A comparative study between two cases of extreme rainfall events in Catalonia

A diagnostic study of the synoptic aspects of two cases of heavy rain producing floods in Catalonia (northeast corner of the Iberian peninsula) is presented. The diagnosis consists in the determination of the area where large scale circulation induces favorable conditions for development of thunderstorms. We identify these conditions as upward quasi-geostrophic forcing, convergence of water vapor at low levels and convective instability in the lower troposphere. A composite chart showing where the three synoptic mechanisms overlap determines the area. Local studies of instability are carried out from upper air data given by the radiosonde ascents of Palma de Mallorca, looking for both the Convective Available Potential Energy and Bulk Richardson number and its temporal evolution during the events.     

Mixed estimation methods for Halphen distributions with applications in extreme hydrologic events

The Halphen family of distributions is a flexible and complete system to fit sets of observations independent and identically distributed. Recently, it is shown that this family of distributions represents a potential alternative to the generalized extreme value distributions to model extreme hydrological events. The existence of jointly sufficient statistics for parameter estimation leads to optimality of the method of maximum likelihood (ML). Nevertheless, the ML method requires numerical approximations leading to less accurate values. However, estimators by the method of moments (MM) are explicit and their computation is fast. Even though MM method leads to good results, it is not optimal. In order to combine the advantages of the ML (optimality) and MM (efficiency and fast computations), two new mixed methods were proposed in this paper. One of the two methods is direct and the other is iterative, denoted respectively direct mixed method (MMD) and iterative mixed method (MMI). An overall comparison of the four estimation methods (MM, ML, MMD and MMI) was performed using Monte Carlo simulations regarding the three Halphen distributions. Generally, the MMI method can be considered for the three Halphen distributions since it is recommended for a majority of cases encountered in hydrology. The principal idea of the mixed methods MMD and MMI could be generalized for other distributions with complicated density functions.     

Improving extreme hydrologic events forecasting using a new criterion for artificial neural network selection

The issue of selecting appropriate model input parameters is addressed using a peak and low flow criterion (PLC). The optimal artificial neural network (ANN) models selected using the PLC significantly outperform those identified with the classical root‐mean‐square error (RMSE) or the conventional Nash–Sutcliffe coefficient (NSC) statistics. The comparative forecast results indicate that the PLC can help to design an appropriate ANN model to improve extreme hydrologic events (peak and low flow) forecast accuracy. Copyright © 2001 John Wiley & Sons, Ltd.     

Change-point detection of long-duration extreme precipitation and the effect on hydrologic design: a case study of south Taiwan

An increase in the global temperature has intensified the hydrologic cycle, which affects the temporal patterns of precipitation. This study analyzed a long-term annual dataset measuring maximum precipitation in south Taiwan, and identified the change point of the time series using the cumulative sum technique. The result reveals a clear change point of the annual maximum rainfall for 24-h durations in 2004 at most observations. The average 24-h-duration precipitation depth in the study area increased by 27 and 36% for 20-year and 100-year extreme events compared with and without data after 2004, respectively. The long-duration precipitation depth demonstrates a significant positive trend following the change point. Furthermore, this study assesses the changes of hydrologic design while precipitation data are updated annually. The designed 20- and 100-year storm will decrease abruptly when the observed data are subsequently updated until 2004. Because of climate change, this issue is worthy of attention in hydrologic designs.     

Uncertainty analysis in statistical modeling of extreme hydrological events

With the increase of both magnitude and frequency of hydrological extreme events such as drought and flooding, the significance of adequately modeling hydrological extreme events is fully recognized. Estimation of extreme rainfall/flood for various return periods is of prime importance for hydrological design or risk assessment. However, due to knowledge and data limitation, uncertainty involved in extrapolating beyond available data is huge. In this paper, different sources of uncertainty in statistical modeling of extreme hydrological events are studied in a systematic way. This is done by focusing on several key uncertainty sources using three different case studies. The chosen case studies highlight a number of projects where there have been questions regarding the uncertainty in extreme rainfall/flood estimation. The results show that the uncertainty originated from the methodology is the largest and could be >40% for a return period of 200 years, while the uncertainty caused by ignoring the dependence among multiple hydrological variables seems the smallest. In the end, it is highly recommended that uncertainty in modeling extreme hydrological events be fully recognized and incorporated into a formal hydrological extreme analysis.     

