Transitional States During Compound Extreme Events: Examples from Cascading Wildfires and Floods
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Jackson, Nicole Danielle [Sandia National Laborato↗
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Transitional states during compound extreme events and power outages
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Jackson, Nicole Danielle [Sandia National Laborato↗
Improving Short Term Predictability of Hydrologic Models with Deep Learning
Focal Area: Focal Area 2. Predictive modeling through the use of AI techniques and AI-derived model components; the use of AI and other tools to design a prediction system comprising of a hierarchy of models (e.g., AI driven model/component/parameterization selection). Science Challenge: A major challenge exists in the lack of reliable predictive modeling of water-cycle extremes using macro-scale hydrologic models that are driven by atmospheric climate data. This causes a critical knowledge gap in understanding the magnitudes, probabilities, and severity of droughts, rainfall, and flooding. Improved understanding of these extreme events requires more accurate modeling at high spatial and temporal resolutions. This shortcoming is especially apparent when complex couplings between atmospheric quantities and engineered systems emerge, such as during extreme precipitation or drought events and at intersections of atmospheric-land-fluvial-ocean systems. Added complexity around representing operations of engineered infrastructure leads to a limited capability to analyze the temporal and spatial implications of compounding extreme events across inland and coastal regions. This results in profound challenges for water and power systems operators, agriculture production, and regional infrastructure planning. The science challenge can be summarized as How can we use AI/ML to improve short term predictability of extreme water-cycle events and associated risk, especially compounding events and extreme rainfall patterns that result in flooding, to inland and coastal communities under different climate scenarios?
54 ENVIRONMENTAL SCIENCES↗
Characterization of Extremes and Compound Impacts: Applications of Machine Learning and Interpretable Neural Networks
Focal Area: This white paper responds to Focal area III by exploring data fusion, learning and explainable AI methods in characterizing hydrological extremes and interconnections. It also addresses Focal area II by using probabilistic AI and ensemble ML for predicting extremes and compound extremes. Science Challenge: A key question associated with the integrated water (or hydrological) cycle grand challenge in the Earth and Environmental Systems Sciences Division (EESSD) strategic plan, is how the frequency and intensity of hydrological events will change. Prediction of the tail behavior (extremes) of the hydrological cycle is especially challenging, because of their stochasticity and low probability. These extreme events and their compound impacts have significant societal and economic consequences. It is anticipated for the next-generation Earth System models (ESMs), that model predictability of the water cycle will improve with increased resolution (e.g., regionally refined E3SM), advanced software and computational architectures, and improved model physics based on the data from ARM measurements and high-fidelity models. However, the challenges for predictability of low-probability high-impact extreme events will unlikely be alleviated with conventional modeling and data-driven approaches, as ESMs are calibrated largely for capturing the high-frequency mean climate states. Recent AI and ML applications have shown great potential in quantifying well-defined climate extremes (e.g., supervised learning of tropical cyclones/atmospheric rivers by ClimateNet1) but few efforts are dedicated to compound events, extreme drivers and uncertainty estimation. We envision the opportunity to develop and apply ML and interpretable AI methods extended on the existing efforts, specifically, for: (1) identification of compound extremes, (2) diagnosing drivers of extremes, (3) bias correction in extreme predictions and (4) probabilistic modeling of extremes.
