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At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Geospatial modeling of near subsurface temperatures of the contiguous United States for assessment of materials degradation

Abstract Understanding subsurface temperature variations is crucial for assessing material degradation in underground structures. This study maps subsurface temperatures across the contiguous United States for depths from 50 to 3500 m, comparing linear interpolation, gradient boosting (LightGBM), neural networks, and a novel hybrid approach combining linear interpolation with LightGBM. Results reveal heterogeneous temperature patterns both horizontally and vertically. The hybrid model performed best achieving a root mean square error of 2.61 °C at shallow depths (50–350 m). Model performance generally decreased with depth, highlighting challenges in deep temperature prediction. State-level analyses emphasized the importance of considering local geological factors. This study provides valuable insights for designing efficient underground facilities and infrastructure, underscoring the need for depth-specific and region-specific modeling approaches in subsurface temperature assessment.

Science & Technology - Other Topics↗
DOE OSTI2025

IrrigationViz: A Geospatial Visualization Application to Facilitate Irrigation Modernization

Irrigation water delivery infrastructure, such as canals and pipelines, are essential to agriculture in the Western U.S., yet many of these systems are reaching the end of their useful lives. Reinvestment can achieve a wide variety of benefits, from water conservation to energy savings or renewable energy generation. Modernization requires significant planning, design, and implementation funding which can be a challenge for many irrigation districts. Here, this paper introduces IrrigationViz, a web-based mapping application designed to help irrigation districts visualize and generate high-level cost and benefit estimates for infrastructure modernization projects. These estimates can help water managers identify projects that benefit from additional engineering resources and ultimately obtain funding. IrrigationViz includes map interactions, graphs, and visualization components to facilitate planning and communication to stakeholders and funders. IrrigationViz combines user-provided information about a water-delivery system with public datasets and basic engineering formulas to generate estimates of the benefits of reinvestment. Estimates include the amount of water seepage in earthen canals, potential hydropower generation associated with replacing a canal with a pressurized pipe, and pipe size recommendations. We found that hydropower generation estimates compared favorably with real world projects, but seepage loss estimates showed high variability relative to on-the-ground measurements

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Geospatial Diffusion for Land Cover Imperviousness Change Forecasting

Land-use and land-cover (LULC) has a significant effect on several Earth system processes. For example, impervious surfaces reduce infiltration and speed water flow, impacting regional hydrology and flood risk. While Earth System models have improved forecasting hydrologic and atmospheric processes at higher resolutions, the ability to forecast LULC change has lagged behind. In this paper, we propose a new paradigm exploiting Generative AI (GenAI) for land cover change forecasting by framing it as a data synthesis problem conditioned on historical and auxiliary data-sources. To demonstrate the feasibility of our methodology, we perform experiments where a diffusion model is trained for decadal forecasting of imperviousness change across the entire United States. We find that our model yields MAE lower than a no-change baseline for resolutions ≥ 0.7 X 0.7km2 on average, demonstrating its ability to capture and project accurate spatiotemporal patterns. Finally, we discuss future research to incorporate Earth's physical properties and enabling scenario simulations via driver variables.

Varshney, Debvrat [ORNL] (ORCID:0000000188981736)↗
DOE OSTI2020

Exploring Renewable Energy Opportunities in Select Southeast Asian Countries: A Geospatial Analysis of the Levelized Cost of Energy of Utility-Scale Wind and Solar Photovoltaics

