Vector Generalized Additive Models for Extreme Rainfall Data Analysis: A case study in South Korea

Information on extreme rainfall events for a given duration and a given frequency of occurrence is commonly required for the design of various urban hydraulic structures. More specifically, this information is extracted from the rainfall “intensity–duration–frequency” (IDF) relations. In Canada, these IDF relations are provided by Environment Canada for approximately 650 stations across Canada with at least 10-year record by fitting the Gumbel distribution to the annual maximum (AM) rainfall data for nine different rainfall durations (from 5 min to one day) using the method of moments. However, it has been widely known that the Generalized Extreme Value (GEV) distribution using the L-moment estimation method can provide more accurate extreme rainfall estimates. In addition, rather than fitting the GEV distribution to AM rainfall series for each duration independently as commonly used in the traditional method, the scale-invariance GEV model has been shown to provide more robust estimates of extreme rainfalls since it can take into account the relationship between the statistical properties of the AM rainfall processes over different durations. Therefore, this study presents the development of more accurate and more robust site-specific IDF relations as well as regional extreme rainfall maps for different locations across Canada based on all available historical AM rainfall data and using the improved scale-invariance GEV model. It is expected that these newly developed IDF relations and regional extreme rainfall maps could produce more cost-effective design of urban infrastructure in Canada.

Extreme value modeling for extreme rainfall is one of the most important processes in the field of hydrology. For the improvement of extreme value modeling and its physical meaning, large-scale climate modes have been widely used as covariates of distribution parameters, as they can physically account for climate variability. This study proposes a novel procedure for extreme value modeling of rainfall based on the significant relationship between the long-term trend of the annual maximum (AM) daily rainfall and large-scale climate indices. This procedure is characterized by two main steps: (a) identifying significant seasonal climate indices (SCIs), which impact the long-term trend of AM daily rainfall using statistical approaches, such as ensemble empirical mode decomposition, and (b) selecting an appropriate generalized extreme value (GEV) distribution among the stationary GEV and nonstationary GEV (NS-GEV) using time and SCIs as covariates by comparing their model fit and uncertainty. Our findings showed significant relationships between the long-term trend of AM daily rainfall over South Korea and SCIs (i.e., the Atlantic Meridional Mode, Atlantic Multidecadal Oscillation in the fall season, and North Atlantic Oscillation in the summer season). In addition, we proposed a model selection procedure considering both the Akaike information criterion and residual bootstrap method to select an appropriate GEV distribution among a total of 59 GEV candidates. As a result, the NS-GEV with SCI covariates generally showed the best performance over South Korea. We expect that this study can contribute to estimating more reliable extreme rainfall quantiles using climate covariates.

A cross-comparison of different extreme value modeling techniques for traffic conflict-based crash risk estimation

A hybrid modelling framework of machine learning and extreme value theory for crash risk estimation using traffic conflicts

Abstract. Extreme rainfall is classically estimated using raingauge data at raingauge locations. An important related issue is to assess return levels of extreme rainfall at ungauged sites. Classical methods consist in interpolating extreme-value models. In this paper, such methods are referred to as regionalization schemes. Our goal is to evaluate three classical regionalization schemes. Each scheme consists of an extreme-value model (block maxima, peaks over threshold) taken from extreme-value theory plus a method to interpolate the parameters of the statistical model throughout the Cévennes-Vivarais region. From the interpolated parameters, the 100-yr quantile level can be estimated over this whole region. A reference regionalization scheme is made of the couple block maxima/kriging, where kriging is an optimal interpolation method. The two other schemes differ from the reference by replacing either the extreme-value model block maxima by peaks over threshold or kriging by a neural network interpolation procedure. Hyper-parameters are selected by cross-validation and the three regionalization schemes are compared by double cross-validation. Our evaluation criteria are based on the ability to interpolate the 100-yr return level both in terms of precision and spatial distribution. It turns out that the best results are obtained by the regionalization scheme combining the peaks-over-threshold method with kriging.

Some events are difficult to avoid and gives considerable influence to humans and the environment is extreme weather and climate change. Many of the problems that require knowledge about the behavior of extreme values and one of the methods used are the Extreme Value Theory (EVT). EVT used to draw up reliable systems in a variety of conditions, so as to minimize the risk of a major disaster. There are two methods for identifying extreme value, Block Maxima with Generalized Extreme Value (GEV) distribution approach and Peaks over Threshold (POT) with Generalized Pareto Distribution (GPD) approach. This research in Indramayu with January 1961-December 2003 period, the method used is Block Maxima with GEV distribution approach. The result showed that there is no climate change in Indramayu with January 1961-December 2003 period.

