|Yakirevich, Alexander -|
|Kouznetsov, Michael -|
|Nicholson, Thomas -|
|Cady, Ralph -|
Submitted to: Nuclear Regulatory Commission Report
Publication Type: Government Publication
Publication Acceptance Date: October 14, 2009
Publication Date: May 12, 2011
Citation: Pachepsky, Y.A., Gish, T.J., Guber, A.K., Yakirevich, A., Kouznetsov, M., Nicholson, T.J., Cady, R.E. 2011. Application of model abstraction techniques to simulate transport in soils. Nuclear Regulatory Commission Report. CR-7026. Interpretive Summary: The complexity of flow and transport pathways in hydrologic systems can be represented in mathematical terms by making different simplifying assumptions. These assumptions can be incorporated into conceptual models that produce results that are consistent with available observations. However, no single set of assumptions may be adequate. Model abstraction techniques seek to use a variety of conceptual approaches rather than attempt to find the best model or to use a single preferred model. This work was undertaken to demonstrate the efficiency of model abstraction in using models to understand how subsurface soil structure affects contaminant transport. Experiments were conducted with chemical tracers that do not interact with soil, such that transport was controlled exclusively by soil structure, i.e., spatial arrangement of soil particles. Two tracer experiments were carried out at the USDA-ARS OPE3 experimental area in Beltsville, Maryland. The tracer chemical, or solute, was introduced into the soil with irrigation water. The tracer was then monitored either in groundwater beneath the application site or down gradient. Monitoring was complemented with hydrogeophysical surveys with ground penetrating radar and electric resistivity sounding to infer the presence and distribution of large soil structural units and layers. Despite collecting a voluminous amount of data, it was not sufficient to clearly decide whether there was preferential solute transport, i.e., transport where only a limited fraction of soil actively conducts the solute. This is the critical question because, under the preferential transport, contaminants move very fast and do not undergo substantial retention or chemical or biological transformations. Transport models were formulated and calibrated assuming no preferential transport occurred in soil and groundwater. Model abstraction techniques were then used to reduce the complexity of the transport models, clearly illustrating their value in simplifying the original conceptual models. Overall, this work demonstrated the utility of model abstraction in soil characterization with respect to solute transport.
Technical Abstract: Successful understanding and modeling of contaminant transport in soils is the precondition of risk-informed predictions of the subsurface contaminant transport. Exceedingly complex models of subsurface contaminant transport are often inefficient. Model abstraction is the methodology for reducing the complexity of a simulation model while maintaining the validity of the simulation. The objective of this work was to apply model abstraction to characterize and understand flow and transport in soils in presence of shallow groundwater. We developed two case studies by carrying two types of field tracer experiments at the USDA-ARS OPE3 Beltsville site and applying a sequence of model simplifications based on the HYDRUS software family and MODFLOW. Soil moisture, soil water potential, tracer concentrations in groundwater and groundwater levels, and weather variables monitoring along with ground penetration radar survey, electric resistivity monitoring, and dilution tests complemented the borehole log data and laboratory hydraulic measurements to characterize soil heterogeneity. The series of model abstractions showed the role of subsurface heterogeneity in vadose zone and groundwater and led to the substantial improvement of the conceptualization of the subsurface. Results of this work demonstrate directions of model abstraction use in NRC licensing staff in their review and performance assessment work.