HYDROLOGIC SIMULATION OF THE LITTLE WASHITA RIVER EXPERIMENTAL WATERSHED USING SWAT1

ABSTRACT: Precipitation and streamflow data from three nested subwatersheds within the Little Washita River Experimental Watershed (LWREW) in southwestern Oklahoma were used to evaluate the capabilities of the Soil and Water Assessment Tool (SWAT) to predict streamflow under varying climatic conditions. Eight years of precipitation and streamflow data were used to calibrate parameters in the model, and 15 years of data were used for model validation. SWAT was calibrated on the smallest and largest sub‐watersheds for a wetter than average period of record. The model was then validated on a third subwatershed for a range in climatic conditions that included dry, average, and wet periods. Calibration of the model involved a multistep approach. A preliminary calibration was conducted to estimate model parameters so that measured versus simulated yearly and monthly runoff were in agreement for the respective calibration periods. Model parameters were then fine tuned based on a visual inspection of daily hydrographs and flow frequency curves. Calibration on a daily basis resulted in higher baseflows and lower peak runoff rates than were obtained in the preliminary calibration. Test results show that once the model was calibrated for wet climatic conditions, it did a good job in predicting streamflow responses over wet, average, and dry climatic conditions selected for model validation. Monthly coefficients of efficiencies were 0.65, 0.86, and 0.45 for the dry, average, and wet validation periods, respectively. Results of this investigation indicate that once calibrated, SWAT is capable of providing adequate simulations for hydrologic investigations related to the impact of climate variations on water resources of the LWREW.     

基于模糊识别的建筑物火灾危险性评价方法

笔者以传统的经典统计和模糊统计为基础 ,依据建筑物火灾危险性的影响因素 ,应用模糊识别理论及系统安全方法 ,建立了建筑物火灾危险性的评价指标体系 ;并应用集值统计方法对各项指标权重进行了处理。该方法改变了过去统计给出一个固定值的做法 ,而给出一个区间值 ,解决了对专家给出的指标“大约是多少”这一概念的定量化。为了验证了评价模型的准确性 ,将该理论应用到某购物商场火灾危险性评价中 ,取得了满意效果 ,为建筑物防火设计以及安全管理提供了可行的依据。     

2007年7-8月国内环境事件

简要统计了2007年7-8月国内发生的各种环境事件148起,包括沙尘天气1起,污染事件24起,地震14起,山体滑坡和泥石流29起,旱灾5起,以及其他自然灾害75起.对自然灾害进行了讨论.     

面向防洪救灾的人口统计数据空间化研究——以扶余县为例

人口是重要的受灾体,准确的人口空间分布信息是防洪救灾工作的重要依据。将人口统计数据与遥感数据相结合,借助GIS手段来空间化人口统计数据,模拟人口空间分布。着力分析两个问题：①居民点类型、面积、位置的精确获取;②居民点类型、面积与人口数量间的关系。研究中,首先根据地理意义和数学意义,对经典的城市人口-面积异速生长模型进行扩展,推导出城乡人口-面积统一模型。进而以扶余县为例,以15万地形图为辅助数据,建立居民点分类体系,利用ETM影像提取居民点信息,在以上模型的基础上建立人口分布的反演模型,得到基于居民点的人口分布矢量数据。     

上海市民用建筑能耗的估算与分析

建筑能耗是重要的社会能源消耗主体。采用宏观能源统计方法,利用综合能源平衡表估算上海市1999-2009年住宅能耗和公共建筑运行能耗的变化及其特征。结果表明,上海市民用建筑总能耗由1999年的687.82万tce增加到2009年的1 774.91万tce,平均年增长率为4.68%;住宅建筑能耗呈上升趋势,而单位面积能耗逐年下降,单位面积住宅建筑能耗均值比公共建筑低35.30 kgce/(m2.a);公共建筑单位建筑面积能耗变化呈略增态势,2009年单位建筑能耗比1999年高9.64 kgce/(m2.a)。研究结果可为建筑节能减排政策的制定提供决策参考和数据支持。     

