Direct assimilation of measured soil water content in Root Zone Water Quality Model calibration for deficit-irrigated maize

Sima, MW; Fang, QX; Qi, Z; Yu, Q

Direct assimilation of measured soil water content in Root Zone Water Quality Model calibration for deficit-irrigated maize

Sima, MW Fang, QX Qi, Z Yu, Q

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Publisher:: WILEY
Publication Type:: Journal Article
Citation:: Agronomy Journal, 2020, 112, (2), pp. 844-860
Issue Date:: 2020

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Field	Value	Language
dc.contributor.author	Sima, MW
dc.contributor.author	Fang, QX
dc.contributor.author	Qi, Z
dc.contributor.author	Yu, Q https://orcid.org/0000-0001-6950-1821
dc.date.accessioned	2020-11-19T04:14:19Z
dc.date.available	2020-11-19T04:14:19Z
dc.date.issued	2020
dc.identifier.citation	Agronomy Journal, 2020, 112, (2), pp. 844-860
dc.identifier.issn	0002-1962
dc.identifier.issn	1435-0645
dc.identifier.uri	http://hdl.handle.net/10453/144156
dc.description.abstract	© 2019 The Authors. Agronomy Journal © 2019 American Society of Agronomy Correct soil water simulation is critical for water balance and plant growth in agricultural systems. Crop production simulation errors have often been attributed to a lack of accuracy in soil water content (SWC) estimates. However, only a few studies have quantified the effects of SWC estimate errors on crop production and evapotranspiration (ET), especially under different irrigation treatments. The objective of this study was to investigate the impacts of direct assimilation of measured SWC during model calibration for deficit irrigated maize (Zea mays L.) on simulated ET, leaf area index (LAI), biomass, and yield. The CERES-Maize model within the Root Zone Water Quality Model (RZWQM) was calibrated using the automatic parameter estimation (PEST) software. Simulation results showed that, using PEST-optimized crop parameters, RZWQM was able to adequately predict crop yield (relative root mean squared error, rRMSE, of 4.8%) and biomass (rRMSE of 7.1%) in response to irrigation levels, in spite of the bias in SWC and ET simulation. However, with the same crop parameters but replacing simulated SWC with measured data, simulations of crop yield and biomass became worse, with higher rRMSE values (14.5% for yield and 21.5% for biomass). This unexpected model performance with SWC assimilation was mainly associated with the water addition and removal from the soil, which was improved only by recalibration of both soil and crop parameters. This study suggested compensating effects between soil and crop parameters during model calibration. Caution should be applied when using measured SWC as model inputs, especially under water stress conditions.
dc.language	English
dc.publisher	WILEY
dc.relation.ispartof	Agronomy Journal
dc.relation.isbasedon	10.1002/agj2.20088
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.subject	0703 Crop and Pasture Production
dc.subject.classification	Agronomy & Agriculture
dc.title	Direct assimilation of measured soil water content in Root Zone Water Quality Model calibration for deficit-irrigated maize
dc.type	Journal Article
utslib.citation.volume	112
utslib.for	0703 Crop and Pasture Production
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Life Sciences
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
pubs.consider-herdc	true
dc.date.updated	2020-11-19T04:14:13Z
pubs.issue	2
pubs.publication-status	Published
pubs.volume	112
utslib.citation.issue	2

Abstract:

© 2019 The Authors. Agronomy Journal © 2019 American Society of Agronomy Correct soil water simulation is critical for water balance and plant growth in agricultural systems. Crop production simulation errors have often been attributed to a lack of accuracy in soil water content (SWC) estimates. However, only a few studies have quantified the effects of SWC estimate errors on crop production and evapotranspiration (ET), especially under different irrigation treatments. The objective of this study was to investigate the impacts of direct assimilation of measured SWC during model calibration for deficit irrigated maize (Zea mays L.) on simulated ET, leaf area index (LAI), biomass, and yield. The CERES-Maize model within the Root Zone Water Quality Model (RZWQM) was calibrated using the automatic parameter estimation (PEST) software. Simulation results showed that, using PEST-optimized crop parameters, RZWQM was able to adequately predict crop yield (relative root mean squared error, rRMSE, of 4.8%) and biomass (rRMSE of 7.1%) in response to irrigation levels, in spite of the bias in SWC and ET simulation. However, with the same crop parameters but replacing simulated SWC with measured data, simulations of crop yield and biomass became worse, with higher rRMSE values (14.5% for yield and 21.5% for biomass). This unexpected model performance with SWC assimilation was mainly associated with the water addition and removal from the soil, which was improved only by recalibration of both soil and crop parameters. This study suggested compensating effects between soil and crop parameters during model calibration. Caution should be applied when using measured SWC as model inputs, especially under water stress conditions.

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http://hdl.handle.net/10453/144156