For drylands with low inherent levels of biological productivity, coping with climate change presents particular problems. The world’s drylands cover over 40 % of the global terrestrial area and house more than 2 billion inhabitants MEA, (2005). The world’s poorest people live in these areas and they will be hit hardest by the adverse effects of climate change. The effects will manifest themselves not through increased temperatures per se but rather via changes in hydrological cycles characterised by both increased droughts and paradoxically, increased risks of flooding.

What are the consequences of rapid global change for the behaviour and ecology of wild species? Answering this very broad question is a challenging but important task. One relatively manageable question under this broad umbrella is how the timing of biological events (i.e., phenology) is changing as the earth's climate changes. In this article, we describe a new programme aimed at assessing the timing of migration of birds that winter in the Indian Subcontinent and monitoring changes

Climate is a major driver of forest species distributions and the growth rate and structure of forests. Thus, climate change can potentially have significant effects on mountain forest hydrology, particularly the amount of water available downstream. However, many other factors influence forest biomass and mountain hydrology, and climate change effects cannot be viewed in isolation from previous land use histories (i.e. forest legacies), altered disturbance regimes (e.g. fire frequency, insect outbreaks, floods) and invasive species.

Data from the satellite-based Special Sensor Microwave Imager (SSM/I) show that the total atmospheric moisture content over oceans has increased by 0.41 kg/m2 per decade since 1988. Results from current climate models indicate that water vapor increases of this magnitude cannot be explained by climate noise alone. In a formal detection and attribution analysis using the pooled results from 22 different climate models, the simulated “fingerprint” pattern of anthropogenically caused changes in water vapor is identifiable with high statistical confidence in the SSM/I data.