Enhancing Satellite Gravimetry for Sustainable Water Resource and Disaster Management in Thailand and Southeast Asia under Climate Change Challenges

The project “Enhancing Satellite Gravimetry for Sustainable Water Resource and Disaster Management in Thailand and Southeast Asia” develops advanced methods to refine GRACE and GRACE-FO satellite data into high-resolution datasets for local water and disaster management. By separating groundwater signals from total water storage and making the results openly accessible, it empowers agencies to better monitor droughts, floods, and land subsidence. Alongside technical innovation, the project builds regional expertise and collaboration, strengthening climate resilience, reducing economic losses, and positioning Thailand as a leader in Earth and Space Technology.

Climate change and rapid environmental shifts are reshaping how water resources and disaster risks must be managed in Thailand and Southeast Asia. Traditional approaches, once effective under relatively stable conditions, are now insufficient to cope with increasingly frequent and severe floods, droughts, and unpredictable hydrological patterns. Evidence shows declining water availability due to altered rainfall, higher evaporation, and excessive groundwater extraction, leading to subsidence and long-term vulnerability. These challenges demand advanced monitoring technologies and high-resolution data to provide timely insights for sustainable management. Satellite gravimetry, pioneered by the GRACE and GRACE-FO missions, offers a powerful means of tracking mass changes on Earth, particularly groundwater dynamics, by detecting subtle variations in the planet’s gravity field. While widely applied internationally, its use in Thailand and the broader region remains limited due to technical barriers and lack of awareness. Current datasets, typically processed to reduce noise, are constrained by coarse spatial resolution (~300 km), which restricts local-scale applications. Unlocking the full potential of this technology requires new regional processing methods, integration with physical models, and capacity building among local experts. By advancing these capabilities, Thailand can strengthen resilience to climate change, improve water resource governance, and reduce disaster risks, while positioning itself as a regional leader in Earth and Space Technology.

 
Variable Name Unit Status
TWS
Terrestrial Water Storage
mm
Ongoing
GWS
Groundwater Storage
mm
Ongoing
Others
Others
Others
Others
Others
Others
Others
Others

This dataset is provided for free, without any charge, as part of the provider’s commitment to the development of knowledge and collaboration in science. Registration helps users understand the scope and impact of data usage and helps the provider prioritize improvements in the future.

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The data from the “Enhancing Satellite Gravimetry for Sustainable Water Resource and Disaster Management in Thailand and Southeast Asia” is presented in various formats. Raster data is provided in NetCDF and GeoTIFF formats for spatial analysis. Time series data is provided in Excel format, suitable for studies at basin, province, or district level. The data is updated every month.

Example

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Reading Spatial Data in Python

Working with geospatial data locally offers numerous advantages including faster processing, enhanced privacy, and the ability to work without an internet connection. This guide focuses on two critical spatial data formats: GeoTIFF and NetCDF files, providing comprehensive instructions for setting up your environment, loading data, visualization techniques, and advanced spatial operations.

 

Whether you’re analysing remote sensing imagery, climate data, or creating specialised maps, these step-by-step instructions will help you build a robust offline geospatial workflow in Python. By the end of this guide, you’ll be able to confidently manipulate spatial data, create informative visualisations, and perform common geographic operations such as clipping to specific regions.

Working with geospatial data locally offers numerous advantages including faster processing, enhanced privacy, and the ability to work without an internet connection. This guide focuses on two critical spatial data formats: GeoTIFF and NetCDF files, providing comprehensive instructions for setting up your environment, loading data, visualization techniques, and advanced spatial operations.

Whether you’re analysing remote sensing imagery, climate data, or creating specialised maps, these step-by-step instructions will help you build a robust offline geospatial workflow in Python. By the end of this guide, you’ll be able to confidently manipulate spatial data, create informative visualisations, and perform common geographic operations such as clipping to specific regions.

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How to read/process/plot time series data using Python

Time series data is essential for tracking changes over time—whether it’s climate measurements, hydrological trends, or environmental indicators. In geospatial workflows, we often work with time series , so this guide walks you through two practical workflows to handle time series data using Python:

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