基于全天空图像的极光活动变化检测方法研究

面对日积月累产生的海量极光数据,快速发现极光现象的发生及活动特征是研究极光的物理机制及相关动力学过程的首要问题,它为研究极光现象提供有效的自动化分析手段,从而能够提供充足而有效的事件用于统计学分析.因为太阳风是等离子体,它有着磁流体力学的特征,本文采用流体力学的连续性方程对极光运动进行建模,提取全天空极光图像序列的运动场,对极光活动进行表征,进而构造极光活动变化曲线,从而有效的检测出极光活动的变化.该方法的优势在于,基于运动场的表征方法能够有效反映极光活动的二维形态和运动特征,生成的极光活动变化曲线可以准确地指示极光发生、变化和消失的时间,为进一步研究极光的活动周期及相关物理变化奠定基础.     

Application of the Bayesian approach in regional analysis of extreme rainfalls

Based on the Partial Duration Series model a regional Bayesian approach is introduced in the modelling of extreme rainfalls from a country-wide system of recording raingauges in Denmark. The application of the Bayesian principles is derived in case of both exponential and generalized Pareto-distributed exceedances. The method is applied to, respectively, the total precipitation depth and the maximum 10 minutes rain intensity of individual storms from 41 stations. By means of the regional analysis prior distributions of the parameters in the Partial Duration Series model are estimated. It is shown that the regional approach significantly reduces the uncertainty of the T-year event estimator compared to estimation based solely on at-site data. In addition, the regional approach provides quantile estimates at non-monitored sites.     

A hierarchical Bayesian approach for the analysis of climate change impact on runoff extremes

In this study the impact of climate change on runoff extremes is investigated over the Pacific Northwest (PNW). This paper aims to address the question of how the runoff extremes change in the future compared to the historical time period, investigate the different behaviors of the regional climate models (RCMs) regarding the runoff extremes and assess the seasonal variations of runoff extremes. Hydrologic modeling is performed by the variable infiltration capacity (VIC) model at a 1/8° resolution and the model is driven by climate scenarios provided by the North American Regional Climate Change Assessment Program (NARCCAP) including nine regional climate model (RCM) simulations. Analysis is performed for both the historical (1971–2000) and future (2041–2070) time periods. Downscaling of the climate variables including precipitation, maximum and minimum temperature and wind speed is done using the quantile‐mapping (QM) approach. A spatial hierarchical Bayesian model is then developed to analyse the annual maximum runoff in different seasons for both historical and future time periods. The estimated spatial changes in extreme runoffs over the future period vary depending on the RCM driving the hydrologic model. The hierarchical Bayesian model characterizes the spatial variations in the marginal distributions of the General Extreme Value (GEV) parameters and the corresponding 100‐year return level runoffs. Results show an increase in the 100‐year return level runoffs for most regions in particular over the high elevation areas during winter. The Canadian portions of the study region reflect higher increases during spring. However, reduction of extreme events in several regions is projected during summer. Copyright © 2013 John Wiley & Sons, Ltd.     

水平界面上P-SV转换波转换点的精确解

转换波共转换点的叠加和道集选取都需要准确地计算转换点的位置.Tessmer和Behle、Taylor分别给出了水平单层介质中P-SV转换波转换点坐标的解析解,由于其表达式的复杂性,在应用中几乎不被采用.在本文中,运用Snell定律重新建立了在水平反射界面上反射的P-SV转换波的转换点坐标的四次方程,并严格地推导出与纵波速度、横波速度、炮检距和反射深度有关的转换点坐标的解析解,确定了惟一的解析表达式.将这一结果应用于P-SV转换波的速度分析和叠加处理中.简化的公式有较好的应用价值.     

Revisiting prior distributions,Part II: Implications of the physical prior in maximum entropy analysis

