Sankarasubramanian Arumugam

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.

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.

Understanding of the frequency of floods is critical for effective risk communication, planning and mitigation. Methods for estimating annual exceedance probabilities (AEPs) (or return intervals) in the United States are codified in the Federal guidelines of Bulletin 17C (England, Jr., and others, 2019). These guidelines acknowledge that floods may be generated by multiple causal mechanisms, such as snowmelt, intense convective rainfall events, or tropical cyclones, representing a mixed population. Floods at a single location may be generated by multiple mechanisms each of which contributes to the overall frequency of design events, such as the 1-percent AEP corresponding to the100-year flood event. Further, mixed population flood events may not only impact the fit of the flood frequency curve in the range of the observed floods but may also impact the quality of AEP estimates in the upper tail of the flood frequency distribution beyond the range of observed floods. Event-by-event information on flood generating mechanisms, or flood type classification, can aid in a mixed population analysis that improves our ability to design and prepare for dangerous flood events. These flood populations can be defined in terms of both proximal atmospheric causal mechanisms, such as different storm types, as well as antecedent watershed conditions, such as soil moisture storage and snowpack water content. Additionally, changes in the mixture of flood generating mechanisms at a given location may be incorporated into estimates of future flood conditions. In October 2022, the Federal Emergency Management Agency (FEMA), the U.S. Army Corps of Engineers (USACE), and the U.S. Geological Survey (USGS) kicked off a four-year interagency collaboration to improve flood-frequency estimates at select pilot sites.

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 1???????????????), 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.

The guiding strategy of the Southeast Climate Science Center (SE CSC) is to provide staffing and institutional support for core SE CSC mission areas. The SE CSC's mission involves supporting researchers and managers to co-produce science connected to management decisions (actionable science), coordinating logistics and communications to bring partners and the community together (within NCSU, with USGS researchers, and across the broader community) to discuss global change impacts to the DOI mission, and training the next generation (graduate students) and current managers on how to use and develop global change science.

Economic development and environmental sustainability are often conflicting objectives (Rogers, 1997). Continued economic development often arises from ensuring environmental safeguards and sustainability (Rogers,1997). This Food-Water-Energy System (FEWS) study presents a synthesis on understanding the regional and global FEW impacts due to uncertain climate and development scenarios on two regions ?????????????????? Southeast US (SEUS) and North China Plain (NCP) ?????????????????? that experience contrasting settings on water and energy availability, but have similar portfolios on crop production (corn, soybeans, fruits, vegetables and cereals ?????????????????? wheat/rice) and water (primarily groundwater) and energy (coal/natural gas) appropriation. FEW system is complex and their nexus typically organizes under different spatial and temporal scales. For instance, pollution from agricultural runoff usually have local signature and has lesser impacts and the energy grid water issues typically organize at watershed scale. However, events triggered by large-scale climatic conditions such as multi-year droughts could impact both surface water and groundwater availability which could impact hydropower generation, cooling of power plants and irrigated and rainfed agriculture. But, it is unclear how much the climatic impacts on regional FEWS could impact global food prices and commodity flow. Similarly, federal policy changes (e.g., tax deductions for solar PV installation) could potentially make the nexus resilient, depending on the nature of FEWS, against climate variability. We intend to explore these research issues and perform a cross-regional synthesis on two regions, Southeast US and North China Plain, for improving food-energy-water system sustainability.

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.

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.

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.

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.