Abstract
Abstract. Vegetation responses to changing climate patterns need to be understood to devise adaptation strategy for a sustainable development, especially in the light of increasing climate related vulnerability. Dynamic Global Vegetation Models(DGVM) have the capacity and scope to develop understanding in this regard, due to their ability in simulating plant-vegetation-climate processes incorporating bioclimatic variables. However, prior to take up modelling using a spatially explicit DGVM, it may be imminent to prioritize the area for vulnerable contexts, so as to calibrate and validate the model optimally. Spatially explicit DGVMs require site level observations at canopy and leaf level/soil strata level for parametrization and implementation. Satellite data in VNIR and thermal regimes provide scope to understand the responses of various vegetation categories and enable to set up baseline addressing the foci of change as regions of vulnerability. Study carried out Western Himalayan transect using MODIS enhanced vegetation index and land surface temperature illustrates potential to differentiate areas that can be vulnerable due to warming trends disturbing cold to warm season energy level transition. Relations of these indices were studied in different vegetation categories and modelled spatially to derive potential vulnerable zones. Many sites showed high vulnerability while some sites showed distinct resilient behaviour by showing increase in EVI during warming periods. Potential zones were studied further using a spatially explicit Dynamic Global Vegetation Model for site level understanding. DGVM results in terms of biomass and carbon were studied to understand the trends in the vulnerable and resilient sites. Detailed characterisation of DGVM based modelling is underway to further diagnose the vulnerability contexts.
Highlights
Global warming is influencing vegetation growth and phenology (Zhang et al, 2004 Parmesan & Yohe, 2003, Myneni et al 1997, Cleland 2007)
A study window covering sufficient biogeographic variation corresponding to diverse phenological categories of vegetation in Western Himalayas was chosen between geographic extent of 30 20 55.68 N, 75 17 51.09 E (Lower left), 33 21 04.23 N, 79 18 19.64 E (Upper right) covering 482 columns and 362 lines of MODIS data
Clusters observed in temperature and greenness gradients were termed Green-warm, Brown-warm or brown-cold with respected to their foliage features as well as their habitat nature (Fig 2)
Summary
Global warming is influencing vegetation growth and phenology (Zhang et al, 2004 Parmesan & Yohe, 2003, Myneni et al 1997, Cleland 2007). The responses can vary from cessation of normal phenological processes to increased physiological activity expressed, for instance, as improved vegetation vigour. Trends such as these may need to be confounded using other associated biophysical properties amenable at synoptic scale, which help to discern the levels of relative responses, in turn enabling decision making. Downscaling regional DGVM operation to site level involving spatially explicit models may require alternate approach to know the vulnerability prevalent across a region. Such knowledge can help to validate these models more robustly. Satellite data in VNIR and thermal regimes provide scope to understand the responses of various vegetation categories and enable to set up baseline addressing the foci or hotspots of change as regions of vulnerability
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
