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At least 991 records · Page 55
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Observed changes in hydroclimate attributed to human forcing

Observational and modeling studies indicate significant changes in the global hydroclimate in the twentieth and early twenty-first centuries due to anthropogenic climate change. In this review, we analyze the recent literature on the observed changes in hydroclimate attributable to anthropogenic forcing, the physical and biological mechanisms underlying those changes, and the advantages and limitations of current detection and attribution methods. Changes in the magnitude and spatial patterns of precipitation minus evaporation (P–E) are consistent with increased water vapor content driven by higher temperatures. While thermodynamics explains most of the observed changes, the contribution of dynamics is not yet well constrained, especially at regional and local scales, due to limitations in observations and climate models. Anthropogenic climate change has also increased the severity and likelihood of contemporaneous droughts in southwestern North America, southwestern South America, the Mediterranean, and the Caribbean. An increased frequency of extreme precipitation events and shifts in phenology has also been attributed to anthropogenic climate change. While considerable uncertainties persist on the role of plant physiology in modulating hydroclimate and vice versa, emerging evidence indicates that increased canopy water demand and longer growing seasons negate the water-saving effects from increased water-use efficiency.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

A genetic tradeoff for tolerance to moderate and severe heat stress in US hybrid maize

Global climate change is increasing both average temperatures and the frequencies of extreme high temperatures. Past studies have documented a strong negative effect of exposures to temperatures >30°C on hybrid maize yields. However, these studies could not disentangle genetic adaptation via artificial selection from changes in agronomic practices. Because most of the earliest maize hybrids are no longer available, side-by-side comparisons with modern hybrids under current field conditions are generally impossible. Here, we report on the collection and curation of 81 years of public yield trial records covering 4,730 maize hybrids, which enabled us to model genetic variation for temperature responses among maize hybrids. We show that selection may have indirectly and inconsistently contributed to the genetic adaptation of maize to moderate heat stress over this time period while preserving genetic variance for continued adaptation. However, our results reveal the existence of a genetic tradeoff for tolerance to moderate and severe heat stress, leading to a decrease in tolerance to severe heat stress over the same time period. Both trends are particularly conspicuous since the mid-1970s. Such a tradeoff poses challenges to the continued adaptation of maize to warming climates due to a projected increase in the frequency of extreme heat events. Nevertheless, given recent advances in phenomics, enviromics, and physiological modeling, our results offer a degree of optimism for the capacity of plant breeders to adapt maize to warming climates, assuming appropriate levels of R&D investment.

Kusmec, Aaron (ORCID:000000032295385X)↗
DOE OSTI2021

Considering uncertainties expands the lower tail of maize yield projections

Crop yields are sensitive to extreme weather events. Improving the understanding of the mechanisms and the drivers of the projection uncertainties can help to improve decisions. Previous studies have provided important insights, but often sample only a small subset of potentially important uncertainties. Here we expand on a previous statistical modeling approach by refining the analyses of two uncertainty sources. Specifically, we assess the effects of uncertainties surrounding crop-yield model parameters and climate forcings on projected crop yield. We focus on maize yield projections in the eastern U.S.in this century. We quantify how considering more uncertainties expands the lower tail of yield projections. We characterized the relative importance of each uncertainty source and show that the uncertainty surrounding yield model parameters is the main driver of yield projection uncertainty.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Building heights and urban canopy parameters for urban modeling

GLObal Building heights for Urban Studies (UT-GLOBUS) is a random forest model based framework that provides a level-of-detail-1 (LoD-1) building height dataset. The primary objective of UT-GLOBUS is not to precisely predict the height and footprint of individual buildings, but rather to offer a functional framework for generating building level information using open-source datasets for modeling applications. Specifically, UT-GLOBUS is tailored to meet the requirements of deriving urban canopy parameters (UCPs) for the multi-layer model within the Weather Research and Forecasting (WRF) model and building heights for the SOLWEIG and SUEWS model. Building-level data is accessible in vector file format (GeoPackage: .gpkg), which can be converted into raster file format (geoTIFF). The vector files employ the Universal Transverse Mercator (UTM) projection. The vector files are compatible with GIS platforms like QGIS and ArcGIS, and can be imported for analysis using programming languages such as Python. We are also providing UCPs required by the multi-layer urban model in the urban WRF in binary file format. Additionally, we provide the urban fractions calculated using ESA world cover dataset (https://esa-worldcover.org/en) for WRF model in binary file format. These binary files can be directly incorporated into the WRF pre-processing system (WPS).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Ameliorating Global Challenges: Globalization, Geopolitics, Basic & Applied Research, and Research Security

