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Doctoral Scholars (PhD completed)
Email: sachincit70@gmail.com
Dr. Sachidanand Kumar
Dr. Sachidanand Kumar
Education
Postdoc: IIT Indore
Year of completion: 2024
PhD: IIT (ISM) Dhanbad
Year of completion: 2023
M.Tech: IIT Madras
Year of completion:2014
B.Tech: Ranchi University
Year of completion: 2014
Publications:
Kumar, S., Chanda, K. & Pasupuleti, S. Spatiotemporal analysis of extreme indices derived from daily precipitation and temperature for climate change detection over India. Theor Appl Climatol 140, 343–357 (2020). DOI: https://doi.org/10.1007/s00704-020-03088-5
Thomas, J., Kumar, S., & Sudheer, K. P. (2020). Channel stability assessment in the lower reaches of the Krishna River (India) using multi-temporal satellite data during 1973–2015. Remote Sensing Applications: Society and Environment, 17, 100274.DOI: https://doi.org/10.1016/j.rsase.2019.100274
Goyal, M. K., Kumar, S., & Gupta, A. (Eds.). (Year). AI innovation for water policy and sustainability. Springer.
Kumar, S., Chanda, K., & Pasupuleti, S. (2022). Pre- and post-1975 scaling relationships of monsoon and non-monsoon hourly precipitation extremes with coincident temperature across urban India. Journal of Hydrology, 612(Part B), 128180.DOI: https://doi.org/10.1016/j.jhydrol.2022.128180
Kumar, S., & Goyal, M. K. (2025). Water policy review: Ensuring sustainable water management for India. Journal of Environmental Management, 388, 125823. DOI: https://doi.org/10.1016/j.jenvman.2025.125823
Kumar, S., Chanda, K. & Pasupuleti, S. Association of tropical daily precipitation extremes with physical covariates in a changing climate. Stoch Environ Res Risk Assess 37, 3021–3039 (2023). DOI: https://doi.org/10.1007/s00477-023-02433-0
Jain, V., Kumar, S., & Goyal, M. K. (2025). Relationship between daily precipitation extremes and temperature in changing climate across smart cities of Central India. Journal of Environmental Management, 380, 125036. DOI: https://doi.org/10.1016/j.jenvman.2025.125036
Goyal, M.K., Kumar, S., Gupta, A. (2024). Basics of AI for Water Management. In: AI Innovation for Water Policy and Sustainability. SpringerBriefs in Water Science and Technology. Springer, Cham. DOI: https://doi.org/10.1007/978-3-031-72014-7_1
Kumar, S., Das, P., Mandal, N., Chanda, K., & Pasupuleti, S. (2024). Joint probabilistic behaviour of climate extremes over the Godavari River basin, India. International Journal of Climatology, 44(9), 2876–2896.DOI: https://doi.org/10.1002/joc.8486
Goyal, M.K., Kumar, S., Gupta, A. (2024). AI for Water Treatment. In: AI Innovation for Water Policy and Sustainability. SpringerBriefs in Water Science and Technology. Springer, Cham. DOI: https://doi.org/10.1007/978-3-031-72014-7_3
Goyal, M.K., Kumar, S., Gupta, A. (2024). AI for Water Policy. In: AI Innovation for Water Policy and Sustainability. SpringerBriefs in Water Science and Technology. Springer, Cham. DOI: https://doi.org/10.1007/978-3-031-72014-7_4
Goyal, M.K., Kumar, S., Gupta, A. (2024). AI for Water Conservation. In: AI Innovation for Water Policy and Sustainability. SpringerBriefs in Water Science and Technology. Springer, Cham. DOI: https://doi.org/10.1007/978-3-031-72014-7_2
Goyal, M.K., Kumar, S., Gupta, A. (2024). AI Framework for Future Water. In: AI Innovation for Water Policy and Sustainability. SpringerBriefs in Water Science and Technology. Springer, Cham. DOI: https://doi.org/10.1007/978-3-031-72014-7_5
Mandal, N. S., Kumar, S., & Chanda, K. (2023). Urban heat islands (UHI) or urban cool islands (UCI): Examining the spatial variation of land surface temperature (LST) in the city of Bongaigaon, India. In Proceedings of the 28th International Conference on Hydraulics, Water Resources, River and Coastal Engineering (HYDRO 2023) , December 21–23, 2023, National Institute of Technology Warangal, India.
