Geographical variability of rainfall extremes in India enhances interpretation of climate change data
In February 2012, the journal Nature Climate Change published a paper on rainfall extremes in India by principal investigator Vipin Kumar of the University of Minnesota’s computer science and engineering department and co-principal investigator Auroop Ganguly of the civil and environmental engineering department at Northeastern University in Boston, members of the National Science Foundation’s (NSF) Expeditions project team.
Nature published the paper online.
Based on new data-driven methods, or novel adaptations for understanding climate change developed by the Expeditions team, the paper identifies a steady and significant increase in geographical variability within India over the past half-century. The data-driven methods used, say the researchers, can be generalized not just to other regions beyond India, but to both observed and model-simulated climate data as well.
“Rainfall extremes are rather difficult to characterize over space and time, particularly at regional or local scales. However, our current understanding of the geographical patterns of heavy rainfall and their changes over time guides water resources and flood hazards management as well as policy negotiations related to urbanization or emissions control,” the researchers note in the paper. “Thus, in vulnerable regions of the world where floods may claim many lives and water drives the economy or in emerging nations which may contribute significantly to the atmospheric inventory of greenhouse gases, major science advances are needed.”
“If we were to use India as a case study, we find that top scientists and peer-reviewed publications do not agree on the nature of observed trends in heavy rainfall over the country,” they add. “This has led to scientific controversies and uncertainties about adaptation and mitigation strategies in a vulnerable yet rapidly growing region of the world.”
“This Expeditions in Computing project brings together interdisciplinary researchers from multiple institutions to pursue a bold, ambitious, research agenda by building reliable predictive models from climate data that could potentially transform how we understand and respond to climate change,” explains Vasant Honavar, NSF program manager in NSF’s Division of Information and Intelligent Systems. “The Nature Climate Change piece provides a hint of how sophisticated data mining methods could help fill gaps in our understanding of climate change, and ultimately, produce actionable insights that can help minimize the negative effects of climate change on humans and the environment.”
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