periods (20y) with upper estimates for 100y return period closing to 40 ms−1.

scholarly journals Uncertainty analysis of hydrological return period estimation, taking the upper Yangtze River as an example

2017 ◽  
Author(s):  
Hemin Sun ◽  
Tong Jiang ◽  
Cheng Jing ◽  
Buda Su ◽  
Guojie Wang
Keyword(s):  
Return Period ◽  
Yangtze River ◽  
Distribution Functions ◽  
Return Level ◽  
Return Periods ◽  
Return Levels ◽  
Upper Yangtze River ◽  
Period Estimation ◽  
Peak Over Threshold ◽  
The Upper Yangtze River

Abstract. Return period estimation plays an important role in the engineering practices of water resources and disaster management, but uncertainties accompany the calculation process. Based on the daily discharge records at two gauging stations (Cuntan and Pingshan) on the upper Yangtze River, three sampling methods (SMs; (annual maximum, peak over threshold, and decadal peak over threshold), five distribution functions (DFs; gamma, Gumbel, lognormal, Pearson III, and general extreme value), and three parameterization methods (PMs; maximum likelihood, L-Moment, and method of moment) were applied to analyze the uncertainties in return period estimation. The estimated return levels based on the different approaches were found to differ considerably at each station. The range of discharge for a 20-year return period was 63,800.8–74,024.1 m3 s−1 for Cuntan and 23,097.8–25,595.3 m3 s−1 for Pingshan, when using the 45 combinations of SMs, DFs, and PMs. For a 1000-year event, the estimated discharge ranges increased to 74,492.5–125,658.0 and 27,339.2–41,718.1 m3 s−1 for Cuntan and Pingshan, respectively. Application of the analysis of variance method showed that the total sum of the squares of the estimated return levels increased with the widening of the return periods, suggestive of increased uncertainties. However, the contributions of the different sources to the uncertainties were different. For Cuntan, where the discharge changed significantly, the SM appeared to be the largest source of uncertainty. For Pingshan, where the discharge series remained almost stable, the DF contributed most to the uncertainty. Therefore, multiple uncertainty sources in estimating return periods should be considered to meet the demands of different planning purposes. The research results also suggest that uncertainties of return level estimation could be reduced if an optimized DF were used, or if the decadal peak over threshold SM were used, which is capable of representing temporal changes of hydrological series.

scholarly journals Comparison of Hurricane Return Levels Using Historical and Geological Records

2008 ◽  
Vol 47 (2) ◽  
pp. 368-374 ◽  
Author(s):  
James B. Elsner ◽  
Thomas H. Jagger ◽  
Kam-biu Liu
Keyword(s):  
Return Period ◽  
Sediment Cores ◽  
Historical Record ◽  
Return Level ◽  
Expected Return ◽  
Hurricane Ivan ◽  
The Past ◽  
Return Levels ◽  
Maximum Likelihood Procedure ◽  
Geological Information

Abstract Hurricane return levels estimated using historical and geological information are quantitatively compared for Lake Shelby, Alabama. The minimum return level of overwash events recorded in sediment cores is estimated using a modern analog (Hurricane Ivan of 2004) to be 54 m s−1 (105 kt) for a return period of 318 yr based on 11 events over 3500 yr. The expected return level of rare hurricanes in the observed records (1851–2005) at this location and for this return period is estimated using a parametric statistical model and a maximum likelihood procedure to be 73 m s−1 (141 kt), with a lower bound on the 95% confidence interval of 64 m s−1 (124 kt). Results are not significantly different if data are taken from the shorter 1880–2005 period. Thus, the estimated sensitivity of Lake Shelby to overwash events is consistent with the historical record given the model. In fact, assuming the past is similar to the present, the sensitivity of the site to overwash events as estimated from the model is likely more accurately set at 64 m s−1.

scholarly journals Has fire policy decreased the return period of the largest wildfire events in France? A Bayesian assessment based on extreme value theory

