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At least 127 records · Page 7

Optimal Electric Vehicle Charging and Discharging Strategies Under DER Compensation Programs: Preprint

The adoption of electric vehicles (EVs) is becoming increasingly popular because of environmental concerns, the greater availability of models, and increased cost-competitiveness with gas vehicles. Because EVs have both charging and discharging capabilities, they provide great potential to help electric utilities with grid operation. When the grid demand is high, EVs can discharge to the grid to reduce the peak load, and vice versa; therefore, electric utilities have designed different policies to encourage EV charging station operators to charge or discharge at certain time periods. The New York State Public Service Commission established the Value of Distributed Energy Resources (VDER), or the Value Stack, to compensate for energy created by distributed energy resources, including EVs. This paper presents an optimization-based approach to identify the "golden hours" and "golden spots," i.e., the effective time periods and geographic locations for EV charging station operators to charge or discharge under the VDER program that can provide them the highest benefit. The proposed methodology can be applied to other compensation mechanisms and distribution systems as well. By working with industry partner NineDot Energy, realistic charging station information is used in this study, and the proposed approach is tested on a distribution feeder. The results from this study can help electric utilities and EV charging station operators determine the ideal charging/discharging time and the ideal locations for the charging station(s) in their distribution systems to achieve maximized benefit.

electric vehicle↗

A Data Processing Pipeline To Extract A Knowledge Graph From Heterogeneous Data For Socio-technical Analysis Of Critical Infrastructure Influence

The code is written in Python and consists of the following pipeline that is implemented in Apache Airflow. This pipeline intends to understand the companies that are directly or indirectly involved with a type of critical infrastructure system at some point in that system's lifecycle. The pipeline takes a configuration file that specifies a list of initial companies to consider, a geographic region of interest, and a set of SEC form types as well as other data sources (e.g. CrunchBase) from which to extract entities and relations. There are four main components to this pipeline as currently implemented: Entity Extraction, Network Construction, Analysis, and Visualization. First, Entity Extraction, is implemented as the `topear-extract_organizations` Apache Airflow workflow. Given an initial query that specifies a geographic region of interest and a time interval, the software will extract CI facilities of interest and organizations that have a direct influence relationship to those facilities (e.g. ownership). During the course of the LDRD, we focused on Electric Vehicle charging stations and this information is available via the Department of Energy (DOE) database on fueling stations maintained by NREL. Within the context of the DOE CESER project, we have focused on Battery Energy Storage Systems (BESS). Second, the Network Extraction component will iteratively construct a social network graph given the set of organizations and people extracted in the previous step. Organizations (and eventually People if desired) are then fed as a query to the `topgear-construct_social_network` Apache Airflow workflow which given a set of initial companies and data sets (e.g. SEC EDGAR form types, OpenCorporates, Crunchbase). This Airflow workflow will iteratively query such data sources to discover relationships with new organizations and people. For example, this module can iteratively query SEC EDGAR for metadata that documents the number of each type of form for the given set of companies and their location. This forms metadata represents a catalog of data sources from SEC EDGAR for the extracted social network knowledge graph. The pipeline then downloads these forms from the website and saves them in a build directory for further processing. These documents are then parsed for entities and relations. Again, we note that in additional to SEC data sources, this step can also pull in information on organizations via API services such as CrunchBase and OpenCorporates or bulk data sources. At the end of this step, the resultant social network, the Critical Infrastructure network, and the edges that encode relationships between organizations and CI facilities, form the Adversarial Socio-Technical Network (ASTN) that informs the analysis. Third, the Analysis component processes these generated ASTN. Previously, that has included the ability to compare prevalence of different vendors for a given infrastructure component type across different regions as well as identify common public and private investors across those vendors. This was demonstrated for EV Charging Stations across several different metropolitan areas within an IEEE PES GridEdge publication. More recently, we have looked at ways to identify infrastructure owners and operators of BESS with the most nameplate capacity across different states as well as other indictors of risk resulting from changes in ownership over time. Finally, the Visualization component consists of an HTML/CSS/JS framework by which users can interact geospatial, operational, and organizational relationships across a given portfolio of Critical Infrastructure facilities. The objective is to provide a library of UI/UX modules that can be repurposed for stakeholder-specific dashboards. All of the modules are related via a common event model that enables UI actions in one view to percolate across the other views.

