Monsoon Mayhem to Market Waves: Forecasting Fisheries Resilience in Sri Lanka

2026-06-27

AI Summary

Between 2019 and 2025, Sri Lanka’s fisheries sector faced multiple shocks from monsoons and disruptive events, yet the interlinkages among climate, production, and market dynamics remain poorly understood. This study develops an integrated analytical framework that jointly incorporates weather variability, exogenous shocks, catch volumes, and price fluctuations. By combining STL seasonal decomposition, Spearman lagged correlations, interrupted time series regression, and SARIMAX forecasting models, the research systematically uncovers the differential responses of marine and inland fisheries to disturbances and identifies cross-sectoral compensatory effects. It provides the first quantitative assessment of heterogeneity between the two fishery types in terms of seasonality and climate sensitivity, evaluates the impacts of major disruptive events, and offers empirically grounded, prediction-driven insights to inform cold-chain optimization, infrastructure planning, and early-warning systems.

Abstract

Sri Lanka's fisheries sector is important for jobs and food supply. Between 2019 and 2025, it faced several major problems at the same time, and how these events together affected fish production and prices is still not well understood. This study develops a framework to connect weather changes, major disruption events, fish production, and prices, with the goal of helping policymakers, traders, and supply chain managers make better decisions. Seasonal patterns are studied using STL decomposition. Spearman lag correlation is used to find delayed effects of climate on production. Interrupted Time Series (ITS) regression measures the impact of major events. SARIMAX models predict monthly production and prices. Hotspot detection identifies unusual patterns. The results show that marine and inland fisheries behave differently in terms of seasons and climate effects. Major disruptions caused different levels of impact, and in some cases, one sector helped compensate for another. These findings can support better planning, for example, improving infrastructure in high-risk areas, strengthening cold storage systems, and using early warning alerts for unusual events. Price forecasting tools should be used as decision-support tools, not as direct market signals.

Problem

• fisheries resilience
• climate disruption
• production and price dynamics
• monsoon impact
• supply chain shocks

Innovation

• SARIMAX modeling
• Interrupted Time Series (ITS)
• STL decomposition
• hotspot detection
• fisheries resilience