Global parameters sensitivity analysis of modeling water, energy and carbon exchange of an arid agricultural ecosystem
Wu, Mousong LU ; Ran, Youhua ; Jansson, Per Erik ; Chen, Peng ; Tan, Xiao and Zhang, Wenxin LU
(2019)
In Agricultural and Forest Meteorology
271.
p.295-306
- Abstract
Agricultural ecosystems are important for regulating terrestrial hydrological and carbon cycles. Hydrological and carbon processes in agricultural ecosystem models are complex due to interactions between parameters. It is therefore crucial to identify parameter sensitivity before a process-based model is applied for simulations and predictions of water, energy and carbon fluxes in agricultural ecosystems. In this study, we investigated the sensitivity and equifinality of the CoupModel parameters in modeling an arid agricultural ecosystem in northwestern China. In total, 27 model parameters were analyzed using a global parameters sensitivity analysis approach and a combination... (More)
(Less)
Agricultural ecosystems are important for regulating terrestrial hydrological and carbon cycles. Hydrological and carbon processes in agricultural ecosystem models are complex due to interactions between parameters. It is therefore crucial to identify parameter sensitivity before a process-based model is applied for simulations and predictions of water, energy and carbon fluxes in agricultural ecosystems. In this study, we investigated the sensitivity and equifinality of the CoupModel parameters in modeling an arid agricultural ecosystem in northwestern China. In total, 27 model parameters were analyzed using a global parameters sensitivity analysis approach and a combination of multiple in situ and remotely sensed data sets. Among the five major model processes, we found that the energy balance process account for much of the importance in the model, followed by soil hydrology, plant growth, soil heat, and soil carbon processes. Meanwhile, parameters from the plant growth process exhibited higher equifinalities than other processes. We found that net ecosystem exchange (NEE) is controlled by soil heat, soil hydrology and energy balance processes, which is mainly due to a high equifinality (0.91) between the parameters g
max
(maximal stomatal conductance) and V
cmax
(maximal carboxylation rate). The equifinalities between different parameters result in a trade-off in model performance metrics (i.e. determination coefficient R
2
and mean error ME) in the water, energy and carbon balance simulations. We revealed that daytime and yearly accumulated eddy fluxes (sensible heat H
s
, latent heat LE and NEE) can constrain the model parameters better. Remotely sensed data were also promising as additional constraints on soil water contents and energy fluxes. This study introduced a systematic global parameter sensitivity analysis approach together with the equifinality identification in an ecosystem model. The approach proposed here is applicable to other studies and the equifinalities detected in this study can be important implications for modelling arid agricultural ecosystems. Additional exploration on remotely sensed data in constraining the model from different aspects are highly recommended in modeling agricultural ecosystems.
- author
- Wu, Mousong
LU
; Ran, Youhua
; Jansson, Per Erik
; Chen, Peng
; Tan, Xiao
and Zhang, Wenxin
LU
- organization
- publishing date
- 2019
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Arid agricultural ecosystem, Equifinality, Parameter sensitivity index, Remote sensing, Water-carbon coupling
- in
- Agricultural and Forest Meteorology
- volume
- 271
- pages
- 12 pages
- publisher
- Elsevier
- external identifiers
-
- scopus:85063072630
- ISSN
- 0168-1923
- DOI
- 10.1016/j.agrformet.2019.03.007
- language
- English
- LU publication?
- yes
- id
- cf126fdc-6278-4dcd-ade8-679679e6bba6
- date added to LUP
- 2019-03-27 12:26:06
- date last changed
- 2022-04-25 22:03:35
@article{cf126fdc-6278-4dcd-ade8-679679e6bba6,
abstract = {{<p><br>
Agricultural ecosystems are important for regulating terrestrial hydrological and carbon cycles. Hydrological and carbon processes in agricultural ecosystem models are complex due to interactions between parameters. It is therefore crucial to identify parameter sensitivity before a process-based model is applied for simulations and predictions of water, energy and carbon fluxes in agricultural ecosystems. In this study, we investigated the sensitivity and equifinality of the CoupModel parameters in modeling an arid agricultural ecosystem in northwestern China. In total, 27 model parameters were analyzed using a global parameters sensitivity analysis approach and a combination of multiple in situ and remotely sensed data sets. Among the five major model processes, we found that the energy balance process account for much of the importance in the model, followed by soil hydrology, plant growth, soil heat, and soil carbon processes. Meanwhile, parameters from the plant growth process exhibited higher equifinalities than other processes. We found that net ecosystem exchange (NEE) is controlled by soil heat, soil hydrology and energy balance processes, which is mainly due to a high equifinality (0.91) between the parameters g <br>
<sub>max</sub><br>
(maximal stomatal conductance) and V <br>
<sub>cmax</sub><br>
(maximal carboxylation rate). The equifinalities between different parameters result in a trade-off in model performance metrics (i.e. determination coefficient R <br>
<sup>2</sup><br>
and mean error ME) in the water, energy and carbon balance simulations. We revealed that daytime and yearly accumulated eddy fluxes (sensible heat H <br>
<sub>s</sub><br>
, latent heat LE and NEE) can constrain the model parameters better. Remotely sensed data were also promising as additional constraints on soil water contents and energy fluxes. This study introduced a systematic global parameter sensitivity analysis approach together with the equifinality identification in an ecosystem model. The approach proposed here is applicable to other studies and the equifinalities detected in this study can be important implications for modelling arid agricultural ecosystems. Additional exploration on remotely sensed data in constraining the model from different aspects are highly recommended in modeling agricultural ecosystems. <br>
</p>}},
author = {{Wu, Mousong and Ran, Youhua and Jansson, Per Erik and Chen, Peng and Tan, Xiao and Zhang, Wenxin}},
issn = {{0168-1923}},
keywords = {{Arid agricultural ecosystem; Equifinality; Parameter sensitivity index; Remote sensing; Water-carbon coupling}},
language = {{eng}},
pages = {{295--306}},
publisher = {{Elsevier}},
series = {{Agricultural and Forest Meteorology}},
title = {{Global parameters sensitivity analysis of modeling water, energy and carbon exchange of an arid agricultural ecosystem}},
url = {{http://dx.doi.org/10.1016/j.agrformet.2019.03.007}},
doi = {{10.1016/j.agrformet.2019.03.007}},
volume = {{271}},
year = {{2019}},
}