Estimating Uncertain Flow and Transport Parameters Using a Sequential Uncertainty Fitting Procedure

By K. C. Abbaspour, C. A. Johnson, M. T. V. Genuchten. 2004

1,079 citations recorded by the source corpus.

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.

Subfields: Civil and Structural Engineering, Environmental Engineering, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/c8ddd726279c39dd20ea69829bb7fdebb53fdc1d

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Citations in the corpus

Calibration and uncertainty analysis of the SWAT model using Genetic Algorithms and Bayesian Model Averaging (2009, methodology): “Yang et al. (2008) compared four uncertainty analysis algorithms, that is GLUE (Beven and Binley, 1992), Sequential Uncertainty Fitting SUFI-2 (Abbaspour et al., 2004), Parameter solutions (ParaSol) (Van Griensven and Meixner, 2006), and Markov Chain Monte Carlo (MCMC) based Bayesian analysis…”
Uncertainty Analysis of SWAT Modeling in the Lancang River Basin Using Four Different Algorithms (2021, background): “[14], which is based on a Bayesian framework [5,48].”
Comparing uncertainty analysis techniques for a SWAT application to the Chaohe Basin in China (2008, methodology): “In fact in SUFI-2 the assumption is that there are many islands of similarly ‘‘good’’ objective functions (Abbaspour et al., 2004) and any uncertainty technique converges to one such island.”
Uncertainty analysis of bias from satellite rainfall estimates using copula method (2014, methodology): “It is noted that in the several literatures (Abbaspour et al., 2004, 2007; Schuol et al., 2008; Yang et al., 2008) the 95PPU band calculated at the 2.5% lower and 97.5% upper limit levels of the output variables was used; however, in our case after a trade-off between different percentages, the…”
Parameter and rating curve uncertainty propagation analysis of the SWAT model for two small Mediterranean catchments (2013, methodology): “…and uncertainty analysis are conducted simultaneously using three different procedures: the Sequential Uncertainty Fitting (SUFI-2) procedure (Abbaspour et al. 2004), the Parameter Solutions (ParaSol) procedure (van Griensven and Meixner 2007) and the Generalized Likelihood Uncertainty…”
Comparison of Three Multiobjective Optimization Algorithms for Inverse Modeling of Vadose Zone Hydraulic Properties (2008, background): “…of interest (e.g., Inoue et al., 1998; Šimůnek et al., 1998; Zurmühl and Durner, 1998; Si and Kachanoski, 2000; Vrugt et al., 2001; Abbasi et al., 2003; Hupet et al., 2003; Roulier and Jarvis, 2003; Abbaspour et al., 2004; Ritter et al., 2004, 2005; Zhang et al., 2004; Kelleners et al., 2005).”
Hydrological modelling of the Chaohe Basin in China: Statistical model formulation and Bayesian inference (2007, positive): “…(Beven and Binley 1992; Lamb, 1999; Beven and Freer 2001), regional or iterative importance sampling and similar approaches (Abbaspour et al., 1997; Abbaspour et al., 2004), and Markov Chain Monte Carlo techniques (Kuczera and Parent, 1998; Bates and Campbell, 2001; Vrugt et al., 2003).”