1960—2009年广西霾日时空变化特征

利用1960—2009年广西80个地面观测站资料,运用EOF、线性倾向估计等统计方法,分析了近50年广西霾日数的时空变化特征。结果表明:近50年广西霾日数总体呈上升趋势,与该地区人类活动和经济发展引起的污染排放增长密切相关;空间分布呈现出主要城市及其周边地区霾日多,边远地区及沿海地区霾日少的特点;广西霾天气主要发生在秋、冬季,以轻微霾 (能见度为5~10 km) 为主,且霾天气发生时的相对湿度8成以上介于70%~90%;除了空气污染之外,近50年风速呈下降趋势可能也导致更多的霾天气。     

Inverse problem in hydrogeology

The state of the groundwater inverse problem is synthesized. Emphasis is placed on aquifer characterization, where modelers have to deal with conceptual model uncertainty (notably spatial and temporal variability), scale dependence, many types of unknown parameters (transmissivity, recharge, boundary conditions, etc.), nonlinearity, and often low sensitivity of state variables (typically heads and concentrations) to aquifer properties. Because of these difficulties, calibration cannot be separated from the modeling process, as it is sometimes done in other fields. Instead, it should be viewed as one step in the process of understanding aquifer behavior. In fact, it is shown that actual parameter estimation methods do not differ from each other in the essence, though they may differ in the computational details. It is argued that there is ample room for improvement in groundwater inversion: development of user-friendly codes, accommodation of variability through geostatistics, incorporation of geological information and different types of data (temperature, occurrence and concentration of isotopes, age, etc.), proper accounting of uncertainty, etc. Despite this, even with existing codes, automatic calibration facilitates enormously the task of modeling. Therefore, it is contended that its use should become standard practice.

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Resumen Se sintetiza el estado del problema inverso en aguas subterráneas. El énfasis se ubica en la caracterización de acuíferos, donde los modeladores tienen que enfrentar la incertidumbre del modelo conceptual (principalmente variabilidad temporal y espacial), dependencia de escala, muchos tipos de parámetros desconocidos (transmisividad, recarga, condiciones limitantes, etc), no linealidad, y frecuentemente baja sensibilidad de variables de estado (típicamente presiones y concentraciones) a las propiedades del acuífero. Debido a estas dificultades, no puede separarse la calibración de los procesos de modelado, como frecuentemente se hace en otros campos. En su lugar, debe de visualizarse como un paso en el proceso de entendimiento del comportamiento del acuífero. En realidad, se muestra que los métodos reales de estimación de parámetros no difieren uno del otro en lo esencial, aunque sí pueden diferir en los detalles computacionales. Se discute que existe amplio espacio para la mejora del problema inverso en aguas subterráneas: desarrollo de códigos amigables al usuario, acomodamiento de variabilidad a través de geoestadística, incorporación de información geológica y diferentes tipos de datos (temperatura, presencia y concentración de isótopos, edad, etc), explicación apropiada de incertidumbre, etc. A pesar de esto, aún con los códigos existentes, la calibración automática facilita enormemente la tarea de modelado. Por lo tanto, se sostiene que su uso debería de convertirse en práctica standard.

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Résumé Létat du problème inverse des eaux souterraines est synthétisé. Laccent est placé sur la caractérisation de laquifère, où les modélisateurs doivent jouer avec lincertitude des modèles conceptuels (notamment la variabilité spatiale et temporelle), les facteurs déchelle, plusieurs inconnues sur différents paramètres (transmissivité, recharge, conditions aux limites, etc.), la non linéarité, et souvent la sensibilité de plusieurs variables détat (charges hydrauliques, concentrations) des propriétés de laquifère. A cause de ces difficultés, le calibrage ne peut être séparé du processus de modélisation, comme cest le cas dans dautres cas de figure. Par ailleurs, il peut être vu comme une des étapes dans le processus de détermination du comportement de laquifère. Il est montré que les méthodes dévaluation des paramètres actuels ne diffèrent pas si ce nest dans les détails des calculs informatiques. Il est montré quil existe une large panoplie de techniques d ‹inversion : codes de calcul utilisables par tout-un-chacun, accommodation de la variabilité via la géostatistique, incorporation dinformations géologiques et de différents types de données (température, occurrence, concentration en isotopes, âge, etc.), détermination de lincertitude. Vu ces développements, la calibration automatique facilite énormément la modélisation. Par ailleurs, il est souhaitable que son utilisation devienne une pratique standardisée.

     

基于神经网络模型的湛江湾水体有色溶解有机物的遥感估算

有色溶解有机物(Colored Dissolved Organic Matter, CDOM)是水体中重要的水质参数之一,是水色遥感的重要研究对象,如何构建适合特定区域的近海二类水体CDOM反演模型一直是国内外研究难点。本文利用2017年5月26～29日对南海西北部海域湛江湾20个站位采集的水样和测量的光谱资料,分析归一化遥感反射率与CDOM浓度a_g(400)的相关性,发现最大负相关系数出现在586nm处,选择580、585、590、595nm这四个波段处的归一化遥感反射率与a_g(400)建立了多元线性回归模型、BP(Back-Propagation)神经网络模型和RBF(Radial-Basis Function)神经网络模型,并与其他算法模型进行对比分析。结果发现, BP和RBF神经网络模型的平均相对误差和均方根误差均远小于多元线性回归模型和其他算法模型,神经网络模型的预测值与实测值拟合效果要优于多元线性回归模型。研究表明,神经网络模型更适合于湛江湾有色溶解有机物的遥感估算。     

