Big Data

The volume, velocity, and variety of water-related data available to researchers and managers is growing rapidly — from satellite imagery archives and real-time sensor networks to social media streams and citizen science observations. Making sense of this data requires new approaches that go beyond traditional analytical methods. Big data analytics, cloud computing, machine learning, and artificial intelligence are increasingly being applied in the water sector to detect patterns, generate forecasts, support decision-making, and unlock insights from datasets that would be impossible to analyse manually.

The WRO makes use of the Google Cloud Platform (GCP) for its data storage and analytics needs. Google Earth Engine (GEE) already hosts an extensive catalogue of satellite imagery and provides powerful tools for spatial and temporal analysis at scale. The resources below provide an introduction to key concepts, cloud platforms relevant to water research, and practical learning resources for those wanting to get started with big data analytics.

Key Concepts

Big Data — extremely large datasets that can be analysed computationally to reveal patterns, trends, and associations, often generated by remote sensing, sensor networks, or social media.

Cloud Computing — the use of remote servers hosted on the internet to store, manage, and process data, rather than relying on local infrastructure. Cloud platforms enable researchers to access enormous computing capacity on demand.

Artificial Intelligence (AI) — the development of computer systems able to perform tasks that normally require human intelligence, such as image recognition, pattern detection, and decision support.

Machine Learning (ML) — a branch of AI in which computer systems learn from data using algorithms and statistical models to identify patterns and make inferences, without being explicitly programmed for each task.

Source: Oxford Languages

Cloud Platforms & Earth Observation Tools

Google Earth Engine (GEE) A cloud-based platform for planetary-scale geospatial analysis, providing access to decades of satellite imagery including Landsat, Sentinel, and MODIS datasets alongside powerful analytical tools. The primary recommended platform for satellite-based water research on the WRO.

Google Earth Engine for Water Resources Management A free online course from Spatial Thoughts covering the use of GEE specifically for water resource applications — highly recommended for WRO users getting started with satellite data analysis.

Microsoft Planetary Computer Microsoft's open geospatial platform providing access to petabytes of global environmental data including satellite imagery, climate data, and land cover products, with built-in analytical tools.

Digital Earth Africa A continental earth observation platform providing free and open satellite data and ready-to-use analysis tools tailored for African contexts, including water body mapping, land cover change, and crop monitoring.

IBM Environmental Intelligence Suite IBM's platform for environmental monitoring and geospatial analytics, including tools for weather forecasting, climate risk assessment, and satellite-based land and water analysis.

Google Cloud Tools for Data Analytics

The WRO uses Google Cloud Platform for data infrastructure. The following GCP tools are particularly relevant for water data analytics:

BigQuery Google's fully managed, serverless data warehouse for large-scale SQL-based analytics — suited to querying very large tabular water quality, streamflow, or monitoring datasets.

BigTable A high-performance NoSQL database for storing and analysing massive unstructured datasets, including time-series sensor data and satellite imagery metadata.

AutoML Google's automated machine learning tool, enabling users to train custom ML models for tasks such as image classification and pattern recognition without deep coding expertise.

Google Colab A free, cloud-based Python notebook environment — an excellent starting point for running data analysis, machine learning, and GEE scripts without any local setup.

AppSheet A no-code app development platform from Google, useful for building simple data collection, visualisation, or field monitoring applications without programming.

Google Cloud APIs for Advanced Analysis

The following Google Cloud APIs may be useful for more advanced WRO applications:

Natural Language API — analyse unstructured text, including sentiment analysis from social media or news streams relevant to water issues
Speech API — speech-to-text conversion, useful for transcribing stakeholder inputs or field recordings
Vision API — image recognition and classification, applicable to analysing field photographs or satellite imagery
Data Catalog — metadata management for organising and discovering datasets across the WRO platform

Learning Resources

Analytics Vidhya – Machine Learning A well-regarded online resource providing practical machine learning tutorials with code examples — a good starting point for water researchers wanting to apply ML to their datasets.

Open Geo Blog A blog covering open-source geospatial tools, Python, and Google Earth Engine with practical tutorials relevant to spatial water data analysis.

SAEON Data Policy The South African Environmental Observation Network's data policy — important reading for researchers using or contributing SAEON data in big data workflows.