Dam safety | Data and dam safety
Two recent studies – one presenting a national-scale dam catchment dataset for India and the other documenting Japan’s dam safety management framework – highlight complementary approaches to strengthening dam safety and hydropower operations through data, regulation, and institutional practice.
Above: Aerial view of Hirakud Dam in Odisha, India
ENSURING THE SAFETY AND performance of hydropower and multipurpose dams requires both reliable technical information and robust governance frameworks. Recent publications from India and Japan illustrate how these elements are being addressed in different national contexts. In India, researchers have developed a comprehensive, open-access dataset describing physical, climatic, and human-influenced characteristics of more than 5,700 dam catchments, providing a consistent foundation for hydrological and dam safety analysis. In Japan, a World Bank report documents the legal, institutional, and operational systems that underpin dam safety management across a large and diverse portfolio of dams operating under complex natural and social conditions. Together, the two reports provide complementary perspectives on the role of data and governance in dam safety practice.
National-scale dataset in India India hosts one of the world’s largest and most diverse
portfolios of dam infrastructure, supporting irrigation, hydropower generation, flood management, water supply, and regional development. The scale and complexity of this infrastructure places significant demands on dam safety assessment, hydrological analysis, and long-term operational planning. Recognising the need for consistent, catchment-scale information to support these activities, Tiwari and Aadhar (2025) have developed a comprehensive, open-access dataset describing physical, climatic, and anthropogenic attributes for dam catchments across the Indian subcontinent. The dataset, referred to as DAM-IN, covers 5715 dam catchments across 18 river basins and brings together more than 45 attributes derived from observed data, reanalysis products, and remote sensing sources. Published in Scientific Data, the study provides a structured description of how the dataset was assembled, validated, and made available for use by engineers, researchers, and water agencies. Rather than evaluating dam performance or safety directly, the work focuses on assembling the foundational data required for
30 | January 2026 |
www.waterpowermagazine.com
hydrological, hydraulic, environmental, and dam safety studies at a national scale. The authors note that while several global and regional dam databases exist, such as Global Dam Watch and the Global Dam Tracker, these resources primarily focus on dam location, physical dimensions, reservoir capacity, and construction history. They generally do not provide the full range of catchment-level attributes required to analyse inflows, flood behaviour, sediment dynamics, or interactions between climate, land use, and hydrology. In India, these information gaps are particularly significant. The country has experienced increased variability in monsoon rainfall and a rise in hydrological extremes over recent decades. According to figures cited from the Central Water Commission, hydrological extremes account for a substantial proportion of dam failure cases. Against this backdrop, the authors emphasise the need for datasets that characterise not only the dam structure itself but also the upstream catchment conditions that influence reservoir inflows and flood risk. The DAM-IN dataset addresses this requirement by providing harmonised catchment attributes grouped into six broad categories: topography, geology and groundwater, soil, land use and land cover (LULC) and vegetation, climate, and human-induced activities.
Dam inventory The dataset includes 5715 dams distributed across 18
major river basins in India. Dam locations were sourced from the National Register of Large Dams (NRLD, 2019). Using these locations as outlet points, the authors delineated upstream catchments for each dam using a 30m resolution Digital Elevation Model from the Shuttle Radar Topography Mission (SRTM). Watershed delineation was carried out using
the Automatic Outlet Relocation (AOR) algorithm implemented within ArcGIS. Prior to delineation, the elevation data were pre-processed to remove sinks and artefacts that could distort flow routing. The authors selected the AOR approach to improve consistency and reduce errors associated with manual outlet placement, particularly at a national scale. The delineated catchments form the spatial basis for all subsequent attribute calculations. Seven topographic attributes were derived for each dam catchment: mean elevation, minimum elevation, maximum elevation, mean slope, catchment area, perimeter, and circularity ratio. These parameters were calculated directly from the SRTM DEM using standard spatial analysis tools. The authors report substantial spatial variability across the dataset. Mean catchment elevations range from approximately 25m to around 1000m asl. Slopes are generally lower in central India and higher in coastal and northern regions. Most dam catchments are relatively small, with the majority covering
Page 1 |
Page 2 |
Page 3 |
Page 4 |
Page 5 |
Page 6 |
Page 7 |
Page 8 |
Page 9 |
Page 10 |
Page 11 |
Page 12 |
Page 13 |
Page 14 |
Page 15 |
Page 16 |
Page 17 |
Page 18 |
Page 19 |
Page 20 |
Page 21 |
Page 22 |
Page 23 |
Page 24 |
Page 25 |
Page 26 |
Page 27 |
Page 28 |
Page 29 |
Page 30 |
Page 31 |
Page 32 |
Page 33 |
Page 34 |
Page 35 |
Page 36 |
Page 37 |
Page 38 |
Page 39 |
Page 40 |
Page 41 |
Page 42 |
Page 43 |
Page 44 |
Page 45
