Sankarasubramanian Arumugam

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 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.

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.

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

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.

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.

The last decade saw a series of catastrophic floods due to hurricanes and storms of increased intensity, and news headlines like Cities Swimming in Raw Sewage After Storm Expose Flaws in System became commonplace. These events exposed the fault lines in flood management. Perhaps more importantly, they also revealed the complex and interconnected nature of engineered, natural and social systems that form the fabric of modern cities. These systems can be conceptualized as a network of networks, or a multiplex, that includes the power grid and transportation network, surface water and groundwater, sewerage and drinking water systems, inland navigation and dams, intertwined with the socioeconomic and public health sectors. Under external pressures and improper management, failures propagating across the Urban Multiplex became obvious - as if viewed under a magnifying glass. Extreme weather is a primary driver of flooding. Its consequences however depend on the interconnectedness of the multiplex components that are, unfortunately, typically designed and/or analyzed independently of one another. For example, a power outage may lead to failure of a storm water network designed to carry the maximum flow during floods, resulting in raw sewage overflow into streets and exposing humans to pathogens. At the same time, storm water overflows could flood critical segments of road networks designed to meet traffic needs, preventing timely evacuation of vulnerable populations. Thus, it is impossible to effectively handle increasingly frequent urban floods by managing these components independently from one another, and ignoring the inherent interconnections of the urban multiplex. Rather, a convergent approach that integrates all interconnected systems and merges state-of-the-art in hydrological and hydraulic engineering; systems analysis, optimization and control; artificial intelligence, data and computer science; epidemiology; socioeconomics; transportation and electrical engineering is proposed in this research.

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.

Continually increasing water demand (due to population growth) and fuel costs threaten the reliability of water and energy systems and also increase operational costs. In addition, both natural climatic variability and the impacts of global climate change increase the vulnerability of these two systems. For instance, reservoir systems depend on precipitation; whereas power systems demand depend on mean daily temperature. Currently, these systems use seasonal averages for their short-term (0-3 months) management, which ignores uncertainty in the climate, thereby resulting in increased spillage and reduced hydropower. While seasonal climate forecasts contain appreciable levels of skill over parts of the US in both winter and summer, the uptake of these forecasts for water and energy systems management has been limited due to lack of a coherent approach to assimilate probabilistic forecasts into management models. We systematically analyze various scenarios that aim at improving the performance of these systems utilizing the multimodel climate forecasts and a high performance computing (HPC) framework.

The main objective of the proposed research is to understand, identify and quantify uncertainties related to the freshwater sustainability under near-term climate change and population growth by incorporating adaptive responses, feedbacks as well as the needs of hydro-ecological systems and human-environmental systems through a cross-regional synthesis. The specific tasks associated with this objective are: 1) Reduce the uncertainty in targeted hydro-ecological variables under near-term climate change by combining projections from multiple GCMs (from AR5), regional climate models (from NAARCAP) and hydrological models (VIC, SWAT and MODFLOW) 2) Ingest the hindcasts and future projections (i.e., time series) of the targeted variables in reservoir models, groundwater models and ecological models to quantify the resilience and vulnerability of water infrastructure systems and ecological systems over the target basins 3) Identify key policy interventions, supply augmentation and demand reduction measures that will ensure freshwater sustainability under projected population growth and climate change 4) Incorporate the identified consumer feedbacks and societal adaptations with the water infrastructure models through an agent-based model 5) Perform an integrative cross-regional analyses to identify critical tipping points and feedback mechanisms that will reduce the uncertainty in freshwater sustainability under near-term climate change and population growth

Inability to maintain proper water quality in natural systems could result not just from excessive point and non-point source loadings, but also from ill-conceived water allocation policies. Given that seasonal to interannual climatic variability modulates both precipitation and temperature, streamflow and river water quality and ecology may respond dramatically to the extremes of climate. We argue that by utilizing climate information, an adaptive approach to integrated water quantity and quality management could be developed which on continual application over long-term could result in improved water resources sustainability. The proposed CAREER-Development Plan (CDP) addresses two key research issues discussed by the National Research Council (NRC, 2001; 2002a): (a) to quantify the impacts of climate variability on water management (b) to explicitly quantify climatic uncertainties on streamflow and water quality and to incorporate them probabilistically into water management models. The proposed plan builds upon previous research by the PI on climate, streamflow forecasting and water management.

The proposed research builds upon the ongoing effort in developing an experimental inflow and storage forecasts portal (http://www.nc-climate.ncsu.edu/inflowforecast) housed at the SCO into an integrated drought management and assessment portal (Figure 1) for NC. Two specific objectives are proposed in this study: 1) Develop operational soil moisture and streamflow forecasts for the entire state of NC by enhancing the existing experimental reservoir inflow and storage forecasts portal with the NASA’s Land Information System (LIS) and distributed modeling framework. 2) Develop adaptive drought management framework that supports monitoring, prediction and drought management including customized drought indices for the entire state of NC.

