Sankarasubramanian Arumugam


University Faculty Scholar
  • Fitts-Woolard Hall 3321

Dr. Sankar Arumugam is a  Professor in the Department of Civil, Construction, and Environmental Engineering at NCSU. He is also a University Faculty Scholar (2013-2018). He is primarily associated with the Environmental, Water Resources, and Coastal Engineering and Computing and Systems groups within the department. His research group, Climate, Hydrology and Water Resources: Modeling and Synthesis, focuses on developing hydroclimatological forecasts and projections for improving water and energy systems management from sub-seasonal to seasonal and decadal time scales.

Dr. Arumugam currently teaches CE 383 – Hydrology and Urban Water Systems, CE 586 – Engineering Hydrology, CE 777 – Stochastic Methods in Water and Environmental Engineering and CE 786 – Hydroclimatology.

Dr. Arumugam currently serves as the associate editor for the Geophysical Research Letters (AGU) and for the Journal of Hydrometeorology (AMS). He also served as the associate editor for  Water Resources Research (AGU), Journal of Hydrology (Elsevier), Journal of Hydrologic Engineering (ASCE) and as the editor of Journal of Water and Climate Change (IWA). Dr. Arumugam is also a member of American Geophysical Union, American Meteorological Society and Environmental Water Research Institute of the American Society of Civil Engineers.


Ph.D. 2002

Water Resources Engineering

Tufts University

M.S. 1996

Civil and Environmental Engineering

Indian Institute of Technology-Madras

B.S. 1991

Agricultural Engineering

Tamilnadu Agricultural University-Coimbatore

Research Description

Dr. Arumugam's primary research interest is at the interface of climate and water management focusing on large-scale hydroclimatology. His current research sponsors include National Science Foundation, National Oceanic and Atmospheric Administration and NC Water Resources Research Institute. Arumugam is interested in understanding, modeling and forecasting hydrological fluxes at large spatial scales based on land surface and climatic indices. Other topics of research include water resources planning and management and environmental assessment in developing countries.


Improved National-Scale Above-Normal Flow Prediction for Gauged and Ungauged Basins Using a Spatio-Temporal Hierarchical Model
Fang, S., Johnson, J. M., Yeghiazarian, L., & Sankarasubramanian, A. (2024), WATER RESOURCES RESEARCH, 60(1).
Influence of long-term observed trends on the performance of seasonal hydroclimate forecasts
Bhowmik, R. D., Budamala, V., & Sankarasubramanian, A. (2024), Advances in Water Resources.
Leveraging Synthetic Aperture Radar (SAR) to improve above-normal flow prediction in ungauged basins
Fang, S., Johnson, J. M., & Sankarasubramanian, A. (2024, April 15). , .
Beyond Simple Trend Tests: Detecting Significant Changes in Design-Flood Quantiles
Awasthi, C., Archfield, S. A., Reich, B. J., & Sankarasubramanian, A. (2023), GEOPHYSICAL RESEARCH LETTERS, 50(13).
Comprehensive Analysis of the NOAA National Water Model: A Call for Heterogeneous Formulations and Diagnostic Model Selection
Johnson, J. M., Fang, S., Sankarasubramanian, A., Rad, A. M., Cunha, L. K., Jennings, K. S., … Yeghiazarian, L. (2023), JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES, 128(24).
Comprehensive analysis of the NOAA National Water Model: A call for heterogeneous formulations and diagnostic model selection
Johnson, J. M., Fang, S., Sankarasubramanian, A., Rad, A. M., Cunha, L. K., Clarke, K. C., … Yeghiazarian, L. (2023, January 19). , .
Contrasting Annual and Summer Phosphorus Export Using a Hybrid Bayesian Watershed Model
Karimi, K., Miller, J. W., Sankarasubramanian, A., & Obenour, D. R. (2023), WATER RESOURCES RESEARCH, 59(1).
Generalizing Reservoir Operations Using a Piecewise Classification and Regression Approach
Ford, L., & Sankarasubramanian, A. (2023), WATER RESOURCES RESEARCH, 59(9).
Generalizing Reservoir Operations using a Piecewise Classification and Regression Approach
Ford, L., & Sankarasubramanian, A. (2023, March 26). , .
Improved National-Scale Flood Prediction for Gauged and Ungauged Basins using a Spatio-temporal Hierarchical Model
Fang, S., Johnson, J. M., Yeghiazarian, L., & Sankarasubramanian, A. (2023, February 9). , .

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EAGER: AI-driven Probabilistic Technique, Quantile Regression Based Artificial Neural Network Model, for Bias Correction and Downscaling of CMIP6 Projections
National Science Foundation (NSF)(12/15/21 - 11/30/25)
CAS-Climate: Understanding the Changing Climatology, Organizing Patterns and Source Attribution of Hazards of Floods over the Southcentral and Southeast US
National Science Foundation (NSF)(9/01/22 - 8/31/25)
Projecting Flood Frequency Curves Under a Changing Climate Using Spatial Extreme Value Analysis
National Science Foundation (NSF)(6/01/22 - 5/31/25)
Flood Typing and Mixed Population Study
US Geological Survey (USGS)(6/01/23 - 12/31/24)
Hosting the Southeast Climate Science Center
US Dept. of the Interior (DOI)(8/01/17 - 7/31/24)
Collaborative Research:nsf-nsfc:improving Few System Sustainability Over the Seus and Ncp: A Cross-regional Synthesis Considering Uncertainties in Climate and Regional Development
National Science Foundation (NSF)(8/15/18 - 12/31/23)
A1: The Urban Flooding Open Knowledge Network (UF-OKN): Delivering Flood Information to AnyOne, AnyTime, AnyWhere
National Science Foundation (NSF)(9/01/20 - 8/31/23)
Evaluation of Tampa Bay Water??????????????????s Water Supply System Under Different Changing Hydroclimatic and Demand Scenarios
Tampa Bay Water(1/01/22 - 6/30/23)
Sustained-Petascale in Action: Blue Waters Enabling Transformative Science and Engineering (Lucas Ford)
National Science Foundation (NSF)(8/01/21 - 12/31/22)
Developing Potential Scenarios of Changes in Hydroclimatic Variables for analyzing the Impacts on the Tampa Bay Water Supply System
Tampa Bay Water(10/01/20 - 7/31/22)