Linear combinations of order statistics to estimate the position and scale parameters of the Generalized Pareto distribution

 Ad hoc techniques for estimating the position and the scale parameters of the Generalized Pareto distribution are introduced. The estimators proposed are simple linear combinations of the order statistics: they provide valuable estimates of the parameters of interest, both when the shape parameter is known and when it is unknown (this latter case being of great relevance in practical applications), and show a good performance as well when the sample size is small. The procedures are tested on simulated data, and comparisons with other techniques are shown.     

Diatoms as indicators of climatic and limnological change in Swedish Lapland: a 100-lake calibration set and its validation for paleoecological reconstructions

This study investigated the distribution of subfossil diatom assemblages in surficial sediments of 100 lakes along steep ecological and climatic gradients in northernmost Sweden (Abisko region, 67.07° N to 68.48° N latitude, 17.67° E to 23.52° E longitude) to develop and cross-validate transfer functions for paleoenvironmental reconstruction. Of 19 environmental variables determined for each site, 15 were included in the statistical analysis. Lake-water pH (8.0%), sedimentary loss-on-ignition (LOI, 5.9% and estimated mean July air temperature (July T, 4.8%) explained the greatest amounts of variation in the distribution of diatom taxa among the 100 lakes. Temperature and pH optima and tolerances were calculated for abundant taxa. Transfer functions, based on WA-PLS (weighted averaging partial least squares), were developed for pH (r² = 0.77, root-mean-square-error of prediction (RMSEP) = 0.19 pH units, maximum bias = 0.31, as assessed by leave-one-out cross-validation) based on 99 lakes and for July T (r² = 0.75, RMSEP = 0.96 °C, max. bias = 1.37 °C) based on the full 100 lake set. We subsequently assessed the ability of the diatom transfer functions to estimate lake-water pH and July T using a form of independent cross-validation. To do this, the 100-lake set was divided in two subsets. An 85-lake training-set (based on single limnological measurements) was used to develop transfer functions with similar performance as those based on the full 100 lakes, and a 15-lake test-set (with 2 years of monthly limnological measurements throughout the ice-free seasons) was used to test the transfer functions developed from the 85-lake training-set. Results from the intra-set cross-validation exercise demonstrated that lake-specific prediction errors (RMSEP) for the 15-lake test-set corresponded closely with the median measured values (pH) and the estimations based on spatial interpolations of data from weather stations (July T). The prediction errors associated with diatom inferences were usually within the range of seasonal and interannual variability. Overall, our results confirm that diatoms can provide reliable and robust estimates of lake-water pH and July T, that WA-PLS is a robust calibration method and that long-term environmental data are needed for further improvement of paleolimnological transfer functions.     

利用广义反射-透射系数方法求解含低速层水平层状介质模型中面波频散曲线问题

在计算面波频散问题的广义反射-透射系数方法基础上研究了含有低速层的均匀平层固体地球模型的面波的频散曲线以及简正振型的计算问题. 当不存在低速层时,仅利用陈晓非1993年提出的单一的久期函数对求解这两个问题已经足够. 但是当模型中存在低速层时,该单一久期函数在数值求解较低阶模式很困难. 本文提出用久期函数族代替该单一久期函数、对不同的简正振型模式采用对应的久期函数来求解的方法,从而更加容易有效地求解较低阶的模式,完善了利用广义反射-透射系数方法求解面波频散问题的理论和算法.     

一种改进的InSAR城市地面沉降监测精度评价方法

针对InSAR沉降监测精度评定方法的单一性,综合考虑建筑物自身沉降和时空基准难以统一的问题,构建了改进的反距离权重插值模型。以宁波东部新城某沉降区域的PS-InSAR监测结果为试验数据,在统一时空基准的前提下,利用改进IDW插值模型对监测结果进行精度评定,并以交叉验证的方式验证了该插值方法的准确性。试验证明：通过对比两个实测水准点,使用改进的IDW插值获取的InSAR监测结果误差为0.3和0.8,相比于普通IDW和Kriging方法提高了大约1.1 mm。     

一种海滩地表水和地下水的全耦合数学模型

海滩地表水与地下水间的相互作用对于海岸带的物质循环及生态环境有重要的影响。基于控制体的思想推导出了统一形式的地表水和地下水的控制方程,在此基础上不仅实现了地表水和地下水的全耦合模拟,也简化了动边界的处理。对一些经典算例的模拟结果验证了该模型的有效性。     

