土地利用/覆盖变化对长江上游非点源污染影响研究

在国内外相关研究的基础上,利用输出系数模型,结合RS和GIS技术,对长江上游的非点源污染负荷进行了空间模拟和负荷估算.模拟结果表明,在不考虑流域损失的前提下,由于土地利用造成的非点源污染负荷TN总量从20世纪70年代的123万t下降至2000年的116万t,基本呈逐年减少的趋势,由土地利用造成的TP的变化趋势与TN基本相同,从70年代的3.7万t下降到2000年的3.5万t左右.就省份、土地利用类型和水系而言,四川省、种植用地和草地以及金沙江水系和嘉陵江水系对长江上游的非点源污染贡献较大.在非点源污染负荷强度上,重庆市和嘉陵江水系单位面积负荷最高,是今后应重点治理的地区.结果表明,该模型可以对长江上游这样的超大尺度空间的非点源污染进行较好的空间模拟.     

GIS在城市生活垃圾回收方案规划研究中的应用——以重庆市主城区为例

以重庆市主城区中的三个区为例,演示了GIS在城市生活垃圾回收方案规划研究中的应用:利用GIS的分类、计算和重叠功能,模拟各垃圾回收方案的运行,预测运行结果;利用GIS的可视化功能,将回收行为的空间分布以形象直观的地图形式表现出来,能为管理部门决策制定规划方案和管理措施提供有力的支持与依据。     

Definition of Homogeneous Environmental Management Units for the Catalan Coast

Geographical areas constitute the basic implementation locus for integrated coastal zone management strategies and activities. Because the definition of territorial planning objectives may be affected by socioeconomic and environmental characteristics, one of the main steps in the process involves dividing the coast into homogeneous environmental management units (HEMUs). This article presents a general and simple method for regionalizing the landside of a coastal zone into HEMUs and illustrates it through application to the Catalan coast. Socioeconomic and natural (biophysical) subsystems were selected as the most appropriate dimensions of the regionalization process. Dimensions were described using 11 spatial themes, which were managed in a geographic information system environment that proved to be an adequate tool for the purpose. A final coastal zone map of four classes of HEMUs connected to local administrative units was obtained, and because it reflects the current natural and socioeconomic dynamics, it can be considered as an initial step in the planning process for the Catalan coast. Although the proposed method was developed based on the characteristics of the Catalan coast, it is general enough to be adapted and applied to most developed or developing coastal areas.     

Synergistic Techniques for Better Understanding and Classifying the Environmental Structure of Landscapes

The desire to capture natural regions in the landscape has been a goal of geographic and environmental classification and ecological land classification (ELC) for decades. Since the increased adoption of data-centric, multivariate, computational methods, the search for natural regions has become the search for the best classification that optimally trades off classification complexity for class homogeneity. In this study, three techniques are investigated for their ability to find the best classification of the physical environments of the Mt. Lofty Ranges in South Australia: AutoClass-C (a Bayesian classifier), a Kohonen Self-Organising Map neural network, and a k-means classifier with homogeneity analysis. AutoClass-C is specifically designed to find the classification that optimally trades off classification complexity for class homogeneity. However, AutoClass analysis was not found to be assumption-free because it was very sensitive to the user-specified level of relative error of input data. The AutoClass results suggest that there may be no way of finding the best classification without making critical assumptions as to the level of class heterogeneity acceptable in the classification when using continuous environmental data. Therefore, rather than relying on adjusting abstract parameters to arrive at a classification of suitable complexity, it is better to quantify and visualize the data structure and the relationship between classification complexity and class homogeneity. Individually and when integrated, the Self-Organizing Map and k-means classification with homogeneity analysis techniques also used in this study facilitate this and provide information upon which the decision of the scale of classification can be made. It is argued that instead of searching for the elusive classification of natural regions in the landscape, it is much better to understand and visualize the environmental structure of the landscape and to use this knowledge to select the best ELC at the required scale of analysis.     

Effects of calibration on L-THIA GIS runoff and pollutant estimation

Urbanization can result in alteration of a watershed's hydrologic response and water quality. To simulate hydrologic and water quality impacts of land use changes, the Long-Term Hydrologic Impact Assessment (L-THIA) system has been used. The L-THIA system estimates pollutant loading based on direct runoff quantity and land use based pollutant coefficients. The accurate estimation of direct runoff is important in assessing water quality impacts of land use changes. An automated program was developed to calibrate the L-THIA model using the millions of curve number (CN) combinations associated with land uses and hydrologic soil groups. L-THIA calibration for the Little Eagle Creek (LEC) watershed near Indianapolis, Indiana was performed using land use data for 1991 and daily rainfall data for six months of 1991 (January 1-June 30) to minimize errors associated with use of different temporal land use data and rainfall data. For the calibration period, the Nash-Sutcliffe coefficient was 0.60 for estimated and observed direct runoff. The calibrated CN values were used for validation of the model for the same year (July 1-December 31), and the Nash-Sutcliffe coefficient was 0.60 for estimated and observed direct runoff. The Nash-Sutcliffe coefficient was 0.52 for January 1, 1991 to December 31, 1991 using uncalibrated CN values. As shown in this study, the use of better input parameters for the L-THIA model can improve accuracy. The effects on direct runoff and pollutant estimation of the calibrated CN values in the L-THIA model were investigated for the LEC. Following calibration, the estimated average annual direct runoff for the LEC watershed increased by 34%, total nitrogen by 24%, total phosphorus by 22%, and total lead by 43%. This study demonstrates that the L-THIA model should be calibrated and validated prior to application in a particular watershed to more accurately assess the effects of land use changes on hydrology and water quality.     

