Abstract:
Sri Lanka has witnessed a struggle with floods and cyclones, transitioning from
occasional emergencies to persistent and growing threats. This study provides the
first consolidated long-term temporal analysis that uniquely integrates 50 years
(1975–2025) of meteorological trends with a parallel critical evaluation of monitoring
technology evolution and institutional governance efficacy. Using Mann-Kendall
trend analysis and Pettitt change-point detection, key statistically significant trends
were demonstrated: cyclonic disturbance frequency increased from 1.3 to 4.1
systems/year (p<0.001), and extreme rainfall intensity rose by +12.2 mm/decade
(p<0.05). Change-point analysis identified 1998 as a statistically significant inflection
year (Pettitt test, p<0.05), after which the cyclonic rainfall contribution increased by
18–25% in the northern and eastern provinces. While monitoring capabilities have
advanced from rain-gauge dependence (pre-1990) through the satellite era (1990–
2000 s) to real-time Earth observation integration (post-2015), a critical institutional
gap remains. This “last-mile” problem is operationally defined as the persistent gap
between the forecast lead time and actual evacuation completion times, revealing
that a 6-fold increase in forecast lead time (from 12 to 24 h in 1975–1990 to 72–
168 h in 2015–2025) has yielded no proportional improvement in evacuation times,
which remain stagnant at 18–36 h. Based on this analytical evidence of persistent
institutional failure despite technological progress, this study conceptually proposes
an integrated Sri Lanka Multi-Hazard Risk Monitoring and Decision-Support Platform
(SL-RISK) that couples technical data integration with community-embedded
monitoring, impact-based forecasting, and pre-arranged institutional response
mechanisms, outlined with a phased 18–36-month implementation roadmap.
Without addressing this governance-technology mismatch, technological advances
remain underutilised, perpetuating cycles of preventable disaster losses among
vulnerable populations.
