Modeling Vegetation as a Dynamic Component in Soil‐vegetation‐atmosphere Transfer Schemes and Hydrological Models

Abstract

[1] Vegetation affects the climate by modifying the energy, momentum, and hydrologic balance of the land surface. Soil-vegetation-atmosphere transfer (SVAT) schemes explicitly consider the role of vegetation in affecting water and energy balance by taking into account its physiological properties, in particular, leaf area index (LAI) and stomatal conductance. These two physiological properties are also the basis of evapotranspiration parameterizations in physically based hydrological models. However, most current SVAT schemes and hydrological models do not parameterize vegetation as a dynamic component. The seasonal evolution of LAI is prescribed, and monthly LAI values are kept constant year after year. The effect of CO 2 on the structure and physiological properties of vegetation is also neglected, which is likely to be important in transient climate simulations with increasing CO 2 concentration and for hydrological models that are used to study climate change impact. The net carbon uptake by vegetation, which is the difference between photosynthesis and respiration, is allocated to leaves, stems, and roots. Carbon allocation to leaves determines their biomass and LAI. The timing of bud burst, leaf senescence, and leaf abscission (i.e., the phenology) determines the length of the growing season. Together, photosynthesis, respiration, allocation, and phenology, which are all strongly dependent on environmental conditions, make vegetation a dynamic component. This paper (1) familiarizes the reader with the basic physical processes associated with the functioning of the terrestrial biosphere using simple nonbiogeochemical terminology, (2) summarizes the range of parameterizations used to model these processes in the current generation of process-based vegetation and plant growth models and discusses their suitability for inclusion in SVAT schemes and hydrological models, and (3) illustrates the manner in which the coupling of vegetation models and SVAT schemes/hydrological models may be accomplished.

A dynamic global vegetation model for studies of the coupled atmosphere‐biosphere system

“…However, the need for a viable prediction of the transitory impact of the human activity on the global climate system implies that unified atmosphere-biosphere models have to be developed that can predict transient vegetation, carbon flux, and climate changes as well as their interactions for the next century at least. This motivates the development of dynamic global vegetation models (DGVMs) [e.g., Foley et al, 1996;Beerling et al, 1997;Kirilenko and Solomon, 1998;Arora, 2002] which are able to simulate the transient structural changes of vegetation cover in response to climatic changes by explicitly modeling competition and disturbance. The coupling of a DGVM with a GCM SVAT and, consequently, with a GCM itself, is one step toward integrated Earth system models.…”

Analysis of leaf area index in the ECMWF land surface model and impact on latent heat and carbon fluxes: Application to West Africa

“…Apart from partitioning surface energy fluxes, vegetation also governs the natural CO 2 exchanges modulating concentration of atmospheric CO 2 at timescales ranging from minutes to months [Tucker et al, 1986]. Within this context, the land surface models (LSMs) aiming to define the boundary conditions for general circulation models have been recently improved to include an interactive vegetation dynamics [Arora, 2002]. Stated differently, the vegetation physiological parameters of interest for the simulation of hydrological processes (leaf area index (LAI) and stomata resistance) are not prescribed and kept constant anymore but evolve interactively with environmental conditions thanks to biophysically based models of vegetation growth and photosynthesis.…”

Climate-driven disturbances in the San Juan River sub-basin of the Colorado River

“…We note that the signals of change for both temperature and precipitation differ from the results offered by the U.S. Department of the Interior Bureau of Reclamation data sets at 1/8th of a degree and downscaled using a slightly different technique (Bureau of Reclamation, 2011). Our study identifies a key challenge in representing land cover: land cover change is represented differently by each of the four ESMs due to the variable representations of land use and the application of different dynamic models within each ESM (Arora, 2002). The dynamic trajectory of change is variable depending on the ESM considered (Fig.…”