气候变化领域集成服务门户

This study develops novel geostatistical methods to investigate the spatial relationship between individual soil moisture sensors placed within native soil and #57 crushed stone aggregate subbase. The subbase sensors are beneath a 0.06 ha (0.15 acre) pervious concrete parking lot in Cincinnati, OH, USA. The parking lot treats runon from a 0.198 ha (0.49 acre) asphalt area. A geostatistical characterization of moisture (measured as permittivity) in the subbase beneath pervious concrete indicates that significant spatial correlation is either not present or only present at very short distances (<2.5 m). A two-stage para-statistical model relating antecedent storm moisture to apparent pervious concrete infiltration was developed to identify temporal trends in the data and to detect the clogging processes with relatively simple parameterization. The results suggest that either the placement of the sensors is not sufficient to detect clogging or that clogging is not problematic for the study period. Suggestions are provided to improve future research installations, based upon the findings here. Subbase moisture analysis results are compared with native soil moisture results. Seasonal trends are more pronounced in the native soil than in the subbase. The statistical analyses are applicable to multiple Storm Control Measures (SCM), Best Management Practices (BMP), agriculture, and soil environments. Other studies can determine the statistical power of their sensor installation using the methods applied here, which are flexible enough for multiple applications. Furthermore, data reduction methods presented serve to easily elucidate short-term moisture responses due to rainfall. A quantile response pattern is provided for sensors installed in both subbase and soil. Published by Elsevier B.V.