中国城市与城市发展理论的历史

论述中国自周期以来的城市概念和理论，指出中国城市性质和城市结构的自有特点及其与西方发展经验为基础的城市概念和理论不同，将中国漫长的演变历史分为封建时代，中华人民共和国建立至1978年和1978年以后的改革开放3个时期。3个时期所形成的城市概念和理论，都有中国特色，是中国城市及城市化向前发展的重要的理解和指导基础。     

长春市旅游产品开发研究

搞好长春市旅游产品的开发与设计对其旅游业的发展极为重要，长春市旅游资源类型多样，内容丰富，主要有生态旅游资源，电影文化旅游资源，产业旅游资源，殖民遗迹旅游资源，辽金文化旅游资源等五大资源优势。文章在分析这些资源优势的基础上，结合长春市国内外旅游客源市场的背景与现状，提出了旅游产品开发的具体思路，即在今后的开发中应突出地方特色。增强参与性，以七大旅游产品系列为依托，重点开发冰雪旅游，生态旅游，文化旅游和产业旅游。     

梅州市名城文化景观的保护与开发研究

从名城文化内涵出发，着眼于作为客家人文中心和名城文化景观的集中的梅州市区，全面分析了其中丰富多样的文化景观资源的特征及其文化内蕴。揭示了梅州名城文化景观保护与开发过程中的成绩与存在问题。并从文化地理学的视觉，结合旅游开发和城市规划，论证了科学保护与开发梅州名城文化景观的下述对策与途径的必要性与可行性：提升名城办的职能并与相关部门共营名城文化氛围；强化名城轴线与标志性文化景观；抢救与保护“老字号”等一批文化景观景区；适度利用，以积极的开发促其效的保护。     

贡嘎山东坡亚高山森林区蒸散力的估算

以海螺沟 30 0 0m气象站的观测资料为基础 ,运用Penman公式法　空气饱和差法和桑斯维特公式法 ,计算了贡嘎山东坡亚高山林林的年平均蒸散力 ,其结果为分别为 431 81mm、171 4mm和 44 6 4mm。通过分析蒸散力的影响因素 ,对这三种计算蒸散力的方法作了比较 ,对计算结果存在的差异作了比较合理的解释 ,认为用Penman公式法可以估计出本研究区的蒸散力。同时 ,对蒸散力及其影响因自进行了相关分析 ,指出湿度和风不是速制约研究区蒸散力的主导因子 ,并分析了蒸散力与水面蒸发的关系 ,由此推导出估算蒸散力的简便方程 :PE =6 6 77 0 6 91E60 1 0 75 1ITm 0 0 396Pm。     

边坡立体绿化工程的稳定性

山地城市有许多大面积锚杆喷浆硬化封闭的边坡与城市景观极不协调，为改变这些生硬，单调的自然坡体现状，政府要求对其进行立体绿化。本文以修建黄花园大桥石板坡立交工程形成的石板坡为例，通过多种方法研究了绿化荷载作用对边坡稳定性的影响，结论表明绿化荷载对边坡的稳定性影响较小，只要施工顺序得当可以保证安全。     

云南省金沙江流域土壤流失方程研究

云南金沙江流域是长江中上游水土流失最严重的区域。本项研究以“通用土壤流失方程”(USLE)为蓝本,运用小区实验等手段,综合分析了各个侵蚀因子,建立了云南金沙江流域土壤流失方程A=R·K·LS·c·P,并确定了方程中诸因子的求算方法和数值,以及该流域土壤允许流失量,为方程的应用提供了基本的技术数据。同时,还进行了方程的检验,方程计算值与小区实测值的相对误差在6.3％以下,表明该方程在实际应用中是可靠的。该方程的建立,可为云南金沙江流域预测预报土壤侵蚀,制定土地合理利用规划方案、水土保持措施和土地生态安全格局提供了一套可靠的科学方法和依据。     

云南省金沙江流域水土流失基本特征分析

在应用新近建立的云南金沙江流域土壤流失方程测算该流域各县(市、区)年均土壤流失总量和各地类年均 土壤流失量基础上,分析了该流域水土流失的总体特征、各地类水土流失特征和不同坡度级坡耕地的水土流失特 征,为因地制宜地防治水土流失提供了依据。     

Total N,total P and organic matters content in floodplain soils of Xianghai Nature Reserve

Soil sediment samples of 10 layers with a spacing of 10 cm each were collected in different floodplain zones adjacent to Huolin River in the Xianghai Nature Reserve, and contents of total N, total P and organic matters were analyzed. The results showed that contents of total N, total P and organic matters were generally decreasing with the increase of distance from sample locations to the river channel, and contents of the three items were generally higher in the upper soil layer than that in the lower soil layer. The content variations displayed how flooding functions influenced nutrient matter content variations in floodplain soils since the flood inundation frequencies of the sample locations varied. The correlation analysis displayed that there were remarkable relativities between total N, total P and organic matters within definite spatial distance from the Huolin River channel.     

Size Scaling of Cross Correlation Between Multiple Variables

Reservoir models have large uncertainty because of spatial variability and limited sample data. The ultimate aim is to use simultaneously all available data sources to reduce uncertainty and provide reliable reservoir models for resource assessment and flow simulation. Seismic impedance or some other attribute provides a key source of data for reservoir modeling. These seismic data are at a coarser scale than the hard well data and it not an exact measurement of facies proportions or porosity. A requirement for data integration is the cross-covariance between the well and seismic data.The size-scaling behavior of the cross correlation for different measurement scales was nvestigated. The size-scaling relationship is derived theoretically and validated by numerical studies (including an example with real data). The limit properties of the cross-correlation coefficient when the averaging volume becomes large is shown. After some averaging volume, the volume-dependent cross-correlation coefficient reaches a limit value. This plateau value is controlled mainly by the large-scale behavior of the cross and direct variograms.The cross correlation can increase or decrease with volume support depending on the relative importance of long- and short-scale covariance structures. If the direct and cross variograms are proportional, there is no change in the cross correlation as the averaging volume changes. Our study shows that the volume-dependent cross-correlation coefficient is sensitive to the shape of the cross variogram and differences between the direct variograms of the well data and seismic data.