Weaver, Gabriel [Idaho National Laboratory (INL), ↗

Toward Improved Regional Hydrological Model Performance Using State-Of-The-Science Data-Informed Soil Parameters

Accurate soil moisture and streamflow data are an aspirational need of many hydrologically relevant fields. Model simulated soil moisture and streamflow hold promise but models require validation prior to application. Calibration methods are commonly used to improve model fidelity but misrepresentation of the true dynamics remains a challenge. In this study, we leverage soil parameter estimates from the Soil Survey Geographic (SSURGO) database and the probability mapping of SSURGO (POLARIS) to improve the representation of hydrologic processes in the Weather Research and Forecasting Hydrological modeling system (WRF-Hydro) over a central California domain. Our results show WRF-Hydro soil moisture exhibits increased correlation coefficients ( r ), reduced biases, and increased Kling-Gupta Efficiencies (KGEs) across seven in situ soil moisture observing stations after updating the model's soil parameters according to POLARIS. Compared to four well-established soil moisture data sets including Soil Moisture Active Passive data and three Phase 2 North American Land Data Assimilation System land surface models, our POLARIS-adjusted WRF-Hydro simulations produce the highest mean KGE (0.69) across the seven stations. More importantly, WRF-Hydro streamflow fidelity also increases, especially in the case where the model domain is set up with SSURGO-informed total soil thickness. The magnitude and timing of peak flow events are better captured, r increases across nine United States Geological Survey stream gages, and the mean KGE across seven of the nine gages increases from 0.12 to 0.66. Our pre-calibration parameter estimate approach, which is transferable to other spatially distributed hydrological models, can substantially improve a model's performance, helping reduce calibration efforts and computational costs.

54 ENVIRONMENTAL SCIENCES↗

A data-driven mobility–energy typology framework for New York State

Exploring multidimensional aspects of differences in technology adoption, travel, and vehicle ownership across settlement types can help inform energy-efficient and affordable mobility system goals. Concurrently, mapping key enablers, barriers, and risks for successfully meeting ambitious goals and targets (e.g. by geography, age, income, education, population density) offer important explanatory power as to context-specific challenges and opportunities. This paper explores how a highly geographically resolved understanding of social, economic, techno-infrastructural, environmental, and governance (SET EG) factors shape variations in technology adoption and associated mobility and energy outcomes in diverse communities of New York State, in terms of electric vehicle adoption rates, alternative commute mode choices, vehicles per household, and vehicle fuel economy. Results indicate the range of two to three times higher adoption rates for electric vehicles by more highly educated, wealthier, “Core Urban” populations relative to the other identified typologies, given the labels “Rural”, “Suburban”, and “Urban” populations. Additionally, commute mode choice is closely linked with population and employment density—more than 89% of Core Urbanites use transit or active modes, compared with just 26% and 18% of Suburban and Rural residents, respectively. Household vehicle ownership varies from approximately 1.9 vehicles per household in Rural areas to only 0.6 in Core Urban settings. Findings on differences among the four settlement types, which go beyond simply rural to urban contexts, suggest an important need to explore how best to manage and anticipate very different types of services that may be supportive in achieving energy-efficient and affordable mobility systems statewide.

