Model comparison of flow through a municipal solid waste incinerator ash landfill

• Published in: Journal of hydrology (Amsterdam) Vol. 243; no. 1; pp. 55 - 72
• Main Authors: Johnson, C.A., Schaap, M.G., Abbaspour, K.C.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01-03-2001; Elsevier Science
• Subjects: Calibration; Discharge; Drainage; Earth sciences; Earth, ocean, space; Engineering and environment geology. Geothermics; Evaporation; Exact sciences and technology; Hydrodynamics; Hydrology; Hydrology. Hydrogeology; Incinerators; Landfill; Landfills; Leachate; Mathematical models; Moisture content; Neural network; Neural networks; Nonlinearity; Pollution, environment geology; Preferential flow; Rainfall; Resistivity; Solid wastes; Storage; Wastes; Central Europe; Switzerland; Drainage; Hydrology; Landfill; Model; Neural network; Leachate; Preferential flow; Discharge; models; physical properties; rainfall; Europe; neural networks; digital simulation; pollution; surface water; physical models; landfills; solid waste; ash; discharge; drainage; leachate; electrical conductivity; evaporation
• ISSN: 0022-1694; 1879-2707
• DOI: 10.1016/S0022-1694(00)00404-2

The drainage discharge of a municipal solid waste incinerator (MSWI) bottom ash landfill was simulated using various modelling approaches. Two functional models including a neural networks approach and a hydrological linear storage model, and two mechanistic models requiring physical/hydrodynamic properties of the waste material, HYDRUS5 and MACRO (Version 4.0) were used. The models were calibrated using an 8-month data set from 1996 and validated on a 3-month data set from winter 1994/1995. The data sets comprised hourly values of rainfall, evaporation (estimated from the Penman–Monteith relationship), drainage discharge and electrical conductivity. Predicted and measured discharges were compared.
The discharge predicted by the functional models more exactly followed the discharge patterns of the measured data but, particularly the linear storage model, could not cope with the non-linearity of the system that was caused by seasonal changes in water content of the MSWI bottom ash. The fit of the neural networks model to the data improved with increasing prior information but was less smooth than the measured data. The mechanistic model that included preferential discharge, MACRO, better modelled the discharge characteristics when inversely applied, indicating that preferential flow does occur in this system. However, even the inverse application of HYDRUS5 could not describe the system discharge as well as the linear storage model. All model approaches would have benefited from a more exact knowledge of initial water content.