NC State has significant strengths in water resources research, including the human dimension, across multiple colleges (e.g., CNR, CALS, CHASS, COS, Design and Engineering). Yet, there in not a forum for sustained interaction among faculty that facilitates understanding of the rich set of interdisciplinary perspectives on campus and fosters new collaborative opportunities. We envision providing this forum through a series of integrative activities including a brownbag seminar series, a graduate student two-day workshop, and a “Water Summit” conference. Each activity will be designed to deepen cross-disciplinary understanding and work toward the goal of assembling multidisciplinary teams that compete successfully for new funding opportunities. Outcomes will include the participants identifying new cross-discipline research collaborations; identification of curricular opportunities related to water resources; and developing a sustainability plan for the network’s continuance.

The reliability of existing water supply systems are threatened due to increase in water demand resulting from urbanization and population growth. This combined with seasonal to interannual variability in streamflow increase the vulnerability of these systems particularly during prolonged droughts. The recent project funded by WRRI provides experimental monthly to seasonal forecasts of streamflows for four major reservoir systems (Falls Lake, Jordan Lake, Kerr Scott and Philpot). These experimental reservoir inflow forecasts (http://www.nc-climate.ncsu.edu/inflowforecast) are developed by downscaling the precipitation forecasts available from ECHAM4.5 General Circulation Model using principal components regression. This prognostic information, which is available as shift in probabilities of streamflows, could be converted to experimental reservoir storage forecasts by combining the inflow forecasts with a reservoir simulation model. The inflow forecasts portal could also be customized to project monthly to seasonal storage scenarios for any user prescribed releases.

The primary objective is to convene climate scientists, hydrologists, forecasting agencies, water utilities, reservoir operators and water management agencies together for understanding the challenges and opportunities in developing hydroclimate forecasts relevant to water resources management. Over four days, the participants will focus on various issues related to hydroclimate forecasts and water management by addressing the following science questions: 1. What are the key sources of uncertainties that challenge development of skillful hydroclimate forecasts at daily, seasonal and interannual time scales? 2. How best do we reduce the uncertainty and improve reliability in downscaling largescale climate information for developing regional hydroclimate forecasts? 3. What are the key challenges in using probabilistic streamflow information in operational water resource management models and decision tools? 4. What are the limitations in applying streamflow forecasts for real-time applications? 5. How can we bridge the gaps between forecast producers (agencies, research institutions) and forecast consumers (water resource managers, operational agencies) for improving forecast applications in water management?

: To manage the stress imposed on water supply through urbanization, population growth, and climate change, engineering solutions typically focus on expanding water infrastructure and finding alternative sources of water. The sustainability of a water resources system, however, emerges from the interactions among the environmental, technological, and social characteristics of the water system and local population. For instance in Wake County, NC, municipal water demand increased by 60% in the last decade due to rapid development and urbanization, which has stressed the reliability of Falls Lake and caused frequent water shortages. Instituting management measures, including water restrictions, results in reduced return flows and revenue loss for the city. Thus, consumer decisions to re-locate to an urban area, use water effectively, and comply with restrictions, can impact the health and availability of water resources. This decision-making process is usually adaptive considering both existing conditions (e.g., droughts) and potential/anticipated changes to the system such as climate change, scarcity of resources, and regulations; therefore a feedback process exists between human activities and the natural and infrastructure systems. The objective of the proposed research is to develop a dynamic modeling approach to couple urban growth dynamics, consumer behaviors, and projections of climate change with watershed and reservoir models for identifying sustainable water management strategies for the Falls Lake.

Although water resources in North Carolina (NC) are abundant6, conflicting demands by growing urban areas, industrial use (including thermoelectric power) and agricultural use have forced the local/regional water supply systems to update their water supply plans once in five years. Accurate development of these water supply plans requires assessment of both water availability and demand over the planning horizon. But, assessment of water availability and demand are not two independent tasks, since increased demand resulting from development could change the land use, which in turn could reduce baseflow with increased overland runoff. This together with regional impacts resulting from global climate change could create considerable uncertainty in the developed water supply plans. In this study, we propose to quantify surface water availability and its uncertainty in the next 10-30 years under potential climate and land cover change scenarios over NC.

Ensuring sustainability of water resources is often challenging owing to the range of time scales over which the water management problems span. In addition, limited scope for developing (expanding) new (existing) water supply infrastructure systems and use of conservative operational guidelines based on observed information only increase the vulnerability of existing systems to continually changing supply and demand variations. Though these issues in managing water supply systems (reservoirs, lakes) are common in many regions of the country, what is lacking is a systematic integrated approach that improves sustainability of existing systems in ensuring the desired reliability and resilience even under these changing conditions. Commonly employed strategies, e.g., water use restrictions and supply augmentation, are often invoked retroactively resulting in increased vulnerability on system management. We propose to research, develop and demonstrate methods that promote proactive and prognostic water management plans conditioned on near-term and short-term streamflow forecasts, which are developed based on weather and climatic information, respectively. By improving the operation of the reservoir systems adaptively using multiple time scale forecasts, we envision that such a prognostic approach will result in better system performance over the long-term and potentially reduce investments associated with additional structural measures or inter basin transfers to meet the supply and demand variations.

