Open AccessPostfire soil water repellency in piñon–juniper woodlands: Extent, severity, and thickness relative to ecological site characteristics and climate
Author(s) -
Zvirzdin Daniel L.,
Roundy Bruce A.,
Barney Nicholas S.,
Petersen Steven L.,
Anderson Val J.,
Madsen Matthew D.
Publication year - 2017
Publication title -
ecology and evolution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.17
H-Index - 63
ISSN - 2045-7758
DOI - 10.1002/ece3.3039
Subject(s) - juniper , woodland , environmental science , canopy , soil water , hydrology (agriculture) , vegetation (pathology) , forestry , ecology , atmospheric sciences , soil science , geography , geology , biology , geotechnical engineering , medicine , pathology
Wildfires can create or intensify water repellency in soil, limiting the soil's capacity to wet and retain water. The objective of this research was to quantify soil water repellency characteristics within burned piñon–juniper woodlands and relate this information to ecological site characteristics. We sampled soil water repellency across forty‐one 1,000 m 2 study plots within three major wildfires that burned in piñon–juniper woodlands. Water repellency was found to be extensive—present at 37% of the total points sampled—and strongly related to piñon–juniper canopy cover. Models developed for predicting SWR extent and severity had R 2 adj values of 0.67 and 0.61, respectively; both models included piñon–juniper canopy cover and relative humidity the month before the fire as coefficient terms. These results are important as they suggest that postfire water repellency will increase in the coming years as infilling processes enhance piñon–juniper canopy cover. Furthermore, reductions in relative humidity brought about by a changing climate have the potential to link additively with infilling processes to increase the frequency and intensity of wildfires and produce stronger water repellency over a greater spatial extent. In working through these challenges, land managers can apply the predictive models developed in this study to prioritize fuel control and postfire restoration treatments.