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Challenges to Evaluating Ecological Risks in Performance Assessments - 18424

Performance assessments (PAs), whether probabilistic or deterministic, have largely focused on evaluating human health effects of radionuclides. In the past, it was asserted that protecting human health would automatically protect the environment. This blanket assertion has been questioned, so tools and methodologies to directly estimate non-human biota dose have been developed and made available since the early 2000's. For example, the Biota Dose Assessment Committee (BDAC) guidance led to the development of the RESRAD-BIOTA (RESRAD stands for Residual Radioactive materials) software to implement this guidance. The BDAC was a US Department of Energy (DOE) led working group with members from academia and consultancies in addition to DOE technical staff. Parallel efforts have been in place in Europe with the development of the ERICA tool (Environmental Risk from Ionising Contaminants: Assessment and Management). More recently, interpretation of the requirements of US Department of Energy (DOE) Order O458.1 have led some waste management facilities to include ecological risks in addition to human health effects in their PAs. We have recently developed methodologies to assess ecological risks within probabilistic PAs (PPAs) for two Federal facilities. One of the challenges in applying the non-human biota assessment models to PPAs is that ecological risk assessments following US Environmental Protection Agency (EPA) or DOE guidance start with protective screening assessments before moving into more site-specific ecological risk assessments. Conservative assumptions bias the screening assessments and greatly overestimate the risks posed by contaminants. However, although the screens are biased they can be useful in ruling out model spatial domain areas or types of biota from requiring further assessment. In some cases, the results of the screen could entirely rule out ecological risks from further assessment. For a more refined and unbiased estimate of dose to non-human biota, one would typically conduct site-specific studies to better define exposure through the food web or adverse effects based on eco-toxicity studies of environmental, and generally weathered, contaminant mixtures. However, for PPAs that consider future site conditions after the loss of institutional control, such site-specific studies are not possible. Rather, site-specific future ecological risk models use probabilistic inputs that result in a distribution of population doses or exposures and calculate the distribution of possible adverse effects to ecological populations. This approach considers both radionuclide and chemical inventory. For both Federal facilities, the model spatial domains include riparian and aquatic environments located downstream of the waste management site. These riparian and aquatic environments represent linear exposure elements for some wildlife populations and require special consideration of exposures to individuals and to populations along such spatial domains. At a minimum, wildlife foraging should be focused along the axis of such water bodies. Another challenge is how to incorporate ecologically relevant spatial scales into models that have contaminant transport as the primary focus. Riparian and aquatic environments also have threatened and endangered (T and E) species, which necessitate greater levels of protection given their rarity. A final challenge for conducting ecological risk assessments is how to evaluate T and E species in the future. This might be addressed by evaluating potential for impacts on individuals of feeding guilds with species that are currently threatened or endangered. We also have to consider how ecological populations might change as a result of climate change. It is a more difficult problem to predict what species might become rare in the future, and how to best assess future populations of threatened or endangered species. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Natural time and nowcasting induced seismicity at the Groningen gas field in the Netherlands

The Groningen gas field in the Netherlands has a significant level of induced seismicity; this seismicity is attributed to fluid withdrawal not fluid injection. This is one of the most productive gas fields in Europe. Production began in 1963 and the first induced earthquake occurred in 1991. Increasing levels of seismicity led to the installation of a sensitive seismic network in 1995 with a minimum magnitude completeness of $M$ ≥ 1.5. The largest induced earthquake was a magnitude 3.6 in 2012, which caused thousands of damage claims and led to a reduction in gas production in 2014 January. In our work, we show that the induced earthquakes satisfy Gutenberg–Richter (GR) frequency–magnitude statistics over the range 1.5 ≤ $M$ ≤ 3.0. We utilize truncated GR scaling to introduce a possible maximum earthquake with $M$ = 3.85. We also demonstrate an almost complete absence of aftershocks following the largest induced earthquakes. To quantify the temporal evolution of the induced seismicity, we perform our nowcasting method. Nowcasting uses the catalogue of seismicity in the region. Two earthquake magnitudes are selected, one large say $M_λ$ ≥ 2.5, and one small say $M_σ$ ≥ 1.5. The method utilizes the number of small earthquakes that occur between pairs of large earthquakes. A major advantage of nowcasting is that it relies on ‘natural time’, earthquake counts, between events rather than clock time. Thus, the results are applicable to induced seismicity that varies in time. We count the rate of occurrence of small induced earthquakes to nowcast the probability of occurrence of larger earthquakes.