2018 ◽  
Vol 18 (10) ◽  
pp. 2641-2651 ◽  
Author(s):  
Guillaume Evin ◽  
Thomas Curt ◽  
Nicolas Eckert
Keyword(s):  
Return Period ◽  
Extreme Value Theory ◽  
Fire Suppression ◽  
Value Theory ◽  
Extreme Value ◽  
Ecological Impacts ◽  
Return Level ◽  
Burned Area ◽  
Return Periods ◽  
Fire Policy

Abstract. Very large wildfires have high human, economic, and ecological impacts so that robust evaluation of their return period is crucial. Preventing such events is a major objective of the new fire policy set up in France in 1994, which is oriented towards fast and massive fire suppression. Whereas this policy is probably efficient for reducing the mean burned area (BA), its effect on the largest fires is still unknown. In this study, we make use of statistical extreme value theory (EVT) to compute return periods of very large BAs in southern France, for two distinct periods (1973 to 1994 and 1995 to 2016) and for three pyroclimatic regions characterized by specific fire activities. Bayesian inference and related predictive simulations are used to fairly evaluate related uncertainties. Results demonstrate that the BA corresponding to a return period of 5 years has actually significantly decreased, but that this is not the case for large return periods (e.g., 50 years). For example, in the most fire-prone region, which includes Corsica and Provence, the median 5-year return level decreased from 5000 to 2400 ha, while the median 50-year return level decreased only from 17 800 to 12 500 ha. This finding is coherent with the recent occurrence of conflagrations of large and intense fires clearly far beyond the suppression capacity of firemen. These fires may belong to a new generation of fires promoted by long-term fuel accumulation, urbanization into the wildland, and ongoing climate change. These findings may help adapt the operational system of fire prevention and suppression to ongoing changes. Also, the proposed methodology may be useful for other case studies worldwide.

scholarly journals Has fire policy decreased the return period of the largest wildfire events in France? A Bayesian assessment based on extreme value theory

2018 ◽  
Author(s):  
Guillaume Evin ◽  
Thomas Curt ◽  
Nicolas Eckert
Keyword(s):  
Return Period ◽  
Extreme Value Theory ◽  
Fire Suppression ◽  
Value Theory ◽  
Extreme Value ◽  
Ecological Impacts ◽  
Return Level ◽  
Burned Area ◽  
Return Periods ◽  
Fire Policy

Abstract. Very large wildfires have high human, economic and ecological impacts so that robust evaluation of their return period is crucial. Preventing such events is a major objective of the new fire policy set up in France in 1994, which is oriented towards fast and massive fire suppression. Whereas this policy is probably efficient for reducing the mean burned area (BA), its effect on the largest fires is still unknown. In this study, we make use of statistical Extreme Value Theory (EVT) to compute return periods of very large BA in southern France, for two distinct periods (1973 to 1994, and 1995 to 2016) and for three pyroclimatic regions characterized by specific fire activities. Bayesian inference and related predictive simulations are used to fairly evaluate related uncertainties. Results demonstrate that the BA corresponding to a return period of 5 years has actually significantly decreased, but that this is not the case for large return periods (e.g. 50 years). For example, in the most fire-prone region, which includes Corsica and Provence, the median 5-year return level decreased from 5,000 ha. to 2,400 ha., while the median 50-year return level decreased only from 17,800 ha. to 12,500 ha. This finding is coherent with the recent occurrence of conflagrations of large and intense fires clearly far beyond the suppression capacity of firemen. These fires may belong to a new generation of fires promoted by long-term fuel accumulation, urbanization into the wildland, and ongoing climate change. These findings may help adapting the operational system of fire prevention and suppression to ongoing changes. Also, the proposed methodology may be useful for other case studies worldwide.

Capstone: Application of NLTS to Real-World Data

2018 ◽  
Author(s):  
Ray Huffaker ◽  
Marco Bittelli ◽  
Rodolfo Rosa
Keyword(s):  
Signal Processing ◽  
Threshold Value ◽  
Return Level ◽  
Extreme Value Statistics ◽  
Time Interval ◽  
Return Periods ◽  
Noise Levels ◽  
Real World Data ◽  
Generalized Pareto ◽  
Return Levels

