Quantifying Climate and Landscape Position Controls on Soil Development in Semiarid Ecosystems

Abstract

All rights reserved. No part of this periodical may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Permission for printing and for reprinting the material contained herein has been obtained by the publisher. Abbreviations: MAP, mean annual precipitation; PDI, profile development index; PET, potential evapotranspiration; SCM, Santa Catalina Mountains; SOC, soil organic carbon. I n the hot, dry ecosystems of the southwestern United States, water availability and topography are central to understanding soil and landscape evolution. Water availability is the dominant driver of soil formation in water-limited climates where precipitation regulates soil production and erosion, vegetation distribution and primary production, soil organic C (SOC) storage, and the degree of primary mineral alteration to secondary products. Topography and landscape position are particularly important in water-limited systems through their control on the redistribution of limited water resources that can lead to significant variation in hillslope-scale soil development and physiochemical properties (Muhs, 1982). It is necessary to understand the coupling of soil, climate, and landscape position in the Desert Southwest where predicted changes in temperature extremes and precipitation variability may drive significant shifts in water availability and biological production (Seager et al., 2007; Intergovernmental Panel on Climate Change,

Mineralogical Transformations and Soil Development in Shale across a Latitudinal Climosequence

“…The degree of soil development varies considerably with changes in climate, with thresholds observed in weathering processes and soil formation between water-limited systems and energy-limited systems (Dahlgren et al, 1997;Rasmussen et al, 2011;Goodfellow et al, 2014). Soil development indices correlated with climate include increased clay content (Dahlgren et al, 1997;Bockheim et al, 2000;Lybrand and Rasmussen, 2015), elemental losses and redistributions during pedogenesis (Muhs et al, 2001;Egli et al, 2003;Stiles et al, 2003;Chadwick et al, 2003) and mineral transformations from parent material to secondary clay minerals in soils (Dahlgren et al, 1997;Mirabella and Egli, 2003).…”

A probabilistic approach to quantifying soil physical properties via time-integrated energy and mass input

“…Weathering and clay production are primary pedogenic processes (Birkeland, 1999;Schaetzl and Anderson, 2005), and because the model assumed that all changes in the soil profile are due to these processes and TPE is closely related to degree of weathering, the model was the most effective at predicting clay content. For initial soil states that begin (2015) and Lybrand and Rasmussen et al (2015); (b) spatial prediction of mass per area clay. When combining the present approach, with a geomorphicbased soil depth model, the combined models together were highly effective at predicting the clay contents for a majority of soils in the Santa Catalina Mountains (Catalina-Jemez CZO), r 2 = 0.74.…”

“…Any predictive soil model or approach must be effective in both simple and complex terrain. To test the ability of the model to predict soil properties in complex terrain, we compiled data from upland catchments with variable parent material and topography from the literature, as well as data available from the US NSF Critical Zone Observatory Network (CZO, www.criticalzone.org) ( Table 1) (Bacon et al, 2012;Dethier et al, 2012;Foster et al, 2015;Holleran et al, 2015;Lybrand and Rasmussen, 2015;Rasmussen, 2008;West et al, 2013). Data from several additional studies from complex terrain were also included to test the model (Table 1) (Dixon et al, 2009;Yoo et al, 2007).…”

“…Additionally, we applied the probabilistic model independent of measured soil data, across a small complex catchment in (Holleran et al, 2015;Lybrand and Rasmussen, 2015). The ∼ 6 ha catchment is located at an elevation between 2300 and 2500 m with mixed conifer vegetation, approximately 30 km northeast of Tucson, AZ (Fig.…”

“…We assumed a constant 50 % rock fragment value for each location. The coupled geomorphic-TPE model outputs were compared with point measures of mass per area clay from Holleran et al (2015) and Lybrand and Rasmussen (2015). Model data were completely independent of the Holleran et al (2015) and Lybrand and Rasmussen (2015) datasets such that they served as validation data for the modeled output.…”