58 GEOSCIENCES↗

Distributionally Robust Decentralized Volt-Var Control With Network Reconfiguration

Here, this paper presents a decentralized volt-var optimization (VVO) and network reconfiguration strategy to address the challenges arising from the growing integration of distributed energy resources, particularly photovoltaic (PV) generation units, in active distribution networks. To reconcile control measures with different time resolutions and empower local control centers to handle intermittency locally, the proposed approach leverages a two-stage distributionally robust optimization; decisions on slow-responding control measures and set points that link neighboring subnetworks are made in advance while considering all plausible distributions of uncertain PV outputs. We present a decomposition algorithm with an acceleration scheme for solving the proposed model. Numerical experiments on the IEEE 123 bus distribution system are given to demonstrate its outstanding out-of-sample performance and computational efficiency, which suggests that the proposed method can effectively localize uncertainty via risk-informed proactive timely decisions.

24 POWER TRANSMISSION AND DISTRIBUTION↗

GRACE Final Technical Report

The GRACE project (Grid that is Risk-Aware for Clean Electricity) was a five-year research initiative funded by the U.S. Department of Energy's Advanced Research Projects Agency- Energy (ARPA-E) under the PERFORM program. Led by Duke University's Nicholas School of the Environment, with contributions from The Ohio State University, North Carolina State University, Dartmouth College, and Pacific Northwest National Laboratory, the project addressed a fundamental challenge in grid management: conventional software plans for a single most-likely outcome and relies on reserves as a buffer, leaving utilities poorly equipped for the growing variability introduced by renewable energy. GRACE demonstrated a better approach: explicitly representing thousands of plausible future scenarios and choosing operating schedules that perform well across all of them.

14 SOLAR ENERGY↗

NASA SPoRT-Land Information System and Vegetation Stress Real-Time Products for Drought, Wildfire, and Pluvial Analysis

The NASA SPoRT Center has been producing a near real-time instance of the NASA Land Information System over a CONUS domain (hereafter “SPoRT-LIS”) since ~2015. The SPoRT-LIS runs the legacy Noah LSM in an observations-constrained manner, with outputs of soil moisture and temperature at layered depths along with surface energy fluxes. The unique configuration of SPoRT-LIS enables decision-making on operational timescales since it incorporates near real-time observations such as VIIRS Green Vegetation Fraction and MRMS QPE. An additional in-house Alaska-LIS is produced in real time to help inform end-users on spring snow melt and summer soil moisture trends during the wildfire season. Use of the SPoRT-LIS has gradually expanded in recent years as a component of drought analysis, feedback to the USDM, and is utilized by State Climate Offices. Operational feedback has led to increased applicability for analyses by other end-users in the drought community. This presentation will provide SPoRT-LIS applications for drought, pluvial, and fire weather case-studies. We will also present preliminary results of 2-week SPoRT-LIS forecast percentiles that were recently implemented using GFS model forecast fields. We will additionally discuss the pathway toward extending these forecasts into the future by incorporating ensemble forecasts for probabilistic guidance on soil moisture trends for potential flash drought and subseasonal outlooks.

Soil Moisture↗

Probabilistic Diffusion Models Advance Extreme Flood Forecasting

Extreme floods pose escalating risks in a changing climate, yet forecasting remains challenging due to peak flow underestimation and high uncertainty. We introduce diffusion-based runoff model (DRUM), a probabilistic deep learning (DL) approach that advances extreme flood forecasting across representative basins in the contiguous United States. DRUM outperforms state-of-the-art benchmarks, enhancing nowcasting skill for the top 1‰ of flows in 72.3% of studied basins. Under operational scenarios, DRUM extends reliable lead times by nearly a full day for 20- and 50-year floods. When evaluated with measured precipitation, an ideal condition, recall improves by 0.3–0.4 and the early warning window extends by 2.3 days for 50-year floods. The enhancement potential varies regionally, with precipitation-driven flood zones in the eastern and northwestern US benefiting most, gaining 3–7 days in lead time. These findings highlight the transformative potential of diffusion models as a cutting-edge generative AI technique for advancing hydrology and broader Earth system sciences.

