Across Africa, farmers rely on water from dams to irrigate their crops through periods of drought. However, farmers and water managers often struggle to accurately estimate and monitor the available water in dams. To address the challenge, International Water Management Institute (IWMI) researchers have worked with Digital Earth Africa to create an innovation that uses satellite images and AI to get timely and accurate dam volume measurements, with the potential to transform reservoir management in southern Africa.
The Loskop Dam, a key component of the Limpopo River Basin, supplies irrigation to over 25 000 hectares of farmland in South Africa. In a region where rainfall is inconsistent and demand is high, uncertainty in dam volume estimation can ripple out into agricultural stress, crop failure and conflict over resources. Traditional field-based measurement methods, while helpful, are often sparse, delayed and logistically limited.
Now, thanks to a collaborative process, water levels in dams such as the Loskop Dam will no longer be a mystery.
Estimating water levels using satellite imagery and machine learning
The innovation uses satellite images showing the amount of visible surface water in a dam over time and the known geometry of the dam itself, both of which are needed to estimate the amount of water in the dam. A mix of several machine learning models predicts water level estimates with a higher level of accuracy compared to field measurements. To achieve high accuracy, the method switches between different models to calculate water volume at different dam levels.
Innovation in action in the Limpopo River Basin
The innovation is already being applied in real-world water management systems, such as the Limpopo Digital Twin project in the Limpopo River Basin, a vast transboundary watershed in southern Africa, covering parts of South Africa, Botswana, Mozambique and Zimbabwe. The basin is a vital resource for millions of people, but faces significant challenges including severe water scarcity, drought, floods and increasing water demands from agriculture, mining and domestic use, necessitating coordinated, integrated water resource management.
The Limpopo Digital Twin, a continuously updated virtual representation of the Limpopo river basin allows water managers to visualise the status of water use and availability for science-based decision making. Uneven water monitoring capacity among the four countries in the Limpopo River basin is a major obstacle to creating an accurate hydrological model of the basin. New sources of data developed from a mixture of satellite images and machine learning go a long way to filling gaps in monitoring capacity.
When technological innovation meets data democratisation
“This innovation shows how open access data can catalyse real world impact, creating a way to track water availability in remote areas with minimal need for investment in data gathering, processing and field monitoring,” says IWMI Research group leader, Mariangel Garcia Andarcia. “With this data, the researchers could focus on developing methodologies that are now easily available for other users such as government water authorities, researchers and NGOs to adapt to more reservoirs and dams.”
The surface water datasets were derived from Landsat satellite imagery by Digital Earth Africa, a digital data infrastructure for accessing and analysing satellite imagery specific to Africa, and made freely accessible on a cloud platform. Digital Earth Africa draws on more than three decades of satellite imagery to address critical challenges facing the African continent and packages Earth Observation (EO) data into accessible and open data sets.
The innovation methodology was made publicly available in an interactive Jupyter Notebook on the Digital Earth Africa platform. This notebook serves as both a learning resource and a practical tool, demonstrating how machine learning and EO data can be combined to generate accurate dam volume estimates in regions with limited in-situ measurements. Users can adapt and apply this workflow to their own reservoirs with minimal coding and infrastructure requirements.
Beyond estimating water availability in reservoirs, the combination of machine learning, earth observation data and cloud computing platforms provides a model to develop further solutions for resilient water governance in a climate-stressed world. “Now that we’re in the era of AI, we’re looking at how we can use AI to simplify the complex science,” says Andarcia. “We need to train communities on what technology can do for them and what AI can do for them, so we can be creative together in trying to provide solutions for communities.”
With platforms like Digital Earth Africa providing open access to analysis-ready satellite data, and organisations like IWMI bringing decades of water management expertise, the continent is well-positioned to leapfrog into a data-driven future.
For more information contact Digital Earth Africa,
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