Education

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.

Publications

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). , . https://doi.org/10.22541/essoar.167415214.45806648/v1

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. https://doi.org/10.1029/2022WR033088

Generalizing Reservoir Operations using a Piecewise Classification and Regression Approach

Ford, L., & Sankarasubramanian, A. (2023, March 26). , . https://doi.org/10.22541/essoar.167979630.09661764/v1

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). , . https://doi.org/10.22541/essoar.167590827.70275868/v1

Spectral Signatures of Flow Regime Alteration by Dams Across the United States

Chalise, D. R., Sankarasubramanian, A., Olden, J. D. D., & Ruhi, A. (2023), EARTHS FUTURE, 11(2). https://doi.org/10.1029/2022EF003078

Co-Optimization of Reservoir and Power Systems (COREGS) for seasonal planning and operation

Ford, L., Queiroz, A., DeCarolis, J., & Sankarasubramanian, A. (2022), ENERGY REPORTS, 8, 8061–8078. https://doi.org/10.1016/j.egyr.2022.06.017

How Does Flow Alteration Propagate Across a Large, Highly Regulated Basin? Dam Attributes, Network Context, and Implications for Biodiversity

Ruhi, A., Hwang, J., Devineni, N., Mukhopadhyay, S., Kumar, H., Comte, L., … Sankarasubramanian, A. (2022), Earth's Future. https://doi.org/10.1029/2021EF002490

Knowledge graphs to support real-time flood impact evaluation

Johnson, J. M., Narock, T., Singh-Mohudpur, J., Fils, D., Clarke, K. C., Saksena, S., … Yeghiazarian, L. (2022), AI MAGAZINE, 43(1), 40–45. https://doi.org/10.1002/aaai.12035

Projecting Flood Frequency Curves Under Near-Term Climate Change

Awasthi, C., Archfield, S. A., Ryberg, K. R., Kiang, J. E., & Sankarasubramanian, A. (2022), WATER RESOURCES RESEARCH, 58(8). https://doi.org/10.1029/2021WR031246

Assessing inter-annual variability in nitrogen sourcing and retention through hybrid Bayesian watershed modeling

Miller, J. W., Karimi, K., Sankarasubramanian, A., & Obenour, D. R. (2021), Hydrology and Earth System Sciences, 2. https://doi.org/10.5194/hess-2021-52

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Grants

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)

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Evaluation of Tampa Bay Water’s Water Supply System Under Different Changing Hydroclimatic and Demand Scenarios

Sponsor: Tampa Bay Water

Start Date: 1/01/22

End Date: 6/30/23

Abstract

The objective of this project is to a) develop streamflow scenarios based on the precipitation and temperature scenarios under stationary conditions as well as under changing precipitation and temperature scenarios; b) run those scenarios with the rainfall-runoff model from TBW and develop streamflow scenarios for the considered precip and temp scenarios; c) use the current synthetic climate generation model and develop streamflow generation scenarios for stationary and potentially changing conditions and d) run the TBW system with the above streamflow generation scenarios (from (b) and (c)) along with current and potential demand scenarios to assess the system performance.

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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)

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Understanding the Changing Climatology, Organizing Patterns and Source Attribution of Hazards of Floods over the Southcentral and Southeast US

Sponsor: National Science Foundation (NSF)

Start Date: 9/01/22

End Date: 8/31/25

Abstract

The Southcentral and Southeast US, comprising six water resource regions, has been experiencing significant growth in population over the last three decades. Among different hazards the region faces, floods occur in all the four seasons accounting more than quarter of the economic losses. The region has faced several major hurricanes – Matthew (2016), Irma (2017), Harvey (2016), Florence (2018) – over the last three seasons resulting in catastrophic flooding and loss of life. The objective of this proposal is to improve the predictability of the hazards of hydrologic extremes of floods and droughts through better understanding of (a) quantifying the changes in climatology, (b) describing their organizational patterns and (c) attributing the sources/drivers – land surface, atmosphere and ocean – that modulate their spatio-temporal variability over the region. Given the continually increasing population over study region, a synthesis on the role of various drivers and their interactions in influencing the predictability of floods will provide related agencies additional insights on developing strategies to improve the community resilience.