54 ENVIRONMENTAL SCIENCES↗
Recreating the California New Year's Flood Event of 1997 in a Regionally Refined Earth System Model
Abstract The 1997 New Year's flood event was the most costly in California's history. This compound extreme event was driven by a category 5 atmospheric river that led to widespread snowmelt. Extreme precipitation, snowmelt, and saturated soils produced heavy runoff causing widespread inundation in the Sacramento Valley. This study recreates the 1997 flood using the Regionally Refined Mesh capabilities of the Energy Exascale Earth System Model (RRM‐E3SM) under prescribed ocean conditions. Understanding the processes causing extreme events informs practical efforts to anticipate and prepare for such events in the future, and also provides a rich context to evaluate model skill in representing extremes. Three California‐focused RRM grids, with horizontal resolution refinement of 14 km down to 3.5 km, and six forecast lead times, 28 December 1996 at 00Z through 30 December 1996 at 12Z, are assessed for their ability to recreate the 1997 flood. Planetary to synoptic scale atmospheric circulations and integrated vapor transport are weakly influenced by horizontal resolution refinement over California. Topography and mesoscale circulations, such as the Sierra barrier jet, are better represented at finer horizontal resolutions resulting in better estimates of storm total precipitation and storm duration snowpack changes. Traditional time‐series and causal analysis frameworks are used to examine runoff sensitivities state‐wide and above major reservoirs. These frameworks show that horizontal resolution plays a more prominent role in shaping reservoir inflows, namely the magnitude and time‐series shape, than forecast lead time, 2‐to‐4 days prior to the 1997 flood onset.
54 ENVIRONMENTAL SCIENCES↗
Hybridizing Machine Learning and Physically-based Earth System Models to Improve Prediction of Multivariate Extreme Events (AI Exploration of Wildland Fire Prediction)
Focal Areas: This project responds to two focal areas identified in the DOE Call for AI4ESP White Papers: 1) Predictive modeling through the use of artificial intelligence (AI) techniques, and 2) insights gleaned from complex data using explainable AI and big data analytics. Science Challenge: Large wildland fires (hereafter wildfires) appearing as high-impact compound climate extreme events are closely related to hydroclimate and water cycle extremes that modulate surface fuel supply and combustibility. These compound events have multivariate climatic features (e.g., temperature, precipitation, relative humidity, wind, lightning) and societal drivers (e.g., forest management, land use change, human caused ignitions). Meanwhile, they induce strong feedbacks to the coupled atmosphere, biosphere, and hydrosphere by perturbing regional and global radiation budget as well as ecological, biogeochemical, and water cycles across multiple spatiotemporal scales. The nonlinear interactions between these natural and anthropogenic components of the Earth system are too complex to be completely and adequately represented in today’s Earth system models (ESMs). The inherent stochastic nature of fire activity at all scales further increases the difficulty of its prediction using ESMs that are usually developed from deterministic equations and parameterizations. Besides, concurrence of long-term (decadal to interdecadal) global climate change and fire regime shifts overlapping with short-term (intraseasonal to interannual) variations of regional fire weather and burning activity confound predictability of these compound extreme events. We propose to address the above scientific challenges by using machine learning (ML)-based data-driven modeling techniques to integrate observations and physically-based ESMs’ simulations in a computationally efficient hybrid prediction system. This prediction system is supposed to characterize the wildfire’s sensitivity to climate and exogenous drivers at high resolution (~ 0.25°) on subseasonal to seasonal (S2S) timescales providing improved predictability and explainability. We will use the system to help identify: (1) What are the computational elements of a hybrid system needed to predict compound climate extreme events such as global wildfires? (2) What are the key drivers (either natural or anthropogenic) that modulate short-term variations of multivariate fire weather and burning activity over different regions? How can one take advantage of those driver-response relationships to improve the predictability of large wildfires on S2S time scales? (3) What are the underlying physical mechanisms and sources of improved predictability? Which ML techniques are optimal in revealing and adapting these mechanisms?
