SPEI
1. Overview of SPEI Dataset
Purpose: Quantifies surface water availability, identifying wet and dry periods over time.
Developed by: Environment and Climate Change Canada (ECCC).
Geographic Coverage: Canada (land mass only).
Time Coverage:
Historical period: 1900–2005
Future projections: 2006–2100
Spatial Resolution: 1° x 1° global grid (~110 km)
Temporal Resolution: Monthly
Key Features
Feature
Description
Drought Index
SPEI measures precipitation minus evapotranspiration (P–PET)
Climate Models Used
29 CMIP5 models
Downscaling Approach
Bias Correction & Multivariate Statistical Adjustment (MBCp)
Emission Scenarios
RCP2.6, RCP4.5, RCP8.5
Time Scales
SPEI-1, SPEI-3, SPEI-12 (1, 3, and 12-month averages)
Reference Period for Anomalies
1950–2005
2. Data and Processing
2.1 Multi-Model Climate Projections
29 CMIP5 climate models were used to generate SPEI projections.
Equal weighting applied to all models ("one model, one vote").
Ensemble median (50th percentile) used as the central estimate, with uncertainty ranges (25th–75th percentiles).
2.2 Bias Correction & Observational Target
Bias correction applied to all climate variables (temperature, precipitation).
Reference dataset: Canadian Gridded Climate Dataset (CANGRD).
Time Period for Bias Correction: 1950–2005.
2.3 Potential Evapotranspiration (PET) Calculation
Method Used: Modified Hargreaves Equation.
Why Modified? It includes precipitation as an input, improving accuracy in cold and dry regions.
Alternative PET Methods:
Penman-Monteith (more complex but requires more data).
Thornthwaite (less accurate, requires only temperature data).
2.4 Log-Logistic Fit & Parameter Estimation
Log-logistic probability distribution used for standardization.
Parameters estimated using Probability Weighted Moments (PWM).
Applied to SPEI data over the full period (1900–2100).
3. SPEI Time Scales & Applications
3.1 SPEI-1 (Short-Term Drought)
Captures rapid-onset drought conditions (flash droughts).
Useful for detecting month-to-month rainfall deficits.
Common applications: Agriculture, water supply monitoring.
3.2 SPEI-3 (Seasonal Drought)
Represents cumulative moisture deficit over three months.
Captures seasonal drought conditions impacting crops, forests, and reservoirs.
Common applications: Wildfire risk assessment, energy demand forecasting.
3.3 SPEI-12 (Long-Term Drought)
Measures persistent droughts lasting over a year.
Best suited for analyzing climate change-driven trends.
Common applications: Hydrological planning, long-term water resource management.
4. Interpretation of SPEI Values
SPEI Value
Drought/Wetness Category
≥ 2.0
Extremely Wet
1.5 to 1.99
Very Wet
1.0 to 1.49
Moderately Wet
-0.99 to 0.99
Near Normal
-1.0 to -1.49
Moderately Dry
-1.5 to -1.99
Very Dry
≤ -2.0
Extremely Dry (Severe Drought)
SPEI is a standardized index, meaning a value of zero indicates "normal" conditions relative to the 1950–2005 baseline.
Negative values indicate dryness, while positive values indicate wetness.
5. Uncertainty Representation
SPEI projections include ensemble percentiles (25th, 50th, 75th).
This range does not capture all uncertainty (e.g., future emissions, local hydrological changes).
Multi-model ensembles are recommended instead of single-model projections.
6. Applications of SPEI in Climate Studies
6.1 Drought Monitoring
Tracking seasonal and long-term drought severity.
Assessing the impact of climate change on Canadian drought risk.
Example: Analyzing multi-year drought patterns in the Prairies.
6.2 Water Resource Management
Planning for municipal and agricultural water supplies.
Assessing reservoir and groundwater recharge trends.
Example: Predicting future water shortages in Ontario and Alberta.
6.3 Wildfire Risk Forecasting
SPEI-3 and SPEI-12 used for wildfire risk assessment.
Drought conditions influence fire spread, intensity, and duration.
Example: Correlating dry periods with wildfire outbreaks in British Columbia.
6.4 Agricultural Impact Assessments
SPEI-1 and SPEI-3 used to assess soil moisture conditions.
Helps determine crop viability and irrigation needs.
Example: Predicting wheat and corn yield losses during dry years.
7. Best Practices for Using SPEI Data
Use multi-model ensembles instead of individual model outputs.
Compare results across different emission scenarios (RCP2.6, RCP4.5, RCP8.5).
Be cautious when applying SPEI in snow/glacier-dominated regions.
Combine SPEI with other hydrological data (streamflow, groundwater) for better decision-making.
Limitations
SPEI does not account for snowpack, glaciers, or permafrost dynamics.
May not fully capture short-term hydrological droughts influenced by local factors.
Best used as an indicator of meteorological drought rather than hydrological drought.
8. Data Access & References
8.1 Where to Access SPEI Data
Environment Canada SPEI Portal:
Climate Data Access
CMIP5 SPEI Datasets:
Earth System Grid Federation (ESGF)
8.2 References
Vicente-Serrano, S. M., et al. (2010). A Multiscalar Drought Index Sensitive to Global Warming. Journal of Climate.
Cannon, A. J. (2016). Multivariate Bias Correction of Climate Model Outputs. Journal of Climate.
Tam, B. Y., et al. (2018). CMIP5 drought projections in Canada based on SPEI. Canadian Water Resources Journal.
Droogers, P. and Allen, R. G. (2002). Estimating Reference Evapotranspiration Under Inaccurate Data Conditions. Irrigation and Drainage Systems.
9. Conclusion
The SPEI dataset is a powerful tool for tracking and predicting drought in Canada, integrating temperature, precipitation, and evapotranspiration for multi-scalar drought assessments.
With multi-model ensemble projections, bias-corrected datasets, and long-term historical records (1900-2100), SPEI supports climate adaptation strategies in agriculture, water management, and disaster risk reduction.
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