Summary And Conclusions

The purpose of the present study has been to introduce a methodology for the optimal integration of satellite data with land surface and vegetation modelling through a technique of data assimilation. First, it is demonstrated that a match between satellite and modelled vegetation cover, expressed as fAPAR, can be achieved without the usual approach of prescribing this quantity directly to a land surface or vegetation scheme. Assimilation has the advantage of preserving internal consistency of the land model's water and carbon balances with the vegetation cover. As a more detailed one-point simulation shows, violation of this consistency requirement can result in greatly underestimated land surface evapotranspiration rates; if the carbon balance is also computed, plant productivity can even become contradictory to the observed presence of vegetation. Another advantage is that gaps in the satellite data can easily be taken into account - something that makes traditional methods of satellite data use complicated - and that model predictions of fAPAR can effectively be used to filter out residual noise in the satellite data.

In a further analysis, focussing on the effect of the data assimilation on simulated soil moisture, it is shown that consistency with satellite derived fAPAR requires rather large values of maximum soil water storage in much of the tropics. Even though the initial, a priori map of soil water storage capacity taken from the ECHAM-4 general circulation model shows rather high values for maximum plant-available soil water - around 250 to 400 mm in the tropics, against only 160 mm globally for the previous ECHAM-3 -, optimised values are even higher and often exceed 500 mm. This has a significant impact on the simulated soil moisture content during the dry season, and for the simulated land climate in the ECHAM-4 GCM. While those changes in simulated 2 m land temperatures are relatively small when compared to differences between GCM and observed temperatures, the changes still compensate for some of the tendency of ECHAM-4 to simulate dry-season temperatures that are too high.

It is certainly true that much of the detail of the results presented here will depend on the qualitity of the precipitation data used to drive the vegetation and land surface model. The data used here are a widely accepted standard for global vegetation models and have been used extensively. However, coverage of meteorological stations can be sparse in parts of the tropics, and some of the very high values of soil moisture storage might be sensitive to possible data problems. Nevertheless, the general tendency of large tropical water reservoirs has also been found from other investigations, based on rooting depth [Canadell et al., 1996; Nepstad et al., 1994], optimised vegetation growth [Kleidon and Heimann, 1998], or the atmospheric moisture balance over the Amazon basin [Zeng, 1999]. This result demonstrates the importance of vegetation for the global land surface water budget, and stresses the importance of including vegetation effects in GCMs. If vegetation is represented as an interactive part of the land surface such that its distribution and amount are predicted, satellite derived greeness can also be used as check on the realism of the simulated climate and surface para-meterisation. The present study is thus intended as a first step in that direction.

There is a further issue related to data availability in land surface and large-scale hydrological modelling. Separate attempts to use microwave remote sensing data to estimate soil moisture have so far only achieved limited results, while similar assimilation techniques have been developed for satellite derived surface temperatures (see above). The advantage of the technique presented here is that, since soil moisture, skin temperature and vegetation cover are all predicted by the surface model, these parameters can in principle be used for integrating diverse types of remotely sensed information into a common data assimilation scheme.

0 0

Post a comment