Seasonal to Interannual Climate Forecasts (SICF) has the potential to improve short-term water management by developing strategies that reduce the vulnerability of water supply systems during extreme years. A wide spectrum of the population in GHA will benefit not only from improved urban water supply utilizing SICF, but also from increased energy availability since most of the reservoir systems are multipurpose. The methodology proposed here is transferable not only to Kenya and GHA but also to other geographic areas where the skill of SICF is significant, particularly in the South Eastern US, Indonesia and North East Brazil. By utilizing NOAA?s Climate Forecast System (CFS) products for downscaling to streamflow over the Tana River basin in Kenya, this application provides a great opportunity to showcase NOAA?s forecasting capabilities and its derived benefits.

This proposal outlines the scope and methodologies to be considered for developing streamflow forecasts for the Lewis River basin, WA. The main intent of the proposal will be towards: (a) Developing 7-day (168 hours) ahead streamflow forecasts into three major reservoirs on the Lewis River and updating the 7 days ahead streamflow forecasts daily to quantify the change in streamflow potential. (b) Developing 15-month ahead streamflow forecasts for Merwin reservoir and updating them monthly to quantify the change in streamflow potential. The study will explore both statistical and physical modeling for developing streamflow forecasts and will also investigate the utility in combining these two forecasts to develop a multimodel streamflow forecasts for the three reservoir sites (Swift, Yale and Merwin).

Two major factors affecting the health of watershed are basin-level land use changes due to civil infrastructure development, and anticipated changes in precipitation and temperature due to global climate change. Both influence changes in stormwater runoff that impacts hydrologic flows. As growth and development continue with inevitable climate change effects, we face a major challenge in regards to taking proactive measures for planning and managing watershed development while ensuring environmental sustainability by matching pre-impact and post-impact conditions. The vision of the proposed research is to develop a quantitative framework for evaluating environmentally sustainable runoff management strategies to effectively respond to impending impacts expected from locally induced land use changes and globally changing climate patterns.

The proposed research intends to capitalize on the recent efforts in developing seasonal streamflow forecasts for the Neuse basin3,6 funded by the NC WRRI. The proposed study will develop seasonal streamflow forecasts, primarily for the winter (January-March) and summer (July-September) seasons, at the HUC-8 basins by downscaling the climate forecasts available from various centers. Downscaled streamflow forecasts expressed as tercile categories (below-normal, normal and above-normal) will be made available through the State Climate Office of NC website for dissemination. We intend to disseminate both downscaled retrospective (1970-2008) streamflow forecasts and real-time streamflow forecasts (to be developed during the 2009 summer and 2010 winter seasons) through the NC Climate Retrieval and Observations Network of the Southeast (NC CRONOS) database. For HUC-8 basins that receive streamflow primarily from controlled releases or from upstream reservoirs (e.g., lower Cape Fear), the study will provide tercile forecasts of precipitation downscaled from climate models.

A strategy to improve water allocation in the Neuse basin is proposed by developing a seamless integration climate-information based streamflow forecasts into water systems planning (3 months to 6 months) and operation. The proposed research will develop long-lead probabilistic streamflow forecasts in the Neuse basin contingent on both local land-surface and exogenous climatic conditions. Retrospective streamflow forecasts will be combined with a reservoir management model to understand the utility of streamflow forecasts in operating the Falls Dam. With the decadal variability in the tropical Atlantic Sea Surface Temperature (above-normal conditions) resulting in more hurricanes, it is imperative to develop a prognostic approach for water management in the Neuse basin given the basin accounts for 22% of state?s population. Such an approach based on climate information could help water managers to prepare well in advance to reduce the impacts resulting from hydroclimatic extremes. Three specific objectives are encompassed in the proposed study: (a) Development of a climate-information based streamflow forecasting model (b) Perform retrospective analyses on the utility of climate forecasts in improving Falls Lake operation (c) Dissemination of results from the analyses with various state agencies that coordinate water-related activities in the Neuse basin. Benefits/Information from this project will support other ongoing activities in the Neuse including Neuse river basin planning program (supported by DENR), National Water Quality Assessment Program (supported by USGS) and NC Drought Monitoring (supported by Division of water resources, DENR) in coordination with the state?s climate office. Analyses from this research will also promote identification of alternate river basin management plans during critical drought conditions including conjunctive use, instream flow maintenance and estuaries management. Informative web portal will de developed that summarizes the hydroclimatic predictability of the Neuse basin as well as updates of streamflow potential for the seasons ahead. Potential implications and its relevance to several ongoing researches in the Neuse basin will include quantitative representation of uncertainty in streamflows to support TMDL process, development of seasonal water management plans considering conjunctive use for the coastal zone and prediction outlooks for floods and droughts. We envisage that this effort for Neuse basin will motivate other basins in NC to incorporate to follow a prognostic, climate-information driven approach towards water management.