Contrary to common belief, Fisher‐Tippett's extreme value (EV) theory does not typically apply to annual rainfall maxima. Similarly, Pickands' extreme excess (EE) theory does not typically apply to rainfall excesses above thresholds on the order of the annual maximum. This is true not just for long‐averaging durationsdbut also for shortdand in the high‐resolution limit asd→ 0. We reach these conclusions by applying large‐deviation theory to multiplicative rainfall models with scale‐invariant structure. We derive several asymptotic results. One is that, asd→ 0, the annual maximum rainfall intensity ind,Iyr,d, has generalized extreme value (GEV) distribution with a shape parameterkthat is significantly higher than that predicted by EV theory and is always in the EV2 range. The value ofkdepends not on the upper tail of the marginal distribution but on regions closer to the body. Under the same conditions, the excesses above levels close to the annual maximum have generalized Pareto distribution with parameterkthat is always higher than that predicted by Pickands' EE theory. For finited, the distribution ofIyr,dis not GEV, but in accordance with empirical evidence is well approximated by a GEV distribution with shape parameterkthat increases asddecreases. We propose a way to estimatekunder preasymptotic conditions from the scaling properties of rainfall and suggest a near‐universalk(d) relationship. The new estimator promises to be more accurate and robust than conventional estimators. These developments represent a significant conceptual change in the way rainfall extremes are viewed and evaluated.

Abstract. In France, nuclear facilities were designed around very low probabilities of failure. Nevertheless, some extreme climatic events have given rise to exceptional observed surges (outliers) much larger than other observations, and have clearly illustrated the potential to underestimate the extreme water levels calculated with the current statistical methods. The objective of the present work is to conduct a comparative study of three approaches to extreme value analysis, including the annual maxima (AM), the peaks-over-threshold (POT) and the r-largest order statistics (r-LOS). These methods are illustrated in a real analysis case study. All data sets were screened for outliers. Non-parametric tests for randomness, homogeneity and stationarity of time series were used. The shape and scale parameter stability plots, the mean excess residual life plot and the stability of the standard errors of return levels were used to select optimal thresholds and r values for the POT and r-LOS method, respectively. The comparison of methods was based on (i) the uncertainty degrees, (ii) the adequacy criteria and tests, and (iii) the visual inspection. It was found that the r-LOS and POT methods have reduced the uncertainty on the distribution parameters and return level estimates and have systematically shown values of the 100 and 500-year return levels smaller than those estimated with the AM method. Results have also shown that none of the compared methods has allowed a good fit at the right tail of the distribution in the presence of outliers. As a perspective, the use of historical information was proposed in order to increase the representativeness of outliers in data sets. Findings are of practical relevance, not only to nuclear energy operators in France, for applications in storm surge hazard analysis and flood management, but also for the optimal planning and design of facilities to withstand extreme environmental conditions, with an appropriate level of risk.

Analysis of Average Annual Rainfall and Average Maximum Annual Temperature for a Period of 30 years to Establish Trends in Kieni, Central Kenya

Bayesian approach in estimating flood waste generation: A case study in South Korea

The selection of explanatory variables is important in modeling prediction of changes in species distribution in response to climate change. In this study, we evaluated the importance of variable selection in species distribution models. We compared two different types of models for predicting the distribution of ant species: temperature-only and both temperature and precipitation. Ants were collected at 343 forest sites across South Korea from 2006 through 2009. We used a generalized additive model (GAM) to predict the future distribution of 16 species that showed significant responses to changes in climatic factors (temperature and/or precipitation). Four types of GAMs were constructed: temperature, temperature with interaction of precipitation, temperature and precipitation without interaction, and temperature and precipitation with interaction. Most species displayed similar results between the temperatureonly and the temperature and precipitation models. The results for predicted changes in species richness were different from the temperature-only model. This indicates higher uncertainty in the prediction of species richness, which is obtained by combining the prediction results of distribution change for each species, than in the prediction of distribution change. The turnover rate of the ant assemblages was predicted to increase with decreases in temperature and increases in elevation, which was consistent with other studies. Finally, our results showed that the prediction of the distribution or diversity of organisms responding to climate change is uncertain because of the high variability of the model outputs induced by the variables used in the models.

