Monsoon-Driven Climate Risk Management in South Asia: Leveraging Indigenous Forecasting and AI For Flood Resilience

Abstract

South Asia faces escalating flood risks due to increasingly erratic monsoon patterns intensified by climate change. Despite advances in satellite-based and AI-driven flood forecasting, gaps persist in accuracy, accessibility, and community trust. Simultaneously, indigenous knowledge systems, based on generations of local environmental observation, remain underutilised. This paper proposes a Hybrid Monsoon Forecasting Model (HMFM) that integrates artificial intelligence with indigenous forecasting practices to enhance flood resilience across the region. The HMFM is built on three principles: fostering trust through community participation, digitally augmenting traditional knowledge, and ensuring bidirectional learning between scientists and local observers. Structured as a three-tier system, the model combines community-led sensing networks, an AI fusion engine for multi-source data integration, and impact-based dissemination tailored to local contexts. Ethical safeguards protect data rights, transparency, and gender inclusivity. Case studies from India, Bangladesh, Nepal, and Pakistan illustrate both the promise and challenges of hybrid systems. Institutional silos, weak cross-border cooperation, and limited policy recognition of indigenous knowledge hinder resilience efforts. The model offers a roadmap for phased implementation, beginning with pilot programs, followed by national scaling, and culminating in regional integration through a SAARC Climate Data Alliance. Policy recommendations include embedding indigenous knowledge in disaster legislation, fostering regional data sharing, and financing inclusive, AI-supported early warning systems. Emphasis is placed on community empowerment, gender equity, and open-source technology governance. By aligning modern forecasting tools with culturally grounded practices, the HMFM enhances both scientific precision and social legitimacy. This integrated approach holds transformative potential for climate risk management in flood-prone, resource-constrained, and socio-culturally diverse regions. It also provides a scalable, participatory model adaptable to other global contexts facing similar climate threats.