Nagarjuna Sagar Dam: By Sumanthk at English Wikipedia - https://commons.wikimedia.org/w/index.php?curid=1974186
A new report by researchers at the Indian Institute of Technology (IIT) Jodhpur presents, for the first time, a highly detailed dataset on dams in India, called DAM-IN. They have created a comprehensive catchment database, DAM-IN, aggregating over 45 attributes for every dam catchment in India. This dataset captures the complete picture of the dam and its surrounding environment, which influences a dam's safety and performance.
The Indian subcontinent is home to roughly 5,715 dams, which are essential for everything from agriculture and industry to flood control and electricity generation. However, this vital infrastructure is increasingly vulnerable to extreme weather, with the Central Water Commission reporting that approximately 44% of dam failures in the past were caused by hydrological extremes. Recent devastating incidents, such as the breach of the Tiware Dam in Maharashtra in 2019 due to heavy flooding, which resulted in significant losses, underscore the need for better risk assessment. At a time when heavy flooding and extreme hydrological events are a growing threat, the new dataset could help improve how the country manages its vast network of water infrastructure.
Kallanai ( also known as the Grand Anicut) is an ancient dam built by Karikala of the Chola dynasty in 150 CE, across the Kaveri river flowing from Tiruchirapalli District to Thanjavur district, Tamil Nadu, India, and is still in use today
Did You Know? Kallanai ( also known as the Grand Anicut) is an ancient dam built by Karikala of the Chola dynasty in 150 CE, across the Kaveri river flowing from Tiruchirapalli District to Thanjavur district, Tamil Nadu, India, and is still in use today |
The new study integrates observed and remote sensing data to build a comprehensive profile based on six critical characteristics: topography, climate, geology and groundwater, soil, Land Use Land Cover (LULC), and human-induced activities.
The researchers began by defining the watershed for each dam, the area of land where all water drains to the dam’s outlet. They achieved this using high-resolution, 30-meter Digital Elevation Map (DEM) data from the Shuttle Radar Topography Mission (SRTM). Once the catchment boundaries were established, they used geographical information systems (GIS) tools to calculate key topographical attributes, such as mean elevation, slope, area, and circularity ratio. These factors are fundamental to understanding how water flows, how sediment is carried, and how erosion occurs within the catchment. For instance, the data revealed that the majority of dam catchments have low slopes, typically less than 7 m/km.
The team incorporated earth science and dynamic climate data to bolster their assessment. Geological attributes drawn from global maps, like the Global Lithological Map (GLiM) and GLobal HYdrogeology MaPS (GLHYMPS), detail the subsurface properties of the rock. This information is vital for understanding groundwater flow, which, in turn, impacts the hydrological conditions of the dam catchment. The study also estimated mean groundwater levels using data from over 4,900 stations, noting that most dam catchments in central India have shallow groundwater. In contrast, the southern and western parts have deeper groundwater levels.
To account for surface-level processes, the researchers included eight primary soil attributes, which serve as the storage component in hydrological models. Drawing on the Harmonised World Soil Database (HWSD), they mapped soil texture, organic carbon content, porosity, and conductivity across each catchment. For the land surface, LULC data from the National Remote Sensing Centre (NRSC) were used to identify the dominant land cover, including fractions of agriculture, forest, scrub, and water. The analysis confirmed that agriculture is the dominant land cover in most catchments, followed by forest and scrub. Seasonal variations in vegetation health were also quantified using the Normalised Difference Vegetation Index (NDVI).
Finally, the study integrated a wealth of climatic and human impact data. Using data from the India Meteorological Department (IMD) and the European Centre for Medium-Range Weather Forecasts (ERA5), they analysed precipitation, temperature, wind, and potential evapotranspiration for the period 1951 to 2019. The report also mapped human influence using four key indicators: road density, human footprint, night-time light, and population count, which are essential as anthropogenic activities continuously alter the natural conditions of dam catchments.
The research significantly improves upon previous global dam datasets, such as the Global Dam Watch or the Global Dam Tracker. While valuable for general location and capacity information, it did not contain the comprehensive static and dynamic catchment attributes necessary for advanced hydrological and hydraulic studies, particularly those focused on dam safety . By delineating the precise watershed for each dam and then populating it with over 45 highly detailed variables, the DAM-IN dataset has created a resource uniquely suited to regional challenges. The dataset could empower engineers, researchers, and policymakers to conduct more accurate analyses of the health of the 5,715 dams across India.
This article was written with the help of generative AI and edited by an editor at Research Matters.