DAM-IN: Mapping the Health of Dam Catchments Across India
A new report by researchers at the Indian Institute of Technology (IIT) Jodhpur has introduced a groundbreaking dataset called DAM-IN, which provides a comprehensive overview of dams and their catchments across India. This dataset aggregates over 45 attributes for every dam catchment in the country, offering crucial insights into the safety and performance of these vital infrastructures.
The Importance of Dams in India
India is home to approximately 5,715 dams, which play a critical role in various sectors including agriculture, industry, flood control, and electricity generation. However, these essential structures are increasingly threatened by extreme weather conditions. The Central Water Commission has reported that around 44% of dam failures in the past have been attributed to hydrological extremes.
Recent incidents, such as the Tiware Dam breach in Maharashtra in 2019, which resulted in significant losses due to heavy flooding, highlight the urgent need for improved risk assessment and management of dam infrastructure. As extreme weather events become more frequent, the DAM-IN dataset could be pivotal in enhancing the management of India’s extensive water infrastructure.
Overview of the DAM-IN Dataset
The DAM-IN dataset is the first of its kind to integrate both observed and remote sensing data, creating a detailed profile of dam catchments based on six critical characteristics:
- Topography
- Climate
- Geology and groundwater
- Soil
- Land Use Land Cover (LULC)
- Human-induced activities
Methodology
The researchers began by defining the watershed for each dam, which is the area of land where all water drains to the dam’s outlet. They utilized high-resolution, 30-meter Digital Elevation Map (DEM) data from the Shuttle Radar Topography Mission (SRTM) to establish these catchment boundaries. Geographic Information Systems (GIS) tools were then employed to calculate key topographical attributes, such as:
- Mean elevation
- Slope
- Area
- Circularity ratio
These factors are essential for understanding water flow, sediment transport, and erosion within the catchment. The analysis revealed that most dam catchments have low slopes, typically less than 7 m/km.
Geological and Groundwater Analysis
To enhance their assessment, the researchers incorporated geological attributes from global maps, including the Global Lithological Map (GLiM) and Global Hydrogeology Maps (GLHYMPS). This information is crucial for understanding groundwater flow, which significantly impacts the hydrological conditions of the dam catchment.
The study estimated mean groundwater levels using data from over 4,900 stations, noting that central India generally has shallow groundwater levels, while southern and western regions exhibit deeper groundwater levels.
Soil and Land Use Analysis
For surface-level processes, the researchers included eight primary soil attributes, which are vital for hydrological models. They utilized the Harmonised World Soil Database (HWSD) to map soil texture, organic carbon content, porosity, and conductivity across each catchment.
Additionally, Land Use Land Cover (LULC) data from the National Remote Sensing Centre (NRSC) was employed to identify the dominant land cover types, including agriculture, forest, scrub, and water. The analysis confirmed that agriculture is the predominant land cover in most catchments, followed by forest and scrub. Seasonal variations in vegetation health were quantified using the Normalised Difference Vegetation Index (NDVI).
Climatic and Human Impact Data
The study integrated a wealth of climatic and human impact data, utilizing information from the India Meteorological Department (IMD) and the European Centre for Medium-Range Weather Forecasts (ERA5). This data encompassed precipitation, temperature, wind, and potential evapotranspiration from 1951 to 2019.
Human influence was mapped using four key indicators: road density, human footprint, night-time light, and population count. These indicators are essential as anthropogenic activities continuously alter the natural conditions of dam catchments.
Significance of the DAM-IN Dataset
The DAM-IN dataset represents a significant advancement over previous global dam datasets, such as the Global Dam Watch and the Global Dam Tracker. While those datasets provided general location and capacity information, they lacked 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 populating it with over 45 highly detailed variables, the DAM-IN dataset serves as a unique resource tailored to regional challenges. It empowers engineers, researchers, and policymakers to conduct more accurate analyses of the health of the 5,715 dams across India.
Note: This article was written with the help of generative AI and edited by an editor at Research Matters. The article was updated to fix a typo. The error is regretted.
