Assessment of the parameter sensitivity for the ORYZA model at the regional scale - A case study in the Yangtze River Basin. (January 2023)

• Record Type: Journal Article
• Authors: Yu, Qianan; Cui, Yuanlai; Liu, Luguang
• Is Part Of: Environmental modelling & software. Volume 159(2023)
• Publication Date: 2023-01
• Subjects: Global sensitivity analysis -- ORYZA model -- Screening -- Ranking -- Regional scale; 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.2022.105575
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: While global sensitivity analysis (GSA) has been widely implemented for crop models, few studies have assessed the sensitivity of model parameters at the regional scale. To fully understand the overall sensitivity of parameters in a specific area, this study carried out a regional-scale global sensitivity analysis (RGSA) by selecting multiple stations through grids within the study area and integrated GSA results obtained by extended Fourier amplitude sensitivity test (EFAST) from these stations by statistical indicators to quantify parameter sensitivity at the regional scale. Taking the Yangtze River Basin (YRB) as the study area, RGSA was implemented for the ORYZA model for paddy rice under two water and nitrogen situations (WNCs) and four levels of number of stations for sensitivity analysis (NSSA). The results suggested that the applicable area for the results of sensitive and insensitive parameters partitioning, sensitivity ranks and sensitivity indices at a specific station were not larger than grid sizes of 8° × 8°, 4° × 4° and 2° × 2° around it, respectively. Thus, the NSSA needed for sensitive parameter identification, sensitivity ranking and sensitivity indices variability analysis at regional scale increased sequentially. It was recommended that a suitable number of stations should be chosen for analysis according to the sensitivity analysis purpose at the regional scale. Since WNC will affect the results of RGSA, such analysis was recommended to be performed separately for different WNCs. Highly sensitive parameters of the ORYZA model for paddy rice in YZB were screened out through RGSA, which were DVRJ, DVRP and RGRLMX. Model users can adopt the RGSA workflow used in this study for parameter sensitivity assessment of other crop models. Highlights: A framework of regional-scale global sensitivity analysis (RGSA) for crop models was proposed. The RGSA results are affected by station selection and water and nitrogen condition settings. The number of stations for RGSA can be determined according to the analysis purpose.