About

Dr. Sankar Arumugam is a Professor in the Department of Civil, Construction, and Environmental Engineering at North Carolina State University. He is also a University Faculty Scholar from 2013 to 2018. His primary research interests lie at the interface of climate and water management, with a focus on large-scale hydroclimatology. His research group, Climate, Hydrology and Water Resources: Modeling and Synthesis, develops hydroclimatological forecasts and projections aimed at improving water and energy systems management across sub-seasonal to decadal and decadal time scales. Dr. Arumugam's work involves understanding, modeling, and forecasting hydrological fluxes based on land surface and climatic indices. His research also encompasses water resources planning and management, as well as environmental assessment in developing countries. He teaches courses such as Hydrology and Urban Water Systems, Engineering Hydrology, Stochastic Methods in Water and Environmental Engineering, and Hydroclimatology. He serves as an associate editor for the Geophysical Research Letters (AGU) and the Journal of Hydrometeorology (AMS), and has previously served as an associate editor for other prominent journals. His professional memberships include the American Geophysical Union, the American Meteorological Society, and the Environmental Water Research Institute of the American Society of Civil Engineers.

Selected publications

• The Role of Daily and Monthly Bias Corrected Data in Preserving the Monthly Cross‐Correlation Between Precipitation and Temperature

  International Journal of Climatology · 2026-04-06

  articleSenior author

  ABSTRACT Bias correction and statistical downscaling of climatic variables from global climate models (GCMs) is vital in climate application studies. This study focuses on the ability of two bias correction procedures, canonical correlation analysis (CCA) and quantile regression (QR), in preserving the monthly cross‐correlation between precipitation and maximum temperature based on monthly bias corrected data and daily bias‐corrected precipitation and maximum temperature over the CONUS. The analyses show CCA reproduces observed cross‐correlation between precipitation and temperature better compared QR. The removal of the dry bias has also resulted in better performance by all the methods. Further, bias‐corrected data at daily time scale preserves the monthly cross‐correlation better compared to the bias‐corrected data available at monthly time scale. Our analysis shows that bias corrected daily time scale data should be aggregated to monthly time scale, even if the climate‐application studies require monthly forcings for developing sectoral (e.g., water, energy) impact analysis.

  PublisherDOI

• Routing Optimization Framework for Exploring Time-Varying Urban Road Network Vulnerability under Floods

  Journal of Infrastructure Systems · 2026-03-26

  article

  This work investigates the problem of vehicle routing and assessment of road network vulnerability during extreme flood conditions by considering inundation levels and their effects on road availability and travel speeds. A stormwater model is used to estimate street-level inundation during three major hurricanes that passed through Wilmington, North Carolina—Florence, Matthew, and Dorian—to determine how different storms can affect the road network system. Toward this, an optimization model was developed that uses flood versus speed reduction functions to integrate flood levels to estimate decreasing the number of available arcs and reducing the interconnectivity of different regions over time. A comparison of the road network analysis using flood estimates from the stormwater model and the 100-year flood maps from the Federal Emergency Management Agency of the United States was performed, and overall results indicate the importance of storm-specific flood estimates to assist emergency planners in the definition of critical roads and affected areas. The paper also explores how different regions are affected during various flood severity levels and identifies areas of vulnerability. It introduces severity scenarios based on route feasibility, shedding light on the dynamic nature of flood impact, and concludes by highlighting the influence of flood changes on the importance of specific road segments in network planning and operation.

  PublisherDOI

• Multireservoir Allocation Framework Considering Societal and Ecological Needs in a Time–Frequency Domain

  Journal of Water Resources Planning and Management · 2026-02-24

  articleSenior author

  Existing reservoir management frameworks traditionally consider historical (predam) flow conditions to deliver environmental flows. Such frameworks may not be feasible because current demand and/or climate could be different from predam conditions. Hence, we developed a multireservoir framework that explicitly considers both human water demands and environmental flow requirements to minimize deviations under current hydroclimatic conditions and demand patterns. The multireservoir framework, Generalized Reservoir Analyses using Probabilistic Streamflow (GRAPS), was modified and implemented to solve the problem of minimizing the flow deviations using feasible sequential quadratic programming for three reservoirs in the Chattahoochee River Basin, Southeastern United States, which is known for its imperiled native biodiversity and productive estuarine ecosystem. Our results show that downstream reservoirs in the cascade system are less influenced by upstream reservoirs’ regulation because the downstream reservoirs receive a significant amount of natural flows. By comparing the average wavelet power spectrum at different periodicities between natural flows and downstream releases, we found that the current release policy and modified releases resulted in highly altered flows under shorter periodicities (e.g., less than 2 months) but synchronized flow variance between natural flow and downstream releases at longer periodicities (e.g., greater than 3 years). This framework of linking the multireservoir allocation model through the time–frequency analysis using wavelet power spectrum could not only advance sustainable water management policies to meet water for human and environmental needs but can also add additional value in meeting the downstream environmental demand at desired periodicities.

