Fig.1. The Taylor diagram showing (a) the performance of all
model data from EC-towers were used.; (b-e) the performance of all model
under different latitudes, where N and S represent the northern and
southern hemispheres, respectively. Color dots represent the models in
the corresponding legend. Taylor diagram is a polar graph in which the
cosine of the angle between the X-axis is the correlation coefficient
between the GPP of the model and EC-tower. The radial direction is the
ratio of model to EC-tower GPP standard deviation. The grey arcs
represent RMSE normalized by standard deviation for each model. The n is
number of EC-towers.
The ability of a model to capture temporal changes is one of the keys to
evaluating the performance of GPP models. The double mass curve
(cumulative GPP predicted by model versus cumulative GPP observed at
EC-towers) computed per site can reflect the ability of models to
simulate the temporal changes of GPP (Fig.2). From the distribution of
double mass curve, results show that, among the 10 models compared, the
distribution cumulated GPP for the LUE-EF was the most concentrated
around the 1:1 correspondence against the cumulated GPP from EC-towers,
which indicates its greater ability to simulate the patterns in temporal
variability in GPP. Fig. 2 also shows the distributions of relative bias
in ratio (PB), which ranged ±0.4 for all models, indicating that the
models had a large heterogeneity in simulating the temporal change of
GPP. The biases were however narrower for the LUE-EF and LUE-NDWI
models, respectively containing 120 and 110 of the EC-towers within ±0.2
in their PB, the largest number of EC-towers amongst all models. This
indicates that these two models have a strong comprehensive ability to
capture the temporal changes of GPP.
<Fig 2 roughly here>