54 ENVIRONMENTAL SCIENCES↗

Mapping brucellosis risk in Kenya and its implications for control strategies in sub-Saharan Africa

In Sub-Saharan Africa (SSA), effective brucellosis control is limited, in part, by the lack of long-term commitments by governments to control the disease and the absence of reliable national human and livestock population-based data to inform policies. Therefore, we conducted a study to establish the national prevalence and develop a risk map for Brucella spp. in cattle to contribute to plans to eliminate the disease in Kenya by the year 2040. We randomly generated 268 geolocations and distributed them across Kenya, proportionate to the area of each of the five agroecological zones and the associated cattle population. Cattle herds closest to each selected geolocation were identified for sampling. Up to 25 cattle were sampled per geolocation and a semi-structured questionnaire was administered to their owners. We tested 6,593 cattle samples for Brucella immunoglobulin G (IgG) antibodies using an Enzyme-linked immunosorbent assay (ELISA). We assessed potential risk factors and performed spatial analyses and prevalence mapping using approximate Bayesian inference implemented via the integrated nested Laplace approximation (INLA) method. The national Brucella spp. prevalence was 6.8% (95% CI: 6.2–7.4%). Exposure levels varied significantly between agro-ecological zones, with a high of 8.5% in the very arid zone with the lowest agricultural potential relative to a low of 0.0% in the agro-alpine zone with the highest agricultural potential. Additionally, seroprevalence increased with herd size, and the odds of seropositivity were significantly higher for females and adult animals than for males or calves. Similarly, animals with a history of abortion, or with multiple reproductive syndromes had higher seropositivity than those without. At the herd level, the risk of Brucella spp. transmission was higher in larger herds, and herds with a history of reproductive problems such as abortion, giving birth to weak calves, or having swollen testes. Geographic localities with high Brucella seroprevalence occurred in northern, eastern, and southern regions of Kenya all primarily characterized by semi-arid or arid agro-ecological zones dominated by livestock pastoralism interspersed with vast areas with mixed livestock-wildlife systems. The large spatial extent of our survey provides compelling evidence for the widespread geographical distribution of brucellosis risk across Kenya in a manner easily understandable for policymakers. Our findings can provide a basis for risk-stratified pilot studies aiming to investigate the cost-effectiveness and efficacy of singular and combined preventive intervention strategies that seek to inform Kenya’s Brucellosis Control Policy.

60 APPLIED LIFE SCIENCES↗

Comprehensive compartmental model and calibration algorithm for the study of clinical implications of the population-level spread of COVID-19: a study protocol

The complex dynamics of the coronavirus disease 2019 (COVID-19) pandemic has made obtaining reliable long-term forecasts of the disease progression difficult. Simple mechanistic models with deterministic parameters are useful for short-term predictions but have ultimately been unsuccessful in extrapolating the trajectory of the pandemic because of unmodelled dynamics and the unrealistic level of certainty that is assumed in the predictions. We propose a 22-compartment epidemiological model that includes compartments not previously considered concurrently, to account for the effects of vaccination, asymptomatic individuals, inadequate access to hospital care, post-acute COVID-19 and recovery with long-term health complications. Additionally, new connections between compartments introduce new dynamics to the system and provide a framework to study the sensitivity of model outputs to several concurrent effects, including temporary immunity, vaccination rate and vaccine effectiveness. Subject to data availability for a given region, we discuss a means by which population demographics (age, comorbidity, socioeconomic status, sex and geographical location) and clinically relevant information (different variants, different vaccines) can be incorporated within the 22-compartment framework. Considering a probabilistic interpretation of the parameters allows the model's predictions to reflect the current state of uncertainty about the model parameters and model states. We propose the use of a sparse Bayesian learning algorithm for parameter calibration and model selection. This methodology considers a combination of prescribed parameter prior distributions for parameters that are known to be essential to the modelled dynamics and automatic relevance determination priors for parameters whose relevance is questionable. This is useful as it helps prevent overfitting the available epidemiological data when calibrating the parameters of the proposed model. Population-level administrative health data will serve as partial observations of the model states.