Raingauge network optimization in a tropical urban area by coupling cross-validation with the geostatistical technique

An adequate and reliable raingauge network is essential for observing rainfall data in hydrology and water resource applications. A raingauge network developed for a catchment area is commonly extended periodically to increase data accuracy. Due to financial constraints, the network is reviewed for the optimal number of stations. A new optimization approach is developed in this study by coupling a cross-validation technique with a geostatistical method for raingauge network optimization to prioritize raingauge stations. The spatial interpolation error of the spatial rainfall distribution, measured as the root mean square error (E_rms) optimization criterion is applied to a raingauge network in a tropical urban area. The results indicate that this method can successfully optimize the number of rainfall stations in an existing raingauge network, as the stations are prioritized based on their importance in the network.     

Application of a data‐based mechanistic modelling (DBM) approach for predicting runoff generation in semi‐arid regions

This paper addresses the application of a data‐based mechanistic (DBM) modelling approach using transfer function models (TFMs) with non‐linear rainfall filtering to predict runoff generation from a semi‐arid catchment (795 km²) in Tanzania. With DBM modelling, time series of rainfall and streamflow were allowed to suggest an appropriate model structure compatible with the data available. The model structures were evaluated by looking at how well the model fitted the data, and how well the parameters of the model were estimated. The results indicated that a parallel model structure is appropriate with a proportion of the runoff being routed through a fast flow pathway and the remainder through a slow flow pathway. Finally, the study employed a Generalized Likelihood Uncertainty Estimation (GLUE) methodology to evaluate the parameter sensitivity and predictive uncertainty based on the feasible parameter ranges chosen from the initial analysis of recession curves and calibration of the TFM. Results showed that parameters that control the slow flow pathway are relatively more sensitive than those that control the fast flow pathway of the hydrograph. Within the GLUE framework, it was found that multiple acceptable parameter sets give a range of predictions. This was found to be an advantage, since it allows the possibility of assessing the uncertainty in predictions as conditioned on the calibration data and then using that uncertainty as part of the decision‐making process arising from any rainfall‐runoff modelling project. Copyright © 2001 John Wiley & Sons, Ltd.     

Streamwise vorticity equation

l.IntroductionTheverticalcomPOnentofthevorticityisthedominantpartinthelarge-scalesystems,therefore,itisgoodenoughtoonlyconsidertheverticalcomPOnentofthevorticityinthevorticityequationforstudyinglarge-scalesystems.Withtheapproximationthatstreamlinesaremainlyquasi-horizontalincontrasttotheirverticalcomponents,hence,thecomPOnentofabsolutevorticityalongthestreamline,so-calledsecondaryvorticity,isverysmallandnearlyomitted.ButinthemesoscaleandsmaIl-scalesystems,especiallyinsmall-scalesys-tems,ver…     

基于小波变换和GRNN神经网络的黑河出山径流模型

对黑河山区流域月降水量和气温做Harr小波变换,并作为GRNN神经网络的输入,对黑河出山径流进行模拟和预测验证,效果较好。应用全球变化成果,在不同的气候情景下,对黑河出山径流进行预测。结果表明,黑河出山径流在未来一段时间内,径流量会有一定程度的增加,最终会减少。但模型对气温反应不敏感。去除气温重构的细节系数后,气温也成为一个敏感因素,但径流量却随气温的增加而增加。可推断,引进Haar小波变换的GRNN神经网络模型可应用于径流量对气温不敏感的流域。     

基于GA-SVR的地铁隧道沉降预测

利用支持向量回归(SVR)和遗传算法(GA)参数寻优,建立了基于GA-SVR的地铁隧道沉降预测模型,可提高地铁隧道沉降预测的精度。利用长期实测的地铁结构监测数据对SVR模型进行训练,并通过GA优化SVR模型的3个参数;利用训练模型均方误差结合留一交叉验证的方法确定GA的适应度。基于南京地铁2号线隧道结构沉降实测数据,将预测值与实测值进行了对比分析。结果表明,该模型预测的地铁隧道沉降预测值准确、可靠,其精度能满足工程实际要求。