Kumar, S., Chanda, K., & Pasupuleti, S. (2022, December). Scaling relationships of sub-daily precipitation extremes with temperature for monsoon and non-monsoon months in India. In AGU Fall Meeting Abstracts (Vol. 2022, pp. H42E-1344).
Chowdary, P.P., Kumar, S., Kumar, S., Villuri, V.G.K., Srinivas, P. (2023). Exploring Geospatial Technology in Kadiri Basin of Ananthapuramu District, A.P. for Demarcation of GWPZ and Identification of Recharge Structures. In: Timbadiya, P.V., Patel, P.L., Singh, V.P., Mirajkar, A.B. (eds) Geospatial and Soft Computing Techniques. HYDRO 2021. Lecture Notes in Civil Engineering, vol 339. Springer, Singapore. DOI: https://doi.org/10.1007/978-981-99-1901-7_18
Kumar, S., Chanda, K., Pasupuleti, S. (2021). Influence of Air Temperature on Local Precipitation Extremes Across India. In: Jha, R., Singh, V.P., Singh, V., Roy, L.B., Thendiyath, R. (eds) Climate Change Impacts on Water Resources. Water Science and Technology Library, vol 98. Springer, Cham. DOI: https://doi.org/10.1007/978-3-030-64202-0_14
Kumar, S., Chanda, K., and Pasupuleti, S.: Spatio-temporal variation of extreme indices derived from observed and reanalysis products for detection of climate change across India, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5837. DOI: https://doi.org/10.5194/egusphere-egu2020-5837, 2020
Dr. Prabal Das
Dr. Prabal Das
Currently: Post-Doctoral Research Associate (Dec 2023 - Present), Department of Civil Engineering, The University of Texas, Arlington
Post-process Numerical Weather Prediction (NWP) model outputs to extract and prepare meteorological variables for downstream machine learning applications.
Develop and apply state-of-the-art ML models to improve precipitation amount and type forecasts across CONUS.
Implement high-performance data pipelines for large-scale weather or climate datasets
Education
PhD: Department of Civil Engineering, Indian Institute of Technology (ISM) – Dhanbad, India CGPA: 9.35/10 (Coursework)
Topic: Unveiling Key Drivers in Hydrometeorological Processes: Feature Selection Using Bayesian Networks for Improved Understanding and Prediction
Year of completion: 2024
M.Tech: Water Resources Engineering and Management, National Institute of Technology Karnataka – Surathkal, India, CGPA: 8.53/10
Topic: Meteorological Drought Forecasting using Machine Learning Techniques
Year of completion: 2017
B.Tech: Civil Engineering, West Bengal University of Technology, Kolkata, India
Year of completion: 2014
Publications:
Das, P., Naganna, S.R., Deka, P.C. et al. Hybrid wavelet packet machine learning approaches for drought modeling. Environ Earth Sci 79, 221 (2020). DOI: https://doi.org/10.1007/s12665-020-08971-y
Das, P., & Chanda, K. (2020). Bayesian network–based modeling of regional rainfall from multiple local meteorological drivers. Journal of Hydrology, 591, Article 125563.DOI: https://doi.org/10.1016/j.jhydrol.2020.125563
Das, P., Sachindra, D.A. & Chanda, K. Machine Learning-Based Rainfall Forecasting with Multiple Non-Linear Feature Selection Algorithms. Water Resour Manage 36, 6043–6071 (2022).DOI: https://doi.org/10.1007/s11269-022-03341-8
Das, P., Chanda, K. A Bayesian network approach for understanding the role of large-scale and local hydro-meteorological variables as drivers of basin-scale rainfall and streamflow. Stoch Environ Res Risk Assess 37, 1535–1556 (2023).DOI: https://doi.org/10.1007/s00477-022-02356-2
Das, P., Chanda, K. Selection of optimum GCMs through Bayesian networks for developing improved machine learning based multi-model ensembles of precipitation and temperature. Stoch Environ Res Risk Assess 39, 155–179 (2025).DOI: https://doi.org/10.1007/s00477-024-02856-3
