Scientists Map Underwater Topography of More Than 1.4 Million Lakes and Reservoirs Worldwide
An online bathymetric dataset will help decision makers manage water resources.
Lakes and reservoirs have a profound influence on ecosystem functions, local stream flow levels, and the movement of water through landscapes. But water managers are often in the dark about subsurface topography, which affects a body of water’s ecology, volume, temperature, and evaporation rate, as well as as inputs and outputs.
Today, a team of scientists has developed artificial intelligence techniques to create a publicly available dataset of the underwater topography, or bathymetry, of more than 1.4 million inland lakes and reservoirs across the world. This information can enable water managers and other decision-makers to better anticipate issues ranging from water availability for cities and farms to ecological changes in wetlands.
“This dataset gives lake modelers and ecologists a more realistic representation of a body of water,” said Bahram Khazaei, who led the creation of the database as a postdoctoral researcher at the National Center for Atmospheric Research (NCAR). “In order to better understand the dynamics of aquatic systems and the properties of freshwater resources, we need to know more about the geophysical characteristics of what lies beneath the surface.”
The Global Lakes Bathymetry Dataset, or GLOBathy, is available online.
Estimating bathymetry with machine learning
Most of the accessible surface fresh water on Earth is stored in more than 100 million lakes and reservoirs. Any change in their volumes or discharges can affect both water availability and quality, with significant impacts on people and ecosystems. To better understand potential changes in water bodies, scientists need computer models that can accurately represent their physical characteristics.
Khazaei, who now works for NOAA’s National Ocean Service, became interested in creating a bathymetric dataset while working on NOAA’s National Water Model, which provides detailed forecasts of the throughput across the United States. As he focused on improving simulations of water levels in rivers and streams, he needed more information about the geophysical characteristics of lakes and reservoirs. Researchers have used advances in geographic information systems (GIS), airborne LiDAR and other technologies to map the underwater topography of thousands of lakes and reservoirs, but lack the ability to determine the bathymetry of millions of others.
To estimate the bathymetry of other lakes and reservoirs, Khazaei and his collaborators turned to a comprehensive dataset called HydroLAKES. This provided them with a comprehensive list of the geophysical characteristics of over 1.4 million water bodies around the world, including shoreline length, area, volume, catchment area, elevation, etc. .
They then developed a machine learning technique, called random forest, that is effective at classifying data to establish relationships between these geophysical features of water bodies. They estimated maximum depth and bathymetry for all lakes and reservoirs in the dataset using these relationships, along with GIS techniques.
To validate the dataset, the scientists turned to datasets of lakes in which the maximum depth had been measured, as well as ground-based bathymetric observations of water bodies in different regions and with a wide range of physical characteristics. The results showed that GLOBathy was able to estimate bathymetry and reproduce patterns of depth variability “reasonably well”, according to the article.
GLOBathy also provides estimates of head-area-volume relationships, derived from its bathymetric maps. These relationships, which indicate water availability and area at different depth levels of water bodies, provide essential information that can be used to improve water balance analyzes and a better understanding of hydrological cycles at the time. local, regional and global scale. GLOBathy also gives geophysicists more flexibility in modeling aquatic systems because it complements several existing datasets of inland water bodies.
“For the first time, we have detailed depth and bathymetry information for all these water bodies around the world,” Khazaei said. “It does not replace ground measurements, but it does give us essential information on many lakes and reservoirs whose underwater topography has never been mapped to this extent.”
Reference: “GLOBathy, the global lakes bathymetry dataset” by Bahram Khazaei, Laura K. Read, Matthew Casali, Kevin M. Sampson and David N. Yates, February 3, 2022, Scientific data.
The work was funded by NOAA, and the results were published in a recent issue of Scientific data. NCAR is sponsored by the National Science Foundation.