Advanced drought monitoring system for German metropolitan areas using Standardized Precipitation Index (SPI) methodology
https://www.kaggle.com/datasets/heidarmirhajisadati/germany-city-rainfall-data/data
This project implements a comprehensive drought assessment framework for major German cities, utilizing advanced statistical methods to transform historical rainfall data into actionable intelligence for water resource management and agricultural planning. The system employs the Standardized Precipitation Index (SPI) methodology, recognized by the World Meteorological Organization as the standard for meteorological drought monitoring.
Key Innovation: Multi-scale temporal analysis (1, 3, 6, and 12-month periods) combined with interactive geospatial visualization for real-time decision support.
- Standardized Precipitation Index (SPI) calculation across multiple time scales
- Statistical modeling using gamma distribution fitting for robust drought classification
- Quality assurance pipeline with comprehensive data validation and outlier detection
- Seasonal pattern analysis with climate type stratification
- Professional web dashboard with Leaflet.js mapping integration
- Temporal layer controls for historical pattern exploration
- Animated heatmaps showing drought progression over time
- Real-time filtering by time scale, year, and geographic region
- Multi-temporal drought classification (9-category WMO standard)
- Risk assessment matrices for agricultural and water management applications
- Trend analysis with statistical significance testing
- Export capabilities for GIS integration and further analysis
Raw Meteorological Data → Quality Assurance → Statistical Modeling → SPI Calculation → Interactive Visualization
- Gamma Distribution Fitting: Robust parameter estimation for precipitation probability modeling
- Standard Normal Transformation: Ensures SPI values are spatially and temporally comparable
- Missing Data Handling: Advanced interpolation and flagging procedures
- Multi-scale Analysis: 1M (agricultural drought), 3M (seasonal patterns), 6M (hydrological impacts), 12M (long-term trends)
- Data Processing: Python (pandas, numpy, scipy)
- Statistical Analysis: Advanced probability distributions and hypothesis testing
- Visualization: Matplotlib, Plotly, Leaflet.js
- Web Technologies: HTML5, CSS3, JavaScript (ES6+)
- Geographic Analysis: Folium, GeoJSON integration
**Purpose**: Analyze how total annual rainfall varies across different climate types over time.
Methodology:
- Aggregates monthly rainfall data to annual totals by city and climate type
- Creates stacked bar charts showing rainfall distribution by climate type
- Applies trend line analysis to identify temporal patterns
- Implements data visualization with value labels for significant rainfall amounts
Outputs:
- Stacked bar chart visualization (
rainfall_stacked_bar_simplified.png) - Processed data export (
rainfall_stacked_data.csv)
Purpose: Examine seasonal rainfall patterns across different climate types and years.
Methodology:
- Maps months to seasons (Winter, Spring, Summer, Autumn)
- Calculates average rainfall per climate type and season
- Generates statistical benchmarks (mean ± standard deviation)
- Creates multi-year seasonal comparison plots
Key Features:
- Statistical context with overall mean and standard deviation bands
- Year-over-year seasonal comparison
- Climate-specific analysis for detailed insights
- Professional visualization with comprehensive legends
Outputs:
- Climate-specific seasonal plots (e.g.,
[Climate_Type]_climate_rainfall.png)
Purpose: Implement the Standardized Precipitation Index for comprehensive drought analysis.
Methodology: The SPI calculation process includes:
- Multi-Scale Analysis: Calculates SPI for 1, 3, 6, and 12-month time scales
- Statistical Modeling:
- Fits gamma distribution to precipitation data
- Accounts for zero precipitation probability
- Transforms to standard normal distribution
- Classification System: Implements standardized drought/wetness categories
- Temporal Processing: Ensures continuous time series for each city
SPI Classification Scale:
- Extremely Wet: ≥ 2.0 | Extremely Dry: < -2.0
- Very Wet: 1.5 to 2.0 | Severely Dry: -2.0 to -1.5
- Moderately Wet: 1.0 to 1.5 | Moderately Dry: -1.5 to -1.0
- Slightly Wet: 0.5 to 1.0 | Slightly Dry: -1.0 to -0.5
- Near Normal: -0.5 to 0.5
Data Products:
- Comprehensive SPI dataset (
spi_tableau_data.csv) - Annual summary statistics (
spi_annual_summary.csv) - Distribution analysis by classification category
Technology Stack:
- Mapping: Leaflet.js for interactive geographic visualization
- Data Visualization: Custom JavaScript implementation
- Web Technologies: HTML5, CSS3, JavaScript
- Geographic Data: Integration with Germany administrative boundaries
Interactive Controls:
- Visualization Type: Toggle between city markers and animated heatmaps
- SPI Time Scale: Select from 1, 3, 6, or 12-month SPI calculations
- Temporal Analysis: Choose between time series or summary views
- Year Filtering: Focus on specific years or overall averages
Visualization Components:
-
City Markers Mode:
- Color-coded circles representing SPI values
- Size proportional to SPI magnitude
- Detailed popup information including all meteorological variables
- Tooltips for quick reference
-
Time Series Analysis:
- Layer control system for temporal navigation
- Month-by-month progression visualization
- Historical pattern analysis
-
Heatmap Animation:
- Animated intensity maps showing drought/wetness patterns
- Temporal progression through the dataset
- Regional pattern identification
-
Summary Views:
- Annual or overall average displays
- Comparative analysis across cities
- Long-term trend identification
Rainfall_Cleaned.csv: Quality-assured datasetspi_tableau_data.csv: Complete SPI analysis resultsgermany_spi_comprehensive_dashboard.html: Interactive web application
- Open
germany_spi_comprehensive_dashboard.htmlin web browser - Select visualization type (markers/heatmap)
- Choose SPI time scale (1M, 3M, 6M, 12M)
- Filter by time period or year
- Click markers for detailed meteorological information
- Reservoir operation optimization during drought periods
- Water allocation strategies for municipal and agricultural users
- Drought preparedness planning and early warning systems
- Crop selection based on historical drought frequency
- Irrigation scheduling and water use efficiency planning
- Risk assessment for crop insurance and financial planning
- Long-term trend analysis for climate adaptation strategies
- Regional vulnerability assessment for infrastructure planning
- Evidence-based policy development for sustainable water management
- Drought monitoring and severity assessment
- Resource allocation during water scarcity events
- Public communication and awareness campaigns
Contributions are welcomed from the data science, hydrology, and water management communities. Please see our contribution guidelines for detailed information on:
- Code standards and documentation requirements
- Data validation protocols
- Statistical methodology enhancements
- Visualization and user experience improvements
- Integration with real-time meteorological data sources
- Machine learning models for drought prediction
- Enhanced mobile responsiveness
- Multi-language support for international applications
This project is licensed under the MIT License - see the LICENSE file for details.
Professional Contact: Nekky Lung | Data Scientist & Water Resources Specialist| https://www.linkedin.com/in/nekkytang/ Research Interests: Hydroclimatology, Drought Monitoring, Water Security, Climate Adaptation
Developed for advancing evidence-based water resource management through innovative data science applications.