Date of Award

5-2017

Document Type

Thesis

Degree Name

Master of Science (MS)

Legacy Department

Plant and Environmental Science

Committee Member

Dr. Elena A. Mikhailova, Committee Chair

Committee Member

Dr. Christopher J. Post

Committee Member

Dr. Charles V. Privette

Abstract

Saturated hydraulic conductivity (Ksat) is a soil property linked to ecosystem services and it is often used in septic tank suitability determination at various scales. Field and laboratory measurements of Ksat and septic tank suitability are time-consuming and expensive. Soil Survey Geographic Database (SSURGO) data are available for the United States, but limitations of using SSURGO data for Ksat and septic suitability determination are not fully understood. The objectives of this study were to quantify and compare depth to limiting layer, thickness of limiting layer, and Ksat values for a 147-hectare Cornell University Willsboro Research Farm, located in upstate New York based on the following procedures: a) using values reported by SSURGO for each soil map unit (SMU) within the farm and applying that value across each SMU; b) averaging the values of soil cores collected within a specific SMU boundary and applying the averaged value across each SMU; and c) interpolating values across the farm based on the individual soil cores. SSURGO overestimated the depth to the limiting layer and the thickness of the limiting layer when compared to field measured values. Average soil core values representing limiting layer, thickness of limiting layer, and Ksat values were not significantly correlated with SSURGO reported values. Similarly, interpolated soil core values of limiting layer, thickness of limiting layer, and Ksat values were not significantly correlated with SSURGO reported values. Both SSURGO data and field measurements are necessary for proper septic tank suitability determination due to the uncertainties, which often arise from field, laboratory and geospatial variability in data necessary for such determinations. Application of technological advances may reduce the uncertainty in data collection.

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