浑善达克沙地黄柳人工林根系分布及生物量研究

 对浑善达克沙地一年生、二年生、三年生黄柳人工林根系分布及生物量进行了调查、测定。结果表明:①在流动沙丘上人工栽植的黄柳,其根系主要由直径小于2.0 mm的中根和细根构成,其数目占剖面根系总数的90%以上,其中又以直径小于0.5 mm细根所占比例最高,其数目占剖面根系总数的50%以上。②根系主要分布在10～30 cm的土层中,可见其根系主要吸收10～30 cm土层内的水分和养分。③根系重量在各土层中呈随土层深度加深而降低的分布趋势。④根系主要呈水平分布,水平根非常发达,随着植株的不断生长,这一特征越来越明显。     

Low complexity adaptive equalizers for underwater acoustic communications

Interference signals due to scattering from surface and reflecting from bottom is one of the most important problems of reliable communications in shallow water channels. To solve this problem, one of the best suggested ways is to use adaptive equalizers. Convergence rate and misadjustment error in adaptive algorithms play important roles in adaptive equalizer performance. In this paper, affine projection algorithm （APA）, selective regressor APA（SR-APA）, family of selective partial update （SPU） algorithms, family of set-membership （SM） algorithms and selective partial update selective regressor APA （SPU-SR-APA） are compared with conventional algorithms such as the least mean square （LMS） in underwater acoustic communications. We apply experimental data from the Strait of Hormuz for demonstrating the efficiency of the proposed methods over shallow water channel. We observe that the values of the steady-state mean square error （MSE） of SR-APA, SPU-APA0 SPU-normalized least mean square （SPU-NLMS）, SPU-SR-APA0 SM-APA and SM-NLMS algorithms decrease in comparison with the LMS algorithm. Also these algorithms have better convergence rates than LMS type algorithm.     

1960—2010年孝感地区的气温变化特征

根据湖北省孝感气象观测站1960—2010年逐月平均气温数据,利用趋势分析、小波变换、M-K突变检验等方法,研究了孝感市近50年来平均气温的时间变化特征。结果表明：孝感市年平均气温总体以0.017℃/年的速率上升,其中冬、春季上升幅度最为显著,夏季呈微下降趋势。年代际尺度上40年以上的周期振荡起主导作用,年际尺度上主要表现为准4年、准2~4年或准3年的周期变化。1997年是年平均气温发生突变的节点。     

Bias-Corrected Short-Range Ensemble Forecasts for Near-Surface Variables during the Summer Season of 2010 in Northern China

A running mean bias （RMB） correction ap- proach was applied to the forecasts of near-surface variables in a seasonal short-range ensemble forecasting experiment with 57 consecutive cases during summer 2010 in the northern China region. To determine a proper training window length for calculating RMB, window lengths from 2 to 20 days were evaluated, and 16 days was taken as an optimal window length, since it receives most of the benefit from extending the window length. The raw and 16-day RMB corrected ensembles were then evaluated for their ensemble mean forecast skills. The results show that the raw ensemble has obvious bias in all near-surface variables. The RMB correction can remove the bias reasonably well, and generate an unbiased ensemble. The bias correction not only reduces the ensemble mean forecast error, but also results in a better spreaderror relationship. Moreover, two methods for computing calibrated probabilistic forecast （PF） were also evaluated through the 57 case dates： 1） using the relative frequency from the RMB-eorrected ensemble; 2） computing the forecasting probabilities based on a historical rank histogram. The first method outperforms the second one, as it can improve both the reliability and the resolution of the PFs, while the second method only has a small effect on the reliability, indicating the necessity and importance of removing the systematic errors from the ensemble.     

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Hyperspectral imagery for disaggregation of land surface temperature with selected regression algorithms over different land use land cover scenes

