Global sensitivity analysis for an integrated model for simulation of nitrogen dynamics under the irrigation with treated wastewater

Abstract

As the amount of water resources that can be utilized for agricultural production is limited, the reuse of treated wastewater (TWW) for irrigation is a practical solution to alleviate the water crisis in China. The process-based models, which estimate nitrogen dynamics under irrigation, are widely used to investigate the best irrigation and fertilization management practices in developed and developing countries. However, for modeling such a complex system for wastewater reuse, it is critical to conduct a sensitivity analysis to determine numerous input parameters and their interactions that contribute most to the variance of the model output for the development of process-based model. In this study, application of a comprehensive global sensitivity analysis for nitrogen dynamics was reported. The objective was to compare different global sensitivity analysis (GSA) on the key parameters for different model predictions of nitrogen and crop growth modules. The analysis was performed as two steps. Firstly, Morris screening method, which is one of the most commonly used screening method, was carried out to select the top affected parameters; then, a variance-based global sensitivity analysis method (extended Fourier amplitude sensitivity test, EFAST) was used to investigate more thoroughly the effects of selected parameters on model predictions. The results of GSA showed that strong parameter interactions exist in crop nitrogen uptake, nitrogen denitrification, crop yield, and evapotranspiration modules. Among all parameters, one of the soil physical-related parameters named as the van Genuchten air entry parameter showed the largest sensitivity effects on major model predictions. These results verified that more effort should be focused on quantifying soil parameters for more accurate model predictions in nitrogen- and crop-related predictions, and stress the need to better calibrate the model in a global sense. This study demonstrates the advantages of the GSA on a more complete analysis of model input parameters and their interactions on the model output for nitrogen modeling.

Global sensitivity and uncertainty analysis of a microalgae model for wastewater treatment

“…Standardized Regression Coefficients, Sobol indices, or Fourier amplitude sensitivity testing) since it is widely used for GSA in the field of wastewater treatment (see e.g. Corominas and Neumann, 2014;Robles et al, 2014a, Robles et al, 2014bRuano et al, 2011;Solimeno et al, 2016;Sun et al, 2015), it represents a well-stablished methodology for data processing, and it is characterized by a relative simple interpretation.…”

Global Sensitivity Analysis of the Advanced ORYZA-N Model with Different Rice Types and Irrigation Regimes

“…The interaction among parameters under SWI was obvious; the ANCR and ANLV of all rice types were affected by a combination of multiple parameters, with the main sensitive parameters of double-season rice being KR0.5, FLV0.5, DVRJ, FRPAR, and RGRLMX (Figure 8a-c). This indicated that the interaction effects of nitrogen-related outputs were greater under water stress conditions, where priority should be provided to the soil waterrelated parameters [44]. The sensitivity results of ANCR and ANLV for single-season rice were both mainly influenced by Kdif2 and FRPAR.…”