Scientific Data

Data

ERA5moistIN

About the Dataset (ERA5moistIN):
ERA5moistIN is a high-resolution gridded dataset of atmospheric moisture budget components constructed from ERA5 reanalysis over the Indian subcontinent and adjoining oceanic regions for the period 1940–2024. The dataset is available at 0.25° spatial resolution and hourly temporal resolution and includes seven components of the column-integrated moisture budget: change in storage, horizontal and vertical advection, horizontal and vertical convergence, and corresponding moisture flux convergence terms. The dataset has been validated against ERA5 single-level products and is suitable for monsoon diagnostics, extreme event attribution, model evaluation, and predictive applications.
Data Record:
The ERA5moistIN dataset is divided into seven components, each available via individual Zenodo repositories: change in storage (vidq_dt.year.nc, https://doi.org/10.5281/zenodo.15751200), horizontal moisture advection (viqadv.year.nc, https://doi.org/10.5281/zenodo.15730248), horizontal wind convergence (viHCM.year.nc, https://doi.org/10.5281/zenodo.15751542), horizontal moisture flux convergence (viHMFC.year.nc, https://doi.org/10.5281/zenodo.15753006), vertical moisture advection (viwdq_dp.year.nc, https://doi.org/10.5281/zenodo.15753073), vertical wind convergence (viqdw_dp.year.nc, https://doi.org/10.5281/zenodo.15753104), and vertical moisture flux convergence (viVMFC.year.nc, https://doi.org/10.5281/zenodo.15753124). Each component is stored as yearly.nc files at hourly resolution, containing three-dimensional data arrays (longitude: 66.5°E–98°E, latitude: 6.5°N–38.5°N, and hourly time steps starting from January 1st at 00:00 UTC). Each file is approximately 549 MB in size, resulting in ~45.4 GB per component and a total dataset size of around 318 GB. Data values are in kg m−2 s−1 and can be converted to hourly accumulated values (kg m−2 hr−1 or mm/hr) by multiplying by 3600, which can further be aggregated to daily totals (mm/day) for analyzing relevant hydrometeorological events.
Associated Publication:
Raghuvanshi, A.S., Agarwal, A. An Hourly Dataset of Moisture Budget Components Over the Indian Subcontinent (1940–2024). Scientific Data, 12, 1770 (2025). https://doi.org/10.1038/s41597-025-06044-y

Non-stationary multivariate bias-corrected CMIP6 climate projections

Title: Non-stationary multivariate bias-corrected CMIP6 climate projections of daily precipitation and temperature over India
Developers: Sachidanand Sharma, Akash Singh Raghuvanshi, Ankit Agarwal
Description:We employed R2D2, a multivariate bias-correction technique, to develop a dependence-preserved daily bias-corrected dataset of precipitation and temperature (mean, maximum, and minimum) at 0.25° spatial resolution over India. The dataset spans the historical period (1951–2014) and future projections (2015–2100) under four Shared Socioeconomic Pathway (SSP) scenarios: SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5, using outputs from 13 CMIP6 Global Climate Models (GCMs). The India Meteorological Department (IMD) gridded dataset at 0.25° resolution for precipitation and temperature (mean, maximum, and minimum) was used as the reference observational dataset for implementing the multivariate bias correction. The bias-corrected dataset was rigorously evaluated against observations using multivariate statistical metrics that capture dependence structures, including Pearson correlation, Kendall’s tau, the Clausius–Clapeyron (CC) relationship, and the representation of compound dry and hot extreme events involving precipitation and temperature (Tmax, Tmin, and Tmean). These evaluations ensure the reliability and quality of the bias-corrected dataset.
The dataset has broad applicability for climate impact and future projection studies, particularly for research focused on precipitation–temperature dependence, compound extreme events, hydrological modelling, and drought assessment.
The complete dataset is divided into four parts, with each part containing the bias-corrected data for an individual variable. The dataset is provided in NetCDF (.nc) format. Each part consists of five compressed files to reduce storage requirements: one historical dataset ({var}_historical.zip) and four future scenario datasets ({var}_SSP126.zip, {var}_SSP245.zip, {var}_SSP370.zip, and {var}_SSP585.zip). Each compressed file contains the bias-corrected NetCDF datasets from 13 CMIP6 GCMs, namely ACCESS-CM2, ACCESS-ESM1-5, BCC-CSM2-MR, CanESM5, EC-Earth3, EC-Earth3-Veg, INM-CM4-8, INM-CM5-0, MPI-ESM1-2-HR, MPI-ESM1-2-LR, MRI-ESM2-0, NorESM2-LM, and NorESM2-MM.
Links to download the dataset:
Precipitation: https://doi.org/10.5281/zenodo.18231865
Mean Temperature: https://doi.org/10.5281/zenodo.18230957
Maximum Temperature: https://doi.org/10.5281/zenodo.18269707
Minimum Temperature: https://doi.org/10.5281/zenodo.18273121

CAMELS-INDIA

CAMELS-IND: hydrometeorological time series and catchment attributes for 472 catchments in Peninsular India
CAMELS-IND: hydrometeorological time series and catchment attributes for 472 catchments in Peninsular India We introduce CAMELS-INDIA (Catchment Attributes and MEteorology for Large-sample Studies – India), which provides daily meteorological time series, available observed and LSTM-based predicted streamflow, and static catchment attributes for 472 catchments in peninsular India, to foster large-sample hydrological studies in India and promote the inclusion of Indian catchments in global hydrological research.
The data set covers 41 years of data between 1st January 1980 and 31st December 2020 for each catchments: daily time series of available streamflow observations, meteorological data such as precipitation, air temperature, solar radiation, relative humidity, wind speed, potential and actual evapotranspiration, and soil moisture. Additionally, CAMELS-INDIA includes regionally trained LSTM-model predicted streamflow for all 472 catchments. The static catchment attributes includes location and topography, climate, hydrological signatures, land-use, land cover, soil, geology, and anthropogenic influences.CAMELS-IND

