基于数值模拟的台风危险性分析综述(Ⅱ)——随机抽样模拟与极值风速预测

台风危险性分析是台风损失估计及防灾减灾的基础。对基于数值模拟的台风危险性分析所采用的随机台风概率模型、随机抽样模拟方法和极值风速预测进行了回顾与评述,包括台风关键参数概率模型及其相关性、台风路径模型、台风登陆衰减和海陆风速转换模型以及台风极值风速概率模型,并针对我国东南沿海地区台风危险性分析数值模拟采用的相关模型给出了相关建议。     

规模效率和技术进步对CO2排放影响的实证

本文利用DEA的方法从全要素生产率中分解出规模效率、技术进步和纯技术效率三个指标,然后基于1998-2008年的省际面板数据分析了不同地区的工业规模效率及技术进步对CO2排放影响的程度及差异化的原因.研究结果显示:从总体来看,技术进步对单位GDP的CO2排放有抑制作用,而规模效率则与CO2排放呈正向关系,但各个地区的情况不同,东中部地区的技术进步对单位GDP的CO2排放起到了抑制作用,东北地区和西部地区的技术进步则对单位GDP的CO2排放起到了促进作用;东中部地区的规模效率对单位GDP CO2排放的影响程度要远小于东部地区和西部地区;产生这些差异的原因可能是与我国区域间技术进步与规模效率所属的种类不同有关,东部地区的技术进步可能属于节能型技术进步,而西部地区则属于耗能型的技术进步;东北地区和西部地区规模效率的提升可能更多地依靠能源消耗,中东部地区则有所改善.     

关于减排技术途径和政策的探讨

通过对发达国家先进的节能减排技术和政策,以及我国现阶段所采取的减排技术政策的分析,通过对我国重点行业二氧化硫和化学需氧量的减排技术现状对策研究,对适合我国减排的技术途径和配套政策进行探讨。     

PARAMETER ESTIMATION OF THE NONLINEAR MUSKINGUM MODEL USING HARMONY SEARCH1

ABSTRACT: A newly developed heuristic algorithm, Harmony Search, is applied to the parameter estimation problem of the nonlinear Muskingum model. Harmony Search found better values of parameters in the nonlinear Muskingum model than five other methods including another heuristic method, genetic algorithm, in terms of SSQ (the sum of the square of the deviations between the observed and routed outflows), SAD (the sum of the absolute value of the deviations between the observed and routed outflows), DPO (deviations of peak of routed and actual flows), and DPOT (deviations of peak time of routed and actual outflow). Harmony Search also has the advantage that it does not require the process of assuming the initial values of design parameters. The sensitivity analysis of Harmony Memory Considering Rate showed that relatively large values of Harmony Memory Considering Rate makes the Harmony Search converge to a better solution.     

Hematology and Some Biochemical Parameters of Wild Rodents in Pistachio Gardens of Kerman Province, Southeast Iran

Wild rodents were collected using live snap traps in pistachio gardens of Kerman Province, Southeast Iran from 2007 to 2009, then some physiological parameters of them were measured. The samples were identified as follow: Nesokia indica, Meriones persicus, Meriones lybicus and Tatera indica. Blood samples were obtained from the heart, then the blood parameters (glucose, cholesterol, triglyceride, total protein, HDL, red and white blood cell number) in wild species of rodents and laboratory rat were compared. The results showed that there were no significant differences in serum glucose, triglyceride, HDL and total protein levels among different experimental groups. The concentration of cholesterol in T. indica was more than that in N. indica (P < 0.01). The total numbers of red blood cells also showed significant difference between wild garden rodent species and laboratory rat (P < 0.01), while the numbers of white blood cells showed no significant difference.     

