DAM-IN: Comprehensive Dam Catchment Attributes in India

We present one of the most comprehensive datasets of dams in India, “DAM-IN”. The dam attributes for India (DAM-IN) consist of six important catchment characteristics, including topography, geology and groundwater, soil, Land Use and Land Cover (LULC), climate, and human-induced activities. These six catchment characteristics were developed using various reanalysis, observed, and remote-sensing data for more than 5700 dams in India. Moreover, a large number of attributes were estimated for each catchment characteristic of the dam. Overall, the comprehensive DAM-IN dataset consists of high-quality catchment attributes for each dam in India for multi-temporal and spatial resolution. The DAM-IN data is useful for various stakeholders to conduct scientific research, dam safety, and impact analysis in India. Moreover, the DAM-IN data can be used for reservoir management, various water quantity and quality studies, environmental risk assessment, and climate change risk analysis. The catchment and meteorological attributes can be used as input for hydrological, hydraulic, and machine-learning modeling. Using the developed attributes, the effective planning and management of sustainable river infrastructure and water resource allocation can be performed.本研究发布印度当前最全面的大坝数据集之一——「DAM-IN」。该印度大坝数据集（DAM-IN）涵盖六大核心流域特征，分别为地形、地质与地下水、土壤、土地利用与土地覆被（Land Use and Land Cover，LULC）、气候以及人类活动影响。上述六大流域特征基于多套再分析、实测与遥感数据构建，覆盖印度境内超5700座大坝。此外，本数据集针对每座大坝的各流域特征均估算了大量相关属性参数。整体而言，完备的DAM-IN数据集为印度境内每座大坝提供了高质量的多时空分辨率流域属性数据。DAM-IN数据集可为印度境内多类利益相关方开展科学研究、大坝安全评估以及影响分析提供支撑。此外，该数据集还可应用于水库管理、各类水量与水质研究、环境风险评估以及气候变化风险分析等场景。流域与气象属性可作为水文、水力学以及机器学习建模的输入数据。依托本数据集构建的属性参数，可实现可持续河流基础设施的高效规划与管理，以及水资源优化配置。