On the choice of data transformation for modelling time series of significant wave height

Autoregressive models have been shown to adequately model the time series of significant wave height. However, since this series exhibits a seasonal component and has a non-gaussian nature, it is necessary to transform the series before a model can be fit to the data. Two different transformations that have been used in earlier work are shown not to be appropriate for all types of applications. A third transformation is proposed here, which combines the better features of the two earlier ones and which is appropriate for simulation work. This is demonstrated with an example of a series from Figueira da Foz, a location of the Portuguese Coast.     

Multicriteria sensitivity analysis as a diagnostic tool for understanding model behaviour and characterizing model uncertainty

Complex hydrological models are being increasingly used nowadays for many purposes such as studying the impact of climate and land‐use change on water resources. However, building a high‐fidelity model, particularly at large scales, remains a challenging task, due to complexities in model functioning and behaviour and uncertainties in model structure, parameterization, and data. Global sensitivity analysis (GSA), which characterizes how the variation in the model response is attributed to variations in its input factors (e.g., parameters and forcing data), provides an opportunity to enhance the development and application of these complex models. In this paper, we advocate using GSA as an integral part of the modelling process by discussing its capabilities as a tool for diagnosing model structure and detecting potential defects, identifying influential factors, characterizing uncertainty, and selecting calibration parameters. Accordingly, we conduct a comprehensive GSA of a complex land surface–hydrology model, Modélisation Environmentale–Surface et Hydrologie (MESH), which combines the Canadian land surface scheme with a hydrological routing component, WATROUTE. Various GSA experiments are carried out using a new technique, called Variogram Analysis of Response Surfaces, for alternative hydroclimatic conditions in Canada using multiple criteria, various model configurations, and a full set of model parameters. Results from this study reveal that, in addition to different hydroclimatic conditions and SA criteria, model configurations can also have a major impact on the assessment of sensitivity. GSA can identify aspects of the model internal functioning that are counter‐intuitive and thus help the modeller to diagnose possible model deficiencies and make recommendations for improving development and application of the model. As a specific outcome of this work, a list of the most influential parameters for the MESH model is developed. This list, along with some specific recommendations, is expected to assist the wide community of MESH and Canadian land surface scheme users, to enhance their modelling applications.     

青藏高原及周缘地壳浅层构造应力场量值特征分析

以"中国大陆地壳应力环境基础数据库"中的实测地应力数据为基础,合理筛选出地理空间范围为21°N-40°N,73°N—110°N的近2000条数据,深度范围0~2 km.通过将研究区内实测地应力扣除重力影响,并考虑数据样本数量沿深度分布不均匀的问题,分析构造应力场的作用.重力影响的扣除采用海姆假说与金尼克假说两种模式估算其下限与上限,给出了青藏高原及周缘及青藏地块、南北地震带北、中、南段上构造应力和构造差应力随深度分布的特征和量值范围,结果显示:(1)青藏高原及周缘地区最大水平应力σ_H、最小水平应力σ_h随深度D呈线性增加:σ_H=22.115D+5.761、σ_h=14.893D+3.269;最大水平构造应力σ_T、最小水平构造应力σ_t的量值估算范围分别为4.609σ_T15.522D+4.609、3.121σ_t6.366D+3.121(D0);构造差应力σ_T-σ_t=7.222D+2.492,地表值(D=0 km)为2.5 MPa左右,随深度增加以7.2 MPa.km~(-1)的梯度增大;(2)在测量深度范围内,青藏地块、南北地震带北、中、南段研究区σ_T、σ_t、σ_T-σ_t,随埋深均呈线性增大;D=1 km时,各地块σ_T的统计回归值中最大为30.1 MPa,最小为17.6 MPa,量值由大到小排序依次为:青藏地块、南北带北段、南北带中段、南北带南段;D=1 km时,各地块σ_T-σ_t,的统计回归值中最大为15.8 MPa,最小为8.9 MPa,量值由大到小排序依次为:青藏地块、南北带北段、南北带中段和南北带南段.总体表现为青藏地块强、南北带较弱的基本特征.(3)与南北带相比较,青藏地块地壳在从南向北的挤压作用下呈现出明显的"浅弱深强"特点.     

精河6.6级地震前精河水管仪、伸缩仪异常特征分析

2017年8月9日精河发生6.6级地震。本文首先利用相关、回归分析研究了气温对水管仪、伸缩仪的影响,并进行了干扰的定量剔除;其次,通过回归残差分析研究地震前水管仪、伸缩仪的异常特征,认为精河6.6级地震前存在3项异常:①2016年5月22日起,水管仪NS分量S倾幅度0.53";②2017年3月24日起,EW分量E倾幅度0.28";③2016年9月10日起,伸缩仪NS分量出现压缩现象。     

