水质信息系统与水环境承载能力模式的研究

水环境承载能力是可持续发展理论的重要体现，它具有很强的动态、空间时空、水量水质兼顾、补偿等特性。建立水质信息系统基础平台，科学地利用和调控水环境承载能力，实现水环境保护目标。对水环境承载能力调控与水质信息系统模式的建立进行了研究分析，并结合我国实情提出了建模总体框架。     

工业生态系统的工业共生原理探讨

提出了共生单元，共生模式、共生环境构成工业共生的3要素。介绍了工业共生的质参量兼容原理，共生能量生成原理、系统进化原理。阐述了工业共生模式的4种类型：共栖，互利型工业共生。寄生型工业共生，附生型（异生型）工业共生，混合型工业共生。分析了工业共生的优势。     

污水处理厂内毒物归宿模型的参数估计及应用

在建立城市污水处理厂毒物归宿模型的基础上，继续探讨了固液相吸附分配系数Ｋｐ，生物降解速率系数Ｋｂ，挥发及吹脱速率系数ＫｖＫｓｔ等模型参数的估计方法，并就模型的检验，模拟及其在风险评价中的应用作了案例研究。     

一种计算粒子电迁移率的新模型

近几年来,粒子电迁移率引起了更多学者的注意。因为它是表征带电粒子在电场中运动的重要参数之一。在这方面,人们已作了大量的研究工作,提出了一些计算粒子电迁移率的理论和半经验模型。基于前人的研究,本文提出了一种计算粒子电迁移率的新模型,并用前人的实验结果和本研究的实验结果对此模型进行了检验。计算结果和实验数据很吻合。就某种意义上讲,此模型是对以前模型的扩展和改进,它可广泛应用于溶胶的研究和工业应用。     

黄土丘陵沟壑区土壤侵蚀专家系统试验研究

本文介绍了黄土丘陵的沟壑区土壤侵蚀专家系统的建造原理与方法.系统采用压缩编码方式存贮各种地学图形与遥感图象数据,具有效据与图象的存贮更新、查询检索、分析处理、图象显示和自动制图功能.该系统可对黄土地区的土壤侵蚀进行准确、快速的预测、预报,并对土壤侵蚀的综合治理,水土保持规划提出几种可行性建议供用户选择。用该系统对山西省离石县王家沟流域羊道沟的土壤侵蚀进行了试验研究,并对试验结果进行了系统评价。     

建筑火灾模拟模型设计

数值模拟是火灾科学研究中的一种重要方法。本文描述了建筑火灾模拟模型的建模思想和方法，该思想可用于研究建筑火灾过程规律、新的火灾安全方案或策略的工作效果和经济性分析，模拟模型可以给系统性能和火灾防治经济性以定量说明。并对在建模中存在的问题进行了探讨．     

AN AUTO-CAD-BASED WATERSHED INFORMATION SYSTEM FOR THE HYDROLOGIC MODEL HEC-11

ABSTRACT: A micro computer based Watershed Information System (W.LS.) is developed to assist in the preparation of input files for the hydrologic simulation model HEC-1. This system consists of three phases. Phase I utilizes the capabilities of AutoCAD version 9 and three programs, BASINS, PLANES, and CHANNELS, to extract, organize, and display watershed data. Phase II uses the program CN to calculate some HEC-1 parameter values. Phase II utilizes the program HECUPDATE to create HEC-1 input files. The system input includes topographic, soils, land use, watershed geometry data, and a skeletal HEC-1 input file. Output from the system includes a summary User Reference File, a Soils File, a Land Use File, a Watershed Geometry File, a Curve Number File, and a HEC-1 input file, which is ready to run. The W.I.S. has been applied to Macks Creek Watershed in southwest Idaho.     