54 ENVIRONMENTAL SCIENCES↗
Elevated Forest Canopy Loss After Wildfires in Moist and Cool Forests in the Pacific Northwest
Wildfire frequency, intensity, and rate of spread are increasing across the Western U.S, resulting in more severe ecosystem impacts. Significant tree mortality can occur years after fire events, but this has received little attention compared to the immediate tree loss during a fire. We overlapped forest cover loss data with burn severity maps in the U.S. Pacific Northwest and quantified the total and delayed forest canopy loss after fires. We found that wildfires resulted in total canopy loss fraction (CLF) of 84%, 53%, and 22% within 3 years in areas burned at high, moderate, and low severity, respectively. The delayed canopy loss accounted for approximately 1/3, 1/2, and 2/3 of the total canopy loss for high, moderate, and low severity burns. Delayed canopy loss was greater in moist and cool areas than in dry and warm areas, likely because tree species in wetter environments were less adapted to survive when fires did occur. Across all forests, delayed CLF doubled as temperature increased from the climatological mean to a hot anomaly and tripled as vapor pressure deficit increased from a wet anomaly to a dry anomaly. Fire impacts on forest ecosystems are likely to intensify under future climate scenarios as wildfires expand into areas that historically experienced infrequent fires. The impacts can also be exacerbated by more frequent compound extreme events, such as droughts, heatwaves, and fires. These findings highlight the urgent need for targeted forest management strategies, particularly in mesic forests, to mitigate future fire impacts.
54 ENVIRONMENTAL SCIENCES↗
Intersection of Hydrologic Change and Hydropower in the United States: Needs for Future Research and Practice
Hydropower is crucial for electric‐grid stability in the context of variable renewables but faces threats from changing hydrology. Here, we summarize the state of the science at the intersection of hydropower operations and planning, hydrologic science, and climate. We focus on the United States, outlining research, development, and training needs. Key knowledge gaps include the risk that intensification of compound extreme events poses to future generation, as well as uncertainties surrounding greenhouse gas emissions from hydropower reservoirs with relevance to hydropower's role in energy decarbonization. Quantifying such impacts and reducing uncertainty are critical where possible, but remaining irreducible or deep uncertainty will require new approaches. Future monitoring and modeling methods must provide a better understanding of the complexity inherent in large watersheds that is critical to managing both hydropower and watersheds in the context of hydrologic change. Yet, research and development will have little impact if they do not inform practice. Standardization and consolidation of platforms are essential for data, modeling, and tool translation to local scales and small operators. An enhanced industry‐academia dialog is pivotal for fostering a robust pipeline of hydropower professionals. Collaboration among researchers, policymakers, authorities, and industry stakeholders emerges as a recurring theme, highlighting the imperative for collective efforts.
13 HYDRO ENERGY↗
Evaluation of Extreme Weather Impacts on Utility-scale Photovoltaic Plant Performance in the United States
The global energy system is undergoing significant changes, including a shift in energy generating technologies to more renewable energy sources. However, the dependence of renewable energy sources on local environmental conditions could also increase disruptions in service through exposures to compound, extreme weather events. By fusing three diverse datasets (operations and maintenance tickets, weather data, and production data), this analysis presents a novel methodology to identify and evaluate performance impacts arising from extreme weather events across diverse geographical regions. Text analysis of maintenance tickets identified snow, hurricanes, and storms as the leading extreme weather events affecting photovoltaic plants in the United States. Statistical techniques and machine learning were then implemented to identify the magnitude and variability of these extreme weather impacts on site performance. Impacts varied between event and non-event days, with snow events causing the greatest reductions in performance (54.5%), followed by hurricanes (12.6%) and storms (1.1%). Machine learning analysis identified key features in determining if a day is categorized as low performing, such as low irradiance, geographic location, weather features, and site size. The analysis improves our understanding of compound, extreme weather event impacts on photovoltaic systems, which can inform planning activities, especially as the industry continues to expand into new geographic and climatic regions around the world.