中国大陆与各活动地块、南北地震带实测应力特征分析

本文以"中国大陆地壳应力环境基础数据库"为基础,补充了迄今为止查阅到的中国大陆水压致裂法与应力解除法的实测地应力数据,在1474个测点上得到3586条数据,研究区经度范围75°E－130°E,纬度范围18°N－47°N,深度范围0~4000 m,基本覆盖了中国大陆的各活动地块与南北地震带各段等研究区.本文采用等深度段分组归纳的方法解决了实测地应力数据样本数量沿深度分布的不均匀问题,给出了中国大陆与各研究区地壳浅层测量深度范围内应力量值、方位特征.结果显示:(1)中国大陆地壳浅层最大水平应力、最小水平应力、垂直应力随深度呈线性增加;(2)中国大陆地区侧压系数随深度的变化特征为:浅部离散,随着深度增加而集中,并趋向0.68,D=465 m是水平作用为主导向垂直作用为主导的转换深度,K_av=1;(3)中国大陆水平差应力在地表为3 MPa左右,随深度增加以5.8 MPa/km的梯度增大;(4)在测量深度范围内,中国大陆各研究区最大水平应力中间值(深度为2000 m时的统计回归值)从大到小的顺序是:青藏地块63.6 MPa、南北带北段57.3 MPa、华南地块51.4 MPa、华北地块50.5 MPa、南北带中段47.9 MPa、西域地块47.5 MPa、南北带南段45.4 MPa、东北地块44.8 MPa,总体表现为"西强东弱"的基本特征,反映了印度板块与欧亚板块的强烈碰撞是中国大陆构造应力场强度总体特征的主要来源;(5)与其他研究区相比较,青藏地块地壳在从南向北的挤压作用下呈现出明显的"浅弱深强"特点;(6)最大水平应力方向的总体特征,基本以青藏高原为中心,呈辐射状展布,由西向东,从近N-S方向逐步顺时针旋转至NNE-SSW、NE-SW、NEE-SWW、NW-SE方向,与深部的震源机制解研究结果有一致性.     

GPS垂直分量与温度、气压的相关性分析及影响机制探讨

选取新疆9个GPS观测站数据和所在地区的气温、 气压数据, 运用相关和回归分析方法进行研究, 并探讨了温度、 气压对GPS垂直分量影响的物理机制。 研究结果表明: ① GPS垂直分量周期近似为1 a的变化为气温的影响; ② 气压对GPS垂直分量的影响基本是同步的, 主要表现为短周期的震荡; ③ GPS周年振幅随着纬度的升高而增大的原因是年温差随纬度的升高而增大。     

Predicting socioeconomic conditions from satellite sensor data in rural developing countries: A case study using female literacy in Assam,India

Social data from census and household surveys provide key information for monitoring the status of populations, but the data utility can be limited by temporal gaps between surveys. Recent studies have pointed to the potential for remotely sensed satellite sensor data to be used as proxies for social data. Such an approach could provide valuable information for the monitoring of populations between enumeration periods. Field observations in Assam, north-east India suggested that socioeconomic conditions could be related to patterns in the type and abundance of local land cover dynamics prompting the development of a more formal approach. This research tested if environmental data derived from remotely sensed satellite sensor data could be used to predict a socioeconomic outcome using a generalised autoregressive error (GAR_err) model. The proportion of female literacy from the 2001 Indian National Census was used as an indicator of socioeconomic conditions. A significant positive correlation was found with woodland and a significant negative correlation with winter cropland (i.e., additional cropping beyond the normal cropping season). The dependence of female literacy on distance to nearest road was very small. The GAR_err model reduced residual spatial autocorrelation and revealed that the logistic regression model over-estimated the significance of the explanatory covariates. The results are promising, while also revealing the complexities of population–environment interactions in rural, developing world contexts. Further research should explore the prediction of socioeconomic conditions using fine spatial resolution satellite sensor data and methods that can account for such complexities.     

重庆及邻区震后烈度分布评估模型构建

选取重庆及邻区作为研究区域,将典型历史地震震例的等震线图数字化,通过回归分析分别构建了基于烈度衰减关系的烈度分布评估模型、基于等震线长短轴半径的“王景来”烈度分布评估模型和基于面积的烈度分布评估模型.最后利用算例验证了上述三种模型的适用性.震后烈度分布评估模型对于震后灾害损失评估有重要的意义.     

基于逐步回归分析的大坝变形分析模型研究

主要介绍对逐步回归分析模型的原理和基本步骤,结合大坝工程实例进行分析。引入了库水位、时效和温度三个因子建立逐步回归模型。得出了运用逐步回归模型分析大坝变形的合理性,可以达到预测的目的。     

Investigating Correlations and Variations of Air Pollutant Concentrations under Conditions of Rapid Industrialization – Kocaeli (1987–2009)

In this study, it was aimed to characterize temporal variations of air pollutants for determining contribution to pollution episodes and to obtain correlations between these pollutants. With this aim we used data analysis for measured sulfur dioxide (SO₂), particulate matter (PM, black fume and PM10), nitrogen oxides (NO_x), ozone (O₃), carbon monoxide (CO), methane (CH₄), and non‐methane hydrocarbons (NMHC) recorded in Kocaeli, one of the most industrilizated cities of Turkey. Pollutant concentrations were the results of continuous and semi‐automatic measurements. Semi‐automatic measurements of SO₂ and PM (black fume) were enclosing period from 1987 to 2008 whereas continuous monitoring of all pollutants included years of 2007–2009. In the first stage of the study daily, monthly, annual, and seasonal variations of pollution were researched. Annual average concentrations were compared with limits set by Air Quality Protection Regulation (AQPR), Air Quality Evaluation and Management Regulation (AQEMR), World Health Organization (WHO), European Union (EU), and National Ambient Air Quality Standards (USEPA). In the following stage relationships between pollutants such as NO₂–O₃, NO_x–CO, NO_x–NMHC, and NO_x–SO₂ were investigated and correlation coefficients were determined as 0.87, 0.56, 0.51, and 0.69, respectively. R² values of regression models developed from these correlations were 0.78, 0.56, 0.34, and 0.72, respectively. Vehicle density of the traffic was evaluated with NO_x–O₃ emissions and decrease was seen in NO_x emissions due to decreasing vehicle density at weekends whereas O₃ concentrations increased. These correlations enable prediction of the parameters that cannot be measured which is important for providing improvement in early warning systems.