The well-known “Maximum Entropy Formalism” offers a powerful framework for deriving probability density functions given a relevant knowledge base and an adequate prior. The majority of results based on this approach have been derived assuming a flat uninformative prior, but this assumption is to a large extent arbitrary (any one-to-one transformation of the random variable will change the flat uninformative prior into some non-constant function). In a companion paper we introduced the notion of a natural reference point for dimensional physical variables, and used this notion to derive a class of physical priors that are form-invariant to changes in the system of dimensional units. The present paper studies effects of these priors on the probability density functions derived using the maximum entropy formalism. Analysis of real data shows that when the maximum entropy formalism uses the physical prior it yields significantly better results than when it is based on the commonly used flat uninformative prior. This improvement reflects the significance of the incorporating additional information (contained in physical priors), which is ignored when flat priors are used in the standard form of the maximum entropy formalism. A potentially serious limitation of the maximum entropy formalism is the assumption that sample moments are available. This is not the case in many macroscopic real-world problems, where the knowledge base available is a finite sample rather than population moments. As a result, the maximum entropy formalism generates a family of “nested models” parameterized by the unknown values of the population parameters. In this work we combine this formalism with a model selection scheme based on Akaike’s information criterion to derive the maximum entropy model that is most consistent with the available sample. This combination establishes a general inference framework of wide applicability in scientific/engineering problems.     

Assessing the impact of land use change on hydrology by ensemble modeling (LUCHEM) III: Scenario analysis

An ensemble of 10 hydrological models was applied to the same set of land use change scenarios. There was general agreement about the direction of changes in the mean annual discharge and 90% discharge percentile predicted by the ensemble members, although a considerable range in the magnitude of predictions for the scenarios and catchments under consideration was obvious. Differences in the magnitude of the increase were attributed to the different mean annual actual evapotranspiration rates for each land use type. The ensemble of model runs was further analyzed with deterministic and probabilistic ensemble methods. The deterministic ensemble method based on a trimmed mean resulted in a single somewhat more reliable scenario prediction. The probabilistic reliability ensemble averaging (REA) method allowed a quantification of the model structure uncertainty in the scenario predictions. It was concluded that the use of a model ensemble has greatly increased our confidence in the reliability of the model predictions.     

An analysis of change in alpine annual maximum discharges: implications for the selection of design discharges

The paper presents an analysis of 17 long annual maximum series (AMS) of flood flows for Swiss Alpine basins, aimed at checking the presence of changes in the frequency regime of annual maxima. We apply Pettitt's change point test, the nonparametric sign test and Sen's test on trends. We also apply a parametric goodness‐of‐fit test for assessing the suitability of distributions estimated on the basis of annual maxima collected up to a certain year for describing the frequency regime of later observations. For a number of series the tests yield consistent indications for significant changes in the frequency regime of annual maxima and increasing trends in the intensity of annual maximum discharges. In most cases, these changes cannot be explained by anthropogenic causes only (e.g. streamflow regulation, construction of dams). Instead, we observe a statistically significant relationship between the year of change and the elevation of the catchment outlet. This evidence is consistent with the findings of recent studies that explain increasing discharges in alpine catchments with an increase in the temperature controlling the portion of mountain catchments above the freezing point. Finally, we analyse the differences in return periods (RPs) estimated for a given flood flow on the basis of recent and past observations. For a large number of the study AMS, we observe that, on average, the 100‐year flood for past observations corresponds to a RP of approximately 10 to 30 years on the basis of more recent observation. From a complementary perspective, we also notice that estimated RP‐year flood (i.e. flood quantile (FQ) associated with RP) increases on average by approximately 20% for the study area, irrespectively of the RP. Practical implications of the observed changes are illustrated and discussed in the paper. Copyright © 2011 John Wiley & Sons, Ltd.     

An ensemble approach for the analysis of extreme rainfall under climate change in Naples (Italy)

In the present paper, an ensemble approach is proposed to estimate possible modifications caused by climate changes in the extreme precipitation regime, with the rain gauge Napoli Servizio Idrografico (Naples, Italy) chosen as test case. The proposed research, focused on the analysis of extremes on the basis of climate model simulations and rainfall observations, is structured in several consecutive steps. In the first step, all the dynamically downscaled EURO‐CORDEX simulations at about 12 km horizontal resolution are collected for the current period 1971–2000 and the future period 2071–2100, for the RCP4.5 and the RCP8.5 concentration scenarios. In the second step, the significance of climate change effects on extreme precipitation is statistically tested by comparing current and future simulated data and bias‐correction is performed by means of a novel approach based on a combination of simple delta change and quantile delta mapping, in compliance with the storm index method. In the third step, two different ensemble models are proposed, accounting for the variabilities given by the use of different climate models and for their hindcast performances. Finally, the ensemble models are used to build novel intensity–duration–frequency curves, and their effects on the early warning system thresholds for the area of interest are evaluated.