基于GIS的乌梁素海水体富营养化状况的模糊模式识别

利用基于MATLAB 7.0实现的模糊模式识别交叉迭代模型对2006年5—10月分布于乌梁素海各水体功能区的21个水质监测点的富营养化等级进行了模糊模式识别,并在GIS技术支持下,用Arcview的空间分析功能绘制富营养化等级识别结果、对富营养化状态影响权重最大的评价指标总氮(TN)含量和富营养化状态控制元素总磷(TP)含量对应的富营养化等级水体和地表水质等级水体的空间分布月变化Grid专题图.将富营养化状态变化及其分布区域与乌梁素海同期水质监测指标浓度变化及其分布区域进行对比分析及验证.结果表明,富营养化等级识别结果的时空分布与实际情况相符,能够比较准确地反映富营养化等级定量的时空变化趋势.      

水环境数学模型与GIS的集成研究

从地理信息系统(GIS)的功能特点、环境信息的特性和水环境数学模型软件应用时存在不足的3个方面分析了GIS和水环境数学模型软件集成的必要性.介绍了GIS和水环境数学模型的集成方式以及苏州河三期综合整治决策支持系统的主要功能结构,重点从GIS和水环境数学模型集成方面介绍了地图矢量化、模型计算结果转化为相应数据库文件、数据库文件和GIS实现绑定以及断面水位动态演示的实现方法和重要作用.     

基于GIS的松花江干流暴雨洪涝灾害风险评估

以黑龙江省内的松花江干流流域作为研究区,从现代灾害风险理论出发,综合运用GIS空间分析和灾害风险评估数学方法,对松花江干流流域的暴雨洪涝灾害风险进行了定量评价.研究利用遥感数据和社会经济数据,在空间分析基础上,通过对暴雨洪涝灾害的危险性、承灾体的暴露性、脆弱性以及区域防灾减灾能力的分析,确定影响各个县市暴雨洪涝灾害风险的具体方面.并将因子集成为洪涝灾害风险指数(FDRI),在此基础之上绘制出松花江干流流域的洪涝灾害风险区划图,结果表明松花江干流洪涝灾害风险以哈尔滨和佳木斯两市最大,上游洪涝灾害风险大于下游.     

Survivor needs or logistical convenience? Factors shaping decisions to deliver relief to earthquake‐affected communities,Pakistan 2005–06

In Bureaucratizing the Good Samaritan , Waters (2001) argues that bureaucratic rationality distracts humanitarian agencies from the needs of the people they are supposed to assist, in favour of other values that their institutional frameworks dictate. We test his claim by investigating the response to the Pakistan 2005 earthquake. One of us (Dittemore) worked with the United Nations Joint Logistics Centre in the theatre, managing a relief cargo shipment database. The response, known as 'Operation Winter Race', was hampered by extreme logistical challenges, but ultimately succeeded in averting a second disaster resulting from cold and starvation. We use statistical models to probe whether survivor needs significantly guided decisions to deliver relief to affected communities. Needs assessments remained incomplete and incoherent. We measure needs through proxy indicators and integrate them, on a Geographic Information System (GIS) platform, with logistics and relief delivery data. We find that, despite strong logistics effects, needs orientations were significant. However, the strength of decision factors varies between commodity types (food versus clothing and shelter versus reconstruction materials) as well as over the different phases of the response. This study confirms Thomas's observation that logistics databases are rich 'repositories of data that can be analyzed to provide post-event learning' (Thomas, 2003, p. 4). This article is an invitation for others to engage in creative humanitarian data management.     

基于GIS技术的汶川8.0级地震诱发地质灾害危险性评价——以四川省安县为例

通过汶川8．0级地震后对绵阳市安县地震诱发地质灾害的应急调查和遥感解译,共获得地质灾害点187处。在此基础上,利用GIS技术对地震诱发地质灾害分布与发震断裂距离、坡度、岩性、水系等因素的关系进行统计分析。结果表明,地震诱发地质灾害在区域上沿断裂带呈带状分布和沿水系呈线状分布的特点;地震诱发的地质灾害与地形坡度有很大的关系,绝大部分的灾害点集中在15°-45°的范围内;地震诱发的地质灾害与地形有很好的对应关系,北部高山地区地质灾害数量明显高于南部平原区;滑坡多发生在千枚岩、泥页岩等软岩中且多为土质滑坡,而岩浆岩等硬岩中多发生崩塌。