Hydrometeorological services play a crucial role in fostering economic prosperity and resilient development by minimizing losses, optimizing production, and informing strategies for climate change adaptation and mitigation. Climate-sensitive sectors such as agriculture, fisheries, and commerce are particularly vulnerable to extremes like droughts, floods, storms, and other hydrometeorological hazards.
To address these challenges, RIMES develops advanced Decision Support Systems (DSS) for climate risk and hazard management. These systems integrate diverse data sources, leverage machine learning and AI for predictive insights, and provide intuitive tools for decision-makers, enabling effective risk assessment, mitigation, and adaptation strategies.
Through its institutional mechanisms, RIMES ensures collaboration among national and sub-national stakeholders to share data and co-design, co-develop, and operationalize DSS tailored to specific needs. By transforming data into actionable insights, RIMES empowers decision-makers to predict, mitigate, and adapt to the growing impacts of climate-related risks. RIMES observe the following comprehensive approach:
Stakeholders: Understand who will use the DSS, such as governments, local authorities, businesses, insurance companies, and climate researchers.
Objectives: Define the goals for the DSS—whether it’s for disaster preparedness, risk mitigation, long-term policy planning, or specific event-based decision-making (e.g., floods, droughts, or heatwaves).
Use Cases: Determine the types of decisions the system will support, like real-time responses to extreme weather events, agricultural planning, or infrastructure resilience analysis.
The effectiveness of the DSS depends on accurate and comprehensive climate risk data. Key data sources include:
Historical Climate Data: Temperature, precipitation, wind patterns, and other climate variables over long periods.
Satellite and Remote Sensing Data: Data from satellites that track real-time environmental changes like sea level rise, glacier melt, or deforestation.
IoT Sensors and Weather Stations: Localized data from on-ground sensors measuring environmental conditions such as temperature, humidity, wind speed, and soil moisture.
Socioeconomic Data: Information on population density, infrastructure, economic activity, and vulnerable communities for risk assessments.
Geospatial Data: Geographic Information System (GIS) data for mapping climate risks to specific locations.
Real-Time Data Sources: Weather forecasts, storm tracking, early warning systems, and hazard alerts.
Data Integration Tools: Use ETL (Extract, Transform, Load) processes, APIs, and cloud platforms (e.g., AWS, GCP) to collect and integrate these disparate data sets into a centralized data warehouse.
Once the data is gathered, it needs to be processed, analyzed, and interpreted using advanced data analytics tools. Key techniques include:
Descriptive Analytics: Visualizing historical climate trends and patterns to provide context for risk assessments (e.g., using Tableau, Power BI).
Predictive Analytics: Applying machine learning models to predict future climate-related events. This includes:
Time Series Analysis: Forecasting temperature, precipitation, or sea level rise trends using techniques like ARIMA or Long Short-Term Memory (LSTM) models.
Machine Learning Models: Using supervised and unsupervised learning models to predict the probability of climate events like droughts, floods, or wildfires based on current data trends.
Climate Models: Incorporating global and regional climate models (e.g., General Circulation Models – GCMs) to simulate future climate conditions under different scenarios (such as the IPCC’s Representative Concentration Pathways or RCPs).
Risk Mapping and Scoring: Using geospatial analytics to map areas of high climate risk, overlaying hazards with socioeconomic data to prioritize vulnerable regions. Scenario Analysis: Create “what-if” scenarios to analyze the impact of different climate policies or interventions (e.g., reducing carbon emissions) and assess how various actions can mitigate future risks.
To make the insights actionable, DSS needs to be intuitive and easy to use. Key components of this phase include:
Dashboards and Visualizations: Creating interactive, real-time dashboards using tools like Power BI, Tableau, or D3.js that display critical climate risk indicators, predictive models, and risk maps.
GIS Tools: Integrating GIS software (e.g., ArcGIS, Google Earth Engine) for visualizing geospatial data, mapping risk zones, and planning interventions at a granular level.
Alerts and Notifications: Implementing systems that automatically notify users (via email, SMS, or mobile apps) when high-risk climate events are forecasted.