54 ENVIRONMENTAL SCIENCES↗

Evaluating decadal predictions of northern hemispheric cyclone frequencies

Mid-latitudinal cyclones are a key factor for understanding regional anomalies in primary meteorological parameters such as temperature or precipitation. Extreme cyclones can produce notable impacts on human society and economy, for example, by causing enormous economic losses through wind damage. Based on 41 annually initialised (1961–2001) hindcast ensembles, this study evaluates the ability of a single-model decadal forecast system (MPI-ESM-LR) to provide skilful probabilistic three-category forecasts (enhanced, normal or decreased) of winter (ONDJFM) extra-tropical cyclone frequency over the Northern Hemisphere with lead times from 1 yr up to a decade. It is shown that these predictions exhibit some significant skill, mainly for lead times of 2–5 yr, especially over the North Atlantic and Pacific. Skill for intense cyclones is generally higher than for all detected systems. A comparison of decadal hindcasts from two different initialisation techniques indicates that initialising from reanalysis fields yields slightly better results for the first forecast winter (month 10–15), while initialisation based on an assimilation experiment provides better skill for lead times between 2 and 5 yr. The reasons and mechanisms behind this predictive skill are subject to future work. Preliminary analyses suggest a strong relationship of the model's skill over the North Atlantic with the ability to predict upper ocean temperatures modulating lower troposphere baroclinicity for the respective area and time scales.

MiKlip↗

National and subnational short-term forecasting of COVID-19 in Germany and Poland during early 2021

During the COVID-19 pandemic there has been a strong interest in forecasts of the short-term development of epidemiological indicators to inform decision makers. In this study we evaluate probabilistic real-time predictions of confirmed cases and deaths from COVID-19 in Germany and Poland for the period from January through April 2021. We evaluate probabilistic real-time predictions of confirmed cases and deaths from COVID-19 in Germany and Poland. These were issued by 15 different forecasting models, run by independent research teams. Moreover, we study the performance of combined ensemble forecasts. Evaluation of probabilistic forecasts is based on proper scoring rules, along with interval coverage proportions to assess calibration. The presented work is part of a pre-registered evaluation study. We find that many, though not all, models outperform a simple baseline model up to four weeks ahead for the considered targets. Ensemble methods show very good relative performance. The addressed time period is characterized by rather stable non-pharmaceutical interventions in both countries, making short-term predictions more straightforward than in previous periods. However, major trend changes in reported cases, like the rebound in cases due to the rise of the B.1.1.7 (Alpha) variant in March 2021, prove challenging to predict. Multi-model approaches can help to improve the performance of epidemiological forecasts. However, while death numbers can be predicted with some success based on current case and hospitalization data, predictability of case numbers remains low beyond quite short time horizons. Additional data sources including sequencing and mobility data, which were not extensively used in the present study, may help to improve performance.

60 APPLIED LIFE SCIENCES↗

pnnl/Forte

Interactive user-interface application for deep probabilistic day-ahead net-load forecasting. This application allows a utility operator and/or other end-user to access through an easy interface the powers of advanced deep AI/ML algorithms for generating day-ahead probabilistic forecast of net-load. Also known as "VRN3P"

