On the frequency of heavy rainfall for the Midwest of the United States

A damaging hydrogeological event (DHE) is characterized by two components: a rainfall event and a subsequent damage event, which is the result of floods and landslides triggered by rainfall. The characteristics of both events depend on climatic, geomorphological and anthropogenic factors. In this paper, a methodology to classify the severity of DHEs is presented. A chart which considers indicators of both the damage (Dscore) and the daily rainfall (Rscore) values recorded in the study area is proposed. According to the chart, the events are classified into four types: ordinary events, with low Dscore and Rscore values; extraordinary events, with high Rscore values but low Dscore values; catastrophic events, characterized by non-exceptional rainfall (low Rscore) and severe damage (high Dscore); major catastrophic events, obtained by both high Dscore and Rscore values. Using this approach, the 2013 DHE that occurred in Calabria (Italy) was classified as an ordinary event, when compared to the previous ones, even though the widespread diffusion of damage data induced the perception of high severity damage. The rainfall that triggered this event confirms the negative trend of heavy daily precipitation detected in Calabria, and the damage can be ascribed more to sub-daily than daily rainfall affecting urbanized flood-prone areas. OPEN ACCESSWater 2014, 6 3653

Section: : In August Was the Jackal Born; The Rain Fell In September;mentioning

confidence: 99%

Damaging Hydrogeological Events: A Procedure for the Assessment of Severity Levels and an Application to Calabria (Southern Italy)

Caloiero

Pasqua

Petrucci

2014

“…Inversely, so-called underdispersion occurs when Φ < 1. Both overdispersion and underdispersion may violate the assumptions of independence and stationarity underlying the Poisson processes [42,43]. The dispersion coefficient (Equation (2)) was calculated to examine whether the extreme daily precipitation events follow a Poisson process.…”

Section: Poisson Distribution Validationmentioning

confidence: 99%

“…Moreover, the dispersion coefficient was calculated to examine the reliability of the Poisson distribution on extreme daily precipitation events. Furthermore, a Poisson regression model could be employed to detect trends in the occurrence of extreme daily precipitation events [42,45,46]. That is, the number of extreme daily precipitation events in the year y (N y , equals SEPF at a given year y) has a conditional Poisson distribution with the rate of occurrence (λ y ).…”

Section: The Sepf To Analyze Extreme Precipitation Frequency and Its mentioning

confidence: 99%

Changes in Extreme Precipitation: A Case Study in the Middle and Lower Reaches of the Yangtze River in China

Pei

et al. 2017

Abstract:Monitoring extreme climate events is of great importance, mainly due to increasingly severe impacts of extreme climate on nature and humanity. However, the characteristics of extreme climate events, especially extreme precipitation, frequently show complex variations in the context of climate change. Taking the middle and lower reaches of the Yangtze River (MLR-YR) in China as a case study, extreme daily precipitation during 1961-2012 was analyzed from the aspects of frequency and intensity. The changes in extreme daily precipitation in the MLR-YR were further attributed to several factors, including large-scale circulation, hydrologic engineering and local topography. Our analyses indicate that both frequency and intensity of the extreme daily precipitation in the MLR-YR showed overall increasing trends from 1961 to 2012. The increase could be associated with weakened East Asian summer monsoon in past decades. In addition, inverse trends could also be found locally between the frequency and the intensity. For instance, extreme precipitation intensity revealed an enhanced trend in the western part of the middle reach of the Yangtze River, while extreme precipitation frequency showed decreasing trends in this region. These phenomena could be associated with the effects of some local factors (e.g., lake regulation, hydropower engineering, topography). Our study highlights the important role of local factors on extreme precipitation changes.

“…GEV distribution has been widely used in the analysis of hydrological extremes because of its flexibility in representing three asymptotic types of extreme value probability distributions [4,16]. The cumulative probability function (CDF) for GEV distribution is given by:…”

Section: Probability Modelingmentioning

confidence: 99%

“…There are two common ways to study the so called extreme events: one is the annual maximum series (AM) and the other is the peaks over a threshold (POT) series [4][5][6][7]. Rao and Hamed [8] showed that the AM series is statistically more efficient than the POT series when λ is small ( λ ă 1.65 ), where λ is the mean number of peaks per year included in the POT series.…”

Section: Introductionmentioning

confidence: 99%

Frequency Analysis of High Flow Extremes in the Yingluoxia Watershed in Northwest China

Wang

Zhao

et al. 2016

Statistical modeling of hydrological extremes is significant to the construction of hydraulic engineering. This paper, taking the Yingluoxia watershed as the study area, compares the annual maximum (AM) series and the peaks over a threshold (POT) series in order to study the hydrological extremes, examines the stationarity and independence assumptions for the two series, and discusses the estimations and uncertainties of return levels from the two series using the Generalized Extreme Value (GEV) and Generalized Pareto distribution (GPD) models. For comparison, the return levels from all threshold excesses with considering the extremal index are also estimated. For the POT series, the threshold is selected by examining the mean excess plot and the stability of the parameter estimates and by using common-sense. The serial correlation is reduced by filtering out a set of dependent threshold excesses. Results show that both series are approximately stationary and independent. The GEV model fits the AM series well and the GPD model fits the POT series well. The estimated return levels are fairly comparable for the AM series, the POT series, and all threshold excesses with considering the extremal index, with the difference being less than 10% for return periods longer than 10 years. The uncertainties of the estimated return levels are the highest for the AM series, and next for the POT series and then for all threshold excesses series in turn.