GeoClimate intelligence platform: A web-based framework for environmental data analysis
Environmental science education faces a critical barrier: programming requirements prevent students, novice researchers, and domain experts from accessing planetary-scale datasets. This study presents the GeoClimate Intelligence Platform, a web-based framework powered by Google Earth Engine (GEE) that eliminates programming barriers while maintaining research-grade analytical capabilities. The platform comprises five integrated modules: GeoData Explorer for climate dataset access, Climate Analytics implementing 20+ ETCCDI-compliant climate indices, Hydrology Analyzer for precipitation analysis and return periods, Product Selector for dataset validation, and Data Visualizer for interactive analysis. This modular design supports integrated workflows while maintaining analytical independence across specialized functions. Development was motivated by workshops where students found programming barriers insurmountable despite strong motivation. Educational validation through university coursework demonstrated effectiveness. Performance evaluation shows robust scalability from educational to research-scale applications. The platform requires only a GEE account and operates through web browsers, eliminating software installation. This accessibility transformation enables broader participation in data-driven environmental problem-solving with scientific rigor, democratizing sophisticated environmental analysis for educational and research communities. • Platform eliminates programming barriers to Google Earth Engine climate analysis. • Five integrated modules enable data access, 23 ETCCDI indices, and validation. • Educational deployment confirms enhanced engagement with large-scale data. • Browser-based system requires no installation and supports open customization.
Authors
- Rocky Talchabhadel (ORCID: https://orcid.org/0000-0003-0526-7663)
- Nawa Raj Pradhan (ORCID: https://orcid.org/0000-0001-5210-0896)
- Saurav Bhattarai (ORCID: https://orcid.org/0009-0006-2627-8563)
Publication Details
- Journal
- Environmental Modelling & Software
- Published
- 2025-12-13
- DOI
- https://doi.org/10.1016/j.envsoft.2025.106826
- Citations
- 1
- Primary Topic
- Scientific Computing and Data Management
- Type
- article
- Field-Weighted Citation Impact
- 4.17