Improvement of modeling plant responses to low soil moisture in JULESvn4.9 and evaluation against flux tower measurements

Anna B. Harper*, Karina E. Williams, Patrick C. Mcguire, Maria Carolina Duran Rojas, Debbie Hemming, Anne Verhoef, Chris Huntingford, Lucy Rowland, Toby Marthews, Cleiton Breder Eller, Camilla Mathison, Rodolfo L.B. Nobrega, Nicola Gedney, Pier Luigi Vidale, Fred Otu-Larbi, Divya Pandey, Sebastien Garrigues, Azin Wright, Darren Slevin, Martin G. De KauweEleanor Blyth, Jonas Ardo¨, Andrew Black, Damien Bonal, Nina Buchmann, Benoit Burban, Kathrin Fuchs, Agne`s De Grandcourt, Ivan Mammarella, Lutz Merbold, Leonardo Montagnani, Yann Nouvellon, Natalia Restrepo-Coupe, Georg Wohlfahrt

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

15 Citations (Scopus)
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Drought is predicted to increase in the future due to climate change, bringing with it myriad impacts on ecosystems. Plants respond to drier soils by reducing stomatal conductance in order to conserve water and avoid hydraulic damage. Despite the importance of plant drought responses for the global carbon cycle and local and regional climate feedbacks, land surface models are unable to capture observed plant responses to soil moisture stress. We assessed the impact of soil moisture stress on simulated gross primary productivity (GPP) and latent energy flux (LE) in the Joint UK Land Environment Simulator (JULES) vn4.9 on seasonal and annual timescales and evaluated 10 different representations of soil moisture stress in the model. For the default configuration, GPP was more realistic in temperate biome sites than in the tropics or high-latitude (cold-region) sites, while LE was best simulated in temperate and high-latitude (cold) sites. Errors that were not due to soil moisture stress, possibly linked to phenology, contributed to model biases for GPP in tropical savanna and deciduous forest sites. We found that three alternative approaches to calculating soil moisture stress produced more realistic results than the default parameterization for most biomes and climates. All of these involved increasing the number of soil layers from 4 to 14 and the soil depth from 3.0 to 10.8 m. In addition, we found improvements when soil matric potential replaced volumetric water content in the stress equation (the "soil14_psi" experiments), when the critical threshold value for inducing soil moisture stress was reduced ("soil14_p0"), and when plants were able to access soil moisture in deeper soil layers ("soil14_dr&z.ast;2"). For LE, the biases were highest in the default configuration in temperate mixed forests, with overestimation occurring during most of the year. At these sites, reducing soil moisture stress (with the new parameterizations mentioned above) increased LE and increased model biases but improved the simulated seasonal cycle and brought the monthly variance closer to the measured variance of LE. Further evaluation of the reason for the high bias in LE at many of the sites would enable improvements in both carbon and energy fluxes with new parameterizations for soil moisture stress. Increasing the soil depth and plant access to deep soil moisture improved many aspects of the simulations, and we recommend these settings in future work using JULES or as a general way to improve land surface carbon and water fluxes in other models. In addition, using soil matric potential presents the opportunity to include plant functional type-specific parameters to further improve modeled fluxes.

Original languageEnglish
Pages (from-to)3269-3294
Number of pages26
JournalGeoscientific Model Development
Issue number6
Publication statusPublished - 3 Jun 2021

Bibliographical note

Funding Information:
The GF-Guy site is supported by an Investissement d’Avenir grant from the Agence Nationale de la Recherche (CEBA: ANR-10-LABX-0025; ARBRE: ANR-11-LABX-0002-01). CA-Oas is part of the Fluxnet Canada network, supported by the Natural Science and Engineering Research Council of Canada (NSERC) and the Canadian Foundation for Climate and Atmospheric Science (CF-CAS).

Funding Information:
Financial support. LBA data was provided with support from National Aeronautics and Space Administration (NASA) LBA investigation CD-32, NASA LBA-DMIP project (no. NNX09AL52G), and the Gordon and Betty Moore Foundation “Simulations from the Interactions between Climate, Forests, and Land Use in the Amazon Basin: Modeling and Mitigating Large Scale Savannization” project. The authors also acknowledge the following funding sources: EPSRC Living with Environmental Change Fellowship EP/N030141/1 (Anna B. Harper); the Met Office Hadley Centre Climate Programme (HCCP) funded by BEIS and Defra (Karina E. Williams, Debbie Hemming, Camilla Mathison); Natural Environment Research Council’s projects: “IMPETUS” (NE/L010488/1) (Anne Verhoef, Azin Wright) and “Newton/NERC/FAPESP Nordeste” (NE/N012488/1) (Rodolfo L. B. Nobrega, Anne Verhoef); Newton Fund through the Met Office Climate Science for Service Partnership Brazil (CSSP Brazil) (Karina E. Williams, Nicola Gedney, Camilla Math-ison, Anna B. Harper, Lucy Rowland); the Research Endowment Trust Fund of the University of Reading (Patrick C. McGuire); Province of South Tyrol “Cycling of carbon and water in mountain ecosystems under changing climate and land use (CYCLAMEN)” (Georg Wohlfahrt); European Commision Horizon 2020 research and innovation programme (project SUPER-G, no. 774124 and project REALM, no. 787203) and the SNF project M4P (40FA40_154245) (Nina Buchmann); European project “Quantification, understanding and prediction of carbon cycle, and other GHG gases, in Sub-Saharan Africa” (CarboAfrica, STREP-CT-037132) (Yann Nouvellon).

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© 2021 International Union of Crystallography. All rights reserved.


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