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ORIGINAL RESEARCH |
a Swiss Federal Inst. for Environmental Science and Technology (EAWAG), Ueberlandstrasse 133, P.O. Box 611, CH-8600 Dübendorf, Switzerland
b USDA-ARS, George E. Brown, Jr. Salinity Lab., 450 West Big Springs Road, Riverside, CA 92507
Correspondence: * Corresponding author (abbaspour{at}eawag.ch)
Received for publication 12 December 2003. Inversely obtained hydrologic parameters are always uncertain (nonunique) because of errors associated with the measurements and the invoked conceptual model, among other factors. Quantification of this uncertainty in multidimensional parameter space is often difficult because of complexities in the structure of the objective function. In this study we describe parameter uncertainties using uniform distributions and fit these distributions iteratively within larger absolute intervals such that two criteria are met: (i) bracketing most of the measured data (>90%) within the 95% prediction uncertainty (95PPU) and (ii) obtaining a small ratio (<1) of the average difference between the upper and lower 95PPU and the standard deviation of the measured data. We define a model as calibrated if, upon reaching these two criteria, a significant R2 exists between the observed and simulated results. A program, SUFI-2, was developed and tested for the calibration of two bottom ash landfills. SUFI-2 performs a combined optimization and uncertainty analysis using a global search procedure and can deal with a large number of parameters through Latin hypercube sampling. We explain the above concepts using an example in which two municipal solid waste incinerator bottom ash monofills were successfully calibrated and tested for flow, and one monofill also for transport. Because of high levels of heavy metals in the leachate, monitoring and modeling of such landfills is critical from environmental points of view.
Abbreviations: EC, electrical conductivity • GLUE, Generalized Likelihood Uncertainty Estimation • MSWI, municipal solid waste incinerator • SSQ, sum of square errors • SUFI, Sequential Uncertainty FItting • 95PPU, 95% prediction uncertainty
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