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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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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Explore Spatio‐Temporal Learning of Large Sample Hydrology Using Graph Neural Networks

Abstract Streamflow forecasting over gauged and ungauged basins play a vital role in water resources planning, especially under the changing climate. Increased availability of large sample hydrology data sets, together with recent advances in deep learning techniques, has presented new opportunities to explore temporal and spatial patterns in hydrological signatures for improving streamflow forecasting. The purpose of this study is to adapt and benchmark several state‐of‐the‐art graph neural network (GNN) architectures, including ChebNet, Graph Convolutional Network (GCN), and GraphWaveNet, for end‐to‐end graph learning. We explicitly represent river basins as nodes in a graph, learn the spatiotemporal nodal dependencies, and then use the learned relations to predict streamflow simultaneously across all nodes in the graph. The efficacy of the developed GNN models is investigated using the Catchment Attributes and MEteorology for Large‐sample Studies (CAMELS) data set under two settings, fixed graph topology (transductive learning), and variable graph topology (inductive learning), with the latter applicable to prediction in ungauged basins (PUB). Results indicate that GNNs are generally robust and computationally efficient, achieving similar or better performance than a baseline model trained using the long short‐term memory (LSTM) network. Further analyses are conducted to interpret the graph learning process at the edge and node levels and to investigate the effect of different model configurations. We conclude that graph learning constitutes a viable machine learning‐based method for aggregating spatiotemporal information from a multitude of sources for streamflow forecasting

Sun, Alexander Y.↗
DOE OSTI2021

Evaluating precipitation, streamflow, and inundation forecasting skills during extreme weather events: A case study for an urban watershed

Integrated forecasting systems for precipitation, streamflow, and floodplain inundation are of critical importance in mitigating the impacts of destructive floods caused by extreme weather events. However, the skills of streamflow and floodplain inundation forecasts derived from various Quantitative Precipitation Forecasts (QPF) require a greater level of understanding. In this paper, a set of QPF developed by the National Weather Service (NWS) were used to drive a flood modeling system obtained utilizing offline coupling of a physics-based distributed hydrological model, the Distributed Hydrology Soil and Vegetation Model (DHSVM), and a hydrodynamic model, the Two-dimensional Runoff Inundation Toolkit for Operational Needs (TRITON). This flood modeling system was used to produce forecasts of streamflow and floodplain inundation maps during three major flood events in the Brays Bayou Watershed (Houston, Texas, USA) for a range of QPF durations (6–72 h). Then, to investigate the effects of increasing QPF durations on the forecasts, the forecasting skills of precipitation, streamflow, and floodplain inundation were quantified. The results show that: 1) QPF skills for more intense and sustained events such as hurricanes and tropical storms are higher than for shorter, less intense events; 2) while QPF and streamflow forecasting skills decrease as QPF durations increase, inundation forecasts under longer QPF durations (24 or 72 h) show higher skills; 3) extending the maximum QPF duration in operational hydrologic modeling from 24 h (under normal circumstances) to 72 h (for extreme events) may increase the skills of long lead time forecasts for large-scale events like Hurricane Harvey.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1975

A study of application of remote sensing to river forecasting. Volume 1: Executive summary

A project is described whose goal was to define, implement and evaluate a pilot demonstration test to show the practicability of applying remotely sensed data to operational river forecasting in gaged or previously ungaged watersheds. A secondary objective was to provide NASA with documentation describing the computer programs that comprise the streamflow forecasting simulation model used. A computer-based simulation model was adapted to a streamflow forecasting application and implemented in an IBM System/360 Model 44 computer, operating in a dedicated mode, with operator interactive control through a Model 2250 keyboard/graphic CRT terminal. The test site whose hydrologic behavior was simulated is a small basin (365 square kilometers) designated Town Creek near Geraldine, Alabama.

