Coupled inverse modeling of vadose zone water, heat, and solute transport: calibration constraints, parameter nonuniqueness, and predictive uncertainty

Friedel, Michael J.

doi:10.1016/j.jhydrol.2005.02.013

Cited by 33 publications

(40 citation statements)

References 38 publications

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“…5). Since the near perfect model correlation values between observed and simulated profiles for each Monte Carlo trial are (by themselves) a poor indicator of model predictability (Friedel 2005(Friedel , 2006a, a statistical summary of estimated runoff curve numbers and rainfall amounts is presented in Tables 2 and 3.…”

Section: Resultsmentioning

confidence: 99%

“…Also, the observations of o are oftentimes comprised of one or more types of measurement information (Friedel 2005) that are preprocessed to ensure: (1) homoscedascity (Bates and Campbell 2001), (2) statistical independence of measurement noise (Kuczera 1983), and (3) that information content associated with the measurement-constraints exert sufficient influence in the estimation process (Doherty and Johnston 2003;Friedel 2006a). When minimization of the measurement objective function U m results in an optimal parameter set p, the upgrade vector, p -p 0 , can be calculated directly by…”

Section: Gauss-marquardt-levenberg Parameter Estimationmentioning

confidence: 99%

“…One problem with adhering to this principle is that there is no a priori means of knowing how many parameter values can be estimated. In this case, estimation of too few parameter values may result in the inability to predict anything of consequence, whereas estimation of too many parameter values relative to the number and/or limited information content (temporal and spatial variation of system stresses reflected in the measurements) in the constraint-measurements may result in an unstable solution where X T WX becomes singular, or near-singular (Doherty and Johnston 2003;Friedel 2005). Perhaps more importantly, the traditional estimation approaches are not amenable to the solution of complex nonlinear inverse problems (such as those involving simultaneous calibration of multiple models and/or relations among parameters).…”

Section: Regularization Proceduresmentioning

confidence: 99%

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Regularized joint inverse estimation of extreme rainfall amounts in ungauged coastal basins of El Salvador

Friedel

2007

Nat Hazards

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A regularized joint inverse procedure is presented and used to estimate the magnitude of extreme rainfall events in ungauged coastal river basins of El Salvador: Paz, Jiboa, Grande de San Miguel, and Goascoran. Since streamflow measurements reflect temporal and spatial rainfall information, peak-flow discharge is hypothesized to represent a similarity measure suitable for regionalization. To test this hypothesis, peak-flow discharge values determined from streamflow recurrence information (10-year, 25-year, and 100-year) collected outside the study basins are used to develop regional (country-wide) regression equations. Peak-flow discharge derived from these equations together with preferred spatial parameter relations as soft prior information are used to constrain the simultaneous calibration of 20 tributary basin models. The nonlinear range of uncertainty in estimated parameter values (1 curve number and 3 recurrent rainfall amounts for each model) is determined using an inverse calibration-constrained Monte Carlo approach. Cumulative probability distributions for rainfall amounts indicate differences among basins for a given return period and an increase in magnitude and range among basins with increasing return interval. Comparison of the estimated median rainfall amounts for all return periods were reasonable but larger (3.2-26%) than rainfall estimates computed using the frequency-duration (traditional) approach and individual rain gauge data. The observed 25-year recurrence rainfall amount at La Hachadura in the Paz River basin during Hurricane Mitch (1998) is similar in value to, but outside and slightly less than, the estimated rainfall confidence limits. The similarity in joint inverse and traditionally computed rainfall events, however, suggests that the rainfall observation may likely be due to under-catch and not model bias.

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Section: Resultsmentioning

confidence: 99%

Section: Gauss-marquardt-levenberg Parameter Estimationmentioning

confidence: 99%

Section: Regularization Proceduresmentioning

confidence: 99%

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Regularized joint inverse estimation of extreme rainfall amounts in ungauged coastal basins of El Salvador

Friedel

2007

Nat Hazards

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“…For example, Friedel (2005) interpreted css for different combinations of data types in a coupled water-heat-solute transport model.…”

Section: Parameter Identifiabilitymentioning

confidence: 99%

Inverse optimization of hydraulic, solute transport, and cation exchange parameters using HP1 and UCODE to simulate cation exchange

Jacques

Smith

Šimůnek

et al. 2012

Journal of Contaminant Hydrology

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“…These are concerns for hydraulic and soil specialists, but they also indicate the uncertainty of parameterization in inverse modeling. Effective models require accurate specification of the variables used (Friedel 2005); however it is usually impossible to accurately describe all observable situations (Köhne et al 2011). Therefore observable variables cannot always be adopted at the operational level (Corradini et al 2011).…”

Section: Introductionmentioning

confidence: 99%

Hydraulic Characterization of Porous Materials Using a Rainfall Simulator to Model Flows in Natural and Artificial Non-Saturated Materials

Masson

Lefer

Boulemia

et al. 2017

JWMM

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Cumulative runoff curves are created by applying constant rainfall of known intensity using a rain simulator. These curves allow us to determine, with good accuracy, the initial loss, the initial time of runoff, and the constant loss. The approach is to model infiltration in an unsaturated medium using a draining reservoir paradigm. Simulated rainfall is applied to a typical soil considered to be composed of two superimposed layers of different permeabilities. This approach enables us to represent the drainage porosity as a function of the constant loss and the initial runoff time, which are measured during the rain simulation. Thus it is possible to calculate the infiltrated volumes, the initial runoff times, and the runoff volumes which are induced by rains of different intensities.Four case studies are used to illustrate the application of our approach.

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Coupled inverse modeling of vadose zone water, heat, and solute transport: calibration constraints, parameter nonuniqueness, and predictive uncertainty

Cited by 33 publications

References 38 publications

Regularized joint inverse estimation of extreme rainfall amounts in ungauged coastal basins of El Salvador

Regularized joint inverse estimation of extreme rainfall amounts in ungauged coastal basins of El Salvador

Inverse optimization of hydraulic, solute transport, and cation exchange parameters using HP1 and UCODE to simulate cation exchange

Hydraulic Characterization of Porous Materials Using a Rainfall Simulator to Model Flows in Natural and Artificial Non-Saturated Materials

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