Custom Reports and Analytics: Allowing users to generate custom reports based on specific regions, climate variables, or risk categories.
AI and ML driven DSS by automating the analysis and decision-making processes:
AI-Driven Risk Assessments: Algorithms that continuously learn from incoming data and refine climate risk assessments.
Natural Language Processing (NLP): For analyzing large volumes of climate-related documents, scientific literature, and policy reports to derive actionable insights.
Reinforcement Learning: To optimize decision-making strategies, where the system learns the best actions over time by simulating different policy choices and their outcomes.
Advanced climate risk management specific models to simulate and predict the impact of climate risks:
Hydrological Models: To predict the risk of floods and droughts by simulating water flow and storage in river basins.
Agricultural Models: To assess the impact of weather/ climate on crops and food security.
Economic Impact Models: To calculate the financial cost of climate events such as storms, cyclones, or heatwaves on infrastructure, agriculture, and the economy.
Processing large-scale climate data requires scalable computing resources:
Cloud Platforms: AWS, Azure, or Google Cloud can provide scalable infrastructure for processing massive amounts of data, running simulations, and storing large datasets.
Big Data Tools: Apache Hadoop, Spark, and other big data platforms help in managing and analyzing the vast amounts of data needed for climate modeling.
Edge Computing: For localized, real-time climate analytics (e.g., processing sensor data in real-time for specific regions).
NWP and Climate Models: Incorporating global and regional Weather climate models (e.g., WRF, General Circulation Models – GCMs) to forecast extreme events/simulate future climate conditions under different scenarios such as the IPCC’s Representative Concentration Pathways or RCPs Climate data and risk assessments shared across sectors for a more holistic approach:
Open Data Platforms: Leveraging platforms like NASA Earthdata, Copernicus Climate Data Store, and World Bank Climate Data for collaboration.
Public-Private Partnerships: Engaging both governmental bodies and private sectors to share data, improve models, and enhance decision-making.
Cross-Agency Collaboration: Promoting collaboration between organizations like meteorological departments, environmental agencies, NGOs, and research institutes.
Automated Decision Engines: Integrating systems that provide real-time, automated recommendations based on data inputs (e.g., evacuation alerts, irrigation adjustments, infrastructure reinforcement).
Feedback Loops: Systems: Continuously learn and improve based on outcomes of past decisions, using reinforcement learning or other AI techniques to improve over time.
Transparency: Ensuring that the decision-making processes are transparent, explainable, and auditable.
Inclusivity: Designing DSS to support climate-vulnerable populations and ensure that the most affected communities have access to decision-making tools.
Pilot Projects: Implementing DSS in small regions or specific use cases (e.g., flood risk in coastal cities).
Scalability: Ensure that the system can scale up to national or global levels by leveraging cloud resources and big data platforms.
DECISION SUPPORT SYSTEMS (DSS) AND TOOLS
Ocean State Forecasting and Advisory System
A web-based system for generation of advisories based on location-specific ocean state forecast information from INCOIS’ Indian Ocean Forecast System (INDOFOS).
ShakeCast: Earthquake Risk Assessment
An online tool for rapid earthquake risk assessment –estimating risks to population and critical facilities using real-/ near real-time data from an earthquake event.
Internet-based Simulation Platform for Inundation and Risk Evaluation (INSPIRE)
A web-based tool for tsunami propagation and inundation simulation and risk assessment.
Low-Cost Bathymetric and Topographic Surveys
A cost-effective survey methodologies to generate high accuracy near-shore bathymetric and topographic datasets for coastal inundation analysis, and exposure dataset for vulnerability assessment.
Earthquake, tsunami, and coastal hazard assessments
Hazard assessments to characterize hazards that at-risk communities face, as inputs to resilience planning.
Basin-based flood forecasting and warning system
A web-based system for generation of basin discharge and river level forecasts based on 3-day and 10-day weather forecasts, analysis and mapping of flood risks, and generation and issue of appropriate advisories.