  PublisherDOI

• Spatial Covariability of Extreme Floods Over the Coterminous United States: Co‐Dependency Measures and Their Statistical Significance

  Water Resources Research · 2026-01-01

  articleOpen accessSenior author

  Abstract Understanding the spatial structure of extreme floods is critical both for reliable design flood estimation and for coordinated development of regional response and flood mitigation strategies. Yet, analysis of rare, high‐magnitude floods is challenged by the limited sample size. This study investigates the spatial covariability of extreme floods across the coterminous United States (CONUS) for large return periods (2–100 years) by proposing three distinct co‐dependency measures: (a) annual co‐occurrence probability (), (b) 7‐day co‐occurrence probability (), and (c) Measures of Co‐occurrence within a 500 km radius. The proposed measures are developed to associate flood spatial dependence with the underlying physical drivers and are evaluated against null distributions of spatially independent floods that preserve their seasonality. Results show that floods co‐occur far more often than expected under independence, with stronger dependence for larger return periods (e.g., for 100‐year floods ≈19%, vs. 1% under independence). The analysis indicates that snowmelt‐driven basins exhibit high dependence for smaller floods (2–25 years), but rainfall‐driven regions (particularly coasts) dominate for extreme events (50–100 years). MOC hotspots confirm that summer tropical storms (East Coast) and winter atmospheric rivers (West Coast) are the primary drivers of widespread extremes. Given the proposed co‐dependency measures' effectiveness flood processes at various spatial and temporal scales, we suggest they could be leveraged for regionally tailored, season‐specific flood mitigation and emergency‐response strategies.

  PublisherOA PDFDOI

• Investigating Streamflow Elasticity in Highly Regulated and Data-Scarce Basins using Bayesian Frameworks

  2026-02-19

  article

  Streamflow elasticity (εₚ) is vital for assessing future water resources, but in human-managed catchments, existing estimation methods often overestimate εₚ due to neglected anthropogenic controls like dam operations or groundwater withdrawal. We address this gap by quantifying εₚ across 78 highly regulated data-scarce catchments in Peninsular India, explicitly accounting for the effects of large-scale storage changes and unobserved water withdrawals. We compare a traditional linear regression model (LM) against two proposed probabilistic frameworks: a Bayesian regression model (BM) and a Bayesian hierarchical model (BHM), across long-term daily average, monsoon sum, and annual sum temporal aggregations. The LM yielded physically implausible elasticity estimates (up to 4.8), with the unexplained variance erroneously attributed to precipitation. Bayesian models that isolates non-climatic factors, achieved substantially improved model skill and εₚ values that were substantially lower than LM-based estimates and more physically consistent, values ranging from 0.28 to 0.68. These revised estimates confirm that the true streamflow elasticity of these basins is far less sensitive than inferred by baseline models. Both Bayesian models (BM and BHM) yielded nearly identical εₚ estimates, and validation checks like comparison of storage change estimates with aridity index showed consistent trends confirming the robustness of the proposed methodology. Elasticity was found to be highest for annual sum aggregation (up to 0.68), reflecting the amplified annual-scale sensitivity of streamflow to precipitation. This work provides a reliable framework for separating climatic signals from anthropogenic noise in regulated systems. The resulting lower εₚ values are crucial for generating realistic climate change impact projections.

  PublisherDOI

• Understanding the organizing scales of winter flood hydroclimatology and the associated drivers over the coterminous United States

  Journal of Hydrology X · 2025-03-04 · 3 citations

  articleOpen accessSenior author

  • Winter flood hydroclimatology of the CONUS is explored at different spatial scales. • Winter flood is influenced by hydroclimate modulators at a subregional scale. • Antecedent wetness dominates winter flood variability in inland regions. • Oceanic/atmospheric conditions affect the subregional variability of winter floods. Floods occur everywhere and in every season. Yet, most studies have focused only on annual maximum floods (AMFs), their climatology, and the associated impacts. Given that monthly/seasonal floods also cause significant damage and disruptions to daily life, this study may be the first to explore winter flood hydroclimatology, a predominantly a non-AMF season, and its associated large-scale climate drivers over the Coterminous US (CONUS). Using a mixed-effects model, we find that the influence of various hydroclimate predictors on winter floods is largely consistent within subregions. Antecedent land-surface conditions are crucial for winter floods in inland areas, while the Pacific sea surface temperatures (SSTs) significantly affects coastal watersheds. The Atlantic SSTs impact winter floods in the south and northeast, while atmospheric conditions influence the Midwest and California. Additional analysis reveals that damage from winter floods is more widespread compared to AMFs across the nation, affecting the entire eastern seaboard, Southwest US, and over the Great Lakes region. Thus, a comprehensive understanding of floods across all seasons (non-AMFs) is critical for developing effective mitigation measures, as it provides information on impacts and required compensation for smaller return period floods.