Data stratification toward advanced flood waste estimation: A case study in South Korea

In this study, the energy consumption and generation characteristics, the operation status of a photovoltaic (PV) system, and the energy balance of a net-zero energy building (nZEB) in South Korea were analyzed based on the data collected over a 10-year period (2012–2021). The average annual power consumption of the nZEB was 101.3 MWh, 6.2% higher than the estimated power consumption. The PV system of the nZEB had an annual power generation capacity of 105.8 MWh, indicating an increase of 10.6% compared to the estimated value. The failure of PV systems such as module cracks, inverter failures, and performance degradation led to a decrease of 21.5% in the power generation. Energy balance analysis was conducted by comparing the energy consumption and generation data based on yearly, monthly, daily, and hourly time intervals. In addition, load coverage factor (LCF) and supply coverage factor (SCF) were used to evaluate the load matching rate. The nZEB achieved a net-zero energy status for 5 out of the 7 years of normal operation (2012–2018) based on average annual data. However, the energy balance analysis using hourly measured data showed that there was both a surplus and a shortage of power every year, and that the average annual power surplus and shortage were 56.4 MWh and 54.3 MWh, respectively. In addition, the load matching analysis showed that the annual LCF and SCF were approximately 0.36 and 0.32, respectively. Thus, the advanced nZEB design, hourly data-based energy analysis, fault diagnosis and maintenance, and the strategies enhancing the self-consumption rate should be considered to expand nZEB dissemination.

<p>Since the impacts of climate change on the environment have been constantly rising over the last decades, scientists have paid much attention to understanding the effects of this phenomenon. Climate change leads to different kinds of extremes, such as heavy rainfall events, characterized by short duration and high intensity, and drought, which can cause the problem of water scarcity over a certain area. These types of extreme events cause several damages for the affected areas since they can result in loss of human lives and economic damages. In particular, heavy rainfall events, which are often associated with convective precipitation because of their characteristics, may result in flash floods, especially when they hit small catchments with low times of concentration, thus causing economic damages and, more relevantly, human lives losses.</p><p>The increasing occurrence of heavy rainfall events in many areas of Europe, also in Italy, over the last few years, has contributed to raising the importance of understanding which factors could be recognized as drivers of these events. In this perspective, it is possible to identify in atmospheric circulation one of the causes of severe rainfall events occurrence since some air fluxes, generated from certain schemes of atmospheric circulation, could lead to the accumulation of moisture within a certain volume of the atmosphere, hence to the occurrence of rainfall.</p><p>Since even the Sicily (Italy) has been experimenting heavy rainfall events and consequent flash floods and urban floods in the last years, this work aims to find out a relationship between some weather circulation patterns, developed by the UK Met Office, and the rainfall Annual MAXima (AMAX) for the Sicily, recorded by the rain gauge network of Autorità di Bacino - Regione Siciliana. The possible connection between AMAX and WPs has been investigated in order to define some specific schemes of atmospheric circulation that are responsible for leading to the occurrence of AMAX in Sicily. In order to do this, a database containing the AMAX of all the available gauges for the Sicily has been used. A distinction between AMAX occurred in summer and winter season and their related WPs has been performed as well, with the goal to understand the possible influence of WPs on the summer and winter AMAX. Furthermore, in order to distinguish convective from stratiform AMAX, some analyses on reanalysis data, namely the CAPE and the Vertical Integral of Divergence of Moisture Flux (VIDMF), have been done.</p>

In modeling spatial extremes, the dependence structure is classically inferred by assuming that block maxima derive from max-stable processes. Weather stations provide daily records rather than just block maxima. The point process approach for univariate extreme value analysis, which uses more historical data and is preferred by some practitioners, does not adapt easily to the spatial setting. We propose a two-step approach with a composite likelihood that utilizes site-wise daily records in addition to block maxima. The procedure separates the estimation of marginal parameters and dependence parameters into two steps. The first step estimates the marginal parameters with an independence likelihood from the point process approach using daily records. Given the marginal parameter estimates, the second step estimates the dependence parameters with a pairwise likelihood using block maxima. In a simulation study, the two-step approach was found to be more efficient than the pairwise likelihood approach using only block maxima. The method was applied to study the effect of El Niño-Southern Oscillation on extreme precipitation in California with maximum daily winter precipitation from 35 sites over 55 years. Using site-specific generalized extreme value models, the two-step approach led to more sites detected with the El Niño effect, narrower confidence intervals for return levels and tighter confidence regions for risk measures of jointly defined events.