This chapter investigates the use of Extreme Value Statistics (EVS) to probabilistically model extreme events separated as unstructured noise in signal processing. We apply a version of EVS that computes the likelihood of extreme discrepancies exceeding a selected threshold value within a given time interval. In theory, exceedances follow a Generalized Pareto (GP) distribution, and we run diagnostics to determine how well the data actually fit this distribution. If we find a reasonable fit, we can invert the GP distribution to solve for quantiles providing a useful noise diagnostic: return level plots. Return level plots show the return periods expected before particular extreme noise levels return levels are realized.

scholarly journals Application of Extreme Value Theory in Predicting Climate Change Induced Extreme Rainfall in Kenya

2019 ◽  
Vol 8 (4) ◽  
pp. 85
Author(s):  
Faithful C. Onwuegbuche ◽  
Alpha B. Kenyatta ◽  
Steeven B. Affognon ◽  
Exavery P. Enock ◽  
Mary O. Akinade
Keyword(s):  
Climate Change ◽  
Extreme Value Theory ◽  
Extreme Rainfall ◽  
Value Theory ◽  
Extreme Value ◽  
Generalized Extreme Value Distribution ◽  
Return Level ◽  
Return Periods ◽  
Optimal Model ◽  
Return Levels

Climate change has brought about unprecedented new weather patterns, one of which is changes in extreme rainfall. In Kenya, heavy rains and severe flash floods have left people dead and displaced hundreds from their settlements. In order to build a resilient society and achieve sustainable development, it is paramount that adequate inference about extreme rainfall be made. To this end, this research modelled and predicted extreme rainfall events in Kenya using Extreme Value Theory for rainfall data from 1901-2016. Maximum Likelihood Estimation was used to estimate the model parameters and block maxima approach was used to fit the Generalized Extreme Value Distribution (GEVD) while the Peak Over Threshold method was used to fit the Generalized Pareto Distribution (GPD). The Gumbel distribution was found to be the optimal model from the GEVD while the Exponential distribution gave the optimal model over the threshold value. Furthermore, prediction for the return periods of 10, 20, 50 and 100 years were made using the return level estimates and their corresponding confidence intervals were presented. It was found that increase in return periods leads to a corresponding increase in return levels. However, the GPD gave higher return levels for 10 and 20 years compared to GEVD. While, for higher return periods 50 and 100 years, the GEVD gave higher return levels compared to the GPD. Model diagnostics using probability, density, quantile and return level plots indicated that the models provided were a good fit for the data.

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 Joint return periods in hydrology: a critical and practical review focusing on synthetic design hydrograph estimation

2012 ◽  
Vol 9 (5) ◽  
pp. 6781-6828 ◽  
Author(s):  
S. Vandenberghe ◽  
M. J. van den Berg ◽  
B. Gräler ◽  
A. Petroselli ◽  
S. Grimaldi ◽  
...  
Keyword(s):  
Frequency Analysis ◽  
Return Period ◽  
Three Dimensional ◽  
Distribution Functions ◽  
Return Periods ◽  
Design Variables ◽  
Joint Return ◽  
Design Values ◽  
Multivariate Frequency Analysis ◽  
Joint Return Period

Abstract. Most of the hydrological and hydraulic studies refer to the notion of a return period to quantify design variables. When dealing with multiple design variables, the well-known univariate statistical analysis is no longer satisfactory and several issues challenge the practitioner. How should one incorporate the dependence between variables? How should the joint return period be defined and applied? In this study, an overview of the state-of-the-art for defining joint return periods is given. The construction of multivariate distribution functions is done through the use of copulas, given their practicality in multivariate frequency analysis and their ability to model numerous types of dependence structures in a flexible way. A case study focusing on the selection of design hydrograph characteristics is presented and the design values of a three-dimensional phenomenon composed of peak discharge, volume and duration are derived. Joint return period methods based on regression analysis, bivariate conditional distributions, bivariate joint distributions, and Kendal distribution functions are investigated and compared highlighting theoretical and practical issues of multivariate frequency analysis. Also an ensemble-based method is introduced. For a given design return period, the method chosen clearly affects the calculated design event. Eventually, light is shed on the practical implications of a chosen method.

