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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 OSTI2022

A Differentiable Hydrology Approach for Modeling With Time-Varying Parameters

In the analysis of observational data with mathematical models, it is often desirable to let one or more model parameters vary with time to account for changing environmental conditions or to offer more flexibility. A standard approach for dynamic models is to allow for the number of parameters to grow as a function of time, leading to a high-dimensional inference problem for long data records. We propose using Hamiltonian Monte Carlo, a gradient-based Markov chain Monte Carlo (MCMC) method, to perform Bayesian inference for time-varying parameters (TVP) in hydrology models. As derivatives of model error functions with regard to parameters are not available in closed form, we implemented the GR4J rainfall-runoff models in Theano/PyMC3, and Jax/NumPyro, allowing for the application of automatic differentiation techniques. A simulation experiment assessing the viability of these methods for recovering underlying temporal variation in parameters indicates that a discrete Gaussian random walk-based prior appears to be best suited across a range of scenarios while the continuous-time Gaussian process compares relatively poorly. In an analysis of 20 years of daily real-world streamflow records from the Model Parameter Estimation Experiment (MOPEX) hydrology data set with increasing data sparsity and at multiple temporal resolutions, we find that TVP inference with gradient-based MCMC is a flexible and reliable approach for analyzing hydrology models with dynamic parameter sets.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Differentiable, Learnable, Regionalized Process-Based Models With Multiphysical Outputs can Approach State-Of-The-Art Hydrologic Prediction Accuracy

Predictions of hydrologic variables across the entire water cycle have significant value for water resources management as well as downstream applications such as ecosystem and water quality modeling. Recently, purely data-driven deep learning models like long short-term memory (LSTM) showed seemingly insurmountable performance in modeling rainfall runoff and other geoscientific variables, yet they cannot predict untrained physical variables and remain challenging to interpret. Here, we show that differentiable, learnable, process-based models (called δ models here) can approach the performance level of LSTM for the intensively observed variable (streamflow) with regionalized parameterization. We use a simple hydrologic model HBV as the backbone and use embedded neural networks, which can only be trained in a differentiable programming framework, to parameterize, enhance, or replace the process-based model's modules. Without using an ensemble or post-processor, δ models can obtain a median Nash-Sutcliffe efficiency of 0.732 for 671 basins across the USA for the Daymet forcing data set, compared to 0.748 from a state-of-the-art LSTM model with the same setup. For another forcing data set, the difference is even smaller: 0.715 versus 0.722. Meanwhile, the resulting learnable process-based models can output a full set of untrained variables, for example, soil and groundwater storage, snowpack, evapotranspiration, and baseflow, and can later be constrained by their observations. Both simulated evapotranspiration and fraction of discharge from baseflow agreed decently with alternative estimates. The general framework can work with models with various process complexity and opens up the path for learning physics from big data.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Prolonged Drought in a Northern California Coastal Region Suppresses Wildfire Impacts on Hydrology

Wildfires naturally occur in many landscapes, however they are undergoing rapid regime shifts. Despite the emphasis in the literature on the most severe hydrological responses to wildfire, there remains a knowledge gap on the thresholds of wildfire (i.e., burned area/drainage area ratio, BAR) required to initiate hydrological responses. We investigated hydrological changes in the Russian River Watershed (RRW) in California, a coastal, Mediterranean, drought-prone, wildfire-adapted ecosystem, following ten wildfires that burned 30% of the watershed. Our findings suggest that sub-watersheds of the RRW have not burned beyond an intrinsic, unknown, threshold required to initiate change. Using paired watersheds, we examined spatiotemporal patterns of pre-and-post wildfire hydrology with a rainfall-runoff hydrological model. Even though these successive wildfires burned 1%–50% of each sub-watershed (1%–30% at moderate/high severity), we found little evidence of wildfire-related shifts in hydrology. As a function of BAR, wildfire imposed limited effects on runoff ratios (runoff/precipitation) and runoff residuals (observations—model simulations). Our findings that post-wildfire runoff enhancements asymptote beyond 30% burn indicate that when a watershed is burned beyond a certain threshold, the magnitude of the hydrologic response no longer increases. Drought and storm conditions explained much of the variability observed in streamflow, whereas wildfire explained only moderate variability in streamflow even when wildfire accounted for >45% BAR. While the BAR in the RRW was sufficiently beyond previously reported minimum disturbance thresholds (>20% burned forest), the lack of hydrological response is attributed to buffering effects of wildfire adaptation and drought factors that are unique to Mediterranean ecoregions.