A NEW LOOK AT FLOOD RISK DETERMINATION1

ABSTRACT: The probability distributions of annual peak flows used in flood risk analysis quantify the risk that a design flood will be exceeded. But the parameters of these distributions are themselves to a degree uncertain and this uncertainty increases the risk that the flood protection provided will in fact prove to be inadequate. The increase in flood risk due to parameter uncertainty is small when a fairly long record of data is available and the annual flood peaks are serially independent, which is the standard assumption in flood frequency analysis. But standard tests for serial independence are insensitive to the type of grouping of high and low values in a time series, which is measured by the Hurst coefficient. This grouping increases the parameter uncertainty considerably. A study of 49 annual peak flow series for Canadian rivers shows that many have a high Hurst coefficient. The corresponding increase in flood risk due to parameter uncertainty is shown to be substantial even for rivers with a long record, and therefore should not be neglected. The paper presents a method of rationally combining parameter uncertainty due to serial correlation, and the stochastic variability of peak flows in a single risk assessment. In addition, a relatively simple time series model that is capable of reproducing the observed serial correlation of flood peaks is presented.     

GEOLOGIC INFERENCE FROM “FLOW NET” TRANSMISSIVITY DETERMINATION: THREE CASE STUDIES1

A graphical inverse method for determining the regional transmissivity distribution was applied to three field problems. The study areas were the Hanford Site, Washington; the Rocky Mountain Arsenal, Colorado; and the Nevada Test Site, Nevada. This method can aid in flow system conceptualization by revealing the location of bedrock controls for groundwater flow. It is a valuable tool for aiding the hydrogeologist in asking questions about the nature of trends in the pattern of transmissivity values. Quantitative estimates of regional transmissivities can be used as starting points for further parameter refinement. Sensitivity analysis using Monte Carlo simulation shows that quantitative estimates of transmissivity can be obtained when measurement error in the hydraulic head does not cause a large error in the hydraulic gradient.     

PARAMETRIC ESTIMATION OF COVARIANCES OF REGIONALIZED VARIABLES1

ABSTRACT: The usefulness of stochastic models in describing the spatial variability of hydrogeologic quantities, such as permeability, storativity, piezometric head, seepage velocity, and solute concentrations is now widely recognized. In practice, these quantities are represented as the sum of a well-structured component, or drift, and a more erratic fluctuation component which is described statistically through its covariance function. This paper reviews some of the most recent and most promising methods for the estimation of parameters of these covariances from existing data. They are maximum likelihood, restricted maximum likelihood, minimum-variance unbiased quadratic estimation, and minimum-norm (weighted least squares) estimation. The applicability of such methods to conditional and unconditional probability problems is discussed.     

IDENTIFICATION OF AQUIFER DISPERSIVITIES: METHODS OF ANALYSIS AND PARAMETER UNCERTAINTY1

ABSTRACT: This paper presents a method for estimating aquifer dispersivities in solute transport models. Sensitivity equations are derived for the calculation of sensitivity coefficients. A modified Gauss-Newton algorithm is used to perform the least-squares minimization. A statistical procedure is outlined to assess reliability of the estimated parameters. The solute transport model is solved by the upstream weighted, multiple cell balance method which combines the concepts of local mass balance and finite element approximations. A one-dimensional solute transport problem in a vertical column system is first used to illustrate the inverse technique. A second example considers the parameter identification problem for three-dimensional solute transport with a unidirectional steady and uniform flow field. The third example solves the parameter identification problem in a three-dimensional, stream-aquifer, solute transport system with steady state flow. Numerical experiments are conducted to study data requirements for parameter identification.     

KRIGED ESTIMATES OF TRANSMISSIVITY IN THE MESILLA BOLSON,NEW MEXICO1

ABSTRACT: Kriging utilizes a statistically based procedure of spatial interpolation that incorporates the spatial correlation structure of the phenomenon, and provides an error estimate. Kriging was applied to a total of 141 transmissivity values in an attempt to quantify the transmissivity distribution of the Santa Fe aquifer in Mesilla Bolson. New Mexico. The analysis produced contour maps of estimated transmissivity values and associated estimation variances. Through variogram analysis and fitting of an exponential variogrsm to 141 natural log of transmissivity (InT) values, the range was determined to be 3 miles, the average variance 2.74 (σ_InT= 1.65) with a mean of 8.65. Kriged estimates were generally lower when compared to estimates based on available transmissivity maps.