scholarly journals On the magnitude and frequency of Karakoram Glacier surges

2013 ◽  
Vol 7 (5) ◽  
pp. 5177-5187
Author(s):  
D. J. Quincey ◽  
A. Luckman
Keyword(s):  
Mass Balance ◽  
Return Period ◽  
Accurate Mass ◽  
Return Periods ◽  
Present Evidence ◽  
Surface Displacements ◽  
Hindu Kush ◽  
Glacier Surges

Abstract. The return periods of Karakoram glacier surges are almost entirely unknown. Here, we present evidence of an historic surge of the Khurdopin Glacier that began in the mid-1970s and peaked in 1979. Measured surface displacements reached > 5 km yr–1, two orders of magnitude faster than during quiescence and twice as large as any previously recorded velocity in the region. The Khurdopin Glacier next surged in the late-1990s, equating to a return period of 20 yr. Surge activity in the region needs to be better understood if accurate mass balance assessments of Hindu-Kush–Karakoram–Himalaya glaciers are to be made.

scholarly journals Using high-resolution regional climate models to estimate return levels of daily extreme precipitation over Bavaria

2021 ◽  
Vol 21 (11) ◽  
pp. 3573-3598
Author(s):  
Benjamin Poschlod
Keyword(s):  
High Resolution ◽  
Climate Models ◽  
Regional Climate ◽  
Daily Rainfall ◽  
Regional Climate Models ◽  
Reanalysis Data ◽  
Extreme Value ◽  
Return Level ◽  
Gev Distribution ◽  
Return Levels

Abstract. Extreme daily rainfall is an important trigger for floods in Bavaria. The dimensioning of water management structures as well as building codes is based on observational rainfall return levels. In this study, three high-resolution regional climate models (RCMs) are employed to produce 10- and 100-year daily rainfall return levels and their performance is evaluated by comparison to observational return levels. The study area is governed by different types of precipitation (stratiform, orographic, convectional) and a complex terrain, with convective precipitation also contributing to daily rainfall levels. The Canadian Regional Climate Model version 5 (CRCM5) at a 12 km spatial resolution and the Weather and Forecasting Research (WRF) model at a 5 km resolution both driven by ERA-Interim reanalysis data use parametrization schemes to simulate convection. WRF at a 1.5 km resolution driven by ERA5 reanalysis data explicitly resolves convectional processes. Applying the generalized extreme value (GEV) distribution, the CRCM5 setup can reproduce the observational 10-year return levels with an areal average bias of +6.6 % and a spatial Spearman rank correlation of ρ=0.72. The higher-resolution 5 km WRF setup is found to improve the performance in terms of bias (+4.7 %) and spatial correlation (ρ=0.82). However, the finer topographic details of the WRF-ERA5 return levels cannot be evaluated with the observation data because their spatial resolution is too low. Hence, this comparison shows no further improvement in the spatial correlation (ρ=0.82) but a small improvement in the bias (2.7 %) compared to the 5 km resolution setup. Uncertainties due to extreme value theory are explored by employing three further approaches. Applied to the WRF-ERA5 data, the GEV distributions with a fixed shape parameter (bias is +2.5 %; ρ=0.79) and the generalized Pareto (GP) distributions (bias is +2.9 %; ρ=0.81) show almost equivalent results for the 10-year return period, whereas the metastatistical extreme value (MEV) distribution leads to a slight underestimation (bias is −7.8 %; ρ=0.84). For the 100-year return level, however, the MEV distribution (bias is +2.7 %; ρ=0.73) outperforms the GEV distribution (bias is +13.3 %; ρ=0.66), the GEV distribution with fixed shape parameter (bias is +12.9 %; ρ=0.70), and the GP distribution (bias is +11.9 %; ρ=0.63). Hence, for applications where the return period is extrapolated, the MEV framework is recommended. From these results, it follows that high-resolution regional climate models are suitable for generating spatially homogeneous rainfall return level products. In regions with a sparse rain gauge density or low spatial representativeness of the stations due to complex topography, RCMs can support the observational data. Further, RCMs driven by global climate models with emission scenarios can project climate-change-induced alterations in rainfall return levels at regional to local scales. This can allow adjustment of structural design and, therefore, adaption to future precipitation conditions.

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