Predicting Dynamic Change of Biomass and Ecosystem CO2 Flux based on Synergy of Remotely Sensed Data and A Svat Model

6.1 The SVAT Model

We used a SVAT model, ISBA-Ags (Calvet et al., 1998). The original version of the 0model, ISBA (interactions between soil, biosphere and atmosphere) was developed at Meteo-France for being implemented as a land-surface scheme in atmospheric weather forecast model and GCM (Noilhan and Mahfouf, 1996). This model solves the surface energy balance and the soil water balance at a 5-min time step. The soil is described by one bulk reservoir corresponding to the maximum root zone (including a thin surface layer and regardless to actual root development). The main surface variables simulated by the model are the surface temperature, the soil moisture in the root zone, and the energy fluxes. The ISBA-Ags has incorporated a physiological submodel to describe photosynthesis and its coupling with stomatal resistance at the leaf level, by adding three parameters; leaf life expectancy, effective biomass per unit leaf area, and a mesophyll conductance. The stomatal conductance is expressed as a function of radiation, temperature, vapor-pressure deficit, and soil moisture. The model requires meteorological variables, albedo, minimum stomatal resistance, LAI and vegetation height, soil texture, wilting point, and field capacity. The computed net vegetation assimilation is used to feed a simple growth submodel and to predict the density of the vegetation cover. Thus, the model is able to simulate water budget, energy and mass fluxes (CO2, sensible and latent heat fluxes, etc.), and LAI in response to changes in the environmental conditions (precipitation, irrigation, water storage of the soil moisture in the root-zone, atmospheric CO2 concentration, etc.).

The model was carefully parameterized with various data sets of comprehensive soil, plant, and meteorological measurements. We further combined the quantitative relationships between canopy surface temperature and SSFCO2 with the model (Inoue et al., 2003). We assumed that the surface

Figure 8: Simulation results of the SVAT model at different values of initial soil water content (SWCi).

temperature can be used for estimating SSFCo2 not only under bare or sparsely vegetated conditions but also under densely vegetated plant conditions because the soil surface temperatures get closer to the canopy temperature accordingly with increasing vegetation coverage.

The performance of the model was excellent provided all necessary inputs and parameters were available; however, the simulation results by the coupled model proved to be sensitive to the input variables such as initial soil water content (Fig. 8), while the soil moisture condition is one of the most important but difficult input variables to be estimated at regional scales. That is, simulation results by the model alone are subject to uncertainty if the thorough set of input data is unavailable.

Was this article helpful?

0 0

Post a comment