Скачать или смотреть When Satellites Predict Risk: Rethinking Extreme Climate Assessment

A methodological framework for extreme climate risk assessment integrating satellite and location based data sets in intelligent
systems

Authors: Srinidhi Jha, Manish K. Goyal, Brij B. Gupta, Ching‐Hsien Hsu, Eric Gilleland, Jew Das

Adaptation and resilience practitioners often face a critical gap: limited guidance on how to understand and manage extreme climate risk using the growing volume of available data. This video presents a methodological framework that integrates satellite-derived observations with location-based socio-economic datasets to estimate extreme climate risk in a systematic and policy-relevant manner.

The framework is demonstrated through a national-scale case study on extreme rainfall risk in India, combining the influence of large-scale climate oscillations with local factors such as population, infrastructure, and economic activity within a probabilistic modeling approach. Using nonstationary extreme value theory and Bayesian uncertainty analysis, the study quantifies how time-varying climate drivers—including El Niño–Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), and North Atlantic Oscillation (NAO)—amplify extreme rainfall risks across 637 districts in 29 states.

The results reveal widespread vulnerability: at least 50% of districts in 8 states face high extreme rainfall risk. Specifically, 198 districts (~31%) show elevated risk from intense downpours, while 249 districts (~39%) are vulnerable to prolonged wet spells.

By linking global climate signals to local-scale impacts, this framework supports evidence-based disaster risk reduction and climate adaptation planning, offering practical insights for policy makers, planners, and resilience practitioners working on extreme rainfall–induced hazards.

DOI: 10.1002/int.22475

This video is created by Saral AI
https://saral.democratiseresearch.in/