基于DEMATEL-AHP模型的海岛绿色发展指数影响因子识别与评价

海岛绿色发展是保护海洋生态环境、建设海洋强国的重要途径和必然要求。本文提出海岛绿色发展指数（IGDI）概念,从能源消耗与资源利用、环境管理与治理、环境质量与安全、经济增长质量、绿色生活水平、公众满意度等6个方面选取22个指标构建海岛绿色发展指数评价指标体系,运用DEMATEL与AHP相结合的方法,对海岛绿色发展关键影响因子进行识别,并以12个海岛县（市、区）（以下简称“海岛县”）为例进行评价与分析。结果显示：（1）经济聚集程度（2.15）、单位GDP能耗（2.01）指标影响度大于2,环境污染治理投资占GDP比重、碳排放强度、生态损害和环境污染情况、岛陆开发程度、第三产业增加值比重、生活垃圾处理率等指标影响度均大于1,反映出环境管理与治理、能源消耗与资源利用、经济增长质量是决定海岛绿色发展成效的关键。（2）12个海岛县的海岛绿色发展指数评价值均值为74.37,其中嵊泗县的评价值最高（88.24）,崇明区的评价值最低（65.57）,反映出我国海岛绿色发展程度整体不高,海岛之间的绿色发展程度差异较大。（3）推动海岛绿色发展需坚持以人民为中心的发展思想,强化基于绿色发展理念的海岛综合管理,推动海岛产业绿色转型升级,实施海岛生态整治修复,提升海岛生态系统碳汇能力。     

基于空间分析技术的海岛地震灾害风险分析研究

我国约有1.1万个海岛,多处于地震活跃带,地震灾害特征明显,目前地震领域研究主要针对城市展开,海岛地震灾害风险的研究较少。此背景下,该研究收集了海岛地震相关的地质和地震数据,形成海岛地震灾害空间数据库;从地震危险性、承灾体易损性和防震减灾能力3个方面构建海岛地震灾害风险评价指标体系;利用ArcGIS空间分析技术,采用熵权法与AHP法相结合建立海岛地震灾害风险综合评价模型,以长岛县、洞头区和东山县为例进行分析,并将海岛地震灾害风险等级划分为4个等级。结果显示:东山县为地震灾害高风险区,洞头区和长岛县为地震灾害较低风险区。该研究对海岛工程抗震规划与设计有重要意义。     

Geospatial and geostatistical approach for groundwater potential zone delineation

Over the past few decades, groundwater has become an essential commodity owing to increased demand as a result of growing population, industrialization, urbanization and so on. The water supply situation is expected to become more severe in the future because of continued unsustainable water use and projected change in hydrometeorological parameters due to climate change. This study is based on the integrated approach of remote sensing, geographical information system and multicriteria decision‐making techniques to determine the most important contributing factors that affect the groundwater resources and to delineate the groundwater potential zones. Ten thematic layers, namely, geomorphology, geology, soil, topographic elevation (digital elevation model), land use/land cover, drainage density, lineament density, proximity of surface water bodies, surface temperature and post‐monsoon groundwater depth, were considered for the present study. These thematic layers were selected for groundwater prospecting based on the literature; discussion with the experts of the Central Ground Water Board, Government of India; field observations; geophysical investigation; and multivariate techniques. The thematic layers and their features were assigned suitable weights on Saaty's scale according to their relative significance for groundwater occurrence. The assigned weights of the layers and their features were normalized by using the analytic hierarchy process and eigenvector method. Finally, the selected thematic maps were integrated using a weighted linear combination method to create the final groundwater potential zone map. The final output map shows different zones of groundwater potential, namely, very good (16%), good (35%), moderate (28%) low (17%) and very low (2.1%). The groundwater potential zone map was finally validated using the discharge and groundwater depth data from 28 and 98 pumping wells, respectively, which showed good correlation. Copyright © 2014 John Wiley & Sons, Ltd.     

1951—2015年乌鲁木齐市升温过程频数及强度气候特征

利用乌鲁木齐市气象站1951年1月1日至2015年12月31日的逐日气温资料,以日最高气温及其升温幅度为指标,整理出乌鲁木齐市近65年升温过程数据库,将升温过程分为Ⅰ级(弱)、Ⅱ级(中等强度)、Ⅲ级(较强)、Ⅳ级(强)以及Ⅴ级(极强)5个等级,分析了乌鲁木齐市各级升温过程发生频数、持续日数、过程不同时段升温幅度、过程最高气温、过程最高气温距平偏高幅度等要素气候特征。结果如下：(1)1951—2015年,乌鲁木齐市出现升温过程5677次,平均每年87.3次,其中Ⅰ级(弱)升温过程占67.8 %。升温过程发生频数的季节分布较均匀,但在春季相对较多。近65年来,年平均升温过程发生频数在7个年代际中差异不大,没有明显的线性变化趋势。(2)1951—2015年,乌鲁木齐市5677次升温过程的平均持续日数为2.14?d,其中持续1 d的过程占43.0 %。随升温过程等级由Ⅰ级到Ⅴ级提高,过程持续日数最高出现频率也从1?d过渡到3?d。升温过程持续日数在春季4、5月份最长。(3)1951—2015年,乌鲁木齐市过程升温幅度平均为5.76℃,在春季最大、秋季最小。Ⅳ级(强)以及Ⅴ级(极强)的过程升温幅度最大的月份分别是5月和3月。65年来,乌鲁木齐市升温过程的最大24h、48h和72h升温幅度平均值分别为3.72℃、6.12℃和8.23℃,最大24 h升温幅度在冬季最大、夏季最小,最大48 h和72 h升温幅度都是在春季最大、秋季最小。(4)1951—2015年,乌鲁木齐市升温过程的最高气温平均值为14.52℃,在夏季7、8月最高,在冬季各月最低,带有显著的季节背景特征。过程最大日气温距平的平均值为2.93℃。Ⅳ级(强)和Ⅴ级(极强)升温过程的日气温距平偏高幅度最大月份分别出现在1月(11.73℃)和12月(19.10℃)。     

