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

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 distri-
butions, 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 34 (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.