Mandal, N., Das, P., and Chanda, K.: Machine-learning-based reconstruction of long-term global terrestrial water storage anomalies from observed, satellite and land-surface model data, Earth Syst. Sci. Data, 17, 2575–2604.DOI: https://doi.org/10.5194/essd-17-2575-2025, 2025
Chanda, K., & Das, P. (2023). Dimensionality reduction of correlated meteorological variables by Bayesian network–based graphical modeling. In S. Eslamian & F. Eslamian (Eds.), Handbook of hydroinformatics (pp. 69–76). Elsevier. DOI: https://doi.org/10.1016/B978-0-12-821961-4.00021-X
Das, P., Palchaudhuri, M., Biswas, S., & Deka, P. C. (2017). Analysis of agricultural drought severity using MODIS satellite images. In Proceedings of the 17th International Conference on Global Computing and Smart Communication Systems (ICGCSC 2017) (p. 277). Mangalore Institute of Technology & Engineering. ISBN 978-93-5267-355-1.
Kumar, S., Das, P., Mandal, N., Chanda, K., & Pasupuleti, S. (2024). Joint probabilistic behaviour of climate extremes over the Godavari River basin, India. International Journal of Climatology, 44(9), 2876–2896. DOI: https://doi.org/10.1002/joc.8486
Das, P., & Deka, P. C. (n.d.). Application of hybrid wavelet packet–ANN in drought forecasting. International Journal of Water Resource Engineering, 3(2).DOI: https://www.journalspub.com
Mandal, N., Das, P., and Chanda, K.: Machine-learning-based reconstruction of long-term global terrestrial water storage anomalies from observed, satellite and land-surface model data, Earth Syst. Sci. Data, 17, 2575–2604. DOI: https://doi.org/10.5194/essd-17-2575-2025, 2025
Das, P., Zhang, Y., Acharya, N., Veenhuis, B., Shafer, P. E., & Hamill, T. M. (2025, January). Machine Learning Based Improvement of Precipitation Type Forecasts. In 105th AMS Annual Meeting. AMS.
Mandal, N., Das, P., and Chanda, K.: Performance of two-step technique for gap-filling and reconstruction of basin-scale Terrestrial Water Storage Anomalies (TWSA) , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18755.DOI: https://doi.org/10.5194/egusphere-egu24-18755, 2024
Das, P. and Chanda, K.: Influence of large-scale climate modes and local hydrometeorological factors in predicting basin scale rainfall and streamflow: A Bayesian network approach, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-3826.DOI: https://doi.org/10.5194/egusphere-egu23-3826, 2023
Das, P., & Chanda, K. (2022, December). Potential of Bayesian Network for regional rainfall prediction from multiple meteorological drivers. In AGU Fall Meeting Abstracts (Vol. 2022, pp. H42E-1336).
Das, P., Chanda, K. (2022). Feature Selection for Rainfall Prediction and Drought Assessment Using Bayesian Network Technique. In: Kolathayar, S., Mondal, A., Chian, S.C. (eds) Climate Change and Water Security. Lecture Notes in Civil Engineering, vol 178. Springer, Singapore.DOI: https://doi.org/10.1007/978-981-16-5501-2_10
Das, P., Chanda, K., & Maity, R. (2020, May). How useful are CORDEX products for the assessment of future agricultural drought propensity across the Indian subcontinent? EGU General Assembly Conference Abstracts, 15885.DOI: https://doi.org/10.5194/egusphere-egu2020-15885
Doctoral Scholars (PhD ongoing)
Position: Ph.D. Research Scholar
Joined: 2019
Broad Topics: Terrestrial water balance, analyzing climate and catchment impacts on basin runoff, and application of machine learning techniques in predictive hydrology
Nehar Mandal
Nehar Mandal
Education
M.Tech: National Institute of Technology Patna, India
Topic:
Year of completion: 2019
B.Tech: West Bengal University of Technology, Kolkata, India
Year of completion: 2016
Publications:
Mandal N, Roshni T, and Chanda K, (2025), Performance of kernel-based and network-based machine learning models in comparison with temperature-based empirical models for estimation of evapotranspiration, HYDRO 2025, NIT Rourkela, India, 18-20 Dec 2025.