Source record↗
NASA NTRSDate not supplied

Economic Impact Assessments (EIA) of application of GEOGLOWS in Ecuador: Data Gaps, Limitations and Recommendations

In 2022, the United Nations launched the Early Warnings for All (EW4ALL) Program to establish global early warning systems by 2027. To assess the impact of the substantial $3.1 billion annual investment over five years, EW4ALL will consider factors that will require national coordination for the data needed for these assessments. In 2023, Ecuador was identified as one of the world's most climate-vulnerable countries, emphasizing the need to enhance its early warning systems. In 2020, the SERVIR Amazonia hub implemented the GEOGLOWS streamflow forecast service in collaboration with Ecuador's national meteorological agency (INAMHI). GEOGloWS provides 15-day ensemble forecasts and 80 years of historical streamflow data for every river worldwide through a free web service. The World Meteorological Organization has recognized this initiative as essential in contributing to the UN's call to ensure an 'Early Warning for All' by 2027. In 2023, as part of NASA's continuous efforts to fund research for Policy-Relevant Implementations, an economic impact assessment (EIA) was performed to understand the potential socioeconomic benefits of Early streamflow predictions in Ecuador using the GEOGLOWS service. Preliminary findings highlighted that gaps remain in effectively integrating socioeconomic and Earth observation (EO) data to capture the total value of these predictions. Implementing GEOGLOWS has led to valuable hydrological forecasts; however, the total economic benefits have yet to be documented. This study addresses the gaps and makes recommendations for future work that should focus on capturing the socio-economic benefits and costs associated with these forecasts, including their impact on decision-making at national and local levels. Despite the daily use of GEOGLOWS by key figures, including the President of Ecuador, the need for comprehensive recommendations and assessments is urgent.

Reetwika Basu↗
DOE OSTI2024

Bridging Hydrological Ensemble Simulation and Learning Using Deep Neural Operators

Ensemble-based simulation and learning (ESnL) has long been used in hydrology for parameter inference, but computational demands of process-based ESnL can be quite high. To address this issue, we propose a deep neural operator learning approach. Neural operators are generic machine learning algorithms that can learn functional mappings between infinite-dimensional spaces, providing a highly flexible tool for scientific machine learning. Our approach is built upon DeepONet, a specific deep neural operator, and is designed to address several common problems in hydrology, namely, model parameter estimation, prediction at ungaged locations, and uncertainty quantification. Here we demonstrate the effectiveness of our DeepONet-based workflow using an existing large model ensemble created for an eastern U.S. watershed that is instrumented with 10 streamflow gages. Results suggest DeepONet achieves high efficiency in learning an ML surrogate model from the model ensemble, with the modified Kling-Gupta Efficiency exceeding 0.9 on holdout test sets. Parameter inference, carried out using the trained DeepONet surrogate model and genetic algorithm, also yields robust results. Additionally, we formulate and train a separate DeepONet model for physics-informed, seq-to-seq streamflow forecasting, which further reduces biases in the pre-trained DeepONet surrogate model. While this study focuses primarily on a single watershed, our approach is general and may be extended to enable learning from model ensembles across multiple basins or models. Thus, this research represents a significant contribution to the application of hybrid machine learning in hydrology.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1981

Applications systems verification and transfer project. Volume 5: Operational applications of satellite snow-cover observations, northwest United States

The study objective was to develop or modify methods in an operational framework that would allow incorporation of satellite derived snow cover observations for prediction of snowmelt derived runoff. Data were reviewed and verified for five basins in the Pacific Northwest. The data were analyzed for up to a 6-year period ending July 1978, and in all cases cover a low, average, and high snow cover/runoff year. Cloud cover is a major problem in these springtime runoff analyses and have hampered data collection for periods of up to 52 days. Tree cover and terrain are sufficiently dense and rugged to have caused problems. The interpretation of snowlines from satellite data was compared with conventional ground truth data and tested in operational streamflow forecasting models. When the satellite snow-covered area (SCA) data are incorporated in the SSARR (Streamflow Synthesis and Reservoir Regulation) model, there is a definite but minor improvement.

Dillard, J. P.↗
DOE OSTI2024

Temporal sequence transformer to advance long-term streamflow prediction

Accurate streamflow prediction is crucial for understanding climate change impacts on water resources and for effective management of extreme hydrological events. While Long Short-Term Memory (LSTM) networks have been the dominant data-driven approach for streamflow forecasting, recent advancements in transformer architectures for time series tasks have shown promise in outperforming traditional LSTM models. This study introduces a transformer-based model that integrates historical streamflow data with climatic variables to enhance streamflow prediction accuracy. We evaluated our transformer model against a benchmark LSTM across five diverse basins in the United States. Results demonstrate that the transformer architecture consistently outperforms the LSTM model across all evaluation metrics, highlighting its potential as a more effective tool for hydrological forecasting. This research contributes to the ongoing development of advanced AI techniques for improved water resource management and climate change adaptation strategies.

