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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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DOE OSTI2024

Pumped Storage Hydropower Potential and Opportunities

Pumped storage hydropower (PSH) is a flexible energy storage technology with the potential to improve grid reliability, resiliency, and stability in the electric grid of the future. NREL has developed a range of data and tools to help understand opportunities for new PSH deployment, including nationwide resource assessment data, a bottom-up component-level cost model, and a lifecycle greenhouse gas emissions calculator. These datasets can then be used to inform grid planning models, analysis, and decision making to understand the role PSH can play in the power sector.

cost↗

DOE OSTI2024

Evaluation of Global Climate Models for Use in Energy Analysis

The interplay between energy, climate, and weather is becoming more complex due to increasing contributions of renewable energy generation, energy storage, electrified end uses, and the increasing frequency of extreme weather events. Energy system analyses commonly rely on meteorological inputs to estimate renewable energy generation and energy demand; however, these inputs rarely represent the estimated impacts of future climate change. Climate models and publicly available climate change datasets can be used for this purpose, but the selection of inputs from the myriad of available models and datasets is a nuanced and subjective process. In this work, we assess datasets from various global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6). We present evaluations of their skills with respect to the historical climate and comparisons of their future projections of climate change for two climate change scenarios. We present the results for different climatic and energy system regions and include interactive figures in the accompanying software repository. Previous work has presented similar GCM evaluations, but none have presented variables and metrics specifically intended for comprehensive energy systems analysis including impacts on energy demand, thermal cooling, hydropower, water availability, solar energy generation, and wind energy generation. We focus on GCM output meteorological variables that directly affect these energy system components including the representation of extreme values that can drive grid resilience events. The objective of this work is not to recommend the best climate model and dataset for a given analysis, but instead to provide a reference to facilitate the selection of climate models and scenarios in subsequent work.

14 SOLAR ENERGY↗

DOE OSTI2024

AquaPV: Foundational Analysis and Industry Guidance on Floating PV Results

This dataset contains results of the technical potential analysis of floating photovoltaics (FPV) on federally owned or permitted reservoirs in the United States. Estimates of the area of reservoirs that is technically feasible for FPV development are provided for reservoirs that are owned by the US Army Corps of Engineers or the US Bureau of Reclamation, or are associated with hydropower dams licensed by the Federal Energy Regulatory Commission. Other associated data are included, such as estimates of the potential evaporative losses of FPV development, and whether the waterbody is associated with an energy community or disadvantaged community that could qualify it for investment or production tax credits. Data is provided both as a spreadsheet and in a geospatial format including the waterbody geometries. Please read the included readme for more detailed field definitions.

area↗

DOE OSTI2026

FFTSF: Revisiting Sub-Seasonal Streamflow Forecasting with Simple Feedforward Network

Accurate short-to-subseasonal streamflow forecasts are vital for water management, including flood preparedness, drought mitigation, hydropower scheduling, and ecosystem protection. However, extending a forecast beyond a few days remains challenging due to complexity of hydrological processes. While recent self-attention based transformer architectures such as iTransformer have gained traction in time-series forecasting, these models suffer from several critical limitations: (1) significant computational overhead that scales quadratically with sequence length, (2) vulnerability to overfitting on limited hydrological datasets, (3) degraded performance on long-horizon forecasts due to attention decay, and (4) excessive architectural complexity that hampers interpretability and operational deployment. In this study, we propose a simple Feedforward Time Series Forecasting (FFTSF) network that directly addresses these limitations through its lightweight architecture and long-range forecasting capabilities. We evaluate FFTSF across 178 USGS stream gauges spanning diverse climate regimes by forecasting lead times of 1-, 7-, 14-, and 30-days. Our results demonstrate that FFTSF achieves competitive performance at short lead times (NSE of 0.778 for 1-day forecasts) while substantially outperforming complex baselines at longer forecast period, achieving the highest NSE (0.271) at 30-day forecasts with greater robustness and stability. For 30-day forecasts, FFTSF achieves a 71% improvement over NLinear, 57% improvement over DLinear and 12% improvement over the computationally intensive iTransformer while requiring fewer computational resources. Our findings reveal that architectural complexity is not necessary for hydrological forecasting, demonstrating that well-designed simple models can outperform attention mechanisms for subseasonal streamflow forecasting. The computational efficiency and consistent long-range performance of FFTSF make it suitable for water management applications where reliable extended forecasts are essential.

