The Natural Hazards and Risk Analysis (NHaRA) group was established in 2019 with a mission to develop technological solutions that assess the potential impacts of natural hazards on regions and communities, providing critical information for risk management strategies.
NHaRA is an interdisciplinary group from the Computer Applications in Science and Engineering Department (CASE) that pushes the boundaries of science and technology for the early identification and forecast of natural hazards, all of them deeply affected by the climate change. The group focuses on the identification of vulnerabilities and the development of risk maps to enhance disaster preparedness and mitigation strategies.
Objectives
- Develop Innovative Methodologies - Design and implement state-of-the-art approaches to reduce vulnerability and exposure while enhancing the resilience of communities, institutions, and businesses against natural and man-made hazards
- Advance Predictive Modeling for Natural Hazards - Develop and refine high-precision models capable of forecasting natural hazards, improving accuracy and reliability for proactive risk mitigation.
- Analyze and Visualize Extensive Datasets - Perform advanced data analysis to identify patterns, trends, and correlations within large datasets, producing actionable insights such as risk maps to inform resource allocation and early emergency planning. 
- Create Resilience Monitoring Indexes - Develop systematic and data-driven resilience monitoring indexes to assess the capacity of communities to withstand, adapt, and recover from predefined risks, supporting evidence-based resilience strategies.
- Build Analytical Tools and Real-Time Decision Platforms - Develop cutting-edge analytical tools and interactive platforms that enable real-time decision-making during hazard events, ensuring rapid and informed responses to emergencies.
RESEARCH LINES
- Hazard and Compound Hazard Risk Modelling for Climate Scenarios - Exploring how individual and interacting natural hazards, such as floods, wildfires, landslides, hailstorms, and severe convective storms, evolve under present conditions and projected climate change, using integrated physical and statistical models to assess exposure, vulnerability, and cascading impacts.
- Integration of Hazard, Risk Models and Early Warning Systems - Designing participatory early warning systems that integrate localized data collection, artificial intelligence, and hazard physics modelling to improve risk awareness, communication, and adaptive capacity at the community level.
- Climate Change Impacts on Public Health, Agriculture, Energy, and the Environment - Assessing the cascading impacts of climate extremes on human health, agricultural productivity, energy infrastructure, and ecosystems, with an emphasis on identifying key vulnerabilities and supporting the development of targeted adaptation strategies.
- Transferability of AI-Based Hazard Models to Data-Scarce Regions - Applying knowledge transfer and domain adaptation techniques to extend the application of machine learning–based hazard models to regions with limited data availability.
- Modelization of Climatic Events with Statistical and AI Approaches - This research explores the use of statistical methods, machine learning, and deep learning to model complex climate processes beyond traditional physical approaches. The goal is to develop fast, scalable tools that enable rapid analysis and actionable insights, even with large datasets.
Map of mainland Spain showing all agricultural categories from the European CORINE Land Cover dataset provided by Copernicus which consists of a total of 44 thematic classes of land cover and land use.
(left) Probability of having one or more hail events over each location of mainland Spain. The map has been constructed using a Machine Learning model developed by the team based on meteorological variables from the ERA5 reanalysis and hail observations. The map is based on the period 1984-2023. (centre) Probability of having one or more hail events over each location of mainland Spain where vineyards are grown. For instance, the map shows that vineyards in Catalonia are more exposed to hail hazards than vineyards in regions such as Andalucia. (right) Probability of having one or more hail events over each location of mainland Spain where fruit trees and berries are grown.