59 BASIC BIOLOGICAL SCIENCES↗

Rapid Characterization and Statistical Analysis of High-Volume Field-Harvested Photovoltaic Connectors

Photovoltaic (PV) installations heavily depend on connectors for efficient module and string interconnections without requiring skilled labor. Yet this seemingly innocuous component of PV systems is a leading cause of module failures, multiple high-profile fires, and lawsuits in the PV industry. This work aims to answer critical questions regarding why connectors fail and the contributing factors to their failure. The study involves collecting and analyzing more than 17,000 field-harvested connectors from various solar installations across the United States. The vast dataset, which includes connector metadata, visual inspections, and resistance measurements, provides unprecedented insight into the state of health of PV connectors across the US, including the geographic locations, connector types, and installation practices most prone to failures. The work presented here describes a novel rapid characterization method for processing large numbers of connectors and is supported by parallel forensic analysis to discern the root causes of failures as well as a levelized cost of lifetime model to determine the economic ramifications of connector failure. Ultimately, the findings may inform PV developers about the best practices to extend connector longevity and lead to more resilient and reliable PV systems.

connectors↗

The CanBikeCO Mini Pilot: Preliminary Results and Lessons Learned

In fall 2020, the Colorado Energy Office, as part of the State of Colorado’s “Can Do Colorado” initiative, initiated a project aimed at encouraging energy-efficient transportation during the COVID-19 pandemic. The initial mini-pilot provided e-bikes to 13 low-income households under an individual ownership model. This report assesses the impact of providing this additional mobility option on the travel behavior of participants. It also outlines the lessons learned from deploying a continuous monitoring platform to track the travel behavior. These lessons will influence the evaluation component for the full pilot, which will cover multiple geographic regions, starting in summer 2021, and run for 2 years. The continuous data collection was enabled by a customized version of the open-source e-mission platform, called CanBikeCO, configured with a behavioral gamification feature. The Colorado Energy Office used this system to collect a unique data set consisting of 3 months of partially automated travel diaries, combining sensed and surveyed data and linked with demographic information, from 12 participants. The data collection process worked well overall: users generally liked the app, appreciated the game, and did not complain about battery life. The long tracking period introduced behavioral challenges in user engagement, which we plan to address using repeated patterns and automated status checks for the full pilot. The analysis results, based on the subset of trips with user-reported labels (68%), indicate that the e-bike was the dominant commute mode share (31%), in sharp contrast to the census bicycle commute mode share (<1%). E-bike trips primarily replaced single-occupancy vehicle (SOV) trips (28%), followed closely by walking (24%) and regular bike (20%). The nonmotorized mode replacement corresponds to lower travel time and increased productivity enabled by the program. The emissions impact analysis of the program, computed using trip-level energy intensity factors, indicates savings of 1,367 lbs. of CO 2 . Although the results are strongly positive, the narrow demographic profile of study participants, their limited mobility alternatives, and nonuniform labeling indicate caution in broader interpretation. These preliminary results do suggest that such programs, supported by real-time education and support from program managers, can simultaneously meet equity and sustainability goals. The planned full pilot, addressing the data collection challenges and broadening the geographic scope, will provide additional insights into the generality of this approach.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Idaho National Laboratory Integrated Multisite SSHAC Level 3: Probabilistic Volcanic Hazards Assessment