Singh, Ruhaan [Farragut High School]↗
DOE OSTI2022

A graph neural network (GNN) approach to basin-scale river network learning: the role of physics-based connectivity and data fusion

Abstract. Rivers and river habitats around the world are under sustained pressure from human activities and the changing global environment. Our ability to quantify and manage the river states in a timely manner is critical for protecting the public safety and natural resources. In recent years, vector-based river network models have enabled modeling of large river basins at increasingly fine resolutions, but are computationally demanding. This work presents a multistage, physics-guided, graph neural network (GNN) approach for basin-scale river network learning and streamflow forecasting. During training, we train a GNN model to approximate outputs of a high-resolution vector-based river network model; we then fine-tune the pretrained GNN model with streamflow observations. We further apply a graph-based, data-fusion step to correct prediction biases. The GNN-based framework is first demonstrated over a snow-dominated watershed in the western United States. A series of experiments are performed to test different training and imputation strategies. Results show that the trained GNN model can effectively serve as a surrogate of the process-based model with high accuracy, with median Kling–Gupta efficiency (KGE) greater than 0.97. Application of the graph-based data fusion further reduces mismatch between the GNN model and observations, with as much as 50 % KGE improvement over some cross-validation gages. To improve scalability, a graph-coarsening procedure is introduced and is demonstrated over a much larger basin. Results show that graph coarsening achieves comparable prediction skills at only a fraction of training cost, thus providing important insights into the degree of physical realism needed for developing large-scale GNN-based river network models.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1981

Applications systems verification and transfer project. Volume 4: Operational applications of satellite snow cover observations. Colorado Field Test Center

The study was conducted on six watersheds ranging in size from 277 km to 3460 km in the Rio Grande and Arkansas River basins of southwestern Colorado. Six years of satellite data in the period 1973-78 were analyzed and snowcover maps prepared for all available image dates. Seven snowmapping techniques were explored; the photointerpretative method was selected as the most accurate. Three schemes to forecast snowmelt runoff employing satellite snowcover observations were investigated. They included a conceptual hydrologic model, a statistical model, and a graphical method. A reduction of 10% in the current average forecast error is estimated when snowcover data in snowmelt runoff forecasting is shown to be extremely promising. Inability to obtain repetitive coverage due to the 18 day cycle of LANDSAT, the occurrence of cloud cover and slow image delivery are obstacles to the immediate implementation of satellite derived snowcover in operational streamflow forecasting programs.

Shafer, B. A.↗
NASA NTRS2019

Definition of a Technology Validation Mission for P-band Reflectometry using Signals of Opportunity

Root-Zone Soil Moisture (RZSM) (moisture profile in the top meter of soil) and Snow Water Equivalent (SWE) (total snow pack water content) are identified as priority target variables in the ESAS 2017 decadal survey [1] with critical roles in hydrology and water management. RZSM estimates are vital for understanding multiple Earth system processes and forecasting (for example, droughts [2]). Simultaneous knowledge of surface and RZSM could enable a breakthrough in estimating key unobserved hydrologic fluxes and reduce uncertainty in net ecosystem exchange (NEE), carbon balance [3] discharge estimates, and crop yield forecasts [4] .With the high albedo and insulating properties of snow, monitoring, SWE accumulation would provide a key constraint on the potential runoff during spring ablation while monitoring SWE disappearance rates would provide a key constraint on SWE partition into runoff vs. infiltration/recharge. [5] demonstrated that knowledge of early-spring SWE generally contributes most to streamflow forecast skill in the Western U.S. SWE is also a source of water storage that provides the water resources during spring snowmelt. Despite such potentially transformative contributions, accurate RZSM and SWE measurements are unattainable with current technology. While active/passive L-band methods (e.g. SMAP, SMOS) can reliably retrieve surface soil moisture in the top 5 cm of soil [6], [7]. RZSM estimates are only available through model assimilation of brightness temperatures with a radiative transfer and land surface models [8]. SWE estimation uses multi-frequency passive microwave techniques (e.g. [9]-[11]), which have significant problems with deeper snow and in forested and mountainous environments [12]. Signals of opportunity (SoOp) in P-band (200-400 MHz) is a new remote sensing technique with the capability of estimating both essential hydrologic variables, RZSM and SWE, circumventing many of the aforementioned limitations under all weather conditions day and night. SoOp is the re-utilization of existing powerful satellite transmissions within bands allocated for communications or navigation. P-band SoOp sensitivity to soil moisture has been demonstrated in an airborne experiment over Oklahoma in 2016 [13]. Recent theory [14] and experiments [15] have also confirmed that the reflection coefficient phase is proportional to SWE.