14 SOLAR ENERGY↗
Climate Adaptation Approaches for Water and Electric Utilities: A compendium of existing strategies in a changing climate
Report analyzes plans developed by water and electric utilities to address the impacts of changes in the climate within their service territories. The analysis identifies the main types of changes that utilities address, such as sea level rise, extreme heat, and compound events such as freezing rain/show and extreme wind events. It then identifies adaptation approaches utilities have proposed or adopted to address the changes that are anticipated in their service territories. The report also identifies the mechanisms utilities (and in some cases the governments which the utilities are part of) use to ensure that energy or environmental equity is increased as a result of the resilience program. In summary, the report provides a compendium of adaptation strategies that can be used by others as they review their own need to increase climate resilience.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
Dataset for Evaluation of Extreme Weather Impacts on Utility-Scale Photovoltaic Plant Performance in the United States
This dataset is a fusion of three data types (operations and maintenance tickets, weather data, and production data) that was used to support machine learning analysis and evaluation of drivers for low performance at photovoltaic (PV) sites during compound, extreme weather events. After being processed with machine learning, the data was used in the "Evaluation of Extreme Weather Impacts on Utility-scale Photovoltaic Plant Performance in the United States" manuscript. Additional details are captured in the associated manuscript.
AI↗
Enhancing Resilience of Urban Systems Against Climate-Induced Floods Using Advanced Data-Driven and Computing Techniques: A Driver-Pressure-State-Impact-Response (DPSIR) Framework
Focal Area: This white paper proposes advanced data-driven and computing techniques to improve the predictability of extreme climate events (precipitation) and focuses on comprehending their impact dynamics to enhance resilience of urban systems. Science Challenge: In urban systems, climate change has presented significant challenges related to accurate spatiotemporal prediction of extreme climate events (heavy precipitation) and their impacts, i.e. flooding. The occurrence of extreme climate events and their compounding repercussions on the built environment make urban systems increasingly more vulnerable. The interactions between extreme climate events, urban systems, and the corresponding impacts are irregular, non-linear, and dynamic, making it difficult to standardize resilience. As it stands, the management of complex urban systems is affected by uncertainties related to the nature of short- and long-term consequences caused by extreme events.
54 ENVIRONMENTAL SCIENCES↗
Regional Heatwave Prediction Using Graph Neural Network and Weather Station Data
Abstract Heatwaves lead to catastrophic consequences on public health and the economy. Accurate and timely predictions of regional heatwaves can improve climate preparedness and foster decision‐making to alleviate the burdens due to climate change. In this paper, we propose a heatwave prediction algorithm based on a novel deep learning model, that is, Graph Neural Network (GNN). This new GNN framework can provide real time warnings of the sudden occurrence of regional heatwaves with high accuracy at lower costs of computation and data collection. In addition, its interpretable structure unravels the spatiotemporal patterns of regional heatwaves and helps to enrich our understanding of the general climate dynamics and the causal influences between locations. The proposed GNN framework can be applied for the detection and prediction of other extreme or compound climate events, which calls for future studies.
54 ENVIRONMENTAL SCIENCES↗
Trustworthy AI for Extreme Event Prediction and Understanding
Our transformational science question is: can we revolutionize both the prediction and understanding of extreme events through trustworthy AI? Our use-cases include extreme weather such as tornadoes and hail as well as water-based events including extreme precipitation, compound flooding, harmful algal blooms, and sea turtle cold stunnings and nest inundations.
54 ENVIRONMENTAL SCIENCES↗
Compound Mesoscale Convective Systems and Low‐Pressure Systems in Tropical Monsoon Regions: Assessing Their Meteorology and Precipitation
Mesoscale convective systems (MCS) and low-pressure systems (LPS) are both strongly associated with precipitation across the regions where they occur, particularly within global monsoon systems; however, their co-occurrence and its relationship to precipitation have not been systematically examined. Here, we use LPS and MCS trackers to detect compound MCS and LPS events in five monsoon regions and assess the association of this co-occurrence with anomalies of winds, precipitation, and other atmospheric variables. Additionally, we investigate the spatial distribution of precipitating MCS and LPS events. Our results show that most (∼60%) MCS and LPS co-occurrences are located in the lower latitudes, where they contribute up to 40% of annual precipitation. We find that compound events generally produce more extreme precipitation than MCS-only or LPS-only events. Furthermore, our assessment of the synoptic and mesoscale composites reveals that the underlying dynamics of compound events exhibit anomalously positive convective available potential energy and an anomalously low-pressure within the location of the event. In terms of the synoptic environment of the features, the compound MCS and LPS events are associated with inverted troughs in three out of five monsoon locations assessed.