Land surface temperature (LST), a key parameter in understanding thermal behavior of various terrestrial processes, changes rapidly and hence mapping and modeling its spatio-temporal evolution requires measurements at frequent intervals and finer resolutions. We designed a series of experiments for disaggregation of LST (DLST) derived from the Landsat ETM + thermal band using narrowband reflectance information derived from the EO1-Hyperion hyperspectral sensor and selected regression algorithms over three geographic locations with different climate and land use land cover (LULC) characteristics. The regression algorithms applied to this end were: partial least square regression (PLS), gradient boosting machine (GBM) and support vector machine (SVM). To understand the scale dependence of regression algorithms for predicting LST, we developed individual models (local models) at four spatial resolutions (480 m, 240 m, 120 m and 60 m) and tested the differences between these using RMSE derived from cross-validated samples. The sharpening capabilities of the models were assessed by predicting LST at finer resolutions using models developed at coarser spatial resolution. The results were also compared with LST produced by DisTrad sharpening model. It was found that scale dependence of the models is a function of the study area characteristics and regression algorithms. Considering the sharpening experiments, both GBM and SVM performed better than PLS which produced noisy LST at finer spatial resolutions. Based on the results, it can be concluded that GBM and SVM are more suitable algorithms for operational implementation of this application. These algorithms outperformed DisTrad model for heterogeneous landscapes with high variation in soil moisture content and photosynthetic activities. The variable importance measure derived from PLS and GBM provided insights about the characteristics of the relevant bands. The results indicate that wavelengths centered around 457, 671, 1488 and 2013–2083 nm are the most important in predicting LST. Nevertheless, further research is needed to improve the performance of regression algorithms when there is a large variability in LST and to examine the utility of narrowband vegetation indices to predict the LST. The benefits of this research may extend to applications such as monitoring urban heat island effect, volcanic activity and wildfire, estimating evapotranspiration and assessing drought severity.     

利用加权平均温度研究探讨GPS大气水汽探测方法

本文基于探空数据,以广东省GPS气象监测网建设项目为基础,研究加权平均温度本地化问题,建立了广东省加权平均温度本地化模型并得出广东地区的加权平均温度本地化模型,确立了模型的相关系数。本文的研究成果已经在广东省GPS气象监测网数据处理中得到应用。     

最小二乘曲面拟合的LiDAR数据滤波方法

机载激光扫描技术(LiDAR)可直接获取数字表面模型,但要获得高精度的数字地面模型(DTM)需要滤波去除地物点,这是一项十分复杂和耗时的工作。本文提出一种基于二次多项式的局部最小二乘拟合滤波算法,对数据进行规则格网化处理,对每个格网的数据进行局部最小二乘拟合滤波处理,并缩小格网大小迭代进行滤波。实验结果表明提出的方法能得到很好的效果。     

Correlation in spectral accelerations for earthquakes in Europe

The shape of a uniform hazard spectrum has been criticized to be unrealistic for a site where the spectral ordinates of the uniform hazard spectrum at different periods are governed by different scenario events and conservative for long‐return‐period earthquake shaking. The conditional mean spectrum considering epsilon (CMS‐ε) takes into account the correlation of spectral demands (represented by values of ε) at different periods, to address these issues. This paper proposes new prediction models for the correlation coefficient of ε(T₁) and ε(T₂), a key component for developing a CMS, using Pan‐European earthquake records from a European ground motion database. Epsilon (ε) for each record is computed using the 2005 Ambraseys ground‐motion prediction equation. The model can be used to develop CMS for European sites, and it can be incorporated in the European seismic standards. Copyright © 2012 John Wiley & Sons, Ltd.     

A stratified optimization method for a multivariate marine environmental monitoring network in the Yangtze River estuary and its adjacent sea

An efficient monitoring network is very important in accessing the marine environmental quality and its protection and management. In an estuary, there are fronts that separate distinctly different water masses and affect material transport, nutrient distribution, pollutant aggregation, and diffusion. This stratified heterogeneous surface neither satisfies the stationary requirements of kriging, nor can be handled adequately by removing a spatially continuous trend. This article presents a stratified optimization method for a multivariate monitoring network. In this method, principal component analysis (PCA) was used to reduce the dimensionality of the correlated targets, and the mean of surface with nonhomogeneity (MSN) method was adopted to produce the best linear unbiased estimator for a spatially stratified heterogeneous surface that failed to satisfy the requirements for a kriging estimate. The existing monitoring network in the Yangtze River estuary and its adjacent sea, which was designed by purposive sampling year ago was optimized as an illustration. The optimization consisted of two steps: reduce the redundant monitoring sites and then optimally add new sites to the remaining sites. After optimization, the inclusion of 51 sites in the monitoring network was found to produce a smaller total estimated error than that of the current network, which has 70 sites; moreover, the use of 55 sites can produce a higher precision of estimation for all three principal components (PCs) than that of the current 70 sites. The results demonstrated that the proposed method is suitable for optimizing environmental monitoring sites that have dominant stratified nonhomogeneity and that involve multiple factors.