期刊
VADOSE ZONE JOURNAL
卷 17, 期 1, 页码 -出版社
WILEY
DOI: 10.2136/vzj2017.10.0178
关键词
-
资金
- National Science Foundation through the Southern Sierra Critical Zone Observatory, a Major Research Instrumentation grant [EAR-1331939, EAR-1126887]
- University of California Center for Information Technology Research in the Interest of Society
Using 6 yr (Water Year [WY] 2009-WY 2014) of hourly in situ measurements from a spatially distributed water-balance cluster, we quantified the long-term accuracy of an algorithm used to predict spatial patterns of depth-integrated soil-water storage within the rain-snow transition zone of the southern Sierra Nevada. The algorithm-the multivariate, non-parametric regression-tree estimator Random Forest-was used to predict soil-water storage based on a combination of attributes at each instrument cluster (soil texture, topographic wetness index, elevation, northness, and canopy cover). Out-of-bag R-2 (similar to cross-validation for Random Forest) was used to quantify the accuracy of the estimator for unobserved data. Accuracy was consistently high during the wet-up, snow-cover, and early recession periods of average and wet years. The accuracy declined at the end of a 3-yr dry period, and the relative rank of the independent variables in the model shifted. Soil texture was the highest-ranked independent variable across all years, followed by elevation and northness. Topographic wetness increased in importance during dry periods. Northness exhibited high importance during the wet-up and early recession periods of most water years. During dry years, the importance of elevation declined. In dry years, notable differences in soil-water storage at each depth include lower-than-average storage in the deeper regolith at the beginning of the water year and lower storage in near-surface layers during the winter resulting from transient snow cover.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据