Spatial Pattern of Ecosystem Function and Ecosystem Conservation

The spatial pattern of ecosystem function can affect ecosystem conservation. Ecosystem functions are often heterogeneous spatially due to physical and biological factors. We can influence ecosystem functions by changing the spatial patterns of the physical and biological elements of an ecosystem and regulating their combinations. The variation–position effect highlights a phenomenon resulting from the spatial pattern of ecosystem function. The effect shows that the identical variation of a factor may produce different effects on the overall situation when this variation occurs in a different spatial position. In a watershed of the Yangtze River, water retention is a primary ecosystem function. The variation–position effect for water retention capacity occurs in the watershed because of the spatial heterogeneity in vegetation, soil, and slope. The change of vegetation that occurs in a complex can affect the overall situation of water retention, and the effect can be different due to the change occurring in the position holding different vegetation-soil-slope complex. To improve the ecosystem in the watershed and to meet the social needs for the ecosystem function of water retention, a strategy called ecosystem function and spatial pattern-based forest extension was proposed to conserve forests. The implementation of the strategy enables the watershed to attain the maximum effective increase in water retention capacity.     

Evaluating Nonpoint Pollution Policy Using a Tightly Coupled Spatial Decision Support System

Policy makers often must rely on the cumulative impact of independent actions taken by local landowners to achieve environmental goals. The connection between policy, regulation, and local action, however, is often not well understood and, thus, the impact of proposed policies may be difficult to predict. In this study we evaluate the effectiveness of alternative policy scenarios for agricultural set aside programs (e.g., the conservation reserve program administered by the United States Department of Agriculture) in reducing nonpoint pollution. Two alternative policy scenarios are developed and analyzed; one based on the erodibility index (detachment), the other sediment yield (transport). An estimate of the cumulative impact of associated land use change on nonpoint pollution is made using the AGNPS distributed parameter watershed model. This work is completed within the Cypress Creek watershed in southern Illinois. An analysis of the resulting data suggests that the most efficacious regulatory strategy for achieving nonpoint water pollution goals depends, in part, on place-specific land use patterns. This conclusion provides a solid argument for place-based regulatory strategies.     

安全投入与事故直接经济损失的问题研究

通过对安全投入及事故直接经济损失的分析研究,利用最小二乘法对安全投入比例(安全投入占GDP的比例)与事故直接经济损失之间的关系进行了回归分析,确定了其对应的函数关系.结果表明,安全投入比例与事故直接经济损失之间呈较明显的负指数关系.探讨了安全投入存在的主要问题,提出了几点关于加强安全投入的建议及措施.     

基于优势隐含周期分析的气象灾害预报

建立了优势因子和优势隐含周期的概念,并提出了多因子逐步回归周期分析的新方法。该方法既能增长预报期,又能充分利用物理因子的重要信息,同时在计算过程中还能挑选出起显著作用的优势因子和优势隐含周期,对预报气象要素的变化趋势效果较好,有一定的实用价值,是气象灾害预报的一条比较有效的途径。     

Water Use by Thermoelectric Power Plants in the United States1

Abstract: Thermoelectric power generation is responsible for the largest annual volume of water withdrawals in the United States although it is only a distant third after irrigation and industrial sectors in consumptive use. The substantial water withdrawals by thermoelectric power plants can have significant impacts on local surface and ground water sources, especially in arid regions. However, there are few studies of the determinants of water use in thermoelectric generation. Analysis of thermoelectric water use data in existing steam thermoelectric power plants shows that there is wide variability in unitary thermoelectric water use (in cubic decimeters per 1 kWh) within and among different types of cooling systems. Multiple‐regression models of unit thermoelectric water use were developed to identify significant determinants of unit thermoelectric water use. The high variability of unit usage rates indicates that there is a significant potential for water conservation in existing thermoelectric power plants.     

THE EFFECT OF DATA INDEPENDENCE IN MODEL CALIBRATION AND MODEL TESTING1

ABSTRACT: With the increased use of models in hydrologic design, there is an immediate need for a comprehensive comparison of hydrologic models, especially those intended for use at ungaged locations (i.e., where measured data are either not available or inadequate for model calibration). But some past comparisons of hydrologic models have used the same data base for both calibration and testing of the different models or implied that the results of model calibration are indicative of the accuracy at ungaged locations. This practice was examined using both the regression equation approach to peak discharge estimation and a unit hydrograph model that was intended for use in urban areas. The results suggested that the lack of data independence in the calibration and testing of regression equations may lead to both biased results and misleading statements about prediction accuracy. Additionally, although split-sample testing is recognized as desirable, the split-samples should be selected using a systematic-random sampling scheme, rather than random sampling, because random sampling with small samples may lead to a testing sample that is not representative of the population. A systematic-random sampling technique should lead to more valid conclusions about model reliability. For models like a unit hydrograph model, which are more complex and for which calibration is a more involved process, data independence is not as critical because the data fitting error variation is not as dominant as the error variation due to the calibration process and the inability of the model structure to conform with data variability.     

RIDGE REGRESSION TECHNIQUES APPLIED TO LANDSAT INVESTIGATION OF WATER QUALITY IN LAKE OKEECHOBEE1

ABSTRACT: Landsat radiance values were processed at two different (single and double) levels of accuracy to estimate chlorophyll a, turbidity, and suspended sediment in Lake Okeechobee, Florida. Both ordinary least square and ridge regression analyses were used to establish a relationship between water quality parameters and Landsat radiance. Radiance measurements made at greater precision (double level) gave a better solution in this application. The ridge regression analysis for double level not only can reduce the total mean square error about 13–20 percent and confidence interval about 6–28 percent as compared to ordinary least square analysis, but it can also change the interpretation of analysis results.     

CAPITAL COST OF RURAL WATER DISTRIBUTION SYSTEMS1

ABSTRACT: The cost of water service to rural residents is very high compared to urban areas. This is true even after subsidization by Farmers Home Administration (FmHA) loans and grants. Capital cost data on 44 projects financed by the Ohio office of the FmHA during the period August 1968 to January 1977 are used to derive cost equations for 26 components of rural water distribution systems. These components represent 92 percent of the capital cost of the pipeline distribution systems studied. The data can be used to economically design rural water supply systems from a capital cost viewpoint. More data are needed on operation and maintenance costs as well as central and cluster well costs before totally economic system designs can be undertaken.     

COMPARING METHODS OF HYDROLOGIC REGIONALIZATION1

ABSTRACT: Data splitting is used to compare methods of determining “homogeneous” hydrologic regions. The methods compared use cluster analysis based on similarity of hydrologic characteristics or similarity of characteristics of a stream's drainage basin. Data for 221 stations in Arizona are used to show that the methods, which are a modification of DeCoursey's scheme for defining regions, improve the fit of estimation data to the model, but that is is necessary to have an independent measure of predictive accuracy, such as that provided by data splitting, to demonstrate improved predictive accuracy. The methods used the complete linkage algorithm for cluster analysis and computed weighted average estimates of hydrologic characteristics at ungaged sites.     

Risk factors for driving into flooded roads

Motor vehicle-related deaths account for more than half of all flood fatalities in the United States, but to date, very little is known about the risk factors associated with why people drive into flooded roads. Using data from survey questionnaires administered in Denver, CO, and Austin, TX, this paper suggests that people who do not take warnings seriously are more likely to drive through flooded roads, as are people aged 18–35, and those that do not know that motor vehicles are involved in more than half of all flood fatalities. In Denver, people who have not experienced a flood previously and those who do not know they live in flood-prone areas are also more likely to drive into flooded roads.