The Idaho National Laboratory (INL) resides on the eastern Snake River Plain (ESRP), part of the Snake River Plain (SRP) with a complex origin and geologic history of volcanism. Much of the Quaternary (last 2.58 million years) volcanism within 400 km of INL is genetically associated with a major thermal anomaly referred to as the Yellowstone hotspot, which is currently located more than 180 km northeast of INL. Near INL, local volcanic sources include silicic domes near its southern border, and numerous dike-fed basaltic vents of the ESRP, some of which are near or within the INL boundaries. Quantitative probabilistic assessments of screened-in volcanic hazardous phenomena for nine different facility complexes at the INL are presented for a Senior Seismic Hazard Analysis Committee (SSHAC) Level 3 (SL3) study. The comprehensive, integrated multisite SSHAC study consists of a single regional Probabilistic Volcanic Hazards Assessment (PVHA) that pertains to all nine INL facility complexes, with site-specific information developed at each respective area of interest (AOI), referred to as the "INL facility AOI" (Figure ES-1). Hazard products are generated for specified INL facility AOIs for use by multiple stakeholders from different agencies in risk-informed decision-making regarding site selection, operations, and design of nuclear facilities at INL, consistent with U.S. Department of Energy (DOE) and U.S. Nuclear Regulatory Commission (NRC) regulatory guidance. The study also serves as the basis for future periodic safety assessments required for existing DOE facilities, such as 10-year evaluations of natural-phenomena hazards. Elements of the INL PVHA including initial characterization, screening, quantitative assessments of eruption and hazard potential, and consideration of facility needs are conducted using the three phases of the SSHAC process: evaluation, integration, and documentation. As per regulatory guidance, the SSHAC framework provided the necessary processes and procedures for the PVHA Technical Integration (TI) team, at three workshops, five formal working meetings and many TI team remote meetings, to conduct initial characterization, screen the volcanic hazards, create PVHA model inputs, exercise those models in the PVHA, consider facility-specific volcanic hazard needs, and perform final hazard calculations. The Participatory Peer Review Panel (PPRP) provided independent oversight and performed process and technical reviews of the PVHA throughout its duration. The study included an extensive New Data Collection and Analyses (NDCA) program developed by the PVHA TI team to reduce uncertainties in hazard-significant elements in the PVHA model. NDCA activities generated 20 reports providing important contributory datasets and results to the project database for characterizing the ESRP. For example, a report compiling the dimensions of ESRP shield volcanoes and lava fields was used to construct volcanic footprints (areas of impact), was compared with data from INL subsurface cores, and was used to validate the results of lava-flow inundation modeling on the contemporary terrain. Another example is the acquisition of aeromagnetic data over INL and its surrounding area, with maps of buried magmatic features (e.g., subsurface volcanoes and swarms of feeder dikes) that informed the PVHA conceptual model of volcanism. The SSHAC evaluation process was used to conduct all elements of the PVHA including initial characterization and screening. Existing data and NDCA activities provided the foundation to develop the tectonomagmatic conceptual model of volcanism for the region of geographical interest in the SRP and Yellowstone hotspot volcanic system, and for considering volcanoes in the western US. Considering Quaternary volcanic sources active during this period, the screening approach identified and evaluated magma compositions, types of eruptive and intrusive phenomena, types of hazardous phenomena, and proximity of sources to INL. The PVHA TI team evaluated 18 types of magmatic sources in terms of 20 potentially hazardous phenomena, resulting in 360 screening decisions. The screening process resulted in 140 screened-in hazardous volcanic phenomena for Quaternary volcanic sources 1) proximal to INL facility complexes in the ESRP (64 basaltic and 54 silicic), 2) regional sources associated with the Yellowstone caldera system and Blackfoot Reservoir volcanic field (7), and 3) more distal sources from thirteen Cascade volcanoes and two volcanoes at Long Valley caldera (CA).

58 GEOSCIENCES↗

Simulating the Impact of Dynamic Rerouting on Metropolitan-scale Traffic Systems

The rapid introduction of mobile navigation aides that use real-time road network information to suggest alternate routes to drivers is making it more difficult for researchers and government transportation agencies to understand and predict the dynamics of congested transportation systems. Computer simulation is a key capability for these organizations to analyze hypothetical scenarios; however, the complexity of transportation systems makes it challenging for them to simulate very large geographical regions, such as multi-city metropolitan areas. In this article, we describe enhancements to the Mobiliti parallel traffic simulator to model dynamic rerouting behavior with the addition of vehicle controller actors and vehicle-to-controller reroute requests. The simulator is designed to support distributed-memory parallel execution using discrete event simulation and be scalable on high-performance computing platforms. We demonstrate the potential of the simulator by analyzing the impact of varying the population penetration rate of dynamic rerouting on the San Francisco Bay Area road network. Using high-performance parallel computing, we can simulate a day in the San Francisco Bay Area with 19 million vehicle trips with 50 percent dynamic rerouting penetration over a road network with 0.5 million nodes and 1 million links in less than three minutes. We present a sensitivity study on the dynamic rerouting parameters, discuss the simulator’s parallel scalability, and analyze system-level impacts of changing the dynamic rerouting penetration. Furthermore, we examine the varying effects on different functional classes and geographical regions and present a validation of the simulation results compared to real-world data.

