94
Himalaya, Singh (2017) has raised the issue of geographical spread of research
areas/region. It was argued that the heterogeneity and complexity of the region
increases manifold when Tibet, Karakoram, and other areas are included to refer the
region as Hindu Kush Himalaya (HKH). This inclusion changes the entire landscape perspective. For instance, the Himalaya represents largely the forest dominated landscapes, as described earlier. However, when we refer to HKH, the
grasslands become most dominant system. This change has a wide ranging implication for: (i) influencing global understanding of the CC in Himalayas, and (ii) bringing in Himalayan perspectives of CC research. Also, it makes the outcome of
analysis erroneous for generalization at regional scale. The consequences of such
erroneous generalization could be fatal, particularly when R&D efforts and understanding is not equally distributed across the region. Recently, while reviewing
Climate Change in the HKH, Ren and Shrestha (2017) have indicated that our
understanding of CC and its impacts is better in few areas including the eastern
Tibet Plateau, the Yunnan-Guizhou Plateau and the low lying plains of northern
India. However, a general data deficiency in terms of CC impacts on ecosystem and
biodiversity (IPCC 2007) and a serious lack of systematic studies and empirical
observations about species-level impacts of CC in the Himalayas, is often highlighted (Gautam et al. 2013). All this obviously leads to a larger bias of regional
projection models of CC impact towards the biome/landform richly represented in
regional/global database (e.g., Tibetan Plateau). On the contrary scanty representation of data-sets from forested landscape (e.g., the Himalayas) makes global projections/scenarios less applicable for such landscapes of the region. This calls for
inclusion of nearly equal data-sets across the landforms/biomes so that projections
become more agreeable.
More importantly, the reorientations of R&D focus needs to be considered more
vigorously on account of human population distribution and dependence on
Ecosystem Services. While the Tibetan Plateau and surrounding uplands areas are
scantily inhabited, the forested landscapes and further dependent downstream areas
are heavily populated. Therefore, further delay in modelled CC projections based on
appropriately included data-sets may affect adaptation and mitigation strategies for
these landscapes thereby leading to a grave unwarranted situation.
6 Conclusions
It can be concluded that the challenges of developing detailed scientific knowledge
base to assess the current situation and/or to make projections of the likely impacts
of climate change needs to be addressed on priority. There is an urgent need to reorient and focus our research in the field of climate change so as to include datasets
from diverse landform/biomes. A separate model projections for grassland dominated Tibetan System and forest dominated Himalayan systems based on data sets
from respective areas needs to be formulated.
S. K. Nandi et al.
Himalaya, Singh (2017) has raised the issue of geographical spread of research
areas/region. It was argued that the heterogeneity and complexity of the region
increases manifold when Tibet, Karakoram, and other areas are included to refer the
region as Hindu Kush Himalaya (HKH). This inclusion changes the entire landscape perspective. For instance, the Himalaya represents largely the forest dominated landscapes, as described earlier. However, when we refer to HKH, the
grasslands become most dominant system. This change has a wide ranging implication for: (i) influencing global understanding of the CC in Himalayas, and (ii) bringing in Himalayan perspectives of CC research. Also, it makes the outcome of
analysis erroneous for generalization at regional scale. The consequences of such
erroneous generalization could be fatal, particularly when R&D efforts and understanding is not equally distributed across the region. Recently, while reviewing
Climate Change in the HKH, Ren and Shrestha (2017) have indicated that our
understanding of CC and its impacts is better in few areas including the eastern
Tibet Plateau, the Yunnan-Guizhou Plateau and the low lying plains of northern
India. However, a general data deficiency in terms of CC impacts on ecosystem and
biodiversity (IPCC 2007) and a serious lack of systematic studies and empirical
observations about species-level impacts of CC in the Himalayas, is often highlighted (Gautam et al. 2013). All this obviously leads to a larger bias of regional
projection models of CC impact towards the biome/landform richly represented in
regional/global database (e.g., Tibetan Plateau). On the contrary scanty representation of data-sets from forested landscape (e.g., the Himalayas) makes global projections/scenarios less applicable for such landscapes of the region. This calls for
inclusion of nearly equal data-sets across the landforms/biomes so that projections
become more agreeable.
More importantly, the reorientations of R&D focus needs to be considered more
vigorously on account of human population distribution and dependence on
Ecosystem Services. While the Tibetan Plateau and surrounding uplands areas are
scantily inhabited, the forested landscapes and further dependent downstream areas
are heavily populated. Therefore, further delay in modelled CC projections based on
appropriately included data-sets may affect adaptation and mitigation strategies for
these landscapes thereby leading to a grave unwarranted situation.
6 Conclusions
It can be concluded that the challenges of developing detailed scientific knowledge
base to assess the current situation and/or to make projections of the likely impacts
of climate change needs to be addressed on priority. There is an urgent need to reorient and focus our research in the field of climate change so as to include datasets
from diverse landform/biomes. A separate model projections for grassland dominated Tibetan System and forest dominated Himalayan systems based on data sets
from respective areas needs to be formulated.
S. K. Nandi et al.