New modelling methodology for improving crop model performance under stress conditions - Archive ouverte HAL Access content directly
Poster Communications Year :

New modelling methodology for improving crop model performance under stress conditions

(1) , (1) , (1) , (1) , (2) , (3) , (4)
1
2
3
4

Abstract

Crop models exhibit large uncertainty in the quantification of risks imposed to food production by climate change (Asseng et al., 2013). A significant step towards reducing this uncertainty is to improve model structure (Tao et al., 2018). Here, we present a new modelling methodology for improving crop model structure based on simultaneous solution of model equations. The new technique is called SEMAC (Simultaneous Equation Modelling for Annual Crops) and is implemented into the GLAM crop model, resulting in a new model version GLAM-Parti (i.e. GLAM Partitioning). The new model has improved structure, it gives a better connection between the model processes and leads to higher internal consistency. The model skill is significantly increased when tested under different stress environments (i.e. water and ozone stress).
Fichier principal
Vignette du fichier
Droutsas_S1-Poster.pdf (226.53 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-02950252 , version 1 (27-09-2020)

Identifiers

  • HAL Id : hal-02950252 , version 1

Cite

Ioannis Droutsas, Andy Challinor, Steve Arnold, Mikolaj Swiderski, Mikhail Semenov, et al.. New modelling methodology for improving crop model performance under stress conditions. ICROPM2020: Second International Crop Modelling Symposium , Feb 2020, Montpellier, France. ⟨hal-02950252⟩
32 View
32 Download

Share

Gmail Facebook Twitter LinkedIn More