Reply on RC1 [* Cross-Reference *]

Here, we examine the ability of an optimality-based model (the Vegetation Optimality Model, VOM) to predict vegetation behaviour for the same savanna sites. The VOM optimizes key vegetation properties such as foliage cover, rooting depth and water use parameters in order to maximize the net carbon profit (NCP), defined as the difference between total carbon taken up by photosynthesis minus the carbon invested in construction and maintenance of plant organs.

Despite a reduced need for input data, the VOM performed similarly to or better than the conventional TBMs in terms of reproducing the seasonal amplitude and mean annual fluxes recorded by flux towers at the different sites. It had a relative error of 0.08 for the seasonal amplitude in ET and was among the three best models tested with the smallest relative error in the seasonal amplitude of gross primary productivity (GPP).

Nevertheless, the VOM displayed some persistent deviations from observations, especially for GPP, namely an underestimation of dry season evapotranspiration at the wettest site, suggesting that the hydrological assumptions (free drainage) have a strong influence on the results. Furthermore, our study exposes a persistent overprediction of vegetation cover and carbon uptake during the wet seasons by the VOM. Our analysis revealed several areas for improvement in the VOM and the applied optimality theory, including a better representation of the hydrological settings as well as the costs and benefits related to plant water transport and light capture by the canopy.

The results of this study imply that vegetation optimality is a promising approach to explain vegetation dynamics and the resulting fluxes. It provides a way to derive vegetation properties independently of observations and allows for a more insightful evaluation of model shortcomings as no calibration or site-specific information is required. ***This entry has been automatically imported via OpenAlex by LIST harvest scripts.

Please refer to doi.org/10.5194/hess-2021-265-ac1 for the original and latest version of the publication***