Mandal, N., Das, P., and Chanda, K.: Machine-learning-based reconstruction of long-term global terrestrial water storage anomalies from observed, satellite and land-surface model data, Earth Syst. Sci. Data, 17, 2575–2604,2025. DOI: https://doi.org/10.5194/essd-17-2575-2025
Mandal, N., & Chanda, K. (2023). Performance of machine learning algorithms for multi-step ahead prediction of reference evapotranspiration across various agro-climatic zones and cropping seasons. Journal of Hydrology, 620(Part A), 129418. DOI: https://doi.org/10.1016/j.jhydrol.2023.129418
Mandal, N., & Chanda, K. (2023). Contribution of climate and catchment characteristics to runoff variations in Indian river basins: a climate elasticity approach. Hydrological Sciences Journal, 68(12), 1693–1710. DOI: https://doi.org/10.1080/02626667.2023.2236092
Kumar, S., Das, P., Mandal, N., Chanda, K., & Pasupuleti, S. (2024). Joint probabilistic behaviour of climate extremes over the Godavari River basin, India. International Journal of Climatology, 44(9), 2876–2896. DOI: https://doi.org/10.1002/joc.8486
Roshni, T., Choudhary, S., Jha, M. K., & Mandal, N. (2020). Probability-based approach for evaluating groundwater risk assessment in Sina basin, India. In P. Samui, D. T. Bui, S. Chakraborty, & R. C. Deo (Eds.), Handbook of probabilistic models (pp. 289–304). Butterworth-Heinemann. DOI: https://doi.org/10.1016/B978-0-12-816514-0.00012-6
Position: Ph.D. Research Scholar
Joined: 2021
Broad Topics: Climate Extremes, Urban Heat Island Effects, Heat waves, Population Exposure to Heatwaves, Soil Moisture Stress, Land Use Land Cover Change, Machine Learning Methods in Hydroclimatology
Email: 21dr0122@cve.iitism.ac.in
Nirup Sundar Mandal
Nirup Sundar Mandal
Education
M.Tech: National Institute of Technology, Rourkela
Topic: Effect of Climate Change in Saraikela-Kharsawan District of Jharkhand and Sedimentation in Chandil Dam Reservoir
Year of completion: 2021
B.Tech: Guru Ghasidas University
Year of completion: 2018
Publications:
Mandal, N.S., Das, P., Mandal, N., and Chanda, K., (2025), Spatio-Temporal Trends of Heatwave Occurrences Across India and Comparison of The Discomfort Indices at Heat Wave Hotspots. HYDRO 2025, NIT Rourkela, India, 18-20 Dec 2025. [ Best Presentation Award ]
Mandal, N.S., Chanda, K. Investigation of Urban Heat Islands and modeling of Land Surface Temperature over selected Indian cities using MODIS products. Theor Appl Climatol 156, 258 (2025). DOI: https://doi.org/10.1007/s00704-025-05480-5
Mandal, N. S., Kumar, S., & Chanda, K. (2023). Urban Heat Islands (UHI) or Urban Cool Islands (UCI): Examining the spatial variation of Land Surface Temperature (LST) in the city of Bongaigaon, India. HYDRO 2023, NIT Warangal India, 19-21 Dec 2023.
Mandal, N. S., & Sahoo, S. N. (2021). Prediction of Precipitation in Saraikela-Kharsawan District of Jharkhand by Statistical Downscaling Method. HYDRO 2021, NIT Rourkela, India, 26-28 Mar 2021.
