Multi-Scale Statistical Approach to Critical-Area Analysis and Modeling of Watersheds and Landscapes

流域和景观关键区域分析和建模的多尺度统计方法

• 批准号: 9524722
• 负责人: Ganapati Patil
• 金额: $ 40.5万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1995
• 资助国家: 美国
• 起止时间: 1995-10-01 至 1999-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9524722&HistoricalAwards=false
• 关键词: Multi; Scale; Statistical; Approach; Critical

Public agencies and corporate landholders are developing and maintaining major environmental databases for computerized mapping and analysis. The cost of such databases increases rapidly with increasing spatial detail. This project undertakes the development of objective and efficient methods for determining patterns of spatial variation from such databases - particularly relating to water resources and landscape ecology - and for making pattern comparisons between databases having different levels of detail. Comparison of patterns emerging from data at different levels of detail will enable managers to select the appropriate spatial resolution necessary for particular purposes. Cost efficiency will also be increased by the ability to indicate likely zones of spatial uncertainty and detect areas of disagreement between alternate hydrologic models, so that detailed data acquisition can be better targeted and modeling will be more precise. Application of these approaches will provide a better basis for districting watersheds, reducing the costs of monitoring and detecting the need for remediation.

公共机构和公司土地所有者正在发展和维持主要的环境数据库，用于计算机化制图和分析。这种数据库的成本随着空间细节的增加而迅速增加。这个项目负责发展客观和有效的方法，从这些数据库确定空间变化的格局- -特别是与水资源和景观生态学有关的格局- -并在具有不同详细程度的数据库之间进行格局比较。比较从不同细节层次的数据中产生的模式将使管理人员能够为特定目的选择必要的适当空间分辨率。成本效率还将通过指出可能存在空间不确定性的区域和检测不同水文模型之间存在分歧的区域的能力得到提高，这样可以更有针对性地获取详细的数据，并使建模更加精确。这些方法的应用将为划分流域提供更好的基础，减少监测和发现需要补救的费用。