97 MATHEMATICS AND COMPUTING↗

LCC-based framework for building envelope and structure co-design considering energy efficiency and natural hazard performance

Herein, this paper presents a life-cycle cost (LCC) informed co-design framework for building structures and envelope systems, holistically considering the influences of energy and natural hazard performance. The proposed method is consisted of a two-stage design and decision-making process, aiming to provide a quantitative guideline for building's structural and envelope co-design based on the its geographic locations. First, the building's structural configuration and envelope type are selected based on the life cycle cost. Then, the long-term cost effectiveness of various energy-saving building envelope options (e.g., high-performance glazing and insulation) is evaluated to refine the envelope design. The proposed co-design framework was demonstrated through the case study of a medium-size office building archetype in three locations with distinct climate conditions and seismic activities (i.e., Los Angeles, Memphis, and Boston). The results highlighted the interplay between building's structural (seismic) performance and the cost-effectiveness of energy-saving design options – e.g., for buildings located in high-seismic regions, seismic enhancing designs greatly reduce the paybak period of high performance building envelope by reducing the seismic loss; whereas for buildings located in regions with cold climate and low seismic risk such as Boston, spatial frame with high insulation building envelope shows the lowest LCC.

42 ENGINEERING↗

Epidemiology, Clinical Features, and Outcomes of Coccidioidomycosis, Utah, 2006–2015

On the basis of a 1957 geographic Coccidioides seropositivity survey, 3 counties in southwestern Utah, USA, were considered coccidioidomycosis-endemic, but there has been a paucity of information on the disease burden in Utah since. We report findings from a recent clinical and epidemiologic study of coccidioidomycosis in Utah. To describe clinical characteristics, we identified all coccidioidomycosis cases in an integrated health system in the state during 2006–2015. For epidemiologic analysis, we used cases reported to the Utah Department of Health during 2009–2015. Mean state incidence was 1.83 cases/100,000 population/year. Washington County, in southwestern Utah, had the highest incidence, 17.2 cases/100,000 population/year. In a generalized linear model with time as a fixed effect, mean annual temperature, population, and new construction were associated with regional variations in incidence. Using these variables in a spatiotemporal model, we estimated the adjusted regional variation by county to predict areas where Coccidioides infections might increase.

60 APPLIED LIFE SCIENCES↗

A typological framework of non-floodplain wetlands for global collaborative research and sustainable use

Non-floodplain wetlands (NFWs) are important but vulnerable inland freshwater systems that are receiving increased attention and protection worldwide. However, a lack of consistent terminology, incohesive research objectives, and inherent heterogeneity in existing knowledge hinder cross-regional information sharing and global collaboration. To address this challenge and facilitate future management decisions, we synthesized recent work to understand the state of NFW science and explore new opportunities for research and sustainable NFW use globally. Results from our synthesis show that although NFWs have been widely studied across all continents, regional biases exist in the literature. We hypothesize these biases in the literature stem from terminology rather than real geographical bias around existence and functionality. To confirm this observation, we explored a set of geographically representative NFW regions around the world and characteristics of research focal areas. We conclude that there is more that unites NFW research and management efforts than we might otherwise appreciate. Furthermore, opportunities for cross-regional information sharing and global collaboration exist, but a unified terminology will be needed, as will a focus on wetland functionality. Based on these findings, we discuss four pathways that aid in better collaboration, including improved cohesion in classification and terminology, and unified approaches to modeling and simulation. In turn, legislative objectives must be informed by science to drive conservation and management priorities. Finally, an educational pathway serves to integrate the measures and to promote new technologies that aid in our collective understanding of NFWs. Our resulting framework from NFW synthesis serves to encourage interdisciplinary collaboration and sustainable use and conservation of wetland systems globally.

