Application of spatial pedotransfer functions to understand soil modulation of vegetation response to climate

TitleApplication of spatial pedotransfer functions to understand soil modulation of vegetation response to climate
Publication TypeJournal Article
Year of Publication2015
AuthorsLevi M, Schaap M, Rasmussen C
JournalVadose Zone Journal
Start Page1
Date Published09/2015
ARIS Log Number310885

A fundamental knowledge gap in understanding land-atmosphere interactions is accurate, high resolution spatial representation of soil physical and hydraulic properties. We present a novel approach to predict hydraulic soil parameters by combining digital soil mapping techniques with pedotransfer functions and demonstrate that simple derived quantities are related to observed spatial patterns in ecosystem production during the North American Monsoon. Landsat reflectance and elevation data were used to predict physical soil properties at a 5 m spatial resolution for a semiarid landscape of 6,265 ha using regression kriging. Resulting soil property maps were applied to the Rosetta pedotransfer function to predict saturated hydraulic conductivity and water retention parameters from which approximate water residence times were derived. Estimated idealized residence time for water lost to the deeper vadose zone and evapotranspiration corresponded to vegetation response. Antecedent precipitation was more important for explaining the relationships between modeled soil properties and vegetation response than the amount of monsoon precipitation. Increased spring precipitation prior to the monsoon produced stronger negative correlations with hydraulic conductivity and stronger positive correlations with plant available water. Modeled water residence times explained the patterns of vegetation and landscape morphology validating our approach as a method of producing functional spatial pedotransfer functions. Linking digital soil mapping with Rosetta led to predictions of hydraulic soil properties that were more closely related to vegetation dynamics compared to data available in the SSURGO soil database.