Development of a calibration approach using DNDC and PEST for improving estimates of management impacts on water and nutrient dynamics in an agricultural system. (November 2022)

• Record Type: Journal Article
• Authors: Bhattarai, Abha; Steinbeck, Garrett; Grant, Brian B.; Kalcic, Margaret; King, Kevin; Smith, Ward; Xu, Nuo; Deng, Jia; Khanal, Sami
• Is Part Of: Environmental modelling & software. Volume 157(2022)
• Publication Date: 2022-11
• Subjects: Agroecosystem model -- Calibration -- DNDC -- Inverse modeling -- PEST; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2022.105494
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Calibration and validation are standardized practices to establish the credibility of biogeochemical models for understanding agroecosystem nutrient dynamics. We evaluated three automatic calibration approaches, including simultaneous, sequential, and separate, for the calibration of model parameters of the biogeochemical DeNitrification DeComposition (DNDC) model through inverse modeling using PEST, open-source parameter estimation, and uncertainty analysis software. While manual calibration by experts performed the best during calibration period, followed by simultaneous calibration, sequential calibration had the best model performance during the validation period. Model sensitivity analyses demonstrated water leaching to be sensitive to curve number and drain spacing, nitrate leaching to be sensitive to porosity and clay content, and corn yield to be sensitive to accumulative temperature and grain C/N ratio. While some level of expertise is required to inform the automated calibration procedure, it represents a more efficient and robust approach toward increasing the performance of biogeochemical models.