54 ENVIRONMENTAL SCIENCES↗

Data Request for the Distribution Grid Atlas

Model-based, distribution powerflow analysis is a foundational component of system planning and grid modernization efforts, but data security is an impediment to collaboration among utility engineers, researchers, developers, community members, and other stakeholders. Pacific Northwest National Laboratory (PNNL) and the National Renewable Energy Laboratory (NREL) are partnering to develop the new Distribution Grid Atlas - a publicly available catalog of realistic, geographically relevant, representative distribution feeder models without sensitive geographic information, customer data, or disclosure of utility models. We are looking for utilities to share data for the Distribution Grid Atlas.

distribution↗

Hydropower Scheduling Toolchains: Comparing Experiences in Brazil, Norway, and USA and Implications for Synergistic Research

While hydropower scheduling is a well-defined problem, there are institutional differences that need to be identified to promote constructive and synergistic research. We study how established toolchains of computer models are organized to assist operational hydropower scheduling in Brazil, Norway, and the United States’ Colorado River System (CRS). These three systems have vast hydropower resources, with numerous, geographically widespread, and complex reservoir systems. Although the underlying objective of hydropower scheduling is essentially the same, the systems are operated in different market contexts and with different alternative uses of water, where the stakeholders’ objectives clearly differ. This in turn leads to different approaches when it comes to the scope, organization, and use of models for operational hydropower scheduling and the information flow between the models. Here, we describe these hydropower scheduling toolchains, identify the similarities and differences, and shed light on the original ideas that motivated their creation. We then discuss the need to improve and extend the current toolchains and the opportunities to synergistic research that embrace those contextual differences.

13 HYDRO ENERGY↗

Updates to a Preliminary Probabilistic Performance Assessment Model for Radiological and Chemical Contamination at the West Valley Site, New York - 20420

The New York State Energy Research and Development Authority (NYSERDA) is the owner of the Western New York Nuclear Service Center (WNYNSC), a 1,351-ha site located approximately 48 km south of Buffalo, New York. In 1962, Nuclear Fuel Services, Inc. (NFS) entered into agreements with the Atomic Energy Commission and New York State to construct the first commercial reprocessing plant of nuclear fuel in the United States at the WNYNSC. NFS built and operated the spent fuel reprocessing plant and waste disposal facilities, processing 640 Mg (640 metric tons) of spent nuclear fuel from 1966 to 1972 under an Atomic Energy Commission license. Nuclear fuel reprocessing operations halted in 1972 and never restarted, leaving behind radioactive and chemical wastes. The U.S. Department of Energy (DOE) was required to complete certain waste management activities under the West Valley Demonstration Project (WVDP) Act of 1980 including decommissioning of WVDP facilities. As collaborating agencies, NYSERDA and the DOE are tasked with making decisions about decommissioning and risk reduction for the West Valley Site. Neptune and Company, Inc. (Neptune) was contracted to develop a probabilistic performance assessment (PPA) model to assist the agencies in their decision making process for decommissioning the WVDP and WNYNSC. One important tool that is needed in order to inform the decision-making process is a science-based model of the West Valley Site that evaluates potential future consequences for human health and the environment. This forms the core of the spatial domain of the West Valley PPA Model. The PPA Model, developed using the GoldSim system modeling software, is a tool intended to provide support for decision making that evaluates uncertainty, in a manner that is transparent, defensible, and robust. The PPA Model includes contaminant transport and health effects components, and is organized around geographically-grouped contaminated facilities. These include the waste disposal areas licensed by the U.S. Nuclear Regulatory Commission and the State of New York, a waste tank farm for storage of high level radioactive waste resulting from reprocessing operations, and several areas contaminated with radioactive and chemical constituents. The PPA Model evaluates contaminant transport from these sources to points of exposure across the site and into receiving surface waters and sediments. Hypothetical people and wildlife could be exposed to contamination at these locations, and the effects of these exposures are evaluated. Contaminant transport processes to be evaluated in the PPA Model include groundwater and surface water transport, contaminant translocation by plants and animals, diffusion, and erosion. The evaluation of exposures to people in this preliminary model is limited to a resident farmer scenario, and ecological assessment is performed at the level of a screening analysis. The results of these preliminary evaluations inform future model developments. PPA Model results are subjected to sensitivity analysis in order to determine those pathways and parameters that are most significant in influencing the results. This information allows analysts and decision makers to focus on those aspects of Site behavior and processes. With this information, the decision makers can drive informed, defensible decisions regarding decommissioning of the Site. This paper includes an update of the information presented at WM2019 [1]. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Supporting Information - How Can Crop Production Adapt to growing groundwater restrictions in the West?