We are confronted with a myriad of global challenges, from extreme weather events, occurring at higher frequencies than at any point in history, to pollution, food insecurity, clean water shortages, and fundamentally limited natural resources and materials. The largest number of people inhabit our planet today and enjoy the highest standard of living - though not equally distributed across the world - compared to any prior moment in history. To sustain this quality of life, we are largely reliant on fossil fuel sources, which are responsible for more greenhouse gas emissions by weight each day than the collective weight of all humans that inhabit the planet. Ameliorating these global challenges will require elements of solutions that include advances in basic and applied research, innovative global engineering, materials discovery, new technologies, manufacturing at scale, resilient and adaptable infrastructure, carbon-free energy sources and storage technologies, and new supply chain networks and markets. Success will require constructive collaborative efforts between researchers in countries located in every continent of this planet. For any of these goals to be realized in a timely fashion, geopolitical leaders must become better educated about this existential challenge and incentivized to act.

applied research↗
DOE OSTI2023

Ocean & Geohazard Analysis Tool

The Ocean & Geohazard Analysis (OGA) software tool is designed to summarize insights into key offshore hazards drawing from a diverse set of approaches, including artificial intelligence, machine learning, probabilistic and statistical, and offshore data sources. The offshore hazards that can be analyzed include submarine landslides, extreme wind/wave/current event probabilities, earthquakes, and metocean pathways (CIAM Climatological Isolation and Attraction Model–Climatological Lagrangian Coherent Structures - Submissions - EDX (doe.gov)). Currently, the tool is developed for use in the Gulf of Mexico. The data underlying the offshore hazard analyses can be found here: https://edx.netl.doe.gov/dataset/gulf-of-mexico-risk-analysis-database-gomrad This work was conducted under the Advanced Offshore Research Portfolio, FWP Number 1022409 at National Energy Technology Laboratory, U.S. Dept. of Energy. Disclaimer This project was funded by the United States Department of Energy, National Energy Technology Laboratory, in part, through a site support contract. Neither the United States Government nor any agency thereof, nor any of their employees, nor the support contractor, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.

AIML↗
DOE OSTI2020

Will Solar Panels Help When the Power Goes Out? Planning for PV Resilience

PV systems, like all energy systems, must chart a difficult course in order to improve resilience. They need to simultaneously withstand extreme conditions that cause grid power disruptions, be configured in a way that allows electricity to be delivered to meet the PV user's needs, and overcome disruptions arising from solar resource intermittency. Certain considerations reveal themselves as key to a PV system's resilience benefits. Choice of hardware is critical, as is site selection for the PV array. Voluminous evidence in a hurricane context demonstrates that durable hardware, properly maintained and sited, leads to PV systems surviving extreme weather with their functionality intact. Structurally weak or poorly maintained PV equipment, on the other hand, has demonstrated failures during extreme weather events. PV system stakeholders should ensure that best practices are followed for hardware procurement and siting. It is also important to understand that PV systems are not one-size-fits-all. While incorporating islanding capabilities and energy storage into the system's electrical topology will typically enhance resilience, the user's needs will determine the optimally resilient conformation of any given system. These factors - appropriate hardware, siting, maintenance, and system design - are the primary drivers of PV resilience. PV systems possess high potential for resilient energy generation due to their distributed, abundant, and free fuel supply, but the technology itself does not inherently lead to resilient outcomes. Realizing PV's potential for resilience requires consideration of these determinative factors.

14 SOLAR ENERGY↗
DOE OSTI2020

Climate Change Science Review 2018 and Associated Social and Economic Impacts

Climate change and its impacts on average temperature, water supply, agriculture, coastal flooding, biodiversity, social and economic stability, human migration, and overall global stability is one of the leading threats emerging for humanity, and is likely to increase as a threat for decades to come. This report represents a snapshot of climate change data, information, and understanding, as of 2018, and can serve as a kind of benchmark for changes going forward in time. This report covers temperature change and heat effects, atmospheric and ocean circulation, freshwater supply changes, sea level rise and flooding, extreme climate events, oceanic deoxygenation, land degredation, social and economic changes, migration, and food. Data and information on all these issues represent a compelling body of knowledge supporting the scientific hypothesis describing climate change, and an important mile marker in our effort to track the unfolding nature of the problem. A slide presentation that summarizes the content of this report is in Appendix A and can serve as an executive summary for the report.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Diagnosis of convective organization and cold pools using ARM datasets and evaluation of a unified convection parameterization (UNICON)