Bias estimation and correction for triangle-based surface area calculations

The calculation of surface area is meaningful for a variety of space-filling phenomena, e.g., the packing of plants or animals within an area of land. With Digital Elevation Model (DEM) data we can calculate the surface area by using a continuous surface model, such as by the Triangulated Irregular Network (TIN). However, just as the triangle-based surface area discussed in this paper, the surface area is generally biased because it is a nonlinear mapping about the DEM data which contain measurement errors. To reduce the bias in the surface area, we propose a second-order bias correction by applying nonlinear error propagation to the triangle-based surface area. This process reveals that the random errors in the DEM data result in a bias in the triangle-based surface area while the systematic errors in the DEM data can be reduced by using the height differences. The bias is theoretically given by a probability integral which can be approximated by numerical approaches including the numerical integral and the Monte Carlo method; but these approaches need a theoretical distribution assumption about the DEM measurement errors, and have a very high computational cost. In most cases, we only have variance information on the measurement errors; thus, a bias estimation based on nonlinear error propagation is proposed. Based on the second-order bias estimation proposed, the variance of the surface area can be improved immediately by removing the bias from the original variance estimation. The main results are verified by the Monte Carlo method and by the numerical integral. They show that an unbiased surface area can be obtained by removing the proposed bias estimation from the triangle-based surface area originally calculated from the DEM data.     

The identification of conceptual hydrological models for surface water acidification

Ambiguity in parameter identification represents a potentially serious limitation to the application of models of surface water acidification. Previous work has concentrated on manipulation of two of the three factors affecting model identifiability, namely model structure and estimator properties. A new technique is proposed which uses different modes of response within the data to improve parameter identification. Preliminary results, obtained using the Birkenes model of surface water acidification, appear to show promise. The technique is robust in recovering model parameters from synthetic data, with and without error, and in assimilating problems of structural error.     

Minimum-variance unbiased quadratic estimation of covariances of regionalized variables

The parameters of covariance functions (or variograms) of regionalized variables must be determined before linear unbiased estimation can be applied. This work examines the problem of minimum-variance unbiased quadratic estimation of the parameters of ordinary or generalized covariance functions of regionalized variables. Attention is limited to covariance functions that are linear in the parameters and the normality assumption is invoked when fourth moments of the data need to be calculated. The main contributions of this work are (1) it shows when and in what sense minimum-variance unbiased quadratic estimation can be achieved, and (2) it yields a well-founded, practicable, and easy-to-automate methodology for the estimation of parameters of covariance functions. Results of simulation studies are very encouraging.     

On the joint robust estimation of parameters and variance

A method of joint estimation of the parameters and the variance σ² in the linear model is presented in this paper. The M-estimators with finite rejection points are adopted for estimating the parameters. In order to estimate the variance σ², the Grubbs statistics and the Kurtosis test statistics are adopted to test the residual sequence {;r_i};. And the sample variance of {;r_i}; after discarding the outliers is taken as the estimation of σ². This method of estimating σ² is less computational demanding and more accurate, compared with the well-known method which takes 1.483 med_i |r_i| as the estimation of σ. The breakdown point of the above estimation of σ² is more than 20%, while the brekdown point is less than 10%, if only the Grubbs statistics is used.     

2006―2016年粤港澳大湾区城市不透水面 时空变化与驱动力分析

以遥感和地理信息系统技术为支撑,从城市地理学视角对粤港澳大湾区城市不透水面的时空变化状况进行分析,并探讨了不透水面变化的驱动力。结果表明：1）湾区内城市不透水面扩张现象明显,面积从2006年的12 127.69 km2增加到2016年的20 188.87 km2,年均增长速度高达6.65%;2）区域内城市不透水面空间分布越来越集中,呈现高密度区域聚集;3）交通道路（省道及以上级别）与城市不透水面扩张的速度和方向关系密切,其中与国道的关系最为紧密;4）国民生产总值（GDP）和常住人口的增长都对城市不透水面的扩张产生深刻的正向影响,相关系数分别为0.840 7和0.834 1;5）近10年来,粤港澳大湾区城市不透水面的空间格局变化显著,湾区各城市的发展阶段存在差异性,在未来城市发展中应该协调好各城市不透水面的扩张范围和速度,避免过高不透水面带来生态环境问题。     

MLP在雷达定量降水估测中的应用

通过临近时次、临近空间降水回波性质相似假设,以人工智能技术结合快速动态分级法(Fast Dynamic Categorical method,FDC)为核心,设计广西区域分钟级雷达定量降水估测产品模型。在单部雷达估测降水时,分两层三次使用感知器寻求合理的降水类型分类Z-I关系,首先将FDC看为一种多分类算法,根据单站雷达各级回波的降水估测结果误差将回波区内的站点分为强站点和弱站点两类,然后分别对这两类站点再次使用FDC建立新的强、弱两类Z-I关系。在多部雷达组网联合估测定量降水时,将各雷达估测值等权重看待,将单部雷达估测作为一个分支,通过连结方式构建一个多层感知器(Multilayer Perceptron,MLP)。无站点回波区采用K近邻算法(K-Nearest Neighbor,KNN)选择合适的MLP求得的Z-I关系估算降水量。对2019年3—10月试验产品进行检验分析,结果表明以区域站组成的训练组小时降水相关系数达0.973 7,以国家级气象台站组成的测试组相关系数达0.825 6。