STREAM DISCHARGE PREDICTION VIA A GRID BASED SOIL WATER ROUTING WITH PADDY FIELDS1

ABSTRACT: A grid based daily hydrologic model for a watershed with paddy fields was developed to predict the stream discharge. ASCII formatted elevation, soil, and land use data supported by the GRASS Geographic Information System are used to generate distributed results such as surface runoff and subsurface flow, soil water content, and evapotranspiration. The model uses a single flow path algorithm and simulates a water balance at each grid element. A linear reservoir assumption was used to predict subsurface runoff components. The model was applied to a 75.6 km² watershed located in the middle of South Korea, and observed stream flow hydrographs from 1995 and 1996 were compared to model predictions. The stream flow predictions of 1995 and 1996 generally agreed with the observed flow, resulting in a Nash‐Sutcliffe efficiency R² of 0.60 and 0.62, respectively. The hydraulic conductivity for percolating water through the saturated layer affected baseflow generation. The levee height of the paddy influenced the time and magnitude of the surface runoff, depending on irrigation management. The model will be used for making low flow management decisions by evaluating the role of each land use to stream flow, especially in case of paddy decrease by gradual urbanization of a watershed.     

GIS在重庆市地质灾害信息管理系统中的应用

重庆市是我国地质灾害非常严重的省市之一。长期以来，地质灾害的发生严重地影响着其城市建设与发展。为了加强对地质灾害规律的研究以及科学地管理地质灾害信息，本文探讨了GIS技术在重庆市地质灾害信息管理系统(CQGHMIS)中的应用。该系统以MapGis和MapInfo、Mapx作为后台支持，利用Visual basic5．0中文企业版作为开发语言，具有数据采集与更新、空间检索与查询、信息时空分析与可视和信息输出以及系统说明与帮助等五大功能。     

应用GIS-ANN进行土地盐碱化危险度评价--以吉林西部平原为例

土地盐碱化可破坏生态环境，制约社会经济的可持续发展，已成为一种严重的环境灾害。应用GIS技术提取土地盐碱化和环境信息，采用关联度法分析了土地盐碱化与各环境要素之间的关系，应用人工神经网络模型(ANN)进行了盐碱化危险度评价。结果表明，吉林西部平原有53．97％的土地盐碱化危险度为Ⅳ级和V级。实践证明，应用GIS—ANN集成技术评价土地盐碱化的危险度是一种较好的方法，为土地盐碱化的防治提供了科学依据。     

利用WEBGIS实现地震灾害信息数据的管理

介绍了WEBGIS技术的特点、基本原理和实现方法，及其在管理海量地理数据中的优势，并构建了基于WEBGIS的地震灾害信息数据的主体框架。     

A Conceptual Framework for Selecting and Analyzing Stressor Data to Study Species Richness at Large Spatial Scales

/ In this paper we develop a conceptual framework for selectingstressor data and analyzing their relationship to geographic patterns ofspecies richness at large spatial scales. Aspects of climate and topography,which are not stressors per se, have been most strongly linked withgeographic patterns of species richness at large spatial scales (e.g.,continental to global scales). The adverse impact of stressors (e.g., habitatloss, pollution) on species has been demonstrated primarily on much smallerspatial scales. To date, there has been a lack of conceptual developmenton how to use stressor data to study geographic patterns of speciesrichness at large spatial scales.The framework we developed includes four components: (1) clarification of theterms stress and stressor and categorization of factors affecting speciesrichness into three groups-anthropogenic stressors, natural stressors, andnatural covariates; (2) synthesis of the existing hypotheses for explaininggeographic patterns of species richness to identify the scales over whichstressors and natural covariates influence species richness and to providesupporting evidence for these relationships through review of previousstudies; (3) identification of three criteria for selection of stressor andcovariate data sets: (a) inclusion of data sets from each of the threecategories identified in item 1, (b) inclusion of data sets representingdifferent aspects of each category, and (c) to the extent possible, analysisof data quality; and (4) identification of two approaches for examiningscale-dependent relationships among stressors, covariates, and patterns ofspecies richness-scaling-up and regression-tree analyses.Based on this framework, we propose 10 data sets as a minimum data base forexamining the effects of stressors and covariates on species richness atlarge spatial scales. These data sets include land cover, roads, wetlands(numbers and loss), exotic species, livestock grazing, surface water pH,pesticide application, climate (and weather), topography, and streams.KEY WORDS: Anthropogenic impacts; Biodiversity; Environmental gradients;Geographic information systems; Hierarchy     

支持灾害管理决策的咨询信息系统

为减少中国大城市灾害造成的损失，运用信息科学、系统科学的方法和计算机技术，建立支持灾害管理决策的咨询信息系统（ＤＭＤＳＣＩＳ）。灾害文献数据库管理系统（ＤＬＤＢＭＳ）是这一咨询信息系统的知识基础和信息来源。本文论述了“ＤＬＤＢＭＳ”和三个主要的子系统“灾害文献数据库管理系统”、“灾害事实数据库管理系统”、“中外减灾法规超文本检索系统”的作用、设计思想、构成、功能和实现方法。     

氯气和光气爆燃事故源强估算

在建立化学品泄漏的气体排放、液体排放、两相排放模式和爆炸燃烧的火球和气爆，蒸气云爆炸及绝热扩散和池蒸发扩展等模式的基础上，估计分析了氯气和光气爆燃事故源强，即爆炸能量及碎片抛射、冲击波、热辐射和毒云等后果影响     

双室火灾烟气特性的实验研究

本文在１／２缩尺建筑模型中，取双室－走廊结构，以标准木垛为火源，对建筑火灾的初始阶段进行了实验模拟。着重研究了火灾初期的烟气特性及其变化，包括烟气温度及烟气中ＣＯ＿２气体和炭颗粒浓度。本文目的在于在探索和发展中等尺寸火灾实验模拟手段和方法的同时，定性地确定火源功率对烟气特性的影响，并积累一定的实验数据，为建筑火灾的分析和模化提供实验基础和依据。     

Using digital image projections to visualize forest landscape changes due to management activities and forest growth

A pedagogic problem in forestry and landscape management is to visualize future landscape effects of forest growth and current management activities in the forest. This paper presents a method for forecasting digital image projections of forest landscape dynamics. Static nonlinear regression functions estimate the digital numbers in a Landsat Thematic Mapper image. Regressors used are forest stand variables. By estimating the future forest stand data, based on intermediate treatment and growth, future satellite digital images are created. In a case study example, the future landscape of a forest block in the province of Västernorrland, Sweden, is projected to demonstrate the application of this visualization technique.     

淮河上游洪水灾害可公度信息系预测

可公度信息系是一种源于天文学的预测方法，其实质是寻求事物发生的经验关系。本文应用此法对淮河上游的洪水灾害进行了预测分析，结果表明淮河上游洪水灾害发生具有２ａ、４ａ、５ａ、７ａ、９ａ、１１ａ、１９ａ及７１ａ等多种周期，并在１９９７年、２０００年等年份的前后有可能发生大洪水。