Experimental Simulations of Extreme Precipitation Based on the Multi-Status Markov Chain Model

A multi-status Markov chain model is proposed to produce daily rainfall, and based on which extreme rainfall is simulated with the generalized Pareto distribution (GPD). The simulated daily rainfall shows high precision at most stations, especially in pluvial regions of East China. The analysis reveals that the multistatus Markov chain model excels the bi-status Markov chain model in simulating climatic features of extreme rainfall. Results from the selected six stations demonstrate excellent simulations in the following aspects:standard deviation of monthly precipitation,daily maximum precipitation,the monthly mean rainfall days,standard deviation of daily precipitation and mean daily precipitation, which are proved to be consistent with the observations. A comparative study involving 78 stations in East China also reveals good consistency in monthly mean rainfall days and mean daily maximum rainfall, except mean daily rainfall. Simulation results at the above 6 stations have shown satisfactory fitting capability of the extreme precipitation GPD method. Good analogy is also found between simulation and observation in threshold and return values. As the errors of the threshold decrease, so do the di?erences between the return and real values. All the above demonstrates the applicability of the Markov chain model to extreme rainfall simulations.     

极值波高建模中不确定性的模糊量化方法

A non-traditional fuzzy quantification method is presented in the modeling of an extreme significant wave height. First, a set of parametric models are selected to fit time series data for the significant wave height and the extrapolation for extremes are obtained based on high quantile estimations. The quality of these results is compared and discussed. Then, the proposed fuzzy model, which combines Poisson process and generalized Pareto distribution(GPD) model, is applied to characterizing the wave extremes in the time series data. The estimations for a long-term return value are considered as time-varying as a threshold is regarded as non-stationary. The estimated intervals coupled with the fuzzy theory are then introduced to construct the probability bounds for the return values. This nontraditional model is analyzed in comparison with the traditional model in the degree of conservatism for the long-term estimate. The impact on the fuzzy bounds of extreme estimations from the non stationary effect in the proposed model is also investigated.     

2004-2013年辽宁省主要气象灾害分布及预警服务分析

极端降水重现期的研究可提升对降水规律性的认识,对预防城市内涝、农田渍害等有重要意义。利用辽宁省14个国家气象观测站1960—2020年5—10月的逐日降水资料,选取第98个百分位值作为极端降水指标,采用泊松-广义帕累托复合极值模型对辽宁省极端降水日数和极端日降水量进行拟合并计算其重现期降水量。结果表明：辽宁省极端降水日数和极端日降水量分别服从泊松分布和广义帕累托分布（均通过α=0.05的显著性检验）;辽宁省极端日降水量从西北向东南方向逐渐增多,其中丹东市的极端日降水量和极端降水日数均为最高;泊松-广义帕累托复合极值模型和经验频率法计算的重现期降水量拟合度较高,不同重现期下辽宁省东南部极端日降水量值均为最高,辽河流域中部极端日降水量较小,西北部极端日降水量最小,辽宁东部山区和辽宁西部丘陵地带极端日降水量高于辽宁中部平原区,且不同重现期分布状态一致。

     

Street hierarchies: a minority of streets account for a majority of traffic flow

Urban streets are hierarchically organized in the sense that a majority of streets are trivial, while a minority of streets is vital. This hierarchy can be simply, but elegantly, characterized by the 80/20 principle, i.e. 80% of streets are less connected (below the average), while 20% of streets are well connected (above the average); out of the 20%, there is 1% of streets that are extremely well connected. This paper, using a European city as an example, examined, at a much more detailed level, such street hierarchies from the perspective of geometric and topological properties. Based on an empirical study, we further proved a previous conjecture that a minority of streets accounts for a majority of traffic flow; more accurately, the 20% of top streets accommodate 80% of traffic flow (20/80), and the 1% of top streets account for more than 20% of traffic flow (1/20). Our study provides new evidence as to how a city is (self‐)organized, contributing to the understanding of cities and their evolution using increasingly available mobility geographic information.     

Statistical characterization of temporal structure of storms

The authors present a statistical procedure to estimate the probability distributions of storm characteristics. The approach uses recent advances in stochastic hydrological modeling. The temporal dynamics of rainfall are modeled via a reward alternating renewal process that describes wet and dry phases of storms. In particular, the wet phase is modeled as a rectangular pulse process with dependent random duration and intensity; the global dependence structure is described using multidimensional copulas. The marginal distributions are described by Generalized Pareto laws. The authors derive both the storm volume statistics and the rainfall volume distribution within a fixed temporal window preceding a storm. Based on these results, they calculate the antecedent moisture conditions. The paper includes a thorough discussion of the validity of the assumptions and approximations introduced, and an application to actual rainfall data. The models presented here have important implications for improved design procedures of water resources and hydrologic systems.     