Bhattacharjee, Kaustav↗

Probabilistic Assessment of Cancer Risk for Astronauts on Lunar Missions

During future lunar missions, exposure to solar particle events (SPEs) is a major safety concern for crew members during extra-vehicular activities (EVAs) on the lunar surface or Earth-to-moon transit. NASA s new lunar program anticipates that up to 15% of crew time may be on EVA, with minimal radiation shielding. For the operational challenge to respond to events of unknown size and duration, a probabilistic risk assessment approach is essential for mission planning and design. Using the historical database of proton measurements during the past 5 solar cycles, a typical hazard function for SPE occurrence was defined using a non-homogeneous Poisson model as a function of time within a non-specific future solar cycle of 4000 days duration. Distributions ranging from the 5th to 95th percentile of particle fluences for a specified mission period were simulated. Organ doses corresponding to particle fluences at the median and at the 95th percentile for a specified mission period were assessed using NASA s baryon transport model, BRYNTRN. The cancer fatality risk for astronauts as functions of age, gender, and solar cycle activity were then analyzed. The probability of exceeding the NASA 30- day limit of blood forming organ (BFO) dose inside a typical spacecraft was calculated. Future work will involve using this probabilistic risk assessment approach to SPE forecasting, combined with a probabilistic approach to the radiobiological factors that contribute to the uncertainties in projecting cancer risks.

Kim, Myung-Hee Y.↗

RAVIS: Resource Forecast and Ramp Visualization for Situational Awareness of Variable Renewable Generation

The Resource Forecast and Ramp Visualization for Situational Awareness (RAVIS) is an innovative, open-source tool for visualizing variable renewable resource forecasts and alerts for significant up- and down-ramps in renewable generation and the consequent system net load. The modular dashboard of RAVIS contains configurable panes for viewing probabilistic time-series forecasts, ramp event alerts on the look-ahead timeline, and spatially resolved resource sites, and forecasts. For comprehensive situational awareness, the tool can add additional data layers from simulation and independent system operator (ISO) market clearing data---including electric line utilization, nodal price, and available generation flexibility---in response to continuously updated renewable forecasts. This paper introduces the RAVIS technology suite employed to provide optimum visualization and flexible design characteristics. The paper also illustrates some use cases of the tool using site-specific solar power forecast data obtained from the IBM Watt-Sun forecasting platform for the California ISO and Midcontinent ISO footprints. The source code for RAVIS is public (https://github.com/ravis-nrel/ravis), and the intended users are forecasters, utility planners, ISO operators, and researchers.

41 EE - Solar Energy Technologies Office (EE-4S)↗

RAVIS: Resource Forecast and Ramp Visualization for Situational Awareness of Variable Renewable Generation: Preprint

The Resource Forecast and Ramp Visualization for Situational Awareness (RAVIS) is an innovative, open-source tool for visualizing variable renewable resource forecasts and alerts for significant up- and down-ramps in renewable generation and the consequent system net load. The modular dashboard of RAVIS contains configurable panes for viewing probabilistic time-series forecasts, ramp event alerts on the look-ahead timeline, and spatially resolved resource sites, and forecasts. For comprehensive situational awareness, the tool can add additional data layers from simulation and independent system operator (ISO) market clearing data—including electric line utilization, nodal price, and available generation flexibility—in response to continuously updated renewable forecasts. This paper introduces the RAVIS technology suite employed to provide optimum visualization and flexible design characteristics. The paper also illustrates some use cases of the tool using site-specific solar power forecast data obtained from the IBM Watt-Sun forecasting platform for the California ISO and Midcontinent ISO footprints. The source code for RAVIS is public (https://github.com/ravis-nrel/ravis), and the intended users are forecasters, utility planners, ISO operators, and researchers.

41 EE - Solar Energy Technologies Office (EE-4S)↗

Resource Forecast and Ramp Visualization for Situational Awareness (RAVIS)

The Resource Forecast and Ramp Visualization for Situational Awareness (RAVIS) is an open-source tool for visualizing variable renewable resource forecasts and ramp alerts for significant up/down ramps in renewable resource and the consequent net-load. The modular dashboard of RAVIS contains configurable panes for viewing- probabilistic time series forecasts, ramp event alerts on the look-ahead timeline, spatially resolved resource sites and forecasts, and system simulation and market clearing data such as transmission lines utilization, nodal prices and available generation flexibility.