Abstract This data plan outlines the structure and content of datasets generated and utilized in the research presented in the manuscript abstract. Groundwater overdraft has led to serious water supply issues in the US West. Most western states manage groundwater use through a permitting system, but California has only recently begun to restrict groundwater use statewide with the 2014 passage of groundwater restrictions which target the elimination of groundwater overdraft practices by 2042. With groundwater extraction curtailed, crop production in the US West (a $95 billion industry annually) will be affected, and appropriate response strategies will be needed to ensure minimal disruption to food production and the regional economy. In this paper, we explore the adoption of alternative adaptive responses: (a) deficit irrigation; (b) switching to less water-intensive crops; (c) changing the extent of irrigated land (including fallowing); and (d) geographically shifting crop production. Employing an integrated modeling approach, we explicitly capture the interactions and feedbacks between local hydrology, changes in crop yields, crop and land use decision-making, changes in crop prices, and regional shifts in crop production. We find that the optimal adaptive response is spatially heterogeneous and comprises a portfolio of strategies. Southwestern states and California will be the most impacted by groundwater restrictions. The optimal responses in these states are to both adopt deficit irrigation strategies and reduce a portion of their irrigated croplands, resulting in a shift in crop production to northwestern states with a larger supply of water. The datasets detailed in this repository represent the output from these integrated models, specifically designed to support the analysis and visualization presented in the manuscript and supplemental information. These datasets, used in conjunction with the scripts available at our associated GitHub repository (https://github.com/pches/Femeena_etal_How_can_crop_production_adapt), enable the reproduction of figures and facilitate a comprehensive understanding of the optimal adaptive strategies in agriculture for mitigating water stress under varying groundwater extraction scenarios, as outlined in the abstract.

DNDCe↗

Supporting Information - How Can Crop Production Adapt to growing groundwater restrictions in the West?

Abstract This data plan outlines the structure and content of datasets generated and utilized in the research presented in the manuscript abstract. Groundwater overdraft has led to serious water supply issues in the US West. Most western states manage groundwater use through a permitting system, but California has only recently begun to restrict groundwater use statewide with the 2014 passage of groundwater restrictions which target the elimination of groundwater overdraft practices by 2042. With groundwater extraction curtailed, crop production in the US West (a $95 billion industry annually) will be affected, and appropriate response strategies will be needed to ensure minimal disruption to food production and the regional economy. In this paper, we explore the adoption of alternative adaptive responses: (a) deficit irrigation; (b) switching to less water-intensive crops; (c) changing the extent of irrigated land (including fallowing); and (d) geographically shifting crop production. Employing an integrated modeling approach, we explicitly capture the interactions and feedbacks between local hydrology, changes in crop yields, crop and land use decision-making, changes in crop prices, and regional shifts in crop production. We find that the optimal adaptive response is spatially heterogeneous and comprises a portfolio of strategies. Southwestern states and California will be the most impacted by groundwater restrictions. The optimal responses in these states are to both adopt deficit irrigation strategies and reduce a portion of their irrigated croplands, resulting in a shift in crop production to northwestern states with a larger supply of water. The datasets detailed in this repository represent the output from these integrated models, specifically designed to support the analysis and visualization presented in the manuscript and supplemental information. These datasets, used in conjunction with the scripts available at our associated GitHub repository (https://github.com/pches/Femeena_etal_How_can_crop_production_adapt), enable the reproduction of figures and facilitate a comprehensive understanding of the optimal adaptive strategies in agriculture for mitigating water stress under varying groundwater extraction scenarios, as outlined in the abstract.

DNDCe↗