A model-data fusion approach to analyse carbon dynamics in managed grasslands

Myrgiotis, Vasileios; Blei, Emanuel; Clement, Rob; Jones, Stephanie K.; Keane, Ben; Lee, Mark A.; Levy, Peter E. ORCID: https://orcid.org/0000-0002-8505-1901; Rees, Robert M.; Skiba, Ute M. ORCID: https://orcid.org/0000-0001-8659-6092; Smallman, Thomas Luke; Toet, Sylvia; Williams, Mathew. 2020 A model-data fusion approach to analyse carbon dynamics in managed grasslands. Agricultural Systems, 184, 102907. 13, pp. https://doi.org/10.1016/j.agsy.2020.102907

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Official URL: http://dx.doi.org/10.1016/j.agsy.2020.102907

Abstract/Summary

Grasslands are an important component of the global carbon (C) cycle, with a strong potential for C sequestration. However, an improved capacity to quantify grassland C stocks and monitor their variation in space and time, particularly in response to management, is needed in order to conserve and enhance grassland C reservoirs. To meet this challenge we outline and test here an approach to combine C cycle modelling with observational data. We implemented an intermediate complexity model, DALEC-Grass, within a probabilistic model-data fusion (MDF) framework, CARDAMOM, at two managed grassland sites (Easter Bush and Crichton) in the UK. We used 3 years (Easter Bush, 2002–2004) of management data and observations of leaf area index (LAI) and Net Ecosystem Exchange (NEE) from eddy covariance to calibrate the distributions of model parameters. Using these refined distributions, we then assimilated the remaining 7 years (Easter Bush, 2005–2010 and Crichton, 2015) of LAI observations and evaluated the simulated NEE, above and below-ground biomass and other C fluxes against independent data from the two grasslands. Our results show that fusing model predictions with LAI observations allowed the CARDAMOM MDF system to diagnose the effects of grazing and cutting realistically. The overlap of MDF-predicted and measured NEE (both sites) and ecosystem respiration (Easter Bush) was 92% and 83% respectively while the correlation coefficient (r) was 0.79 for both variables. This study lays the foundation for using MDF with satellite data on LAI to produce the spatially and temporally-resolved estimates of C cycling needed in shaping and monitoring the implementation of relevant policies and farm-management decisions.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.1016/j.agsy.2020.102907
UKCEH and CEH Sections/Science Areas:	Atmospheric Chemistry and Effects (Science Area 2017-)
ISSN:	0308-521X
Additional Keywords:	UK grasslands, primary production, carbon sequestration, model-data fusion
NORA Subject Terms:	Agriculture and Soil Science
Date made live:	10 Sep 2020 15:16 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/528453

Item Type:

Publication - Article

Digital Object Identifier (DOI):

https://doi.org/10.1016/j.agsy.2020.102907

UKCEH and CEH Sections/Science Areas:

Atmospheric Chemistry and Effects (Science Area 2017-)

ISSN:

0308-521X

Additional Keywords:

UK grasslands, primary production, carbon sequestration, model-data fusion

NORA Subject Terms:

Agriculture and Soil Science