Importance of crop varieties and management practices: evaluation of a process-based model for simulating CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; and H&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt;O fluxes at five European maize (&amp;lt;i&amp;gt;Zea mays&amp;lt;/i&amp;gt; L.) sites

Li, Longhui; Vuichard, Nicolas; Viovy, Nicolas; Ciais, P.; Wang, Tao; Ceschia, Éric; Jans, W.W.P.; Wattenbach, Martin; Béziat, Pierre; Gruenwald, T.; Lehuger, Simon; Bernhofer, C.

doi:10.5194/bg-8-1721-2011

Cited by 24 publications

(20 citation statements)

References 58 publications

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“…This suggests that modifying the model's root water uptake function and (or) incorporating hydraulic redistribution into the model does not impact the model's performance when soil moisture is readily available during the wet season. Most biases in the simulated NEE during the wet season at all three sites result from errors in the simulated ecosystem respiration by CABLE, as also found for other land surface models [Baker et al, 2008;Li et al, 2011]. During dry seasons, however, the performances of the default CABLE and the other two configurations with either modification of an alternative root water uptake function or a hydraulic redistribution function were much poorer than the simulations including both revised root functions (as in S4).…”

Section: Sensitivity Analysissupporting

confidence: 57%

Improving the responses of the Australian community land surface model (CABLE) to seasonal drought

Wang

et al. 2012

J. Geophys. Res.

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[1] Correct representations of root functioning, such as root water uptake and hydraulic redistribution, are critically important for modeling the responses of vegetation to droughts and seasonal changes in soil moisture content. However, these processes are poorly represented in global land surface models. In this study, we incorporated two root functions: a root water uptake function which assumes root water uptake efficiency varies with rooting depth, and a hydraulic redistribution function into a global land surface model, CABLE. The water uptake function developed by Lai and Katul (2000) was also compared with the default one (see Wang et al., 2010) that assumes that efficiency of water uptake per unit root length is constant. Using eddy flux measurements of CO 2 and water vapor fluxes at three sites experiencing different patterns of seasonal changes in soil water content, we showed that the two root functions significantly improved the agreement between the simulated fluxes of net ecosystem exchange and latent heat flux and soil moisture dynamics with those observed during the dry season while having little impact on the model simulation during the wet seasons at all three sites. Sensitivity analysis showed that varying several model parameters influencing soil water dynamics in CABLE did not significantly affect the model's performance. We conclude that these root functions represent a valuable improvement for land surface modeling and should be implemented into CABLE and other land surface models for studying carbon and water dynamics where rainfall varies seasonally or interannually.

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Section: Sensitivity Analysissupporting

confidence: 57%

Improving the responses of the Australian community land surface model (CABLE) to seasonal drought

Wang

et al. 2012

J. Geophys. Res.

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“…Therefore, we expect that the performance of CLM can be improved if it is coupled to a crop model which can treat specific plant traits and include management practices. Li et al (2011) found that crop variety and management factors like irrigation, fertilization and planting date have a significant impact on NEE (and evapotranspiration) for the coupled LSM-crop model ORCHIDEE-STICS. Wu et al (2016) showed that a crop model coupled to the LSM ORCHIDEE, combined with assimilation of many different data types, was able to reproduce many measurement data up to the measurement uncertainty, whereas other remaining errors could be related to management activities which were not correctly represented in the model.…”

Section: Discussionmentioning

confidence: 98%

Evaluation and uncertainty analysis of regional-scale CLM4.5 net carbon flux estimates

Post

Franssen²,

Han³

et al. 2018

Biogeosciences

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Abstract. Modeling net ecosystem exchange (NEE) at the regional scale with land surface models (LSMs) is relevant for the estimation of regional carbon balances, but studies on it are very limited. Furthermore, it is essential to better understand and quantify the uncertainty of LSMs in order to improve them. An important key variable in this respect is the prognostic leaf area index (LAI), which is very sensitive to forcing data and strongly affects the modeled NEE. We applied the Community Land Model (CLM4.5-BGC) to the Rur catchment in western Germany and compared estimated and default ecological key parameters for modeling carbon fluxes and LAI. The parameter estimates were previously estimated with the Markov chain Monte Carlo (MCMC) approach DREAM (zs) for four of the most widespread plant functional types in the catchment. It was found that the catchment-scale annual NEE was strongly positive with default parameter values but negative (and closer to observations) with the estimated values. Thus, the estimation of CLM parameters with local NEE observations can be highly relevant when determining regional carbon balances. To obtain a more comprehensive picture of model uncertainty, CLM ensembles were set up with perturbed meteorological input and uncertain initial states in addition to uncertain parameters. C 3 grass and C 3 crops were particularly sensitive to the perturbed meteorological input, which resulted in a strong increase in the standard deviation of the annual NEE sum (σ NEE ) for the different ensemble members from ∼ 2 to 3 g C m −2 yr −1 (with uncertain parameters) to ∼ 45 g C m −2 yr −1 (C 3 grass) and ∼ 75 g C m −2 yr −1 (C 3 crops) with perturbed forcings. This increase in uncertainty is related to the impact of the meteorological forcings on leaf onset and senescence, and enhanced/reduced drought stress related to perturbation of precipitation. The NEE uncertainty for the forest plant functional type (PFT) was considerably lower (σ NEE ∼ 4.0-13.5 g C m −2 yr −1 with perturbed parameters, meteorological forcings and initial states). We conclude that LAI and NEE uncertainty with CLM is clearly underestimated if uncertain meteorological forcings and initial states are not taken into account.

