Predicting tipping points of vegetation resilience as a response to precipitation: Implications for understanding impacts of climate change in India

Although the impacts of climate change are long-term in nature, the transformation in climate regime can lead to an ecosystem change from one stable to another stable state through intermediate bistable or metastable states. Such state transition or resilience to change in ecosystems is seldom sharp and therefore difficult to quantify with a single tipping point. Rather, the change can be understood through a tipping point range (tipping zone) across hysteresis loop(s). This study uses a satellite data-derived actual vegetation cover map of India and categorizes it into different vegetation types: forest, scrubland, grassland, and vegetation-less. We used high-resolution long-term average precipitation data to develop moisture-driven hysteresis curves and predict various tipping point ranges for vegetation-type regime shifts. Our results reveal that the forest and vegetation-less states could have one-way, while scrubland and grassland have two-way transition probabilities with a probable shift in precipitation regime. In the dry conditions, the precipitation tipping zone predicted between 154 and 452 mm for the forest to scrubland transitions, while the reverse transition (from scrubland to forest) could occur in wet conditions between 1080 and 1400 mm. Similarly, the transition between scrubland and grassland, between grassland and vegetation-less state, may occur in contrasting dry and wet conditions, creating a hysteresis loop. The results indicate that the reversal of state change requires differential energy spent during the onward and reverse transitions. The study proposes a novel characteristic curve demonstrating the varied precipitation tipping points/zones, precipitation overlaps and distribution of the various vegetation types, and co-existence zones, which has huge implications for understanding the climate change impacts.