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Projecting Flood Frequency Curves Under a Changing Climate Using Spatial Extreme Value Analysis

National Science Foundation (NSF)(6/01/22 - 5/31/25)

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Projecting Flood Frequency Curves Under a Changing Climate Using Spatial Extreme Value Analysis

Sponsor: National Science Foundation (NSF)

Start Date: 6/01/22

End Date: 5/31/25

Abstract

Climate change is often described in terms of the mean, but it will be felt most acutely in terms of extreme events. In particular, the International Panel of Climate Change’s recent Sixth Assessment warns of an increase in the likelihood and magnitude of extreme flooding events in upcoming decades. Understanding the spatiotemporal variability of these changes is critical to mitigating their impact. However, current methods for spatial extreme value analysis are limited in their modeling flexibility and computational capabilities, and thus methodological work is required to analyze extreme events across the United States. Therefore, in this proposal, we develop new methodological and computational tools for spatial extreme value analysis and apply them to forecasting flood risk under a changing climate. The analysis combines fifty years of annual maximum streamflow observations at hundreds of gauges provided by the United States Geological Survey with CMIP6 climate model output produced under different shared socio-economic pathways. This analysis will provide high-resolution maps of anticipated change in flood risk and local flood frequency curves to inform water infrastructure projects. This project will result in major advances in both spatial extreme value analysis and hydrology. We will pursue two methods that exploit recent developments in distributed computing and machine learning/artificial intelligence, respectively, to improve computation for spatial extreme value analysis. Computation for spatial extremes is challenging because the most common model is the max-stable process, and this model gives an intractable likelihood function and is thus not conducive to direct application of maximum likelihood or Bayesian analysis. To overcome this difficulty, we propose a divide-and-conquer method that analyzes data separately by subregion and then combines the results using generalized method of moments techniques. We show that this procedure has desirable theoretical frequentist properties and gives substantial performance gain over state-of-the-art methods. We also propose a new method under the Bayesian framework that is preferred for uncertainty quantification. We decompose the intractable likelihood function into a sequence of simpler functions, and use deep-learning distribution regression to approximate these simpler functions. We argue that this approximation can be arbitrarily precise and scales linearly with the number of spatial locations, facilitating analysis of large datasets. The project culminates with the analysis of flood-frequency curves across the US. Compared to current methods, by using spatial extreme value analysis we are able to borrow information across space to improve estimation of small probabilities and estimate the probability of multiple locations simultaneously experiencing an extreme event. We have assembled an interdisciplinary group of statisticians and hydrologists to accomplish these ambitious objectives and ensure that the results are disseminated to the appropriate communities through journal publications, free and accessible software packages, and seminars, conferences and workshops. The proposed workshop will foster synergy between statisticians and hydrologists by encouraging the sharing of ideas, approaches and solutions to flood risk prediction, and aid in the formulation of a common language shared by statisticians and hydrologists for successful transfer of knowledge across disciplines. This proposal will also train two graduate students and four undergraduate students in theoretical, computational and applied extreme value value analysis in hydrology with a strong emphasis on interdisciplinary ideas.

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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 - 11/30/23)

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EAGER: AI-driven Probabilistic Technique, Quantile Regression Based Artificial Neural Network Model, for Bias Correction and Downscaling of CMIP6 Projections

Sponsor: National Science Foundation (NSF)