Quantitative simulation and prediction of extreme geological events

Science China Earth Sciences - The systematic study of extreme geological events (such as plate collision and subduction, extreme cold and extreme hot events, biological extinction and revival,...     

Return period of bivariate distributed extreme hydrological events

 Extreme hydrological events are inevitable and stochastic in nature. Characterized by multiple properties, the multivariate distribution is a better approach to represent this complex phenomenon than the univariate frequency analysis. However, it requires considerably more data and more sophisticated mathematical analysis. Therefore, a bivariate distribution is the most common method for modeling these extreme events. The return periods for a bivariate distribution can be defined using either separate single random variables or two joint random variables. In the latter case, the return periods can be defined using one random variable equaling or exceeding a certain magnitude and/or another random variable equaling or exceeding another magnitude or the conditional return periods of one random variable given another random variable equaling or exceeding a certain magnitude. In this study, the bivariate extreme value distribution with the Gumbel marginal distributions is used to model extreme flood events characterized by flood volume and flood peak. The proposed methodology is applied to the recorded daily streamflow from Ichu of the Pachang River located in Southern Taiwan. The results show a good agreement between the theoretical models and observed flood data. The author wishes to thank the two anonymous reviewers for their constructive comments that improving the quality of this work.     

Climate impacts on extreme ice-jam events in Canadian rivers

Abstract

River ice jams can produce extreme flood events with major social, economic and ecological impacts throughout Canada. Ice breakup and jamming processes are briefly reviewed and shown to be governed by the flow hydrograph, the thickness and strength of the winter ice cover, and the stream morphology. These factors are directly or indirectly influenced by weather conditions which implies potential impacts of climate change and variability on the severity of ice-jamming. Relevant work has to date focused on simple measures of climatic effects, such as the timing of freeze-up and breakup, and indicates trends that are consistent with concomitant changes in air temperature. More recently, it has been found that increased incidence of mid-winter breakup events and higher freshet flows in certain parts of Canada could enhance the frequency and severity of ice jams. Possible future trends under climate warming scenarios are discussed and associated impacts identified in a general manner.     

Rainfall uncertainty for extreme events in NWP downscaling model

Limited area numerical weather predication (NWP) models such as MM5 have become a popular method for generating rainfall estimates for hydrological analysis, particularly for catchments where rainfall data are sparse. Although several studies have been undertaken to investigate the appropriateness of MM5 parameterization schemes for hydrological applications, the size of the nested domains and the distance between them have been overlooked as a source of uncertainty in model precipitation estimates for hydrological purposes. This study examines the uncertainty of model rainfall estimates derived from MM5 by varying the domain size and the distance between the domains. The results from this study show that domain size and buffer zone have a significant impact on model rainfall estimates, which should not be overlooked by hydrologists. Copyright © 2010 John Wiley & Sons, Ltd.     

Frequency analysis of climate extreme events in Zanjan, Iran

In this study, generalized extreme value distribution (GEV) and generalized Pareto distribution (GPD) were fitted to the maximum and minimum temperature, maximum wind speed, and maximum precipitation series of Zanjan. Maximum (minimum) daily and absolute annual observations of Zanjan station from 1961 to 2011 were used. The parameters of the distributions were estimated using the maximum likelihood estimation method. Quantiles corresponding to 2, 5, 10, 25, 50, and 100 years return periods were calculated. It was found that both candidate distributions fitted to extreme events series, were statistically reasonable. Most of the observations from 1961 to 2011 were found to fall within 1–10 years return period. Low extremal index (θ) values were found for excess maximum and minimum temperatures over a high threshold, indicating the occurrence of consecutively high peaks. For the purpose of filtering the dependent observations to obtain a set of approximately independent threshold excesses, a declustering method was performed, which separated the excesses into clusters, then the de-clustered peaks were fitted to the GPD. In both models, values of the shape parameters of extreme precipitation and extreme wind speed were close to zero. The shape parameter was less negative in the GPD than the GEV. This leads to significantly lower return period estimates for high extremes with the GPD model.     