  PublisherDOI

• Is Reservoir Storage Effectively Utilized in the Southeastern US? A Regional Assessment to Improve Water Supply Availability Considering Potential Storage and Flood Scenarios

  Earth s Future · 2025-02-01 · 2 citations

  articleOpen accessSenior author

  Abstract Most of the world's population faces freshwater scarcity threats, and reservoirs, built both for ensuring water supply during prolonged droughts and reducing downstream flood risks, are critical infrastructure for water sustainability. Historical inflow data and water demand were used to estimate reservoir storage allocation and operation policies when designing and building reservoirs, 50–100 years ago. This study assesses historical reservoir operations in 16 Southeastern reservoirs and evaluates the potential for utilizing existing flood control storage for alternative purposes without increasing downstream flood risk. Using a reservoir simulation model, we evaluate the resulting storage under four initial storage conditions for observed and synthetic seasonal maximum 6‐day flood pulses. For most reservoirs, we find conservation storage is depleting and did not exceed the flood storage capacity in their historical operation. The simulation model resulted in most of the reservoirs' storage levels staying within the flood control pool for all scenarios (for observed and synthetic floods). Additional flood risk was lowest for initial storage condition 1 (flood control pool empty) and highest with condition 2 (50% of the flood control pool full). Flood risk increased the most for reservoirs with small ratios of flood control to conservation pool storage. Our study shows the potential for reallocation and utilization of flood control storage to meet the increasing demand. As limited opportunities for new reservoirs exist, utilizing current reservoir storage without introducing additional downstream risk may be an effective management strategy to mitigate flood and drought risk under climate change and population growth.

  PublisherOA PDFDOI

• A generalized design flood estimation framework under stationary and non-stationary scenarios

  Journal of Hydrology X · 2025-10-29

  articleOpen accessSenior author

  The publisher regrets that this article has been temporarily removed. A replacement will appear as soon as possible in which the reason for the removal of the article will be specified, or the article will be reinstated. The full Elsevier Policy on Article Withdrawal can be found at https://www.elsevier.com/about/policies-and-standards/article-withdrawal

  PublisherDOI

• A Complete Density Correction using Normalizing Flows (CDC-NF) for CMIP6 GCMs

  Scientific Data · 2025-07-23 · 1 citations

  articleOpen accessSenior author

  Global Climate Models (GCMs) are essential for climate projections but often exhibit biases, particularly in representing extremes and multivariate dependencies, which limit their utility in impact assessments. Traditional bias correction (BC) methods, such as quantile mapping, address marginal distributions but fail to correct joint extremes and cross-variable relationships. To address these challenges, we propose a Complete Density Correction using Normalizing Flows (CDC-NF), a novel method leveraging invertible transformations to adjust the full joint distribution of GCM outputs. Using observational data from NOAA nClimGrid-daily and CMIP6 GCM projections, The CDC-NF method was applied at a daily temporal resolution to precipitation and maximum temperature outputs from CMIP6 GCM projections. Compared to traditional BC methods, CDC-NF demonstrated substantial improvements in Wasserstein Distance, RMSE, and PBIAS, particularly for the 90th percentile extremes. Additionally, it preserved cross-correlation structure, enhancing reliability in modeling compound extremes. CDC-NF represents a significant advancement in BC, providing a robust framework for addressing GCM biases and improving climate impact studies in a changing climate.

  PublisherOA PDFDOI

• Large‐Scale Moisture Sources and Delivery Pathways Contributing to Winter Floods in the US

  Water Resources Research · 2025-11-27

  articleOpen accessSenior author

  Abstract Previous seminal studies on US flood hydroclimatology have recognized large‐scale seasonal precipitation water from the lower atmosphere as a significant factor influencing flood patterns and have identified potential moisture delivery pathways associated with seasonal flooding across the conterminous United States (CONUS). Given the changing climate, however, an update of these studies is warranted, leveraging the more extensive and longer series of data sets currently available on floods and exogenous climate drivers. Using a Lagrangian particle tracking model (HYSPLIT), this study explores the large‐scale moisture delivery pathways associated with monthly floods during the winter season across the CONUS. The contributions of various moisture sources (i.e., land, extratropical Pacific, tropical Pacific, extratropical Atlantic, tropical Atlantic, and Arctic) to winter floods are summarized for each hydrologic region (HUC02) based on the moisture delivery pathways identified by the HYSPLIT model. Our results show that major oceanic sources are the primary contributors to winter floods in coastal regions, whereas land remains a significant source for hydrologic regions over the Midwest. Humidity profiles along the trajectories further indicate that air masses from the Pacific lose substantial moisture when crossing western mountain ranges and subsequently gain over east of the Rockies, making land a key contributor to floods over the upper Midwest. We also find substantial variability in moisture source contributions depending on flood severity in specific regions. In addition, the circulation patterns associated with extreme flood trajectories reveal consistent large‐scale features, typically involving a deep low‐pressure system delivering moisture to hydrologic regions.

  PublisherOA PDFDOI

Labs

• Climate, Hydrology and Water Resources: Modeling and SynthesisPI

Awards & honors

• University Faculty Scholar (2013-2018)