CAMELS-INDIA: hydrometeorological time series and catchment attributes for 472 catchments in Peninsular India

Abstract. We introduce CAMELS-INDIA (Catchment Attributes and MEteorology for Large-sample Studies – India), the hydrometeorological time series, and catchment attributes for 472 catchments in Peninsular India. Peninsular India covers 15 intrastate river basins defined by the Central Water Commission (CWC), where river flow and water level datasets are available for several gauge stations through the open-source India Water Resources Information System (India-WRIS). However, many of these gauge stations lack reliable metadata, and data are not in an analysis-ready format for large-sample hydrological studies. Therefore, we utilized 472 gauge stations and their catchment boundaries, characterized as stations with reliable metadata, from the 'Geospatial dataset for Hydrologic analyses in India (GHI)' (Goteti, 2023). For each of these catchments, the CAMELS-INDIA provides a catchment mean time series of meteorological forcings for 41 years (1980–2020) and around 211 catchment attributes representing hydroclimatic and land cover characteristics extracted from multiple data sources (including ground-based observations, remote sensing-based products, and reanalyses datasets). The CAMELS-INDIA follows the same standards of the previously developed CAMELS datasets for the USA, Chile, Brazil, Great Britain, Australia, Switzerland, Germany, and Denmark to facilitate comparisons with catchments of those countries and inclusion in global hydrological studies. Notably, the CAMELS-INDIA includes available observed streamflow and catchment mean time series of 19 meteorological forcings, including precipitation, maximum, minimum, and average temperature, long-wave and short-wave radiation flux, U and V-components of wind, relative humidity, evaporation rates from canopy and soil surface, actual and potential evapotranspiration, and soil moisture of four layers (covering depth up to 3 m below ground) for detailed hydrometeorological studies. We also derived catchment attributes representing human influences, including the number of dams and their utilization, total volume contents of dams in catchments, population density, and increase in urban and agricultural land covers to facilitate studies to understand human influences on catchment hydrology. Furthermore, the dataset includes predicted streamflow time series from a regionally trained Long-Short Term Memory (LSTM)-based hydrological model, which can fill gaps in observed streamflow data or serve as a benchmark for testing and developing new hydrological models. We envision that CAMELS-INDIA will provide a strong foundation for a community-led effort toward gaining new hydrological insights from hydrologically distinct Indian catchments and solving pertinent issues related to water management, quantification and risk assessment of hydrologic extremes, unraveling regional-scale hydrologic functioning, and climate change impact assessment of catchments across India. The CAMELS-INDIA dataset is available at https://doi.org/10.5281/zenodo.13221214 (Mangukiya et al., 2024).