一个创新研究——大气数值模式变量的物理分解及其在极端事件预报中的应用

大气温度、湿度、位势高度和风等数值模式变量可以物理分解为纬圈-时间平均的对称部分和时间平均的非对称部分,以及行星尺度瞬变扰动和天气尺度瞬变扰动等四个部分.区域持续性干旱、暴雨、热浪、低温和雨雪冰冻等极端天气事件与前期及同期数值模式中的行星尺度和天气尺度大气扰动系统之间呈现出密切的关系.瞬变扰动天气图可成为预报极端天气事件的新工具.本文在归纳本期9篇原创性文章的基础上,探讨大气变量物理分解后需要进一步研究的理论问题和应用前景.     

Space transformations in the study of multidimensional functions in the hydrologic sciences

In hydrological sciences we often deal with complex phenomena which take place in several dimensions, such as two-dimensional distributions of rainfall over a region or three-dimensional chemical transport within an aquifer. The aim of this paper is to show benefits obtained by use of space transformations in the solution of multidimensional problems. The approach is to transform the original space to a space of lower dimensionality, where analysis is easier and involves less computational effort. In calculations involving isotropic functions, analytically and computationally tractable expressions are available for both the space transformations and their inverses. Particular attention is paid to the frequency domain forms of the space transformations. Differential equations describing flow through porous media, structural identification of spatially distributed soil variables, and estimation and simulation of hydrologic fields are a few applications where the use of these transforms may be fruitful. Simple but useful examples are included to illustrate the methodology, and in occasion we indicate some areas of further work.     

Seismic response of high plasticity clays during extreme events

Mexico City high plasticity clays exhibit a small degree of nonlinearity for shear strains as large as 0.1%, which leads to both moderate shear stiffness degradation and small to medium damping increment, even for long duration subduction strong ground motions, such as the 8.1M_w 1985Michoacan earthquake. Nonetheless, current seismic design criteria of strategic infrastructure used worldwide have striven for having larger return periods for establishing the seismic environment, considering recent large magnitude (M>8.5M_w) events. This paper presents the study of the seismic response of typical high plasticity clays found in the so-called Texcoco Lake, in the surrounding of Mexico City valley, for larger to extreme earthquakes. The shear wave velocity profile was characterized using a down-hole test. The seismic environment was established from a set of uniform hazard response spectra developed for a nearby rock outcrop for return periods of 125, 250, 475 and 2475 years. A time-domain spectral matching was used to develop acceleration time histories compatible with each uniform hazard response spectrum. Both frequency and time domain site response analyses were carried out considering each seismic scenario. Ground nonlinearities were clearly observed in the soil response during extreme ground shaken, which increases rapidly with the return period. This fact must be taken into account to avoid costly and potentially unsafe seismic designs.     

A dependence modelling study of extreme rainfall in Madeira Island

The dependence between variables plays a central role in multivariate extremes. In this paper, spatial dependence of Madeira Island's rainfall data is addressed within an extreme value copula approach through an analysis of maximum annual data. The impact of altitude, slope orientation, distance between rain gauge stations and distance from the stations to the sea are investigated for two different periods of time. The results obtained highlight the influence of the island's complex topography on the spatial distribution of extreme rainfall in Madeira Island.     

