Indian Climate Change Project 🌏

A public project aimed at gathering and analyzing data to predict climate change trends in India. This project focuses on collecting a variety of environmental, atmospheric, and geographical data to build predictive models for climate scenarios. The insights derived can be used to inform policies, promote sustainability, and mitigate the effects of climate change.

Project Goals

• Collect comprehensive data on climate-related variables like temperature, rainfall, air quality, soil moisture, and more.
• Analyze historical and current data to identify trends and anomalies.
• Develop models to predict future climate conditions in India.
• Provide open-access data and tools to empower researchers, policymakers, and citizens.

Key Features

• Data Collection: From meteorological stations, IoT devices, and satellite imagery.
• Analysis Tools: Python scripts and Jupyter Notebooks for data cleaning, visualization, and machine learning.
• Open Data Access: Data will be structured and shared for public use.
• Predictive Models: AI/ML algorithms to predict climate scenarios.

Climate Data Collection Parameters for India

Core Atmospheric Parameters

1. Temperature
   • Surface temperature (minimum, maximum, and average daily temperatures)
   • Atmospheric temperature at various altitudes (if feasible)
2. Humidity
   • Relative humidity
   • Dew point temperature
3. Pressure
   • Atmospheric pressure (at sea level and local levels)
4. Precipitation
   • Rainfall (daily, monthly totals)
   • Intensity and duration of rainfall events
5. Wind
   • Speed and direction (at surface level and higher altitudes)
6. Solar Radiation
   • Solar irradiance (direct, diffuse, and total)

Land Surface Parameters

1. Soil Moisture
   • Particularly important for monsoon and drought predictions.
2. Land Use and Vegetation
   • Cropping patterns, deforestation, and urbanization.
3. Surface Albedo
   • Reflectivity of land, especially in snow-covered or desert areas.

Water and Oceanic Parameters

1. Surface Water Levels
   • Rivers, lakes, and reservoirs (monitoring for droughts and floods).
2. Sea Surface Temperature (SST)
   • For coastal regions, critical for cyclone predictions.
3. Groundwater Levels
   • Seasonal variations, especially in drought-prone areas.

Greenhouse Gases and Air Quality

1. Carbon Dioxide (CO2)
   • Local sources if possible (industrial areas).
2. Methane (CH4)
   • From wetlands, agriculture, and livestock.
3. Aerosols
   • Dust, soot, and particulate matter (PM2.5, PM10).

Seasonal and Cyclical Influencers

1. Monsoon Patterns
   • Onset, retreat, and variability.
2. El Niño–Southern Oscillation (ENSO)
   • Links to Indian monsoon.
3. Extreme Events
   • Historical data on cyclones, heatwaves, and floods.

Key Sources

1. IMD (Indian Meteorological Department)
   • Data on temperature, humidity, precipitation, wind, solar radiation, and extreme weather events such as cyclones, heatwaves, and floods.
2. ISRO (Indian Space Research Organisation)
   • Satellite data on land use, vegetation, oceanic conditions, and environmental monitoring, which can contribute to land surface parameters, water levels, and urbanization patterns.
3. CPCB (Central Pollution Control Board)
   • Data on air quality, including greenhouse gases (CO2, methane) and aerosols (PM2.5, PM10), which will help in understanding pollution levels and air quality trends.
4. ICAR (Indian Council of Agricultural Research)
   • Information on soil moisture, crop patterns, and agricultural data relevant to climate modeling and predictions, particularly for monsoon and drought events.
5. IWMI (International Water Management Institute)
   • Data on groundwater levels, water bodies, and surface water, useful for monitoring droughts, floods, and water resource management.
6. NRSC (National Remote Sensing Centre)
   • Satellite-based remote sensing data for monitoring land use, land cover, vegetation, and surface albedo, crucial for understanding land surface parameters and their impact on climate.
7. NABARD (National Bank for Agriculture and Rural Development)
   • Provides data on rural development, agriculture, and environmental sustainability, which could inform policy decisions around climate resilience in agriculture.
8. MoEFCC (Ministry of Environment, Forest and Climate Change)
   • Data on forest cover, biodiversity, and climate change, as well as data on carbon sequestration and conservation efforts that affect climate patterns.
9. FSI (Forest Survey of India)
   • Data on forest cover, deforestation rates, and forest management that can contribute to understanding seasonal cycles and land use changes.
10. CES (Centre for Earth Science Studies)
    • Geospatial data related to coastal areas, natural disasters, and climate change impact on ecosystems, which can aid in predicting extreme events like cyclones and floods.
11. WRI India (World Resources Institute India)
    • Provides datasets related to sustainable development, natural resource management, and climate adaptation strategies.
12. NHAI (National Highways Authority of India)
    • Data on road infrastructure and traffic patterns, especially useful for understanding the effects of climate change on transportation networks.
13. IMD (Indian Meteorological Department)
    • Key resource for weather, monsoon patterns, and extreme weather event data, as well as their seasonal variations.

Live Pages

1. Real time Air Quality Index from various locations