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DIVISION S-1-SOIL PHYSICS

Inverse Estimation of Parameters in a Nitrogen Model Using Field Data

Swiss Federal Institute of Technology, Dep. of Soil Protection, Grabenstrasse 3, 8952 Schlieren, Switzerland

barbara.schmied{at}ito.umnw.ethz.ch

An important step in numerical modeling is the determination of model parameters. Because of practical limitations, as well as time and financial constraints, inverse algorithms have in recent years presented an attractive alternative to direct methods of parameter estimation. In this study we linked the inverse algorithm of SUFI with the simulation program LEACHM to study N turnover of an agricultural field. Addressing the inherent modeling uncertainties, we introduce the concept of conditioned parameter distributions as being a more appropriate alternative to best-fit parameters. Conditioned parameter distributions are quantified within uncertainty domains, and the task of an inverse model then is to reduce or condition this domain through minimization of an appropriate objective function. Propagating the uncertainty in the conditioned parameter distributions will result in simulations where most of the measurements are respected or fall within the 95% confidence interval of the Bayesian distribution (95PCIBD). In this study we used measured pressure heads and NO₃ concentrations to estimate 12 hydraulic parameters and up to 14 N turnover–related parameters. Most of the measurements in three soil layers fell within the 95PCIBD. Exceptions were some observed pressure heads corresponding to intense rainfall events and periods of soil freezing, as well as some high NO₃ concentrations in the subsoil between 40- and 70-cm depth. We attributed the discrepancies to processes that were not addressed by the simulation model such as freezing and short-circuiting due to macropore flow.

Abbreviations: 95PCIBD, the 95% confidence interval of the Bayesian distribution

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