Tropical thunderstorms often cluster together. Studies have suggested that the degree to which the tropical thunderstorms are clustered impacts Earth's energy balance and water cycle, as well as extreme precipitation events. However, the processes controlling the spatial distribution of the thunderstorms are poorly understood and are not properly represented in most computer models for weather and climate prediction. Under the goals of better understanding how convection organizes at the mesoscale and advancing the representation of mesoscale convective organization in global climate models, we i) objectively quantified the degrees of convective organization and diagnosed cold pool processes using ARM field campaign observations, ii) examined the organization processes in storm-resolving model simulations, and iii) evaluated the impacts of mesoscale convective organization in global model simulations. The project yielded a firm reference against which the global model representation of mesoscale convective organization and cold pools can be evaluated against and shed new light into the role of parameterized convective organization in the global model simulation of the basic state and variability. Our results revealed two distinct phases of convective clustering during the two-day rain episodes (Cheng et al. 2018) and a new mechanism through which vertical wind shear in the low-troposphere can aid convective organization over tropical oceans (Cheng et al. 2020). It was demonstrated that the interactive representation of cold pools and mesoscale convective organization is key for global models to successfully simulate both the mean state and intraseasonal variability in the tropics (Ahn et al. 2019; 2020).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

A Quantum-AI Framework for Extreme Weather Prediction

The frequency and intensity of extreme weather events in North America will likely increase with a changing climate. High resolution simulations are necessary to advance process-based understanding of weather events at local scales accompanying future changes in global atmospheric circulation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

AI Automated Discovery of New Climate Water System Knowledge from Models and Observations

This paper addresses focus area 3, “Insight gleaned from complex data.” Science Challenge: The climate-water system is highly complex, containing a multitude of positive and negative feedbacks, time scales stretching many orders-of-magnitudes, and non-linear, connected processes. Powerful artificial intelligence (AI) methods can revolutionize and automate the discovery of new knowledge and relationships in the climate-water system, which will improve understanding and predictability of extreme hydrological events.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Deep Learning for Ensemble Forecasting

Focal Area: (2) Predictive modeling through the use of AI techniques and AI-derived model components and the use of AI and other tools to design a prediction system comprising a hierarchy of models. Science Challenge: While both climate and weather forecast systems have continued to improve due to substantial efforts to improve computational capabilities, observations, and numerical models, the atmosphere is a chaotic system, and this puts a fundamental limit on our ability to make predictions. Forecasts made by high-resolution models initialized with only slightly different atmospheric states can quickly diverge. Quantifying uncertainty in forecasts is essential to adequately understand them and to make the best-informed policy decisions particularly when it comes to hydrology, extreme weather (including extreme precipitation events), and climate.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Predictability and feedbacks of the ocean-soil-plant-atmosphere water cycle: deep learning water conductance in Earth System Model

This white paper responds to Focal Area 2. We seek to build predictive models of leaf and surface conductance of water by implementing deep learning (DL) data assimilation techniques. These new models would then be implemented in existing Land Surface Models (LSMs) and Earth System models (ESMs), generating novel water cycle feedbacks. In doing so, we would improve predictability of expected changes in land precipitation, soil moisture, and vegetation dynamics in the long-term, and the role of land cover on the impacts and feedbacks of extreme weather events in the short-term

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Using AI to build a hydrobiogeochemical soil model

Soil water content is a function of inputs from precipitation and outputs via evaporation, transpiration, lateral flow, and vertical percolation, and is sensitive to biogeochemical processes. As such, soils serve as an ideal integrator of atmospheric, hydrological, and biogeochemical processes affecting the water cycle. In addition, soil water retention capacity, infiltration rates, and hydraulic conductivity can buffer or exacerbate the effects of extreme precipitation events (e.g., flooding, runoff, subsurface transport, erosion, greenhouse gas emissions) and mitigate the impact of droughts and heat waves on land systems (e.g., fire, crop failure). However, integrating water cycle measurements spanning different land atmosphere compartments across scales is a fundamental barrier for numerical model predictability. A significant challenge is that each domain (soil, hydrology, biology, and atmosphere) typically collects different sets of data at different temporal and spatial frequencies/scales, and even different dimensionalities (2D vs 3D). To implement soil as an integrator of the water cycle in land models, we suggest that novel machine learning (ML) tools can be developed to effectively simulate complex landscapes across various domains and scales, extended to regions with sparse or no data. The ultimate goals are to improve predictive understanding of land-atmosphere interactions and to extend the predictability of current Earth System Models (ESMs) through better integration of hydrological and biogeochemical data. We envision a framework in which: (1) ML-aided data reconstructions enable the merger of data sources into a unified geospatial product; (2) automated detection techniques are used to improve the knowledge of complex soil processes and interactions; and (3) this knowledge is leveraged and incorporated into models through AI-based emulators to distinctly connect the land and atmospheric compartments of the water cycle in models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

