2015 GSA Annual Meeting in Baltimore, Maryland, USA (1-4 November 2015)

Paper No. 291-4
Presentation Time: 9:00 AM-6:30 PM

PARAMETER CALIBRATION OF MINI-LEO HILL SLOPE MODEL


SIEGEL, Helen, Landscape Evolution Observatory, Biosphere 2, Oracle, AZ 85739; Hydrology, University of Arizona, Tucson, AZ 85721; Geology, College of Wooster, Wooster, OH 44691, hsiegel17@wooster.edu

The mini-LEO hill slope, located at Biosphere 2, is a small-scale catchment model that is used to study the ways landscapes change in response to biological, chemical, and hydrological processes. Previous experiments have shown that soil heterogeneity can develop as a result of groundwater flow; changing the characteristics of the landscape. To determine whether or not flow has caused heterogeneity within the mini-LEO hill slope, numerical models were used to simulate the observed seepage flow, water table height, and storativity.

To begin a numerical model of the hill slope was created using CATchment Hydrology (CATHY). The model was then brought to an initial steady state by applying a rainfall event of 5mm/day for 180 days. Then a specific rainfall experiment of alternating intensities was applied to the model. Next, a parameter calibration was conducted, to fit the model to the observed data, by changing soil parameters individually. The parameters of the best fitting calibration were taken to be the most representative of those present within the mini-LEO hill slope.

Our model concluded that heterogeneities had indeed arisen as a result of the rainfall event, resulting in a lower hydraulic conductivity downslope. The lower hydraulic conductivity downslope in turn caused in an increased storage of water and a decrease in seepage flow compared to homogeneous models. This shows that the hydraulic processes acting within a landscape can change the very characteristics of the landscape itself, namely the permeability and conductivity of the soil. In the future results from the excavation of soil in mini-LEO can be compared to the models results to improve the model and validate its findings.