Position: Ph.D. Research Scholar
Joined: 2022
Broad Topics: Bioremediation of hydrocarbons
Email: 22dr0057@iitism.ac.in
Arindam Roy
Arindam Roy
Education
M.Tech: National Institute of Technology Silchar
Topic: Sediment Transport Analysis using HEC-RAS: A Case Study in Godavari River Basin
Year of completion: 2022
B.Tech: Maulana Abul Kalam Azad University of Technology, West Bengal
Year of completion: 2019
Publications:
Roy, A., Valsala, R., and Chanda, K., (2025), Numerical Modeling on the Transport of Benzene under Variable-density Flow. IGWC 2025, NIH Roorkee, India, 5 – 7 March 2025.
Roy, A., Valsala, R., Chanda, K., and Sarukkalige, R., (2025), Numerical Comparison of Benzene Biodegradation in Aerobic and Anaerobic Systems within a Variable-Density Unsaturated Zone. HWRS 2025, Canberra, Australia, 25 – 28 November 2025.
Prashant Kumar
Prashant Kumar
Education
M.Tech: NIT Jamshedpur
Topic:
Year of completion: 2023
B.Tech:
Year of completion:
Publications:
Kumar P, and Chanda K, (2025), Causal discovery of the dependence process between large-scale teleconnection indices and monsoon rainfall in Sabri Basin in Lower Godavari, India, HYDRO 2025, NIT Rourkela, India, 18-20 Dec 2025.
Position: Ph.D. Research Scholar
Joined: 2024
Broad Topics: Soil Mositure Stress
Email: 24dr0215@iitism.ac.in
Ved Prakash Kumar
Ved Prakash Kumar
Education
M.Tech: National Institute of Technology, Patna
Topic: Studying the impact of rainfall and LULC changes in the reservoir water level of the Hatia dam, Ranchi, India
Year of completion: 2024
B.Tech: Kurukshetra University, Haryana
Year of completion: 2020
Publications:
Kumar, V P., Chanda, K., and Dutta, R. (2025), Development and progression of flash droughts across India using root zone soil moisture information from land surface model. HYDRO 2025, NIT Rourkela, India, 18-20 Dec 2025.
Sneh Kumar, Vivekanand Singh, Ved Prakash Kumar, Thendiyath Roshni; Studying the impact of rainfall and LULC changes in the reservoir water level of the Hatia dam, Ranchi, India. Water Supply 1 March 2025; 25 (3): 522–544. DOI: https://doi.org/10.2166/ws.2025.018
Position: Ph.D. Research Scholar
Joined: 2024
Broad Topics: Drought and flash Drought
Email: 24dr0337@iitism.ac.in
Utkarsh Deepankar
Utkarsh Deepankar
Education
M.Tech: National Institute of Technology, Patna
Topic: Impact of LULC and Climate change on the hydrology of Punpun basin.
Year of completion: 2024
B.Tech: Vellore Institute of Technology , Chennai
Year of completion: 2019
Job Experience: 1.8 years (Rapti Developers, Noida)
Publications:
Deepankar U., Kumar, VP., Chanda, K., and Dutta, R. (2025), Comparison of Evaporative Stress and Soil Moisture Stress for historical droughts across India using multiple reanalysis and land surface model products. HYDRO 2025, NIT Rourkela, India, 18-20 Dec 2025. [ Best Presentation Award ]
Pratyasha Mishra
Pratyasha Mishra
Education
M.Tech: Indian Agricultural Research Institute, New Delhi in Soil and Water Conservation Engineering
Topic: Modelling Water and Nutrient Dynamics of Mustard Crop under Deficit Irrigation
Year of completion: 2024
B.Tech: Odisha University of Agriculture and Technology in Agricultural Engineering
Year of completion: 2022
Publications:
Arijit Guha
Arijit Guha
Education
M.Tech: KIIT University
Topic: Trend analysis of seasonal and annual rainfall of Odisha
Year of completion: 2023
During M.Tech he was president of ASCE, student chapter.
B.Tech: KIITUniversity
Year of completion: 2021
Job Experience:
Deyan Infratech, Intern
The ElevateDigi, Operation Manager
Publications:
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