Climate Change Analysis with GIS Training Course

Participants can select and interpret climate variables, baseline periods and future scenarios for GIS work.

• Climate normals, anomalies and trends calculated against stated baseline periods
• CMIP6 scenarios interpreted through SSP pathways and projection time slices
• Temperature, precipitation and humidity variables matched to decision requirements
• Downscaling methods compared for regional, national and local-scale applications
• Return periods and extreme-event metrics interpreted without overstating certainty
• Ensemble means, ranges and percentiles used to describe projection uncertainty

Participants can prepare spatial datasets with appropriate projections, extents and documented quality controls.

• Coordinate reference systems selected for area, distance and raster calculations
• Raster cell size aligned with climate-data resolution and intended decisions
• Spatial extent, temporal coverage and missing values checked before analysis
• Metadata records capture source, version, licence, processing and limitations
• Resampling methods chosen for continuous, categorical and directional climate surfaces
• Quality-assurance checks identify duplicates, nulls, outliers and geometry errors

Participants can acquire, organise and prepare climate, satellite and contextual data for analysis.

• NetCDF climate files inspected for dimensions, variables, units and timestamps
• GeoTIFF rasters clipped, reprojected and masked to study boundaries
• Station observations joined with location records and quality flags
• Satellite scenes screened for cloud cover, acquisition date and sensor characteristics
• Vector exposure layers standardised using common fields and controlled vocabularies
• Project geodatabases organised with raw, processed, output and documentation folders

Participants can generate interpretable raster-based indicators from climate and environmental datasets.

• Raster algebra combines temperature, rainfall and elevation into derived surfaces
• Zonal statistics summarise climate indicators by districts, assets and catchments
• Reclassification converts continuous values into documented risk categories
• Focal statistics identify local temperature patterns and neighbourhood variation
• Cell statistics compare multiple years, seasons and climate-model outputs
• Raster masks exclude water bodies, clouds and irrelevant land areas

Participants can analyse heat patterns and identify priority locations for heat-risk interventions.

• Land-surface temperature derived from thermal imagery using documented assumptions
• Urban heat island patterns compared with vegetation and impervious surfaces
• Heatwave indicators mapped from threshold exceedance and duration records
• Population-weighted exposure calculated for neighbourhood heat-risk assessment
• Cooling interventions screened using canopy cover, albedo and accessibility data
• Heat maps designed with medically meaningful classes and readable legends

Participants can map rainfall variability, drought conditions and water-related climate pressures.

• Monthly precipitation anomalies calculated against a stated climatological baseline
• Standardized Precipitation Index mapped across multiple accumulation periods
• Drought severity classified using rainfall, vegetation and soil-moisture indicators
• Watershed summaries compare upstream rainfall patterns with downstream demand
• Temporal charts connect mapped anomalies with seasonal agricultural calendars
• Water-stress maps distinguish meteorological drought from operational supply constraints

Participants can derive and interpret satellite-based vegetation indicators for climate analysis.

• Sentinel-2 and Landsat imagery compared for resolution, revisit and spectral bands
• NDVI calculated to assess vegetation condition and seasonal response
• Cloud masking removes contaminated pixels before vegetation-index calculations
• False-colour composites distinguish vegetation, water, bare soil and built surfaces
• Time-series profiles identify vegetation decline, recovery and seasonal variability
• Ground observations used to interpret satellite indicators and anomalies

Participants can classify land cover and quantify changes relevant to climate vulnerability and mitigation.

• Supervised classification separates land-cover classes using labelled training samples
• Confusion matrices report overall accuracy, producer accuracy and user accuracy
• Post-classification comparison quantifies transitions between mapped land-cover periods
• Change detection identifies forest loss, urban growth and wetland conversion
• Class definitions documented using observable spectral and land-use criteria
• Area calculations account for projection, pixel size and classification uncertainty

Participants can combine terrain, hydrological and exposure data to map flood-related climate risk.