Predicting the distributions of marine organisms at the global scale

We present and evaluate AquaMaps, a presence-only species distribution modelling system that allows the incorporation of expert knowledge about habitat usage and was designed for maximum output of standardized species range maps at the global scale. In the marine environment there is a significant challenge to the production of range maps due to large biases in the amount and location of occurrence data for most species. AquaMaps is compared with traditional presence-only species distribution modelling methods to determine the quality of outputs under equivalently automated conditions. The effect of the inclusion of expert knowledge to AquaMaps is also investigated. Model outputs were tested internally, through data partitioning, and externally against independent survey data to determine the ability of models to predict presence versus absence. Models were also tested externally by assessing correlation with independent survey estimates of relative species abundance. AquaMaps outputs compare well to the existing methods tested, and inclusion of expert knowledge results in a general improvement in model outputs. The transparency, speed and adaptability of the AquaMaps system, as well as the existing online framework which allows expert review to compensate for sampling biases and thus improve model predictions are proposed as additional benefits for public and research use alike.     

Assessing the effects of pseudo-absences on predictive distribution model performance

Modelling species distributions with presence data from atlases, museum collections and databases is challenging. In this paper, we compare seven procedures to generate pseudo-absence data, which in turn are used to generate GLM-logistic regressed models when reliable absence data are not available. We use pseudo-absences selected randomly or by means of presence-only methods (ENFA and MDE) to model the distribution of a threatened endemic Iberian moth species (Graellsia isabelae). The results show that the pseudo-absence selection method greatly influences the percentage of explained variability, the scores of the accuracy measures and, most importantly, the degree of constraint in the distribution estimated. As we extract pseudo-absences from environmental regions further from the optimum established by presence data, the models generated obtain better accuracy scores, and over-prediction increases. When variables other than environmental ones influence the distribution of the species (i.e., non-equilibrium state) and precise information on absences is non-existent, the random selection of pseudo-absences or their selection from environmental localities similar to those of species presence data generates the most constrained predictive distribution maps, because pseudo-absences can be located within environmentally suitable areas. This study shows that if we do not have reliable absence data, the method of pseudo-absence selection strongly conditions the obtained model, generating different model predictions in the gradient between potential and realized distributions.     

Impact of generated solar radiation on simulated crop growth and yield

The availability of observed daily solar radiation (OSR) is restricted to recent years. Its estimation through different methods is necessary to develop long-term data sets for agricultural and environmental applications. The objective of this study was to analyze the impact of using generated daily solar radiation (GSR) on simulated growth and yield of cotton, maize, and peanut. Nine locations representing Georgia's major crop belt were selected. Daily weather data from the Georgia Automated Environmental Monitoring Network (AEMN), including solar radiation, maximum and minimum temperature, and precipitation, were duplicated. The OSR was removed from one set and then generated using a stochastic procedure. The Cropping System Models (CSM)-CROPGRO-Cotton, CERES-Maize, and CROPGRO-Peanut of the Decision Support System for Agrotechnology Transfer (DSSAT) v4 were used to simulate crop growth and yield at each location with both OSR and GSR and for rainfed and irrigated conditions. The statistical analysis included summary statistics, Pearson's coefficient of correlation, mean squared deviation (MSD) and its components, namely: squared bias (SB), squared difference between standard deviations (SDSD), lack of correlation weighted by the standard deviations (LCS), and regressions. Within locations, for the three crops under rainfed and irrigated conditions, GSR did not significantly affect simulated total evapotranspiration and aboveground biomass and yields. For the three crops, deviations of simulated water use and yields from GSR with respect to simulated water use and yields from OSR were lower for the rainfed than for the irrigated conditions. Yields from the CSM-CROPGRO-Cotton and -Peanut models had lower deviations than yields from the CSM-CERES-Maize model. LCS was the major component of the MSD suggesting that the extent of the difference between standard deviations of GSR and OSRG could affect the outputs of the crop models. Nevertheless, for most locations none of the MSD components of the GSR showed significant correlation with simulated yields and the overall performance of the models was not affected. It can be concluded based on the results of this study that GSR can be used as an input for crop model simulation models when OSR is not available.