Interpretation and nonuniqueness of CTRW transition distributions: Insights from an alternative solute transport formulation

The continuous time random walk (CTRW) has both an elegant mathematical theory and a successful record at modeling solute transport in the subsurface. However, there are some interpretation ambiguities relating to the relationship between the discrete CTRW transition distributions and the underlying continuous movement of solute that have not been addressed in existing literature. These include the exact definition of “transition”, and the extent to which transition probability distributions are unique/quantifiable from data. Here, we present some theoretical results which address these uncertainties in systems with an advective bias. Simultaneously, we present an alternative, reduced parameter CTRW formulation for general advective transport in heterogeneous porous media, which models early- and late-time transport by use of random transition times between sparse, imaginary planes normal to flow. We show that even in the context of this reduced-parameter formulation there is nonuniqueness in the definitions of both transition lengths and waiting time distributions, and that neither may be uniquely determined from experimental data. For practical use of this formulation, we suggest Pareto transition time distributions, leading to a two-degree-of-freedom modeling approach. We then demonstrate the power of this approach in fitting two sets of existing experimental data. While the primary focus is the presentation of new results, the discussion is designed to be pedagogical and to provide a good entry point into practical modeling of solute transport with the CTRW.     

Response Surface Modeling and Optimization of Microwave‐Activated Persulfate Oxidation of Olive Oil Mill Wastewater

Olive oil mill wastewater (OMW) is environmentally hazardous not only because it contains high recalcitrant and toxic compounds, but also due to its high organic load and turbidity. In this study, oxidation of OMW by microwave (MW)‐activated persulfate is investigated. Box–Behnken design is applied to investigate the effects of operating conditions on operating cost, organic matter, and color removal. Multi response optimization is performed according to minimum operating cost, maximum organic matter and color removal efficiencies. At optimum conditions (persulfate anion dose of 266 g L^?1, oxidation duration of 23.58 min, MW power of 567 W, and initial pH 2), chemical oxygen demand (COD) removal of 63.38%, color removal of 94.85%, and operating cost of 0.0633 Euro/g total organic carbon (TOC) removal are found. The biochemical oxygen demand (BOD₅)/COD ratio is increased from 0.144 to 0.285. Results of Pareto analysis show individual effect of MW power is 92.81% for TOC removal, 15.52% for color removal, 68.99% for operating cost, respectively. According to the results, it is not recommended to use this process as an ultimate treatment unit due to the high amount of oxidizing agent consumed. Instead, it is recommended to be used as a pre‐ or post‐treatment step.     

Single station and regional analysis of daily rainfall extremes

A peaks over threshold (POT) method of analysing daily rainfall values is developed using a Poisson process of occurrences and a generalised Pareto distribution (GPD) for the exceedances. The parameters of the GPD are estimated by the method of probability weighted moments (PWM) and a method of combining the individual estimates to define a regional curve is proposed.     

基于广义帕累托分布的地震震级分布尾部特征分析

极值理论在地震危险性分析中有着重要应用, 发震震级超过某一阈值的超出量分布可以近似为广义帕累托分布. 基于广义帕累托分布给出了若干地震活动性参数的估计公式, 包括强震震级分布、 地震复发周期和重现水平、 期望重现震级、 地震危险性概率和潜在震级上限等; 以云南地区震级资料为基础数据, 讨论了阈值选取、 模型拟合诊断和参数估计; 在此基础上计算了该地区的地震活动性参数. 结果表明, 广义帕累托分布较好地刻画了强震震级分布, 通过超阈值(POT)模型计算的复发周期与实际复发间隔统计基本一致, 高分位数估计在一定阈值范围内表现稳定, 为工程抗震中潜在震级上限的确定提供了一种途径.     

基于广义帕累托分布的马尼拉海沟俯冲带地震危险性估计

选取马尼拉海沟俯冲带作为潜源区, 基于广义帕累托分布, 通过对一定时段内超过某一阈值的震级数据进行拟合, 建立该潜源区地震危险性估计模型, 估计强震重现水平和震级上限, 并对估计结果的不确定性进行了分析, 得到马尼拉海沟俯冲带震级上限为9.0级, 10 a、 50 a、 100 a、 200 a马尼拉海沟俯冲带的震级重现水平期望值分别为7.1级、 7.6级、 7.7级、 7.9级。