A Closed-Loop Distribution System Restoration Tool for Natural Disaster Recovery. Final CRADA report

We propose to continue the development of the restoration algorithms and the tool’s user interface, both of which have been developed under the GMLC-0131 project. This project aims to develop a distribution restoration decision support tool that will assist utilities in performing distribution restoration after extreme weather events in an optimal and efficient manner. The tool will integrate the weather information/forecasts and system fragility assessment together with the field measurement data to improve the situational awareness and estimate the system damage status. Advanced optimization models will be leveraged to dispatch repair crews and associated resources. New resources (e.g., automatic switches and distributed generators (DGs)) enabled by distribution automation and smart grid development will be utilized to reconfigure distribution grids and pick up loads promptly to reduce the outage sizes and durations. The closed-loop feature of the tool will make the tool adaptive to the evolving weather events and varying restoration capabilities. In this CRADA, we will collaborate with S&C Electric Company (S&C) to refine ANL-1060 (07/17/2019) the restoration algorithms that can be potentially applicable for any utility systems and vendor products, including: 1) Customizable information from both utility users and vendor users; 2) User interface design.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Net-Zero Carbon Microgrids

The microgrid concept has been effective in creating aggregations of distributed energy resources—generation, storage and loads—for resiliency, in the form of energy security. The success of microgrids in bringing energy security to a wide range of customers—from individual residences to commercial and industrial installations to military bases—has been exemplified during power disruptions and extended outages due to extreme weather events, cybersecurity attacks, and equipment failures. Now microgrids have an opportunity to meet the challenges of climate change and contribute to a carbon-free power delivery system. The transition to net-zero starts within microgrids themselves. In fact, today’s microgrids are largely dominated by generators using fossil fuels, natural gas and diesel, with high greenhouse gas emissions. In short, the transition to net-zero means replacing fossil fueled generators with renewable generation in microgrids. This transition is extended by including new dispatchable generation technologies that are 100% carbon-free and that offer additional advantage of a more-dependable and sustainable source of energy and power. Basically, the decarbonization of microgrids requires three elements: 1) maximizing generation from renewable energy resources, 2) management of storage and flexible loads to balance the variability and intermittency of renewable energy resources, and 3) introducing new clean power sources, including hydrogen-based generation and small modular reactors. This report affirms a need for specific focus by governmental agencies at national, regional, and local levels to establish technology, policy, and investment in this area. The intention of the Net-Zero Microgrid (NZM) Program is to inform these constituencies with cross-cutting research and tools for the reduction of GHG in microgrids – to net-zero in the near term eventually to zero in the longer term. The NZM Program is committed to achieving decarbonization for resiliency and for providing clean energy at the local or distribution level, from remote communities to underserved communities, and large industrial and military facilities. The NZM Planning and Design Platform is a core tool to be developed as an early deliverable of the NZM Program because only a fully integrated microgrid-design approach will ensure maximum carbon reduction in energy production.

13 HYDRO ENERGY↗
DOE OSTI2021

Optimization Framework for Solar Energy Integrated Resilient Distribution Grid

The increasing penetration of distributed photovoltaic (PV) energy and other distributed energy resources (DERs) such as energy storage batteries, diesel generators, and mobile generators deployed in distribution grids must be properly controlled and coordinated to ensure reliable, resilient, and affordable grid operation in response to various operating conditions. Particularly, weather-induced power outages, such as natural disasters, are among the most common causes of power supply interruptions for distribution grid operations [E-1]. Distributed PV and DERs feature advanced grid supporting capabilities and can potentially enhance power supply continuity during and after extreme weather events. To leverage PV and other DERs to provide resilience benefits while ensuring operational and economic feasibility, distribution grids must be carefully designed, proactively managed, and safely operated through a comprehensive framework.

14 SOLAR ENERGY↗
DOE OSTI2022

Reliability and Resiliency in South Asia's Power Sector - Pathways for Research, Modeling, and Implementation

Reliability and resilience are the core principles of power system planning and operations around the world. Power systems in South Asia are transforming with increasing penetration of clean energy generation resources, emerging technologies, increasing electricity demand and electrification. At the same time, these power systems are facing challenges posed by extreme weather events and climate change. All these factors would add furthermore importance to the reliability and resilience of future power systems in South Asia. This has motivated us to better understand the country specific challenges and chalk out the pathways for research, modelling and implementation in South Asia. Our research, experience in the region and feedback from key stakeholders indicate following as the key areas where more work is needed to improve reliability and resilience of power systems in the region: Renewable energy Data for power system studies, New Tools and Studies, Resilience Planning, Resource Adequacy, Advanced RE Forecasting, Cybersecurity, Load Forecasting, and Coordinated Planning and Operations.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