Garrison, J. L.↗
NASA NTRS1977

The use of snowcovered area in runoff forecasts

Long-term snowcovered area data from aircraft and satellite observations have proven useful in reducing seasonal runoff forecast error on the Kern river watershed. Similar use of snowcovered area on the Kings river watershed produced results that were about equivalent to methods based solely on conventional data. Snowcovered area will be most effective in reducing forecast procedural error on watersheds with: (1) a substantial amount of area within a limited elevation range; (2) an erratic precipitation and/or snowpack accumulation pattern not strongly related to elevation; and (3) poor coverage by precipitation stations or snow courses restricting adequate indexing of water supply conditions. When satellite data acquisition and delivery problems are resolved, the derived snowcover information should provide a means for enhancing operational streamflow forecasts for areas that depend primarily on snowmelt for their water supply.

Rango, A.↗
DOE OSTI2024

Machine Learning Classification Strategy to Improve Streamflow Estimates in Diverse River Basins in the Colorado River Basin

Streamflow in the Colorado River Basin (CRB) is significantly altered by human activities including land use/cover alterations, reservoir operation, irrigation, and water exports. Climate is also highly varied across the CRB which contains snowpack-dominated watersheds and arid, precipitation-dominated basins. Recently, machine learning methods have improved the generalizability and accuracy of streamflow models. Previous successes with LSTM modeling have primarily focused on unimpacted basins, and few studies have included human impacted systems in either regional or single-basin modeling. We demonstrate that the diverse hydrological behavior of river basins in the CRB are too difficult to model with a single, regional model. We propose a method to delineate catchments into categories based on the level of predictability, hydrological characteristics, and the level of human influence. Lastly, we model streamflow in each category with climate and anthropogenic proxy data sets and use feature importance methods to assess whether model performance improves with additional relevant data. Overall, land use cover data at a low temporal resolution was not sufficient to capture the irregular patterns of reservoir releases, demonstrating the importance of having high-resolution reservoir release data sets at a global scale. On the other hand, the classification approach reduced the complexity of the data and has the potential to improve streamflow forecasts in human-altered regions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

A Comprehensive Calibration Framework for the Northwest River Forecast Center

We present a comprehensive framework developed by the Northwest River Forecast Center for calibrating hydrologically diverse basins. The framework includes models for snow, soil moisture, routing, channel loss, and consumptive use. Data inputs include a wide range of open-access datasets for meteorology, land use, topography, and land cover. The framework uses conceptual hydrologic models to handle basins with various hydrologic regimes including rain-driven and snowmelt-dominated basins. We also develop a flexible automatic calibration system that can handle numerous unobservable model parameters in a computationally efficient manner. A single-basin automatic calibration run can typically be completed on a modern laptop in under 10 min. We found that model performance metrics for this new approach match the quality of the NWRFC's previous labor-intensive manual calibrations. The model performance also rivals that of a state-of-the-art deep learning model at a fraction of the computational cost. This framework presents a new standard for the quality of calibrations possible with lumped conceptual hydrologic models, combining careful data curation, an objective calibration framework, and expert local knowledge. In addition, we have made software packages available for the entire suite of National Weather Service River Forecast System models, including SAC-SMA, SNOW-17, and Lag-K. These modern interfaces are intended to increase accessibility and facilitate future research.

Forecasting↗
NASA NTRS1973

Application of remote sensing to hydrology

Streamflow forecasting and hydrologic modelling are considered in a feasibility assessment of using the data produced by remote observation from space and/or aircraft to reduce the time and expense normally involved in achieving the ability to predict the hydrological behavior of an ungaged watershed. Existing watershed models are described, and both stochastic and parametric techniques are discussed towards the selection of a suitable simulation model. Technical progress and applications are reported and recommendations are made for additional research.

Ambaruch, R.↗
NASA NTRS1980

Operational Applications of Satellite Snowcover Observations

The history of remote sensing of snow cover is reviewed and the following topics are covered: various techniques for interpreting LANDSAT and NOAA satellite data; the status of future systems for continuing snow hydrology applications; the use of snow cover observations in streamflow forecasts by Applications Systems Verification and Transfer participants and selected foreign investigators; and the benefits of using satellite snow cover data in runoff prediction.

