Inverse Estimation of Parameters in a Nitrogen Model Using Field Data

Abstract

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.

Calibration of LEACHN model using LH-OAT sensitivity analysis

“…If the NSEF value is closer to one, the efficiency of a model is higher. Sumner (2000) Air entry value (kPa) AVE -17 -0.149 Ramos and Carbonell (1991), Schmied et al (2000) Exponent for Campbell's equation BCAM 0.14 31.5 Lesikar et al (1997), Schmied et al (2000) Conductivity (mm day -1 ) K 264 13400 Ramos and Carbonell (1991), Schmied et al (2000) Dispersivity (mm) D 1 300 Ramos and Carbonell (1991) Crop data Maximum ratio of actual to potential RT 1 1.55 Acutis et al (2000), Schmied et al (2000) Relative root depth Denitrification rate (day -1 ) Kdenitr 2 9 10 -5 0.12 Johnson et al (1999), Schmied et al (2000) Residue rate (day -1 ) Kres 1.5 9 10 -3 0.01 Acutis et al (2000), Borah and Kalita (1999) Manure rate (day -1 ) Kman 8 9 10 -3 0.04 Sogbedji et al (2006), Acutis et al (2000) Humus rate (day -1 ) Khum 1.1 9 10 -5 7.5 9 10 -5 Acutis et al (2000), Schmied et al (2000) Nutr Cycl Agroecosyst (2010) Drained water at deeper soil depth is less, but the change of drainage was also smaller at deeper soil depth. Therefore they became more sensitive at deeper soil depth.…”

“…The parameters related with water movement, such as AVE and BCAM, did not exhibit any distinct trend. Schmied et al (2000) showed that six parameters related to the litter pool, its mineralization, and the denitrification rate are most sensitive for nitrate leaching using simple one-at-a time approach. Mahmood et al (2002), however, concluded that the bulk density, air entry value, BCAM, mineralization rate, base temperature, and Q10 factor are more sensitive than soil moisture content, organic carbon, and nitrification and denitrification rates.…”

“…In some studies, it was used to estimate the change of both water content and nitrate concentration at different soil depth, soil texture, and weather conditions (Ramos and Carbonell 1991;Johnson et al 1999;Schmied et al 2000;Roy et al 2000;Mahmood 2005). In other studies, nitrate leaching was simulated with the model at different fertilizer levels and crops, and then the simulation results were compared to the measured field data (Jemison et al 1994;Acutis et al 2000;Ng et al 2000;Singh and Sondhi 2001;Mahmood et al 2002).…”

“…LEACHN has been applied to many different cases in many previous researches as shown above, which exemplifies the robust and wide field applicability of the model. Sensitivity analyses of LEACHN model were previously performed by Acutis et al (2000), Schmied et al (2000), Mahmood et al (2002), and Vachaud and Chen (2002a, b). These studies, however, analyzed the sensitivities of only a limited number of parameters at specific conditions.…”

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

“…So-called "inverse modeling" has been widely used in uncertainty schemes over recent years (e.g. Abbaspour et al, 1999Abbaspour et al, , 2000Schmied et al, 2000;Beven and Freer, 2001;Vrugt et al, 2003, and Most of these schemes use Bayes' theorem, as this allows a "prior" distribution of uncertain input and parameter values to change in response to the amount and perceived (weighted) value of information available (Frenc and Smith, 1997;Bernado and Smith, 2001). The prior is sampled to provide a range of possible models for simulating the same problem, and uncertainties are propagated to model outputs by implementing the sample (running the models) and recording the results.…”