Estimation of the generalized logistic distribution of extreme events using partial L-moments

Abstract

Statistical analysis of extreme events is often carried out to predict large return period events. In this paper, the use of partial L-moments (PL-moments) for estimating hydrological extremes from censored data is compared to that of simple L-moments. Expressions of parameter estimation are derived to fit the generalized logistic (GLO) distribution based on the PL-moments approach. Monte Carlo analysis is used to examine the sampling properties of PL-moments in fitting the GLO distribution to both GLO and non-GLO samples. Finally, both PL-moments and L-moments are used to fit the GLO distribution to 37 annual maximum rainfall series of raingauge station Kampung Lui (3118102) in Selangor, Malaysia, and it is found that analysis of censored rainfall samples of PL-moments would improve the estimation of large return period events.

Editor D. Koutsoyiannis; Associate editor K. Hamed

Citation Zakaria, Z.A., Shabri, A. and Ahmad, U.N., 2012. Estimation of the generalized logistic distribution of extreme events using partial L-moments. Hydrological Sciences Journal, 57 (3), 424–432.     

Effect of extreme rainfall events on the water resources of the Jordan River

As a response to climate change, shifting rainfall trends including increased multi-year droughts and an escalation in extreme rainfall events are expected in the Middle East. The purpose of this study is to evaluate the potential impact of these shifting trends on stream flow in the Jordan River and its tributaries. We use a non-homogeneous hidden Markov model to generate artificial daily rainfall simulations which capture independently shifting trends of increased droughts and escalated extreme. These simulations are then used as input into a hydrological model calibrated for the upper catchments of the Jordan River to compare the impact on stream flow and water resources between the different rainfall scenarios. We compare the predicted baseflow and surface flow components of the tested watersheds, and find that while an increase in extreme rainfall events increases the intensity and frequency of surface flow, the over all flow to the Jordan River, and the characteristics of the baseflow in the Jordan River system is not largely impacted. In addition, though it has been suggested that in the case of a multi-year drought the karstic nature of the aquifer might lead to more intense, non-linear reductions in stream flow, here we quantify and show the conditions when annual stream flow reduce linearly with rainfall, and when these relations will become non-linear.     

CHOSEN: A synthesis of hydrometeorological data from intensively monitored catchments and comparative analysis of hydrologic extremes

Comparative hydrology has been hampered by limited availability of geographically extensive, intercompatible monitoring data on comprehensive water balance stores and fluxes. These limitations have, for example, restricted comprehensive assessment of multiple dimensions of wetting and drying related to climate change and hampered understanding of why widespread changes in precipitation extremes are uncorrelated with changes in streamflow extremes. Here, we address this knowledge gap and underlying data gap by developing a new data synthesis product and using that product to detect trends in the frequencies and magnitudes of a comprehensive set of hydroclimatic and hydrologic extremes. CHOSEN (Comprehensive Hydrologic Observatory Sensor Network) is a database of streamflow, soil moisture, and other hydroclimatic and hydrologic variables from 30 study areas across the United States. An accompanying data pipeline provides a reproducible, semi-automated approach for assimilating data from multiple sources, performing quality assurance and control, gap-filling and writing to a standard format. Based on the analysis of extreme events in the CHOSEN dataset, we detected hotspots, characterized by unusually large proportions of monitored variables exhibiting trends, in the Pacific Northwest, New England, Florida and Alaska. Extreme streamflow wetting and drying trends exhibited regional coherence. Drying trends in the Pacific Northwest and Southeast were often associated with trends in soil moisture and precipitation (Pacific Northwest) and evapotranspiration-related variables (Southeast). In contrast, wetting trends in the upper Midwest and the Rocky Mountains showed few univariate associations with other hydroclimatic extremes, but their latitudes and elevations suggested the importance of changing snowmelt characteristics. On the whole, observed trends are incompatible with a ‘drying-in-dry, wetting-in-wet’ paradigm for climate-induced hydrologic changes over land. Our analysis underscores the need for more extensive, longer-term observational data for soil moisture, snow and evapotranspiration.