CAMELS-IND

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

Nikunj K. Mangukiya1, Kanneganti Bhargav Kumar2, Pankaj Dey1, Shailza Sharma2, Vijaykumar Bejagam1, Pradeep P. Mujumdar2,3, Ashutosh Sharma1,4,* 

1 Department of Hydrology, Indian Institute of Technology Roorkee, Roorkee, 247667, Uttarakhand, India

2 Department of Civil Engineering, Indian Institute of Science, Bangalore 560012, Karnataka, India

3 Interdisciplinary Centre for Water Research, Indian Institute of Science, Bangalore 560012, Karnataka, India

4 International Centre of Excellence for Dams, Indian Institute of Technology Roorkee, Roorkee, 247667, Uttarakhand, India

Data:  https://doi.org/10.5281/zenodo.14999580 

Paper: https://doi.org/10.5194/essd-17-461-2025 

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.

Special Collection on CAMELS in Earth System Science Data (ESSD) journal: https://essd.copernicus.org/articles/collection6.html 

Key Features:

• Catchment Coverage: 472 catchments in Peninsular India, including 242 with observed streamflow data for over 30% of the period between 1980-2020.

• Meteorological Data: 19 meteorological variables, including precipitation, temperature, radiation flux, wind, humidity, evapotranspiration, and soil moisture.

• Catchment Attributes: 211 attributes related to topography, hydroclimate, land cover, soil, geology, and human influences (e.g., dams, population density, land-use changes).

• Streamflow Data: Available observed streamflow data and predicted streamflow using an LSTM-based hydrological model for all catchments.

• Global Comparison: Follows CAMELS dataset standards for international comparison (USA, Chile, Brazil, etc.).

• Human Impact: Includes data on dams, population density, and land cover changes for studying anthropogenic influence on hydrology.

Link to data: https://zenodo.org/records/14005378 

Link to manuscript: https://doi.org/10.5194/essd-17-461-2025  

CAMELS-IND Side Event at IAHS 2025

Publications using CAMELS-IND

https://scholar.google.co.in/scholar?oi=bibs&hl=en&cites=7447677849803945098 

1. Mangukiya, NK, & Sharma, A. (2025) "Integrating Reservoir Dynamics into Differentiable Process-Based Hydrological Model for Enhanced Streamflow Estimation", Water Resources Research, 61 (7), e2025WR040268. https://doi.org/10.1029/2025WR040268

2. Ambika, A. K., Tayal, K., Mishra, V., & Lu, D. (2025). Novel deep learning transformer model for short to sub‐seasonal streamflow forecast. Geophysical Research Letters, 52(14), e2025GL116707.

3. Kalura, P., Pandey, A., Chowdary, V. M., & Dayal, D. (2025). A TOPSIS‐Based Multicriteria Assessment of Hydrologic Model Calibration Using Satellite‐Derived Evapotranspiration and Streamflow Data. Hydrological Processes, 39(7), e70191.

4. Mangukiya, NK, & Sharma, A. (2025) "Deep learning‐based approach for enhancing streamflow prediction in watersheds with aggregated and intermittent observations", Water Resources Research, 61 (1), e2024WR037331. https://doi.org/10.1029/2024WR037331

5. Kumar, K.B., Sharma, S., Das Bhowmik, R., Mujumdar, P.P. (2025) “Spatial synchronization of river floods growing beyond the basin boundaries in Peninsular India”, Scientific Reports, 15, 18160. https://doi.org/10.1038/s41598-025-02922-y

6. Mangukiya, NK, & Sharma, A. (2024) "Alternate pathway for regional flood frequency analysis in data-sparse region", Journal of Hydrology, 629 (February 2024), 130635. https://doi.org/10.1016/j.jhydrol.2024.130635

7. Mangukiya, N.K., Kushwaha, S., Sharma, A. (2024). “A novel multi-model ensemble framework for fluvial flood inundation mapping”. Environmental Modelling & Software, 180, 106163. https://doi.org/10.1016/j.envsoft.2024.106163

8. Mangukiya, N.K., Sharma, A., Shen, C. (2023) “How to enhance hydrological predictions in hydrologically distinct watersheds of the Indian subcontinent?”, Hydrological Processes, 37(7), e14936. https://doi.org/10.1002/hyp.14936

9. Abbas, A., Yang, Y., Pan, M., Tramblay, Y., Shen, C., Ji, H., Gebrechorkos, S. H., Pappenberger, F., Pyo, J. C., Feng, D., Huffman, G., Nguyen, P., Massari, C., Brocca, L., Jackson, T., and Beck, H. E.: Comprehensive Global Assessment of 23 Gridded Precipitation Datasets Across 16,295 Catchments Using Hydrological Modeling, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2024-4194, 2025.

Contribute to CAMELS-IND

If you are working with a data set covering Indian catchments and would like to contribute catchment averages to CAMELS-INDIA, please get in touch.

We are committed to identifying and correcting errors. If you encounter any unrealistic or suspicious values, please notify us as soon as possible. Thank you for your assistance.

Contacts:

• Nikunj K. Mangukiya (nikk.mangukiya@gmail.com)

• Ashutosh Sharma (ashutosh.sharma@hy.iitr.ac.in)

Disclaimer

The CAMELS-INDIA dataset provided on this webpage is openly accessible for academic and research purposes. While efforts have been made to ensure data accuracy, the authors do not take any responsibility for errors, omissions, or misuse of the data. Users must cite the following paper when utilizing the dataset and acknowledge that all interpretations and conclusions drawn from the data are their own. The dataset is provided "as is" without any warranties, and users are advised to check for updates. It is strongly recommended that users exercise caution and verify the data before using it for any purpose. The authors assume no responsibility for any consequences arising from the use or misuse of this dataset.

Acknowledgments

The authors gratefully acknowledge the Central Water Commission (CWC), the National Water Informatics Centre (NWIC), and the Ministry of Jal Shakti (MoJS) for providing the streamflow dataset through the online portal, India – Water Resources Information System (India-WRIS; https://indiawris.gov.in/wris/#/). The authors also extend their gratitude to the India Meteorological Department (IMD), Ministry of Earth Sciences, Government of India, for providing the gridded rainfall and temperature datasets through their respective websites. Additionally, the authors gratefully acknowledge the National Centre for Medium Range Weather Forecasting (NCMRWF), Ministry of Earth Sciences, Government of India, for the Indian Monsoon Data Assimilation and Analysis (IMDAA) reanalysis. The IMDAA reanalysis was produced under the collaboration between UK Met Office, NCMRWF, and IMD, with financial support from the Ministry of Earth Sciences under the National Monsoon Mission programme. The authors utilized numerous publicly available datasets for compiling catchment attributes and meteorological forcing time series, duly acknowledging and citing them where applicable. The authors extend their gratitude to all the researchers and contributing authors of these open-source datasets.

How to cite?

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.