About CAMELS Initiative
Catchment Attributes and Meteorology for Large-sample Studies
CAMELS hydro-meteorological datasets are nationwide compilations of hundreds of identified catchments, their physical attributes, records of their drainage dynamics and corresponding meteorological time series. CAMELS data have revolutionised the way hydrological predictions, catchment classifications, and analyses of characteristics and change are carried out. They have unified approaches to the identification of hydrological signatures, serve as benchmarks for model development in a variety of settings, and give rise to a new branch of machine learning in water management. CAMELS data are a testimony to international open data initiatives and demonstrate how regional to global science, education and management practice benefit from open data.

Data: https://doi.org/10.5281/zenodo.14999580
Paper: https://doi.org/10.5194/essd-17-461-2025
Citation: Mangukiya, N. K., Kumar, K. B., Dey, P., Sharma, S., Bejagam, V., Mujumdar, P. P., and Sharma, A.: CAMELS-IND: hydrometeorological time series and catchment attributes for 228 catchments in Peninsular India, Earth Syst. Sci. Data, 17, 461–491, https://doi.org/10.5194/essd-17-461-2025, 2025

Key statistics:

7000+ downloads

472 catchments covered

19 meteorological variables

211 catchment attributes

40+ years (1980-2020)

10+ publications used data

GloRESatE — Global Rainfall Erosivity Dataset

Dataset name: GloRESatE (Global Rainfall Erosivity from Reanalysis and Satellite Estimates)
Description: GloRESatE provides global rainfall erosivity estimates derived from multi-source precipitation products, including satellite and reanalysis datasets. It supports large-scale soil erosion assessment and climate impact studies by offering long-term, spatially consistent erosivity factors required for RUSLE-type models and erosion risk analysis. Paper: Das, S., Jain, M. K., Gupta, V., et al. (2024). GloRESatE: A dataset for global rainfall erosivity derived from multi-source data. Scientific Data, 11, 926.DOI (paper): https://doi.org/10.1038/s41597-024-03756-5: https://doi.org/10.1038/s41597-024-03756-5) Data access: Zenodo repository (dataset): https://doi.org/10.5281/zenodo.8406085 ESDAC portal (European Commission): https://esdac.jrc.ec.europa.eu/content/gloresate-global-rainfall-erosivity-reanalysis-and-satellite-estimates

SAWEMD — South Asian Water Erosion Modeled Dataset

Dataset name: SAWEMD (South Asian Water Erosion Modeled Dataset)
Description: SAWEMD provides modeled estimates of soil erosion across South Asia under historical and future climate and land-use scenarios. The dataset integrates climate projections and land-use change information to evaluate spatio-temporal trends in water erosion dynamics, supporting regional land degradation and climate adaptation studies. Paper: Das, S., Jain, M. K., & Gupta, V. (2024). An assessment of anticipated future changes in water erosion dynamics under climate and land use change scenarios in South Asia. Journal of Hydrology, 637, 131341. https://doi.org/10.1016/j.jhydrol.2024.131341 Data access: Zenodo repository (dataset): https://doi.org/10.5281/zenodo.11081878

InRESatE — Indian Regional Satellite-based Erosivity Estimates

Dataset name: InRESatE (Indian Regional Satellite-based Erosivity Estimates)
Description: InRESatE provides high-resolution rainfall erosivity estimates for India derived from GPM satellite rainfall products. The dataset enables detailed regional-scale soil erosion assessments and supports watershed-level conservation planning and erosion modeling.
Paper: Das, S., Jain, M. K., & Gupta, V. (2022). A step towards mapping rainfall erosivity for India using high-resolution GPM satellite rainfall products. CATENA, 212, 106067.https://doi.org/10.1016/j.catena.2022.106067 Data access: Zenodo repository (dataset): https://doi.org/10.5281/zenodo.11064154

Bias-corrected CMIP6 Climate & ETo Dataset for South Asia

Dataset name: Daily Downscaled and Bias-Corrected CMIP6 Climate and Reference Evapotranspiration (ETo) Dataset for South Asia
Description: This dataset provides daily, bias-corrected, and statistically downscaled CMIP6 climate variables (temperature, solar radiation, wind speed, relative humidity) and derived reference evapotranspiration (ETo) using the FAO Penman–Monteith method (corrected for CO2 concentration for global warming) for historical and future SSP scenarios. It is intended for hydrological, agricultural, and climate impact modeling across South Asia.
Paper: Saha, A., Jain, M. K., Joshi, P., et al. (2025). Development of daily downscaled, bias-corrected CMIP6 climate datasets for estimating reference evapotranspiration (ETo) in South Asia. Scientific Data, 12, 1879. DOI (paper): https://doi.org/10.1038/s41597-025-06149-4
Data access: Zenodo repository (dataset): https://doi.org/10.5281/zenodo.15670655

Delineation of homogenous drought regions over India

Dataset name: Delineation of distinct homogenous drought regions over India
Description: Preparation of homogenous drought regions over India using drought characteristics estimated using historical precipitation data. K-means clustering approach has been used to generate the six homogenous drought clusters.
Paper: Gupta, V., & Jain, M. K. (2018). Investigation of multi-model spatiotemporal mesoscale drought projections over India under climate change scenario. Journal of Hydrology, 567, 489-509. DOI (paper): https://doi.org/10.1038/s41597-025-06149-4