Rango, A.↗
NASA NTRSDate not supplied

The GeoGLOWS Project: Essential Water Variables and Observations – Expected Value Chain, Products, Examples

Essential Water Variables (EWV) were developed over the last decade to ensure that key datasets were made available for closing the global water budget, and addressing the many international actions that require water data. Satellite, surface, and numerical model data all have a role in providing EWVs, and it is a key point that these data must be merged and then fed into decision support applications to answer all the needs of the panoply of user communities. The more specialized the applications become, the higher the demand for development and maintenance resources. Some examples of applications include the NASA Goddard Giovanni, which provides on-line data access and simple analysis; the Global Flood Monitor, which focuses on precipitation-driven flooding; the NASA Landslide Hazard Assessment for Situational Awareness, which similarly treats landslides; and the Global Streamflow Forecasting Project, which provides multiple decision support interfaces, as well as a general toolkit for building new interfaces.

Essential Water Variables↗
NASA NTRS2022

Assimilation of NASA’s Airborne Snow Observatory Snow Measurements for Improved Hydrological Modeling: A Case Study Enabled by the Coupled LIS/WRF-Hydro System

he NASA LIS/WRF-Hydro system is a coupled modeling framework that combines the modeling and data assimilation (DA) capabilities of the NASA Land Information System (LIS) with the multi-scale surface hydrological modeling capabilities of the WRF-Hydro model, both of which are widely used in both operations and research. This coupled modeling framework builds on the linkage between land surface models (LSMs), which simulate surface boundary conditions in atmospheric models, and distributed hydrologic models, which simulate horizontal surface and sub-surface flow, adding new land DA capabilities. In the present study, we employ this modeling framework in the Tuolumne River basin in central California. We demonstrate the added value of the assimilation of NASA Airborne Snow Observatory (ASO) snow water equivalent (SWE) estimates in the Tuolumne basin. This analysis is performed in both LIS as an LSM column model and LIS/WRF-Hydro, with hydrologic routing. Results demonstrate that ASO DA in the basin reduced snow bias by as much as 30% from an open-loop (OL) simulation compared to three independent datasets. It also reduces downstream streamflow runoff biases by as much as 40%, and improves streamflow skill scores in both wet and dry years. Analysis of soil moisture and evapotranspiration (ET) also reveals the impacts of hydrologic routing from WRF-Hydro in the simulations, which would otherwise not be resolved in an LSM column model. By demonstrating the beneficial impact of SWE DA on the improving streamflow forecasts, the article outlines the importance of such observational inputs for reservoir operations and related water management applications.

Hydrologic modeling↗
NASA NTRS2022

Assimilation of NASA's Airborne Snow Observatory Snow Measurements for Improved Hydrological Modeling: A Case Study Enabled by the Coupled LIS/WRF-Hydro System

The NASA LIS/WRF-Hydro system is a coupled modeling framework that combines the modeling and data assimilation (DA) capabilities of the NASA Land Information System (LIS) with the multi-scale surface hydrological modeling capabilities of the WRF-Hydro model, both of which are widely used in both operations and research. This coupled modeling framework builds on the linkage between land surface models (LSMs), which simulate surface boundary conditions in atmospheric models, and distributed hydrologic models, which simulate horizontal surface and sub-surface flow, adding new land DA capabilities. In the present study, we employ this modeling framework in the Tuolumne River basin in central California. We demonstrate the added value of the assimilation of NASA Airborne Snow Observatory (ASO) snow water equivalent (SWE) estimates in the Tuolumne basin. This analysis is performed in both LIS as an LSM column model and LIS/WRF-Hydro, with hydrologic routing. Results demonstrate that ASO DA in the basin reduced snow bias by as much as 30% from an open-loop (OL) simulation compared to three independent datasets. It also reduces downstream streamflow runoff biases by as much as 40%, and improves streamflow skill scores in both wet and dry years. Analysis of soil moisture and evapotranspiration (ET) also reveals the impacts of hydrologic routing from WRF-Hydro in the simulations, which would otherwise not be resolved in an LSM column model. By demonstrating the beneficial impact of SWE DA on the improving streamflow forecasts, the article outlines the importance of such observational inputs for reservoir operations and related water management applications.

Timothy M. Lahmers↗