Vibrational Data Assimilation for Flood Dynamics in Nigeria (2021 to 2025): Quantifying Climate-Driven Changes in Hazard

Abstract

Flooding remains one of the most devastating natural hazards in Nigeria, with increasing frequency and intensity driven by climate variability, poor infrastructure, and inadequate disaster preparedness. This study assessed flood hazards across Adamawa, Borno, Niger, Bayelsa, and Lagos States between 2021 and 2025, integrating Vibrational Data Assimilation (VDA) with conventional modeling approaches to improve prediction accuracy. Model performance evaluation revealed that VDA consistently reduced Root Mean Square Error (RMSE) values and improved Nash–Sutcliffe Efficiency (NSE) and coefficient of determination (R²), indicating a significant enhancement over traditional methods. The analysis further showed a steady rise in populations exposed to flooding, increasing from about 585,000 in 2021 to over 1.53 million in 2025. Niger State experienced the most severe impact, with more than half a million people affected in a single year. Mortality patterns also worsened, with estimated deaths rising from 155 in 2021 to over 1,200 by 2025, highlighting the growing human cost of climate-induced flooding. These outcomes emphasize the urgent need for effective risk reduction strategies. The study concludes that while climate-driven flood hazards are intensifying in Nigeria, the adoption of VDA can significantly improve flood forecasting and disaster preparedness. By strengthening early warning systems and reducing uncertainties in hazard assessments, VDA provides a valuable framework for policymakers and disaster management agencies. However, predictive improvements must be complemented by infrastructural development, coordinated water management, and community-based adaptation strategies to mitigate future flood risks.