• Digital elevation models conditioned before slope, flow direction and accumulation analysis
• Flow accumulation identifies drainage pathways and probable surface-water concentrations
• Topographic wetness index highlights areas susceptible to saturation
• Flood extents intersected with buildings, roads and critical facilities
• River buffers, floodplains and historical inundation compared for plausibility
• Hazard maps distinguish observed inundation from modelled susceptibility

Participants can construct spatial exposure and vulnerability indicators for climate-risk assessment.

• Asset inventories geocoded and classified by function, value and criticality
• Population exposure estimated using census grids and settlement footprints
• Social vulnerability indicators selected through transparent inclusion criteria
• Data normalisation applies min-max scaling, ranks or standard scores
• Composite indices document weighting choices, correlations and interpretation limits
• Disaggregation reveals unequal climate risk among population groups

Participants can build and test a weighted GIS model for prioritising climate-risk locations.

• Weighted overlay combines hazard, exposure and vulnerability raster criteria
• Analytic Hierarchy Process structures pairwise comparisons and weighting decisions
• Suitability thresholds reflect policy criteria, engineering limits and local knowledge
• Sensitivity analysis tests how alternative weights change priority areas
• Boolean logic identifies locations meeting mandatory exclusion and inclusion rules
• Model assumptions recorded in an auditable decision-rule table

Participants can test spatial patterns and distinguish apparent clusters from meaningful relationships.

• Global Moran's I tests overall spatial autocorrelation in mapped indicators
• Local Moran's I identifies high-high, low-low and spatial outlier locations
• Hot Spot Analysis maps statistically significant concentrations of climate impacts
• Kernel density estimation visualises incident concentrations and reporting intensity
• Spatial joins compare hazards with assets, population and service locations
• Ecological fallacy and modifiable areal unit problems addressed explicitly

Participants can evaluate analytical reliability and document methods for review and reuse.

• Uncertainty sources identified across observations, models, processing and classifications
• Validation samples separated from training data for independent accuracy assessment
• Cross-validation evaluates predictive performance where reference data permit
• Processing workflows recorded using parameters, dates, software versions and scripts
• Peer review checks assumptions, calculations, map claims and source citations
• Limitations statements distinguish measured findings from plausible interpretations

Participants can produce maps and briefings that communicate climate evidence without misleading audiences.

• Visual hierarchy directs attention to priority areas and decision-relevant evidence
• Colour schemes selected for ordered, diverging and categorical climate variables
• Classification methods compared using equal interval, quantile and natural breaks
• Map layouts include scale, projection, sources, date and uncertainty notes
• Story maps sequence evidence, trade-offs and recommended actions clearly
• Executive briefings translate spatial results into decisions, costs and next steps

Participants can tailor GIS climate analysis methods to sector-specific operational decisions.

• Municipal heat plans prioritise cooling centres, shade corridors and vulnerable residents
• Infrastructure screening maps flood, heat and landslide exposure for asset portfolios
• Agricultural analysis links drought indicators with crop calendars and irrigation access
• Public health surveillance relates heat exposure to service demand and access
• Financial risk screening identifies physical hazards affecting collateral locations
• Ecosystem-based adaptation maps restoration opportunities using vulnerability criteria

Participants can plan a governed climate GIS project with realistic data, people and delivery requirements.

• Problem statements define users, decisions, geography, timescale and required accuracy
• Stakeholder mapping assigns data owners, reviewers and decision authorities
• Data management plans specify storage, access, retention and update responsibilities
• Work breakdown structures sequence acquisition, processing, review and publication tasks
• Quality-control gates define acceptance criteria for datasets and final outputs
• Implementation roadmaps identify resources, dependencies, risks and reporting milestones

Participants can build, review and present a complete climate-change GIS analysis for a defined decision.

• Project scope states decision context, users, geographic extent and climate hazards
• Source register documents climate, satellite, exposure and vulnerability datasets
• Analytical workflow produces indicators, risk surfaces and priority-location outputs
• Validation record reports checks, uncertainty sources and interpretation limitations
• Decision map series communicates findings through consistent layouts and legends
• Final presentation defends methods, recommendations and implementation priorities