Sedgwick Reserve

21 Mar, 02 AM - 30 Jun, 02 AM

Demand has risen quickly for explicit ecosystem models that predict landscape properties and processes across spatial and temporal domains. To date, integrated models that attempt to quantify ?ecosystem services? (e.g., InVEST) generally emphasize plant community dynamics, despite the significant controls exerted by soils on biophysical and biogeochemical processes. This bias towards plant ecology is due in part to the lack of spatially explicit soil-landscape models. Although the dominant paradigm for characterizing soil-landscape relationships (Jenny 1941) has provided a wealth of information linking soil properties to ecosystem function, these efforts have largely taken place in the absence of a robustly quantitative framework ? a prerequisite for inclusion in emergent ecosystem simulation models. Specifically, missing or poor quality terrain data has limited our ability to accurately characterize feedbacks between soils, landforms, hydrology, and plant floristics. Recent advances in technology and geostatistics are dissolving this limitation. For example, high resolution LiDAR digital terrain maps are now a cost-effective means of defining soil-landscape units, while numerical and statistical techniques (Goovaerts 1999, Park et. al 2002) allow for developing testable hypotheses on relationships between soils and environmental variables. Utilizing an existing LiDAR dataset and multiple past research efforts in Sedgwick Reserve, our study will further explore this approach for characterizing the influence of topography on the spatial variation of soil properties. Emphasis will be placed on two goals: three-dimensional modeling of stratigraphic soil patterns (i.e., soil horizons) within Sedgwick Reserve, and identification of discrete, scale-dependent hillslope process domains. While the first goal tests our ability to combine geocomputational techniques with soil mapping, the second goal aims more broadly to advance our understanding of scale dependency on soil-mediated ecosystem functions. The driving hypothesis is that vertical soil variability, specifically surface and subsurface horizon thickness and composition, can be accurately interpolated using ancillary digital terrain information. To the degree this holds true, we further expect our predictive soil maps to divulge insights as to the magnitude and direction (i.e., scalability) of specific soil processes, from the pedon to the landform level. Thus, in addition to an enhanced map of Sedgwick Reserve soils, the final output will yield information on the evolution of regional soil-landscape patterns as well as insights into the scale-dependent nature of ecosystem services in California oak savannahs.

Visit #24559 @Sedgwick Reserve

Approved

Under Project # 23384 | Research

Assessing scale-dependent soil process domains with high resolution DEM

graduate_student - University of California, Santa Barbara

Reservation Members(s)

Samuel Prentice Mar 21 - Jun 30, 2011 (102 days)
Group of 2 Research Assistant (non-student/faculty/postdoc) Mar 21 - Jun 30, 2011 (102 days)
Samuel Prentice Mar 21 - Jun 30, 2011 (102 days)

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Ranch House Not Available 4 Mar 21 - Jun 30, 2011