| Dam safety less than 500km2 . Only around 15% of catchments


exceed this area threshold. The circularity ratio, used to describe catchment shape, indicates that most dam catchments in India are elongated rather than compact. According to the authors, these topographic characteristics are relevant for understanding runoff response, sediment transport, and flood hydrograph behaviour, although the dataset itself does not attempt to model these processes.


Geological, groundwater and


soil attributes To characterise subsurface conditions, the dataset includes geological and hydrogeological attributes derived from global datasets. Dominant lithological class for each catchment was identified using the Global Lithological Map (GLiM), focusing on first-level lithological classifications. Ten dominant lithological classes are represented across Indian dam catchments, including basic volcanic rocks, mixed sedimentary rocks, unconsolidated sediments, metamorphic rocks, and carbonate sedimentary rocks. These classifications provide a broad indication of bedrock type and its influence on hydrological behaviour. Subsurface permeability and porosity were estimated using the Global Hydrogeology Maps (GLHYMPS) dataset. Area-weighted averages were calculated for each catchment. These attributes describe the capacity of subsurface materials to transmit and store water, which is relevant for groundwater–surface water interactions and baseflow contributions. In addition, mean groundwater depth was estimated using data from more than 4900 monitoring wells maintained by the Central Ground Water Board, accessed through the India-WRIS platform. Groundwater data cover the period from 1996 to 2020. Where no wells were present within a catchment, the nearest available observation was used. The dataset records considerable regional variability, with many central Indian catchments exhibiting shallow groundwater levels, while deeper groundwater tables are more common in parts of southern and western India. Eight soil attributes were compiled for each dam


catchment: soil texture fractions (sand, silt, clay, and coarse fragments), organic carbon content, available water capacity, saturated hydraulic conductivity, porosity, bulk density, and maximum water content. These attributes were derived primarily from the Harmonised World Soil Database (HWSD) and supplementary FAO soil datasets. Because soil properties vary with depth, the authors calculated weighted averages to represent conditions down to 200cm. Spatial patterns show that clay- and silt-rich soils are common in many regions and are generally associated with higher porosity and conductivity values, while bulk density tends to vary inversely with porosity. The authors present these relationships descriptively, without drawing conclusions about hydrological performance or suitability for specific dam types .


Land use, land cover and vegetation Land use and land cover attributes were derived from


datasets produced by India’s National Remote Sensing Centre at 56m resolution for the 2015–2016 period. The original classifications were reclassified into five primary categories: built-up areas, agriculture, forest, scrubland, and water bodies. For each catchment, the dataset records the dominant LULC class as well as the fractional


area of each category. Agriculture is identified as the dominant land cover in the majority of dam catchments, followed by forest and scrubland. To capture seasonal vegetation dynamics, the dataset also includes Normalised Difference Vegetation Index (NDVI) values for four climatological seasons: December– February, March–May, June–August, and September– November. NDVI data were derived from AVHRR satellite products covering the period 1981–2020 and resampled to a coarser resolution for consistency. Seasonal NDVI patterns show higher vegetation density during and following the monsoon period, particularly in September–November and December– February. The dataset presents these values as descriptive indicators of vegetation cover rather than as inputs to specific ecological or hydrological models


Climatic attributes


Climatic information is a major component of the DAM-IN dataset. Daily, area-averaged time series were generated for precipitation, minimum and maximum temperature, wind speed, actual evapotranspiration, and potential evapotranspiration for each catchment over the period 1951–2019. Precipitation and temperature data were sourced from the India Meteorological Department for areas within India and from the ERA5 reanalysis dataset for regions outside national boundaries. Potential evapotranspiration was estimated using the Hargreaves–Samani method, based on temperature and radiation data. In addition to mean values, the dataset includes derived climatic indicators such as aridity index, precipitation seasonality, maximum daily precipitation, and the frequency and duration of high- and low- precipitation events. These metrics were calculated following established definitions used in climate variability and hydrological studies. The authors report strong spatial variability across the country. For example, dam catchments in the Western Ghats receive substantially higher mean rainfall than those in parts of Gujarat and leeward regions. Seasonality indices indicate that most precipitation occurs within a limited number of months, reflecting the dominance of the southwest monsoon.


Human-induced activities To represent anthropogenic pressures within dam


catchments, the dataset includes four indicators: road density, human footprint, night-time light intensity, and population count. Road density data were obtained from the Global Roads Inventory Project, while human


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Above: Sardar Sarovar Dam in India


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