基于层次分析法和GIS的西昌市空间形态变化特征研究

城市空间形态变化研究涉及到自然条件、资源分布、人类活动、社会经济发展等诸多方面,运用层次分析法对复杂因素构成的整体问题进行层次分解和重新构造,建立多目标的综合评价指标体系和模型。以西昌市为例,将影响城市化发展和空间形态发展变化的特征因子概括分解为自然因子和人类活动因子两大类,并基于DEM数据分别提取出坡度、相对高程、坡向、地面粗糙度数据,利用两个不同时期的遥感数据求取出土地变化率数据,从而作为分析西昌市空间形态变化特征的驱动力因子。结合层次分析法计算出来的5个因子权重值,在ArcGIS 9.3中利用栅格计算器进行叠置分析,求取出西昌市空间形态变化模式趋势图和空间发展模式分类图。     

区域雷电灾害风险评估模型与应用

在分析国内雷电灾害风险区划发展现状进行的基础上,从区域雷电灾害事前风险评估的角度出发,采用灾害评估的承灾体、致灾体模式,引入雷电风险、地域风险和承灾体风险作为评估指标,针对每个指标选取与其发生紧密度较高的参数作为2级评价标准,对区域雷电灾害风险进行基于事前致灾因子的区域雷电灾害风险评估研究,并以福建省为例,应用该模型进行了计算。结果表明,基于承灾体、致灾体模式的区域雷电灾害风险评估模型能较好地反应出区域雷电灾害发生的损失程度,对于行政区域范围的雷电灾害风险度区域与政府决策支持具有积极的指导意义。     

太原市饮用水源地水质评价研究

运用层次分析法对汾河流域静乐县断面、岚县断面及娄烦县断面的水体质量进行评价,并确定水质优劣顺序,为优先污染控制以及水环境决策提供科学依据。评价结果为岚县断面监测水质较好,娄烦县断面次之,静乐县断面监测水质较差。在水质较差的断面应加大环保投入,加强水质管理,从根本上改善流域水质现状。     

层次分析法的黄河上游滑坡易发性评价

针对黄河上游龙羊峡至积石峡段滑坡灾害分布易发性评价与区划成图问题,该文以ArcGIS为平台,联系评价区的实际特点,选取地貌类型、地层岩性、降雨、断层、坡度为评价因子,运用层次分析法(AHP)确定各评价因子的权重,建立研究区滑坡易发性评价模型,结合GIS的空间分析功能实现研究区内滑坡灾害的易发性区划。结果表明,滑坡灾害主要集中在龙羊峡库区右岸和群科-尖扎盆地。区划结果与野外实际调查基本吻合,为今后GIS应用于地质灾害区划提供了思路,同时可为区内地质单位进行灾害监测提供基础数据和依据。     

软土深基坑变形与控制技术初探

通过对软土深基坑变形机理与影响因素的分析,提出了基坑工程变形全过程控制的理念,探讨了控制变形的设计与施工技术方法及针对变形异常应采取的应急措施。结合工程实践,分析了软土深基坑变形的特征,应用地层损失法原理计算了墙后地表沉降量,与实测值比较,得出了该方法可以应用于工程实践的认识。     

National indicators for observing ecosystem service change

Earth’s life-support systems are in rapid decline, yet we have few metrics or indicators with which to track these changes. The world’s governments are calling for biodiversity and ecosystem-service monitoring to guide and evaluate international conservation policy as well as to incorporate natural capital into their national accounts. The Group on Earth Observations Biodiversity Observation Network (GEO BON) has been tasked with setting up this monitoring system. Here we explore the immediate feasibility of creating a global ecosystem-service monitoring platform under the GEO BON framework through combining data from national statistics, global vegetation models, and production function models. We found that nine ecosystem services could be annually reported at a national scale in the short term: carbon sequestration, water supply for hydropower, and non-fisheries marine products, crop, livestock, game meat, fisheries, mariculture, and timber production. Reported changes in service delivery over time reflected ecological shocks (e.g., droughts and disease outbreaks), highlighting the immediate utility of this monitoring system. Our work also identified three opportunities for creating a more comprehensive monitoring system. First, investing in input data for ecological process models (e.g., global land-use maps) would allow many more regulating services to be monitored. Currently, only 1 of 9 services that can be reported is a regulating service. Second, household surveys and censuses could help evaluate how nature affects people and provides non-monetary benefits. Finally, to forecast the sustainability of service delivery, research efforts could focus on calculating the total remaining biophysical stocks of provisioning services. Regardless, we demonstrated that a preliminary ecosystem-service monitoring platform is immediately feasible. With sufficient international investment, the platform could evolve further into a much-needed system to track changes in our planet's life-support systems.