Start Date: 12/15/21

End Date: 11/30/23

Abstract

Recently released the Sixth assessment report of Intergovernmental Panel on Climate Change highlights the role of humans in warming the climate and also attribute it to the increase in the frequency of occurrence of hydroclimatic extremes. To obtain these future projections of hydroclimatic extremes, Global Climate Models (GCMs), with coarser resolutions, are typically used to develop climate projections until the end of the century. Due to continually increasing computational power, spatial resolution of GCM projections have improved a lot (around 1ï‚°), but still they are inadequate for watershed-scale (e.g., HUC8) applications. Further, historical projections of GCMs inherently have bias with observed climate. Hence, they have been bias-corrected and statistically downscaled (BCSD) and such products are available for the past CMIP runs. Existing BCSD methods (e.g., asynchronous regression) have been shown not to preserve the spatio-temporal dependency across variables due to the high dimensionality in the data. But, Artificial Intelligence (AI) techniques are highly equipped to handle high dimensional data and can preserve spatio-temporal dependency across the variables. Hence, we propose an innovative, high risk and high reward, AI-based probabilistic approach that uses Quantile Regression based Artificial Neural Network (ANN) (QR-AI) model for BCSD CMIP6 projections. The main objective of this EAGER proposal is to develop a BCSD methodology using QR-AI and apply them for recently issued CMIP6 projections to facilitate the rapid uptake of the AI methodology and BCSD products. Specifically, we will develop three BCSD data products of CMIP6 projections over the CONUS: 1) Historical simulations (1950-2014) of precipitation and temperature of GCMs; 2) Near-term (30 year) hindcasts of precipitation and temperature from relevant GCMs and 3) Near-term (30 year) projections of precipitation and temperature for four different Shared Socioeconomic Pathways.

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Sustained-Petascale in Action: Blue Waters Enabling Transformative Science and Engineering (Lucas Ford)

National Science Foundation (NSF)(8/01/21 - 7/31/22)

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Sustained-Petascale in Action: Blue Waters Enabling Transformative Science and Engineering (Lucas Ford)

Sponsor: National Science Foundation (NSF)

Start Date: 8/01/21

End Date: 7/31/22

Abstract

Lucas Ford will develop a geospatial model to improve stream flow prediction in ungauged and controlled basins. He will also attend the mandatory workshops/seminars at NCSA- UIUC as part of his fellowship.

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Hydropower and Pumped Storage Hydro Preliminary Assessments

Mesa Associates, Inc.(7/01/21 - 12/31/21)

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Hydropower and Pumped Storage Hydro Preliminary Assessments

Sponsor: Mesa Associates, Inc.

Start Date: 7/01/21

End Date: 12/31/21

Abstract

The Sponsor's project team will need help from North Carolina State University (NCSU) to support Mesa’s Principal Investigator (PI) and the Mesa team with these preliminary assessments. Specific tasks for NCSU are summarized below: • Help and support with data analysis and site assessments, approximate storage, water flows, installed capacity, and potential energy produced. • Help and support with conceptual design and preliminary cost estimates • Help and support with the technical approach and analysis. • Help and support with the estimated cost (construction and equipment) for the sites. • Review draft and final reports

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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)

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Developing Potential Scenarios of Changes in Hydroclimatic Variables for analyzing the Impacts on the Tampa Bay Water Supply System

Sponsor: Tampa Bay Water

Start Date: 10/01/20

End Date: 7/31/22

Abstract

Tampa Bay Water, the largest wholesale water provider in the southeast United States, provides drinking water to its six-member governments; three cities including New Port Richey, St. Petersburg and Tampa and three counties including Hillsborough, Pasco and Pinellas. Total service population is about 2.5 million residents. Tampa Bay Water, the operating agency, has built an integrated water supply system which includes a surface water system, groundwater wells, and a seawater desalination plant. This has enabled the Tampa Bay to shift from being 100 percent reliant on groundwater to a mixture of sources with an increasing reliance on surface waters. Close monitoring of hydroclimatic variables is thus important for the agency to rotate different supply sources to meet regional demands. Examining the impact of potential hydroclimatic changes, e.g., changes in precipitation, temperature, and streamflow, on Tampa Bay water supply system (TBWSS) is critical to understand the system vulnerability and reliability under potential climate change.

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RAISE: C-Accel Pilot – Track A1: The Urban Flooding Open Knowledge Network - Delivering Flood Information to AnyOne, AnyTime, AnyWhere

National Science Foundation (NSF)(9/01/20 - 8/31/23)

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RAISE: C-Accel Pilot – Track A1: The Urban Flooding Open Knowledge Network - Delivering Flood Information to AnyOne, AnyTime, AnyWhere

Sponsor: National Science Foundation (NSF)

