Based on the equations of motion and the assumption that ocean turbulence is of isotropy or quasi-isotropy, we derived the closure equations of the second-order moments and the variation equations for characteristic quantities, which describe the mechanisms of advection transport and shear instability by the sum of wave-like and eddy-like motions and circulation. Given that ocean turbulence generated by wave breaking is dominant at the ocean surface, we presented the boundary conditions of the turbulence kinetic energy and its dissipation rate, which are determined by energy loss from wave breaking and entrainment depth respectively. According to the equilibrium solution of the variation equations and available data of the dissipation rate, we obtained an analytical estimation of the characteristic quantities of surface-wave-generated turbulence in the upper ocean and its related mixing coefficient. The derived kinetic dissipation rate was validated by field measurements qualitatively and quantitatively, and the mixing coefficient had fairly good consistency with previous results based on the Prandtl mixing length theory.
In the surface wind drift layer with constant momentum flux, two sets of the consistent surface eleva- tion expressions with breaking and occurrence conditions for breaking are deduced from the first in- tegrals of the energy and vortex variations and the kinetic and mathematic breaking criterions, then the expression of the surface elevation with wave breaking is established by using the Heaviside function. On the basis of the form of the sea surface elevation with wave breaking and the understanding of small slope sea waves, a triple composite function of real sea waves is presented including the func- tions for the breaking, weak-nonlinear and basic waves. The expression of the triple composite func- tion and the normal distribution of basic waves are the expected theoretical model for surface elevation statistics.
The CO2 efflux from forest soil (FCO2) is one of the largest components of the global carbon cycle. Accurate estimation of FCO2 can help us better understand the carbon cycle in forested areas and precisely predict future climate change. However, the scarcity of field-measured FCO2 data in the subtropical forested area greatly limits our understanding of FCO2 dynamics at regional and global scales. This study used an automatic cavity ring-down spectrophotometer (CRDS) analyzer to measure FCO2 in a typical subtropical forest of southern China in the dry season. We found that the measured FCO2 at two experimental areas experienced similar temporal trends in the dry season and reached the minima around December, whereas the mean FCO2 differed apparently across the two areas (9.05 vs. 5.03 g C m−2 day−1) during the dry season. Moreover, we found that both abiotic (soil temperature and moisture) and biotic (vegetation productivity) factors are significantly and positively correlated, respectively, with the FCO2 variation during the study period. Furthermore, a machine-learning random forest model (RF model) that incorporates remote sensing data is developed and used to predict the FCO2 pattern in the subtropical forest, and the topographic effects on spatiotemporal patterns of FCO2 were further investigated. The model evaluation indicated that the proposed model illustrated high prediction accuracy for the training and testing dataset. Based on the proposed model, the spatiotemporal patterns of FCO2 in the forested watershed that encloses the two monitoring sites were mapped. Results showed that the spatial distribution of FCO2 is obviously affected by topography: the high FCO2 values mainly occur in relatively high altitudinal areas, in slopes of 10–25°, and in sunny slopes. The results emphasized that future studies should consider topographical effects when simulating FCO2 in subtropical forests. Overall, our study unraveled the spatiotemporal variations of FCO2 and their driving factors in a subtropical forest of southern China in the dry season, and demonstrated that the proposed RF model in combination with remote sensing data can be a useful tool for predicting FCO2 in forested areas, particularly in subtropical and tropical forest ecosystems.
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