car2go completes pilot program, enters new agreement with the City of Austin’s Car-Share Austin Program; more than 21,000 registered members
Dow and AKSA forming JV for carbon fiber and derivatives; up to $1B investment in project within 5 years

Study using extreme value theory finds lack of uniform trends but increasing spatial variability in rainfall extremes in India

A new study utilizing extreme value theory (EVT) reports no evidence for uniformly increasing trends in rainfall extremes averaged over the entire Indian region. It does, however, find a steady and significant increase in the spatial variability of rainfall extremes over the region. These findings, published in Nature Climate Change, are contrary to results of some earlier work on this subject.

Extreme value theory, used in science, engineering, insurance and risk management, provides a theoretical foundation for building statistical models describing extreme events.

Recent studies disagree on how rainfall extremes over India have changed in space and time over the past half century as well as on whether the changes observed are due to global warming or regional urbanization. Although a uniform and consistent decrease in moderate rainfall has been reported, a lack of agreement about trends in heavy rainfall may be due in part to differences in the characterization and spatial averaging of extremes.

Here we use extreme value theory to examine trends in Indian rainfall over the past half century in the context of long-term, low-frequency variability. We show that when generalized extreme value theory is applied to annual maximum rainfall over India, no statistically significant spatially uniform trends are observed, in agreement with previous studies using different approaches. Furthermore, our space–time regression analysis of the return levels points to increasing spatial variability of rainfall extremes over India. Our findings highlight the need for systematic examination of global versus regional drivers of trends in Indian rainfall extremes, and may help to inform flood hazard preparedness and water resource management in the region.

—Ghosh et al.

Auroop Ganguly—then at Oak Ridge National Laboratory (ORNL), now a faculty member at Northeastern University—and co-authors Subimal Ghosh (Indian Institute of Technology Bombay, Debasish Das (Temple University) and Shih-Chieh Kao (ORNL) used their statistical methodologies to analyze data from 1,803 stations from 1951 to 2003. This information was provided in 1-by-1-degree spatial grids by the India Meteorological Department.

The research team noted that statistical observations offer complementary insights compared to the current generation of physics-based computational models. This is especially the case if the goal is to understand climate and rainfall variability at local to regional scales.

Our research suggests that one needs to be aware of the different characterizations of extremes and that these characterizations require both interpretability and statistical rigor.

—Auroop Ganguly

In addition, it makes sense to look at local and regional drivers such as urbanization and deforestation in addition to global scale issues. Although this study focused on rainfall variability in India, the same methodology can be applied to any region of the world, Ganguly said.

Understanding climate model-simulated trends of precipitation extremes and developing metrics relevant for water resources decisions were the focus of a paper published earlier this year in the Journal of Geophysical Research. In that paper, Ganguly and co-author Kao showed that while models provide relatively credible predictive insights of precipitation extremes at aggregate spatial scales, the uncertainty begins to increase significantly at localized spatial scales—especially over the tropical regions.

Even as higher resolution models are attempting to get to the stage where spatially explicit insights can be generated, the kind of insights generated from observations in this study can be used as methods for model diagnostics and can help address science gaps.Shih-Chieh Kao

Ganguly noted that the Nature Climate Change paper is the result of a team effort with researchers from diverse disciplines. Ghosh, the first author, is a hydro-climate scientist and civil engineer; Das is a graduate student in computer science and data mining; Kao is a statistical who specializes in water availability and flood frequency analysis; and Ganguly, a civil engineer, specializes in climate extremes and water sustainability as well as data sciences for complex systems.

This research concept was initiated when all the authors were working with Ganguly at ORNL and was funded by the Laboratory Directed Research and Development program. The National Science Foundation‘s Expeditions in Computing program and the Department of Science and Technology of India also provided funding.


  • Subimal Ghosh, Debasish Das, Shih-Chieh Kao & Auroop R. Ganguly (2011) Lack of uniform trends but increasing spatial variability in observed Indian rainfall extremes. Nature Climate Change (2011) doi: 10.1038/nclimate1327



Is there anything here that good meteorology cannot do better?

The comments to this entry are closed.