Assessment of uncertainty in simulation of nitrate leaching to aquifers at catchment scale

Thorsen, Mette; Refsgaard, Jens Christian; Hansen, Søren; Pebesma, Edzer; Jensen, Jacob Birk; Kleeschulte, S.

doi:10.1016/s0022-1694(00)00396-6

Cited by 76 publications

(46 citation statements)

References 25 publications

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“…The Monte Carlo method has been widely used to propagate uncertainties through soil hydrological models and has become increasingly popular over recent years as the computational overheads of performing such an analysis have declined. Examples in soil hydrology can be found in Petach et al (1991); Bennett et al (1998); Duke et al (1998); Hansen et al (1999); Kros et al (1999); Dillah and Protopapas (2000); Thorsen et al (2001); Keller et al (2002); De Vries et al (2003). For distributed modeling of soil water flows and solute transport, the sampling algorithm must accommodate spatial and temporal dependencies in model inputs and parameters (De Roo et al, 1992;Endreny and Wood, 2001).…”

Section: Monte Carlo Methodsmentioning

confidence: 99%

Assessing Uncertainty Propagation through Physically Based Models of Soil Water Flow and Solute Transport

Brown

Heuvelink

2005

Encyclopedia of Hydrological Sciences

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Soil hydrological models are inherently imperfect because they abstract and simplify “real” hydrological patterns and processes. Indeed, an important aim of modeling is to establish the simplest description possible for adequately addressing a particular problem. Also, models are frequently based on input data that are known to be inadequate for some practical purpose. Thus, uncertainties in model outputs originate from uncertainties in input data, which include measurement and interpolation errors, and in models that include conceptual, logical, mathematical, and computational errors. Understanding the causes and consequences of uncertainty in soil hydrological modeling is useful for: (i) establishing the utility of data and models as decision‐support tools; (ii) directing resources towards improving data and models, and (iii) seeking alternative ways of managing soils when the opportunities for accurate modeling are limited. This chapter focuses on statistical methods for assessing uncertainty in soil data and models, propagating uncertainties through models, and assessing the contribution of different sources of uncertainty to the overall uncertainties in model predictions. In addition, it explores the impacts of scale, and changes between scales, on the outcomes of an uncertainty analysis. It concentrates on physically based models of soil water flow and solute transport, and provides numerous examples from the literature here

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Section: Monte Carlo Methodsmentioning

confidence: 99%

Assessing Uncertainty Propagation through Physically Based Models of Soil Water Flow and Solute Transport

Brown

Heuvelink

2005

Encyclopedia of Hydrological Sciences

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“…In several papers, two or more of the error sources are included in the estimation of the total modelling uncertainty. Thorsen et al (2001), Refsgaard et al (1983) and Storm et al (1988) conclude that the uncertainty in precipitation is more important than that in the parameters. Krzysztofowicz (1999) shows that, in hydrological forecasts, the uncertainties in the precipitation forecasts are more important than those in the hydrological model.…”

Section: Uncertainties In Hydrological Modellingmentioning

confidence: 99%

Bayesian estimation of parameters in a regional hydrological model

Engeland

Gottschalk

2002

Hydrol. Earth Syst. Sci.

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This study evaluates the applicability of the distributed, process-oriented Ecomag model for prediction of daily streamflow in ungauged basins. The Ecomag model is applied as a regional model to nine catchments in the NOPEX area, using Bayesian statistics to estimate the posterior distribution of the model parameters conditioned on the observed streamflow. The distribution is calculated by Markov Chain Monte Carlo (MCMC) analysis. The Bayesian method requires formulation of a likelihood function for the parameters and three alternative formulations are used. The first is a subjectively chosen objective function that describes the goodness of fit between the simulated and observed streamflow, as defined in the GLUE framework. The second and third formulations are more statistically correct likelihood models that describe the simulation errors. The full statistical likelihood model describes the simulation errors as an AR (1) process, whereas the simple model excludes the auto-regressive part. The statistical parameters depend on the catchments and the hydrological processes and the statistical and the hydrological parameters are estimated simultaneously. The results show that the simple likelihood model gives the most robust parameter estimates. The simulation error may be explained to a large extent by the catchment characteristics and climatic conditions, so it is possible to transfer knowledge about them to ungauged catchments. The statistical models for the simulation errors indicate that structural errors in the model are more important than parameter uncertainties.

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“…Kuczera, 1988;Bates and Townley, 1988), Monte Carlo sampling coupled with frequency analysis (e.g. Bates and Townley, 1988;Yu et al, 2001; Thorsen et al, 2001;Zehe and Blöschl, 2004;Arnold et al, 2009), Bayesian analysis (e.g. Beven and Binley, 1992;Romanowicz et al, 1994;Thiemann et al, 2001;Misirli et al, 2003;Engeland et al, 2005;Rojas et al, 2010;Renard et al, 2010) and imprecise probabilities (Hall, 2006;Ghosh and Mujumdar, 2009;Nijssen et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

Possibilistic uncertainty analysis of a conceptual model of snowmelt runoff

Jacquin

2010

Hydrol. Earth Syst. Sci.

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Abstract. This study presents the analysis of predictive uncertainty of a conceptual type snowmelt runoff model. The method applied uses possibilistic rather than probabilistic calculus for the evaluation of predictive uncertainty. Possibility theory is an information theory meant to model uncertainties caused by imprecise or incomplete knowledge about a real system rather than by randomness. A snow dominated catchment in the Chilean Andes is used as case study. Predictive uncertainty arising from parameter uncertainties of the watershed model is assessed. Model performance is evaluated according to several criteria, in order to define the possibility distribution of the parameter vector. The plausibility of the simulated glacier mass balance and snow cover are used for further constraining the model representations. Possibility distributions of the discharge estimates and prediction uncertainty bounds are subsequently derived. The results of the study indicate that the use of additional information allows a reduction of predictive uncertainty. In particular, the assessment of the simulated glacier mass balance and snow cover helps to reduce the width of the uncertainty bounds without a significant increment in the number of unbounded observations.

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Assessment of uncertainty in simulation of nitrate leaching to aquifers at catchment scale

Cited by 76 publications

References 25 publications

Assessing Uncertainty Propagation through Physically Based Models of Soil Water Flow and Solute Transport

Assessing Uncertainty Propagation through Physically Based Models of Soil Water Flow and Solute Transport

Bayesian estimation of parameters in a regional hydrological model

Possibilistic uncertainty analysis of a conceptual model of snowmelt runoff

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