Impacts of Green Vegetation Fraction Derivation Methods on Regional Climate Simulations

Abstract

The representation of vegetation in land surface models (LSM) is crucial for modeling atmospheric processes in regional climate models (RCMs). Vegetation is characterized by the green fractional vegetation cover (FVC) and/or the leaf area index (LAI) that are obtained from nearest difference vegetation index (NDVI) data. Most regional climate models use a constant FVC for each month and grid cell. In this work, three FVC datasets have been constructed using three methods: ZENG, WETZEL and GUTMAN. These datasets have been implemented in a RCM to explore, through sensitivity experiments over the Iberian Peninsula (IP), the effects of the differences among the FVC data-sets on the near surface temperature (T2m). Firstly, we noted that the selection of the NDVI database is of crucial importance, because there are important bias in mean and variability among them. The comparison between the three methods extracted from the same NDVI database, the global inventory modeling and mapping studies (GIMMS), reveals important differences reaching up to 12% in spatial average and and 35% locally. Such differences depend on the FVC magnitude and type of biome. The methods that use the frequency distribution of NDVI (ZENG and GUTMAN) are more similar, and the differences mainly depends on the land type. The comparison of the RCM experiments exhibits a not negligible effect of the FVC uncertainty on the monthly T2m values. Differences of 30% in FVC can produce bias of 1 ∘ C in monthly T2m, although they depend on the time of the year. Therefore, the selection of a certain FVC dataset will introduce bias in T2m and will affect the annual cycle. On the other hand, fixing a FVC database, the use of synchronized FVC instead of climatological values produces differences up to 1 ∘ C, that will modify the T2m interannual variability.

Fractional Vegetation Cover Estimation Algorithm for FY-3B Reflectance Data Based on Random Forest Regression Method

“…Thus, investigating the performances of these methods on the FVC estimation for FY-3 reflectance would be significant in the future. In addition, several empirical and pixel unmixing methods also achieved reliable FVC estimation with the regional scale [69][70][71], which would be evaluated for FY-3 FVC estimation during the future work. Finally, this study adopted the FY-3B reflectance as the representative to analyze the capability of FY-3 data on global FVC estimating.…”

Lower Atmosphere Meteorology

“…Characterizing vegetation also plays an important role in modelling the atmospheric processes involved in climate simulations. The research carried out by Jiménez et al [7] contributes further in this matter. Remote sensing was used to obtain the nearest different vegetation index database which represents vegetation in land surface models.…”

Temperature Response to Changes in Vegetation Fraction Cover in a Regional Climate Model

“…The green vegetation fraction, also know as fraction of vegetation cover (FVC), represents the horizontal density of live vegetation. FVC is calculated through the Normalized Difference Vegetation Index (NDVI) and several algorithms have been developed for this task [12]. LAI quantifies the vertical density of vegetation cover and can be calculated from NDVI index, although NDVI has limited sense to LAI values greater than 3-4.…”

“…FVC is calculated through NDVI and several algorithms have been developed for this task; simple linear [19], quadratic [13], etc. These models as well as the various NDVI database available produce some uncertainty in the estimation of FVC that influence climate simulations [12,16,20]. On the other hand, some differences are also found when comparing climatological FVC datasets (constant monthly values, [19]) with those that account for temporal evolution.…”