Inverse estimation of parameters in a nitrogen model using field data

An important step in numerical modeling is the determination of model param eters. Because of practical limitations, as well as time and financial cons traints, inverse algorithms have in recent years presented an attractive al ternative to direct methods of parameter estimation. In this study we linke d the inverse algorithm of SUFI with the simulation program LEACHM to study N turnover of an agricultural held. Addressing the inherent modeling uncer tainties, we introduce the concept of conditioned parameter distributions a s 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 uncerta inty in the conditioned parameter distributions will result in simulations where most of the measurements are respected or fall within the 95% confide nce interval of the Bayesian distribution (95PCIBD). In this study rye used measured pressure heads and NO3 concentrations to estimate 12 hydraulic pa rameters and up to 14 N turnover-related parameters. Most of the measuremen ts in three soil layers fell within the 95PCIBD. Exceptions were some obser ved pressure heads corresponding to intense rainfall events and periods of soil freezing, as well as some high NO3 concentrations in the subsoil betwe en 40- and 70-cm depth. We attributed the discrepancies to processes that w ere not addressed by the simulation model such as freezing and short-circui ting due to macropore flow.