...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Ecological Modelling >Spatial and temporal prediction and uncertainty of soil loss using the revised universal soil loss equation: a case study of the rainfall-runoff erosivity R factor
【24h】

Spatial and temporal prediction and uncertainty of soil loss using the revised universal soil loss equation: a case study of the rainfall-runoff erosivity R factor

机译：修正后的通用土壤流失方程对土壤流失的时空预测和不确定性：以降雨径流侵蚀力R因子为例

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Soil loss is commonly predicted using the revised universal soil loss equation consisting of rainfall-runoff erosivity, soil erodibility, slope steepness and length, cover management, and support practice factors. Because of the multiple factors, their interactions, and spatial and temporal variability, soil erosion varies considerably over space and time. For these reasons, modeling soil loss is very complicated. Decision-makers need local and regional estimates of soil loss as well as their corresponding uncertainties. Neglecting the local and detailed information may lead to improper decision-making. This paper demonstrates a strategy based on a sample data set and a geostatistical method called sequential Gaussian simulation to derive local estimates and their uncertainties for the input factors of a soil erosion system. This strategy models the spatial and temporal variability of the factors and derives their estimates and variances at any unknown location and time. This strategy was applied to a case study at which the rainfall-runoff erosivity R factor was spatially and temporally estimated using a data set of rainfall. The results showed that the correlation between the observations and estimates by the strategy ranged from 0.89 to 0.97, and most of the mean estimates fell into their confidence intervals at a probability of 95%. Comparing the estimates of the R factor using a traditional isoerodent map to the observed values suggested that the R factor might have increased and a new map may be needed. The method developed in this study may also be useful for modeling other complex ecological systems. (C) 2002 Elsevier Science B.V. All rights reserved. [References: 18]
机译：通常使用修正后的通用土壤流失方程来预测土壤流失，该方程式包括降雨径流侵蚀力，土壤易蚀性，坡度和长度，坡度管理和支持实践因素。由于多种因素，它们的相互作用以及时空变化，土壤侵蚀随时间和空间变化很大。由于这些原因，对土壤流失进行建模非常复杂。决策者需要对土壤流失及其相应的不确定性进行本地和区域估算。忽略本地和详细信息可能会导致决策不当。本文演示了一种基于样本数据集和称为顺序高斯模拟的地统计学方法的策略，该方法可得出土壤侵蚀系统输入因子的局部估计值及其不确定性。该策略对因素的时空变化建模，并在任何未知的位置和时间得出其估计和方差。将该策略应用于一个案例研究，其中使用降雨数据集在空间和时间上估算降雨径流侵蚀力R因子。结果表明，该策略的观察值与估计值之间的相关性介于0.89至0.97之间，大多数平均估计值以95％的概率落入其置信区间。使用传统的等规线图将R因子的估计值与观测值进行比较，表明R因子可能已经增加，可能需要新的图。本研究中开发的方法也可能对其他复杂的生态系统建模有用。 （C）2002 Elsevier Science B.V.保留所有权利。 [参考：18]

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号