Krishnan, Venkat↗

VRN3P: Variational Recurrent Neural Network Based Net-Load Prediction under High Solar Penetration

This is the final technical report for the SETO-funded VRN3P project (PNNL# 76914). The goal of this project, led by Pacific Northwest National Laboratory (PNNL), in collaboration with Lawrence Livermore National Laboratory (LLNL) and Portland General Electric (PGE), was to develop and validate a deep variational recurrent neural network-based net-load prediction (VRN3P) framework for probabilistic time-series forecasting of day-ahead net-load under high solar penetration scenarios. The project team reports successful design of a novel probabilistic net-load forecasting architecture, comprising of a variational autoencoder and a recurrent neural network, which demonstrates 30% improvement in forecast performance, 60% improvement in training time, and consumes 44% less memory, when compared with conventional baseline models. The team tested the VRN3P model performance on GridLAB-D test-cases representing varying BTM solar penetration levels of 20%, 30%, and 50%, with integrated time-series net-load profiles provided by the utility partner (PGE). The VRN3P model demonstrate <2% hourly MAPE (averaged over the year) for day- ahead net-load forecast on the test scenario with 20% BTM solar. Transfer learning extension of the VRN3P model has demonstrated 8.33× speed-up in training, while still achieving acceptable forecast performance of 2.24% hourly MAPE on the 30% BTM solar penetration test-scenario. A preliminary version of the VRN3P GridAPPS-D™has been developed, along with a web-based interactive user-interface (named ‘Forte’) which has made available on GitHub for public use.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Assessing probabilistic predictions of ENSO phase and intensity from the North American Multimodel Ensemble

Here we examine the skill of three, five, and seven-category monthly ENSO probability forecasts (1982–2015) from single and multi-model ensemble integrations of the North American Multimodel Ensemble (NMME) project. Three-category forecasts are typical and provide probabilities for the ENSO phase (El Niño, La Niña or neutral). Additional forecast categories indicate the likelihood of ENSO conditions being weak, moderate or strong. The level of skill observed for differing numbers of forecast categories can help to determine the appropriate degree of forecast precision. However, the dependence of the skill score itself on the number of forecast categories must be taken into account. For reliable forecasts with same quality, the ranked probability skill score (RPSS) is fairly insensitive to the number of categories, while the logarithmic skill score (LSS) is an information measure and increases as categories are added. The ignorance skill score decreases to zero as forecast categories are added, regardless of skill level. For all models, forecast formats and skill scores, the northern spring predictability barrier explains much of the dependence of skill on target month and forecast lead. RPSS values for monthly ENSO forecasts show little dependence on the number of categories. However, the LSS of multimodel ensemble forecasts with five and seven categories show statistically significant advantages over the three-category forecasts for the targets and leads that are least affected by the spring predictability barrier. These findings indicate that current prediction systems are capable of providing more detailed probabilistic forecasts of ENSO phase and amplitude than are typically provided.

54 ENVIRONMENTAL SCIENCES↗

Stochastic Optimization with Risk Aversion for Virtual Power Plant Operations: A Rolling Horizon Control

While the concept of aggregating and controlling renewable distributed energy resources (DERs) to provide grid services is not new, increasing policy support of DER market participation has driven research and development in algorithms to pool DERs for economically viable market participation. Sandia National Laboratories recently undertook a three-year research program to create the components of a real-world virtual power plant (VPP) that can simultaneously participate in multiple markets. Our research extends current state-of-the-art rolling horizon control through the application of stochastic programming with risk aversion at various time resolutions. Our rolling horizon control consists of (1) day-ahead optimization to produce an hourly aggregate schedule for the VPP operator and (2) sub-hourly optimization for real-time dispatch of each VPP subresource. Both optimization routines leverage a two-stage stochastic program (SP) with risk aversion, and integrate the most up-to-date forecasts to generate probabilistic scenarios in real operating time. Our results demonstrate the benefits to the VPP operator of constructing a stochastic solution regardless of the weather. In more extreme weather, applying risk optimization strategies can dramatically increase the financial viability of the VPP. As a result, the methodologies presented here can be further tailored for optimal control of any VPP asset fleet and its operational requirements.

24 POWER TRANSMISSION AND DISTRIBUTION↗