The Climate Modeling Lab focuses on understanding and projecting climate variability and change across multiple spatial and temporal scales, with a special emphasis on water resources, ecosystems, agriculture, and urban environments. The lab integrates numerical climate models, remote sensing, and statistical/AI-based approaches by incorporating field observations and measurements to:

• Assess climate extremes (heatwaves, droughts, intense rainfall, floods)

• Quantify impacts on hydrology, agriculture, forests, and coastal systems.

• Downscale global climate projections to regional and local scales

• Support evidence-based decision-making for adaptation and mitigation policies.

The lab works closely with government agencies, international organizations, and academic partners to co-develop climate-smart solutions and tools.

PI: Dr. Hammad Gilani

Expertise/Capabilities of lab:

• Hydroclimate & Impact Assessment: Linking climate projections with water resources, floods/droughts, groundwater, agriculture, vegetation, ecosystems, and urban heat islands.
• Climate Modeling & Downscaling: Regional climate modeling, bias correction, statistical/dynamical downscaling, and climate indices (extremes, drought, heat stress).
• Remote Sensing & Geospatial Analysis: Integrating satellite data with models for calibration/validation and mapping vegetation, soil moisture, and land cover change.
• Data Science & AI: Machine learning and statistical modeling, time-series trend analysis and simulation, and uncertainty assessment.
• Policy & Decision Support: Supporting SDGs, adaptation planning, climate risk assessments, and stakeholder-focused capacity-building and training

Projects:

• Planning a winter school or workshop in December on climate modeling, data analysis, and remote sensing
• Joint R&D proposals with industry/government (e.g., weather-insurance, flood risk modeling)
• Consultancy opportunities (impact assessments, climate resilience for infrastructure)

Lab Equipment /Resources:

• Computing & Software
  • High-performance workstations and access to Super and Cloud Computing Resources
  • GIS and Remote Sensing software (QGIS, ArcGIS, SNAP, R, Python, Google Earth Engine, etc.)
  • Climate and Earth system modeling tools (e.g., WRF, CMIP/ESGF datasets, RCM outputs)
  • Statistical and machine learning environments (Jupyter, R, scikit-learn, TensorFlow, etc.)
• Data Resources
  • Global and regional climate model datasets (CMIP, CORDEX, ERA5/ERA5-Land, reanalysis)
  • Satellite-based products (e.g., MODIS, Sentinel, Landsat, CHIRPS, TRMM/GPM, SMAP, etc.)
  • Gridded datasets for hydrology, land cover, soil, and socioeconomics, etc.
  • In-house databases of meteorological stations, climate indices, extremes, and impact indicators of climate
  • Field data on groundwater, forest carbon, soil characteristics, and more 

Human Resources

Typical Enrollment of lab researchers: 5-6 researchers

Contact Us:

Dr. Hammad Gillani
Room No.: SINES, NUST Campus, H-12, Islamabad
Contact No.:  
Email:  [email protected]