High mountain rainforests are vital in the global energy and carbon cycle. Understanding the exchange of energy and carbon plays an important role in reflecting responses to climate change. In this study, an eddy covariance (EC) measurement system installed in the high Andean Mountains of southern Ecuador was used. As EC measurements are affected by heterogeneous topography and the vegetation height, the main objective was to estimate the effect of the sloped terrain and the forest on the turbulent energy and carbon fluxes considering the energy balance closure (EBC) and the heat storage. The results showed that the performance of the EBC was generally good and estimated it to be 79.5%. This could be improved when the heat storage effect was considered. Based on the variability of the residuals in the diel, modifications in the imbalances were highlighted. Particularly, during daytime, the residuals were largest (56.9 W/m2 on average), with a clear overestimation. At nighttime, mean imbalances were rather weak (6.5 W/m2) and mostly positive while strongest underestimations developed in the transition period to morning hours (down to −100 W/m2). With respect to the Monin–Obukhov stability parameter ((z − d)/L) and the friction velocity (u*), it was revealed that the largest overestimations evolved in weak unstable and very stable conditions associated with large u* values. In contrast, underestimation was related to very unstable conditions. The estimated carbon fluxes were independently modelled with a non-linear regression using a light-response relationship and reached a good performance value (R2 = 0.51). All fluxes were additionally examined in the annual course to estimate whether both the energy and carbon fluxes resembled the microclimatological conditions of the study site. This unique study demonstrated that EC measurements provide valuable insights into land-surface–atmosphere interactions and contribute to our understanding of energy and carbon exchanges. Moreover, the flux data provide an important basis to validate coupled atmosphere ecosystem models.
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