Soil-related developments of the Biome-BGCMuSo v6.2 terrestrial ecosystem model

Abstract

Abstract. Terrestrial biogeochemical models are essential tools to quantify climate–carbon cycle feedback and plant–soil relations from local to global scale. In this study, a theoretical basis is provided for the latest version of the Biome-BGCMuSo biogeochemical model (version 6.2). Biome-BGCMuSo is a branch of the original Biome-BGC model with a large number of developments and structural changes. Earlier model versions performed poorly in terms of soil water content (SWC) dynamics in different environments. Moreover, lack of detailed nitrogen cycle representation was a major limitation of the model. Since problems associated with these internal drivers might influence the final results and parameter estimation, additional structural improvements were necessary. In this paper the improved soil hydrology as well as the soil carbon and nitrogen cycle calculation methods are described in detail. Capabilities of the Biome-BGCMuSo v6.2 model are demonstrated via case studies focusing on soil hydrology, soil nitrogen cycle, and soil organic carbon content estimation. Soil-hydrology-related results are compared to observation data from an experimental lysimeter station. The results indicate improved performance for Biome-BGCMuSo v6.2 compared to v4.0 (explained variance increased from 0.121 to 0.8 for SWC and from 0.084 to 0.46 for soil evaporation; bias changed from −0.047 to −0.007 m3 m−3 for SWC and from −0.68 to −0.2 mm d−1 for soil evaporation). Simulations related to nitrogen balance and soil CO2 efflux were evaluated based on observations made in a long-term field experiment under crop rotation. The results indicated that the model is able to provide realistic nitrate content estimation for the topsoil. Soil nitrous oxide (N2O) efflux and soil respiration simulations were also realistic, with overall correspondence with the observations (for the N2O efflux simulation bias was between −0.13 and −0.1 mgNm-2d-1, and normalized root mean squared error (NRMSE) was 32.4 %–37.6 %; for CO2 efflux simulations bias was 0.04–0.17 gCm-2d-1, while NRMSE was 34.1 %–40.1 %). Sensitivity analysis and optimization of the decomposition scheme are presented to support practical application of the model. The improved version of Biome-BGCMuSo has the ability to provide more realistic soil hydrology representation as well as nitrification and denitrification process estimation, which represents a major milestone.

A Comprehensive Assessment of Anthropogenic and Natural Sources and Sinks of Australasia's Carbon Budget

“…While inland water uncertainties from both F RI and F LR were calculated based on different peer-reviewed published studies (as reviewed in Lauerwald et al (2023); Table S1 in Supporting Information S1), uncertainties from lateral carbon transport such as DOC, POC, and DIC were assumed to be a relative uncertainty of 50% as described in Ciais et al (2021). Biome BGCMuso (Hidy et al, 2016(Hidy et al, , 2022, CenW model (Kirschbaum, 1999). Uncertainties were defined as the standard deviation between CABLE-POP BIOS3 and 14 DGVM models Biome BGCMuso (Hidy et al, 2016(Hidy et al, , 2022, CenW model (Kirschbaum, 1999) (Darmenov and da Silva, 2015), GFAS (Kaiser et al, 2012).…”

RECCAP2 Future Component: Consistency and Potential for Regional Assessment to Constrain Global Projections

“…Major carbon flux components (GPP, NPP, NEP) for Australasia RECCAP2 assessment were calculated by combining three different model simulations: CABLE-POP model (Australia), Biome-BGC MuSO (NZ), and CenW (NZ) simulations. RECCAP2 Carbon fluxes for New Zealand were simulated with the Biome-BGC MuSO model (Hidy et al, 2016(Hidy et al, , 2022 and CenW model (Kirschbaum & Watt, 2011). Biome-BGC MuSO was used to represent grasslands and native forest (evergreen broadleaf forest), whereas CenW was used to model non-native forests (Pinus radiata/plantation forest) and shrub (∼60% Manuka/Kanuka) landcover classifications.…”

Conditional interval reduction method: A possible new direction for the optimization of process based models

“…The novel method introduced in the present study was linked with the Biome-BGCMuSo process-based model. Biome-BGCMuSo is a general purpose, process-based, biogeochemical model that simulates the full carbon, nitrogen and water budget of terrestrial ecosystems (Hidy et al, 2012(Hidy et al, , 2016(Hidy et al, , 2021(Hidy et al, , 2022. Biome-BGCMuSo is a branch of the well-known Biome-BGC model (Running and Hunt, 1993;Thornton, 1998;Thornton et al, 2002;Churkina et al, 2009;Di Vittorio et al, 2010).…”

“…In this study Biome-BGCMuSo v6.3 was used. Detailed description about the developments can be found in Hidy et al (2012Hidy et al ( , 2016Hidy et al ( , 2022, Fodor et al (2021), while additional details are available in the User's Guide (Hidy et al, 2021).…”

“…Process based models are typically associated with many parameters that have to be set by the user in any modeling exercise (Therond et al, 2011;Wöhling et al, 2013;Hararuk et al, 2014;Bilionis et al, 2015;Hidy et al, 2022). Model parameterization is a complex procedure as generally some of the adjustable constants of the model cannot be measured directly (e.g.…”