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


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2015-11

AUTHORS

Huaiwei Sun, Yan Zhu, Jinzhong Yang, Xiugui Wang

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. More... »

PAGES

16664-16675

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11356-015-4860-5

DOI

http://dx.doi.org/10.1007/s11356-015-4860-5

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1008406210

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/26084558


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Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s11356-015-4860-5'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s11356-015-4860-5'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s11356-015-4860-5'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s11356-015-4860-5'


 

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