The costs of renewable energy-based electricity generation have fallen precipitously in recent years to levels that are increasingly competitive with traditional generation such as fossil fuel-based generation. As these costs become increasingly competitive, private developers, policymakers, and energy system planners are searching for opportunities to harness high-quality renewable energy resources. Developing economies are setting ambitious targets and exploring how cost-effective, grid-connected renewable energy options can help power economic growth and meet growing electricity demands. This includes the member states of the Association of Southeast Asian Nations (ASEAN) that are determined to reach a target of 23% of renewable energy in the region's total primary energy supply by 2025. A critical gap to identifying opportunities and scaling up renewable energy is the lack of quality data and analyses to support decisions on the investment and deployment of renewables - including wind and solar photovoltaics (PV). This work supports decision making by providing high-quality data and spatial analysis of the cost of utility-scale wind and solar PV generation in select countries of Southeast Asia - specifically, the ASEAN member states. Generation costs are expressed as the levelized cost of energy (LCOE) - a commonly used metric that represents the net present value of the unit cost of electricity during the lifetime of a particular electricity generation technology. This is the first spatial estimate of LCOE for these technologies within the ASEAN member states - providing insights into the roles that renewable energy resource quality and other factors may play in generation costs.

14 SOLAR ENERGY↗
DOE OSTI2024

Behavioral Segmentation and Clustering of Geospatial Trajectories

The rapid growth of global positioning system (GPS) devices has led to a corresponding increase in the size of GPS datasets. While these large GPS datasets contain a wealth of information about the behaviors of the moving objects in them, manual classification and anomaly detection are prohibitively time consuming. We utilize unsupervised machine learning techniques to first identify the behaviors for individual moving objects and then cluster those objects by their behavioral sequences. In this way, trajectories behaving unusually as well as common patterns of behavior are both detectable in large datasets without requiring an a priori definition of "unusual" or "common."

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Stepping into the Midwest Bioeconomy: Stakeholder Engagement and Geospatial Tools to Assist in Perennial Bioenergy Crop Decision Making and Entrepreneurship

This project, “Ecosystem Services and Farm Entrepreneurship Technical Assistance,” was a three-year project originally planned for FY22–FY24. Due to a late start and a few extensions, it is being completed in early FY25. This project explored opportunities to support the deployment of a bioeconomy with a circular, more sustainable supply chain. Using a tool developed by Argonne to identify agricultural areas suitable for use in the bioeconomy, we sought to create opportunities in the bioeconomy as biomass producers, bioenergy users, and environmental entrepreneurs. We proposed to focus at the beginning on enhancing the tool’s capabilities, while engaging with key stakeholders to improve and expand the tool’s functionality for all potential stakeholders in the bioeconomy. We believe that expanding our tools and technologies, coupled with conversations in agricultural spaces, will be needed as we continue to explore how best to offer farmers whole-of supply-chain opportunities to participate in the bioeconomy. Through this project we have continued to gain a better understanding of the ways in which farmers, landowners, bioenergy users, and environmental entrepreneurs may approach the bioeconomy. In addition, as we improve our analytic toolkit, we can continue to refine our communication and the ways in which we can valuate the bioeconomy. Refining these tools allows us to dive deeper into conversations around plausible policies and drivers for future bioeconomy investment and engagement by stakeholders. By working with farmers and agricultural landowners to enable a sustainable bioeconomy business model, enhance their energy options, and recover resources from their waste streams, this project directly responds to the Bioenergy Technology Office’s (BETO) priorities of building a resilient energy economy. It addresses BETO’s focus on fostering the development and adoption of energy technologies that enable the conversion of waste to energy, efficient land use, and robust job creation. By establishing a technical assistance program that develops capabilities and practices in agricultural areas to implement a bioeconomy future, this program will develop an important linkage between technology being developed at U.S. Department of Energy national laboratories and the agricultural communities of the Midwest. This project focuses on farmers with lower productivity farmland. Because less productive lands create a more difficult revenue stream for conventional crops, these farmers may therefore be more open to alternative agricultural land management regimes. Consequently, the technical assistance program and the methodologies for targeting perennial bioenergy crop application on marginal land provide a distinct opportunity to engage with and invest in the bioeconomy in these economically stressed areas. Stakeholders in this project include farmers and landowners, local conservation organizations (NRCS, SWCS, etc.), universities, non-profit environmental and agricultural entities, farm consultants, environmental regulators, and industry, including the industries working on conversion technologies, anaerobic digestion, pyrolysis, and biochar generation, and the companies interested in trading or purchasing/supporting the valuation of ecosystem services (ES).

09 BIOMASS FUELS↗