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Section: Discussionmentioning

confidence: 98%

“…restrial compartments at the regional level. Therefore, comprehensive input and evaluation data are available for the catchment, including information on land use (Lussem and Waldhoff, 2013), LAIs (Ali et al, 2015;Reichenau et al, 2016) and EC data (Schmidt et al, 2012;Graf et al, 2014;Kessomkiat et al, 2013;Post et al, 2015).…”

Section: The Rur Catchmentmentioning

confidence: 99%

“…To apply CLM for the Rur catchment domain, a land surface input data set was generated with a spatial resolution of 1 km 2 . The land unit for each grid cell and the PFT distribution of each vegetated land unit were defined based on the land use classification derived from supervised, multitemporal remote sensing data analysis using RapidEye and ASTER data (Waldhoff et al, 2012;Lussem and Waldhoff, 2013). In addition to the land use coverage, CLM requires information on the percentages of clay and sand for each of the 15 soil columns of the vegetated area per grid cell.…”

Section: Community Land Model Setupmentioning

confidence: 99%

See 1 more Smart Citation

Response to anonymous Referee #1

Post¹

2017

Preprint

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Modeling net ecosystem exchange (NEE) at the regional scale with land surface models (LSMs) is relevant for the estimation of regional carbon balances, but studies on it are very limited. Furthermore, it is essential to better understand and quantify the uncertainty of LSMs in order to improve them. An important key variable in this respect is the prognostic leaf area index (LAI), which is very sensitive to forcing data and strongly affects the modeled NEE. We applied the Community Land Model (CLM4.5-BGC) to the Rur catchment in western Germany and compared estimated and default ecological key parameters for modeling carbon fluxes and LAI. The parameter estimates were previously estimated with the Markov chain Monte Carlo (MCMC) approach DREAM (zs) for four of the most widespread plant functional types in the catchment. It was found that the catchment-scale annual NEE was strongly positive with default parameter values but negative (and closer to observations) with the estimated values. Thus, the estimation of CLM parameters with local NEE observations can be highly relevant when determining regional carbon balances. To obtain a more comprehensive picture of model uncertainty, CLM ensembles were set up with perturbed meteorological input and uncertain initial states in addition to uncertain parameters. C 3 grass and C 3 crops were particularly sensitive to the perturbed meteorological input, which resulted in a strong increase in the standard deviation of the annual NEE sum (σ NEE ) for the different ensemble members from ∼ 2 to 3 g C m −2 yr −1 (with uncertain parameters) to ∼ 45 g C m −2 yr −1 (C 3 grass) and ∼ 75 g C m −2 yr −1 (C 3 crops) with perturbed forcings. This increase in uncertainty is related to the impact of the meteorological forcings on leaf onset and senescence, and enhanced/reduced drought stress related to perturbation of precipitation. The NEE uncertainty for the forest plant functional type (PFT) was considerably lower (σ NEE ∼ 4.0-13.5 g C m −2 yr −1 with perturbed parameters, meteorological forcings and initial states). We conclude that LAI and NEE uncertainty with CLM is clearly underestimated if uncertain meteorological forcings and initial states are not taken into account.

show abstract

Importance of crop varieties and management practices: evaluation of a process-based model for simulating CO<sub>2</sub> and H<sub>2</sub>O fluxes at five European maize (<i>Zea mays</i> L.) sites

Cited by 24 publications

References 58 publications

Improving the responses of the Australian community land surface model (CABLE) to seasonal drought

Improving the responses of the Australian community land surface model (CABLE) to seasonal drought

Evaluation and uncertainty analysis of regional-scale CLM4.5 net carbon flux estimates

Response to anonymous Referee #1

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