我国大学生旅游者环境意识与行为特征研究

作为潜在的生态旅游者群体,大学生对环境问题的认识、态度及其行为倾向对于旅游地生态环境的可持续发展至关重要。文章以南京林业大学为研究对象,通过问卷调查,以配额抽样法探讨大学生旅游者学科专业、性别因素对环境意识的影响,了解环境心理与行为特征,并与国内生态旅游景区旅游者进行对比,研究发现当代大学生旅游者表现出较好的环境态度和倾向,行为意愿上显示出"绿色"的特征,同时专业课程学习以及实践经历能够增强环境认知和情感,对旅游环境与生态知识的渴求和认知程度对整体环境意识水平有较大影响,然而大学生旅游者的环境意识多来源于理论,存在着知行脱节,环境行为滞后于环境情感、意志。应将旅游环境教育与各学科教学结合起来,开展培养环境责任和环保能力的旅游实践活动,加强大学校园环境及文化建设,积极培育大学生旅游环境意识及管理旅游环境行为。     

应急管理综合统计指标体系设计与构建——基于北京市应急管理实践探索

为解决我国近些年应急管理体制改革背景下出现的现行统计指标与现行应急管理职责职能不匹配、业务需求不协调的问题,更好地适应我国应急管理新形势新任务新要求,在梳理北京市应急管理综合统计指标体系演化脉络的基础上,从应急管理业务职能入手,构建包含3个层级、11大类、523项统计指标的应急管理综合统计指标体系及相关工作机制,且经过北京多年的实践应用和持续改进表明,该综合统计指标体系具有较好的科学性和适用性,对其他同类应急管理主体单位的综合统计指标体系建立具有一定的启示和借鉴作用。     

DETECTING BASE FLOW IMPACTS IN COASTAL PLAIN STREAMS1

ABSTRACT: An established trend analysis methodology was applied to the problem of identifying and quantifying stream base flow impacts from water withdrawals and water loss through interbasin transfers. Impacts were simulated using base flow values selected from two U.S. Geological Survey (USGS) continuous record streamflow sites located within the Pinelands of southern New Jersey. Study site base flows were regressed against index site base flows with monotonic and step trend tests applied to the residuals from the regression model. The smallest, significantly detectable (α= 0.10) percentage reduction within a given simulation was used as an estimate of the sensitivity of a trend test. Evaluation of the trend analysis methodology led to the following practical considerations regarding trend test sensitivity. The proportion of study site base flow variability explained by index site base flows should be maximized, while at the same time minimizing positive, first-order autocorrelation in the regression residuals. Given the importance of detecting autocorrelation, missing values should be avoided or minimized. The quarterly (three-month) interval reduced the magnitude of autocorrelation relative to a shorter two-month sampling interval. Sensitivity appeared to improve when equalizing the number of values before and after a base flow impact(s) while seasonally biased sampling appeared to reduce sensitivity. Based primarily on past trend detection studies, nonparametric tests were deemed a better choice over their parametric counterparts, due to the lack of stringent data distributional requirements coupled with little or no loss of power even when applied to normally distributed data.     

Spatial structure effects on the detection of patches boundaries using local operators

Landscapes exhibit various degrees of spatial heterogeneity according to the differential intensity and interactions among processes and disturbances that they are subjected to. The management of these spatially dynamical landscapes requires that we can accurately map them and monitor the evolution of their spatial arrangement through time. Such a mapping requires first the delineation of various spatial features present in the landscape such as patches and their boundaries. However, there are several environmental (spatial variability) as well as technical (spatial resolution) factors that impair our ability to accurately delineate patches and their boundaries as polygons. Here, we investigate how the spatial structure and spatial resolution of the data affect the accuracy of detecting patches and their boundaries over simulated landscapes and real data. Simulated landscapes consisted of two patches with parameterized spatial properties (patches’ level of spatial autocorrelation, mean value and variance) separated by a boundary of known location. Real data allowed the investigation of a more complex landscape where there is a known transition between two forest domains with unknown spatial properties. Boundary locations are defined using the lattice-wombling edge detector at various aggregation levels and the degree of patch homogeneity is determined using Getis-Ord’s G*. Results show that boundary detection using a local edge detector is greatly affected by the spatial conditions of the data, namely variance, abruptness of the spatial gradient between two patches and patches’ level of spatial autocorrelation. They also suggest that data aggregation is not a panacea for bringing out the ecological process creating the patches and that indicators derived from local measures of spatial association can be complementary tools for analysing spatial structures affecting boundary delineation.