Start Date: 9/01/20

End Date: 8/31/23

Abstract

Floods impact a series of interconnected urban systems (referred to in this project as the Urban Multiplex) that include the power grid and transportation networks, surface water and groundwater, sewerage and drinking water systems, inland navigation and dams, and other system, all of which are intertwined with the socioeconomic and public health sectors. This project uses a convergent approach to integrate these multiple interconnected systems and merges state-of-the-art practices in hydrologic and hydraulic engineering; systems analysis, optimization and control; machine learning, data and computer science; epidemiology; socioeconomics; and transportation and electrical engineering to develop an Urban Flood Open Knowledge Network (UF-OKN). The UF-OKN will be built by bringing together academic and non-academic researchers from engineering, computer science, social science, and economics. The UF-OKN is envisioned to empower decision makers and the general public by providing information not just on how much flooding may occur from a future event, but also to show the cascading impact of a flood event on natural and engineered infrastructure of an urban area, so that more effective planning and decision-making can occur.

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A Global Synthesis of Land-Surface Fluxes Under Natural and Human-Altered Watersheds Using the Budyko Framework

US Geological Survey (USGS)(11/21/19 - 11/20/21)

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A Global Synthesis of Land-Surface Fluxes Under Natural and Human-Altered Watersheds Using the Budyko Framework

Sponsor: US Geological Survey (USGS)

Start Date: 11/21/19

End Date: 11/20/21

Abstract

Global hydroclimate over the last century has been significantly altered by anthropogenic influences that arise from changes in global climate and also from local impacts stemming from man-made storage structures, increased groundwater withdrawal and land-use changes. Understanding and explaining how the spatio-temporal variability of land-surface fluxes differs in natural and human-altered watersheds is the goal of this synthesis study focusing at the global scale. For instance, annual average flow and annual total precipitation have increased during the period of 1948–1997 across the eastern United States, and the trends appear to arise primarily from the increase in autumn precipitation (Small et al., 2006). Irrigation in the U.S. high plains increases the rainfall and streamflow during the summer season in the Midwest (Kustu et al., 2011). Based on hydroclimatic observations in 100 large hydrological basins from 1901 to 2008, globally, Jaramillo and Destouni (2015) found consistent and dominant effects of increasing relative evapotranspiration from flow regulation and irrigation, and decreasing temporal runoff variability from flow regulation. Understanding the hydroclimatology and associated changes in natural/virgin (human-altered) watersheds will quantify the influence/role of changes in global hydroclimate (and local anthropogenic influences), thereby providing a perspective on the local vs global signals that impact watershed-level hydroclimate.

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Assessing Controls on Nutrient Loading at the Watershed Scale through Data-Driven Modeling

NCSU Water Resources Research Institute(3/01/20 - 12/31/21)

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Assessing Controls on Nutrient Loading at the Watershed Scale through Data-Driven Modeling

Sponsor: NCSU Water Resources Research Institute

Start Date: 3/01/20

End Date: 12/31/21

Abstract

Anthropogenic nutrient loading is a critical driver of water quality throughout North Carolina and much of the world. Nutrient loading has increased over the last century due to fertilization of crops and green spaces, as well as waste from humans, pets, and livestock. The most salient outcome of nutrient loading is increased eutrophication (organic matter accumulation in surface waters), often leading to harmful algal blooms and hypoxia, which jeopardize water supplies and public recreation. As such, developing nutrient criteria and management strategies is a timely objective for state water resources managers. While sources of nutrients have been identified and many nutrient control measures have been proposed, there remains a need to quantitatively assess these sources and controls, particularly at the watershed scale. In this study, we propose a modern, data-driven approach to update our knowledge of the magnitudes of various sources and the effectiveness of various nutrient control strategies. The approach leverages large databases of water quality, hydro-meteorology, and watershed attributes, which have been developed by federal, state, and local governments over the last few decades. The approach will also leverage a sophisticated “hybrid” watershed model that combines a mechanistic representation of nutrient fate and transport within a probabilistic (Bayesian) framework where prior knowledge of loading and transport rates is updated through data-driven inference, and where uncertainty is rigorously quantified. Our project will focus on the Falls and Jordan Lake watersheds of North Carolina, for which preliminary models and data are already available. Key objectives include (1) development of an integrated geospatial database on watershed development, (2) adaptation of the hybrid watershed model to assess watershed development practices, and (3) application of the model to assess future management scenarios. Expected outcomes include quantitative guidance for developing nutrient reduction goals and watershed management strategies.

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