Environmental sciences↗
Precipitation trends determine future occurrences of compound hot–dry events
Compound hot–dry events—co-occurring hot and dry extremes—frequently cause damages to human and natural systems, often exceeding separate impacts from heatwaves and droughts. Strong increases in the occurrence of these events are projected with warming, but associated uncertainties remain large and poorly understood. Here, using climate model large ensembles, we show that mean precipitation trends exclusively modulate the future occurrence of compound hot–dry events over land. This occurs because local warming will be large enough that future droughts will always coincide with at least moderately hot extremes, even in a 2°C warmer world. By contrast, precipitation trends are often weak and equivocal in sign, depending on the model, region and internal climate variability. Therefore, constraining regional precipitation trends will also constrain future compound hot–dry events. These results help to assess future frequencies of other compound extremes characterized by strongly different trends in the drivers.
54 ENVIRONMENTAL SCIENCES↗
Impacts of climate change on future hurricane induced rainfall and flooding in a coastal watershed: A case study on Hurricane Harvey
The warming climate is likely to increase hurricane-associated extreme rainfall and lead to sea-level rise (SLR). Thus, how the floods induced by intense hurricanes respond to these potential changes is of great concern. This study investigates the future warmer climate impacts on hurricane-induced extreme rainfall, and—more importantly—the subsequent compound flooding at the watershed scale (from an event-based analysis perspective). To this goal, a modeling framework is designed based on the Distributed Hydrology Soil Vegetation Model (DHSVM), the Two-Dimensional fvand the Regional Community Earth System Model (R-CESM). The framework was applied to Hurricane Harvey (2017) at the Clear Creek watershed (a coastal watershed in the southern Houston) as a case study. The results show that the projected maximum rainfall totals over the watershed would be exacerbated by 17.7 % and 49.7 % in the 2050s and 2090s (respectively) under Representative Concentration Pathway 8.5 (RCP 8.5). This means a 16.1 % increase in Harvey rainfall over the watershed per degree Celsius increase in Mean Surface Temperature over the Gulf of Mexico region (18°~31° N, 77°~98° W). Meanwhile, the increases in maximum inundation extent would be 11.0 % (2050s) and 19.5 % (2090s). Furthermore, considerable increases in maximum inundation depth and duration in regions along the middle and downstream of Clear Creek (and also those around Clear Lake) are expected. The projected SLR will have little effect on the maximum inundation depth and extent if storm surge changes are not taken into account; meanwhile, it will influence the inundation duration at downstream locations. In conclusion, this modeling framework can be also applied at other coastal watersheds to evaluate the projected climate change impacts on the compound flooding induced by extreme climate events.
54 ENVIRONMENTAL SCIENCES↗
Extreme flood return levels in a U.S. mid-Atlantic estuary using 40-year fluvial-coastal model simulations
Using an integrated watershed-coastal modeling framework, we conducted long-term historical simulations (1980-2019) of fluvial and coastal flooding in the Delaware Bay and River, a vulnerable estuarine system in the U.S., at high spatial resolutions. By applying process-based models, we generated physically consistent and spatially detailed estimates of estuarine, riverine, and surge-driven extreme water level compared to previous studies that used field data only. We then evaluated changes in the magnitude of flood events using the 40-year simulations and detrended Floodwater Depth values with stationary extreme value analysis. Our detailed assessment of spatial-varying extreme values revealed how different flood-generation mechanisms can dominate various zones in the estuary. The datasets produced through this work will be valuable for long-term flood hazard mitigation planning in coastal communities in the Delaware Bay and River region. Additionally, this work will serve as a benchmark for other coastal flood hazard modeling communities worldwide, aiding them in systematically modeling long-term and continuous extreme flood events.
Coastal and Riverine Flooding↗