Krishnan Kutty Ambika, Anukesh [ORNL] (ORCID:00000↗

NASA NTRS2017

Validation of Globsnow-2 Snow Water Equivalent Over Eastern Canada

In Qubec, Eastern Canada, snowmelt runoff contributes more than 30% of the annual energy reserve for hydroelectricity production, and uncertainties in annual maximum snow water equivalent (SWE) over the region are one of the main constraints for improved hydrological forecasting. Current satellite-based methods for mapping SWE over Qubec's main hydropower basins do not meet Hydro-Qubec operational requirements for SWE accuracies with less than 15% error. This paper assesses the accuracy of the GlobSnow-2 (GS-2) SWE product, which combines microwave satellite data and in situ measurements, for hydrological applications in Qubec. GS-2 SWE values for a 30-year period (1980 to 2009) were compared with space- and time-matched values from a comprehensive dataset of in situ SWE measurements (a total of 38,990 observations in Eastern Canada). The root mean square error (RMSE) of the GS-2 SWE product is 94.1+/- 20.3 mm, corresponding to an overall relative percentage error (RPE) of 35.9%. The main sources of uncertainty are wet and deep snow conditions (when SWE is higher than 150 mm), and forest cover type. However, compared to a typical stand-alone brightness temperature channel difference algorithm, the assimilation of surface information in the GS-2 algorithm clearly improves SWE accuracy by reducing the RPE by about 30%. Comparison of trends in annual mean and maximum SWE between surface observations and GS-2 over 1980-2009 showed agreement for increasing trends over southern Qubec, but less agreement on the sign and magnitude of trends over northern Qubec. Extended at a continental scale, the GS-2 SWE trends highlight a strong regional variability.

Snow water equivalent↗

DOE OSTI2024

CMIP6-based Multi-model Streamflow Projections over the Conterminous US, Version 1.1

This dataset presents an ensemble of streamflow projections covering the conterminous United States (CONUS), developed to support the SECURE Water Act Section 9505 Assessment for the US Department of Energy (DOE) Water Power Technologies Office (WPTO). Multiple Coupled Models Intercomparison Project phase 6 (CMIP6) Global Climate Models (GCMs) were downscaled using either statistical (DBCCA) or dynamical (RegCM) downscaling methods, based on two meteorological reference datasets (Daymet and Livneh). Subsequently, the downscaled precipitation, temperature, and wind speed data were used to drive two calibrated hydrologic models (VIC and PRMS), with total runoff routed through the Routing Application for Parallel computatIon of Discharge (RAPID) routing model, producing an ensemble of streamflow projections across 2.7 million NHDPlusV2 stream reaches across the CONUS. Each ensemble member covers the 1980-2019 baseline and 2020-2059 near-future periods under the high-end (SSP585) emission scenario. Additionally, using only DBCCA and Daymet, the projections extend to the 2060-2099 far-future period and encompass three additional emission scenarios (SSP370, SSP245, and SSP126). This dataset is designed to support the SECURE Water Act Section 9505 Assessment for the US Department of Energy (DOE) Water Power Technologies Office (WPTO). For further details, refer to Kao et al. (2022), Rastogi et al. (2022), and Ghimire et al. (2023).

13 HYDRO ENERGY↗

DOE OSTI2025

Dayflow-PR: High-Resolution Streamflow Reanalysis for Puerto Rico, Version 1.0

This dataset presents a high-resolution historical streamflow reanalysis for NHDPlusV2 stream reaches across Puerto Rico (PR) spanning 1950 - 2019. The reanalysis is generated using the calibrated VIC-RAPID hydrologic modeling framework at the Hydrologic Unit Code Sub-basin (HUC08) scale, forced with sub-daily and daily meteorological forcings from Daymet. Runoff is simulated on 1- and 6-km grids, and the resulting total runoff is routed through the NHDPlusV2 river network using the RAPID routing model to produce Naturalized Streamflow Reanalysis. Where complete observational records are available over 1980 - 2019, streamflows are assimilated (substituted) and subsequently routed downstream through the river network to produce Assimilated Streamflow Reanalysis. The dataset includes streamflow outputs from eight distinct hydrologic modeling configurations along with key performanc evaluation metrics at daily and monthly scales, supporting a wide range of water resource applications. This dataset is derived to support the Non-Powered Dam Assessment, as well as 9505 Secure Water Assessment projects for the US Department of Energy (DOE) Water Power Technologies Office (WPTO). For further details, refer to Ghimire et al. (2023), Kao et al. (2024), and Ghimire et al. (2025).

13 HYDRO ENERGY↗