Newcomer, Michelle E.↗
DOE OSTI2024

A Prefire Approach for Probabilistic Assessments of Postfire Debris‐Flow Inundation

Increases in wildfire activity and rainfall intensification are driving more postfire debris flows (PFDF) in many regions around the world. PFDFs are most common in the first postfire year and may even occur before a fire is fully controlled. This underscores the importance of assessing postfire hazards before a fire starts. Evaluation of PFDF hazards prior to fire can help strategize interventions lessening the negative effects of future fires. However, debris-flow runout and inundation analyses are not routine in PFDF hazard assessments, partially due to time constraints and substantial uncertainties in boundary conditions. Here, we propose a prefire PFDF inundation assessment framework using a debris-flow runout model based on the Herschel-Bulkley (HB) rheology (HEC-RAS v6.1). We constrain model inputs and parameters using Bayesian posterior analysis, rainfall-runoff simulations, and a debris-flow volume model. We use observations from recent PFDF incidents in northern Arizona, USA, to calibrate model components and then apply our prefire inundation assessment framework in a nearby unburned area. Specifically, we (a) identify yield stress as the most influential factor on inundation extent and arrival time in a HB model, (b) establish posterior distributions for model parameters suitable for forward modeling by leveraging uncertainties in field observations, and (c) implement a predictive forward analysis in an area that has not burned recently to evaluate PFDF inundation under several future fire scenarios. This study improves our ability to assess postfire debris-flow hazards before a fire begins and provides guidance for future applications of single-phase rheological models when assessing PFDF hazards.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Improving River Routing Using a Differentiable Muskingum‐Cunge Model and Physics‐Informed Machine Learning

Recently, rainfall-runoff simulations in small headwater basins have been improved by methodological advances such as deep neural networks (NNs) and hybrid physics-NN models—particularly, a genre called differentiable modeling that intermingles NNs with physics to learn relationships between variables. However, hydrologic routing simulations, necessary for simulating floods in stem rivers downstream of large heterogeneous basins, had not yet benefited from these advances and it was unclear if the routing process could be improved via coupled NNs. We present a novel differentiable routing method (δMC-Juniata-hydroDL2) that mimics the classical Muskingum-Cunge routing model over a river network but embeds an NN to infer parameterizations for Manning's roughness (n) and channel geometries from raw reach-scale attributes like catchment areas and sinuosity. The NN was trained solely on downstream hydrographs. Synthetic experiments show that while the channel geometry parameter was unidentifiable, n can be identified with moderate precision. With real-world data, the trained differentiable routing model produced more accurate long-term routing results for both the training gage and untrained inner gages for larger subbasins (>2,000 km2) than either a machine learning model assuming homogeneity, or simply using the sum of runoff from subbasins. The n parameterization trained on short periods gave high performance in other periods, despite significant errors in runoff inputs. The learned n pattern was consistent with literature expectations, demonstrating the framework's potential for knowledge discovery, but the absolute values can vary depending on training periods. The trained n parameterization can be coupled with traditional models to improve national-scale hydrologic flood simulations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

A Mass‐Conserving‐Perceptron for Machine‐Learning‐Based Modeling of Geoscientific Systems

Although decades of effort have been devoted to building Physical-Conceptual (PC) models for predicting the time-series evolution of geoscientific systems, recent work shows that Machine Learning (ML) based Gated Recurrent Neural Network technology can be used to develop models that are much more accurate. However, the difficulty of extracting physical understanding from ML-based models complicates their utility for enhancing scientific knowledge regarding system structure and function. Here, we propose a physically interpretable Mass-Conserving-Perceptron (MCP) as a way to bridge the gap between PC-based and ML-based modeling approaches. The MCP exploits the inherent isomorphism between the directed graph structures underlying both PC models and GRNNs to explicitly represent the mass-conserving nature of physical processes while enabling the functional nature of such processes to be directly learned (in an interpretable manner) from available data using off-the-shelf ML technology. As a proof of concept, we investigate the functional expressivity (capacity) of the MCP, explore its ability to parsimoniously represent the rainfall-runoff (RR) dynamics of the Leaf River Basin, and demonstrate its utility for scientific hypothesis testing. To conclude, we discuss extensions of the concept to enable ML-based physical-conceptual representation of the coupled nature of mass-energy-information flows through geoscientific systems.

58 GEOSCIENCES↗
DOE OSTI2022

Storm and Annual Time Scale Hydrological Data for the Russian River Watershed 1996-2022

These files contains observed and simulated hydrological data (discharge, precipitation) that have been aggregated to the storm-event scale and to the annual time scale for the Russian River Watershed (RRW), California. Observed data are obtained from 12 different USGS hydrological stations located throughout the watershed, and discharge is simulated using the GR5H hourly hydrological model. Precipitation data are obtained from the NASA NLDAS NOAH Community Land Surface Model. All simulated and observed discharge are aggregated to the storm and annual time scales. We investigated changes in watershed hydrological conditions in the Russian River Watershed, a Mediterranean, drought prone, wildfire-adapted ecosystem, following eleven wildfires that occurred from 2017-2020. We ask two research questions: 1) How do interacting wildfire events, drought, and atmospheric rivers impact hydrological conditions of the watershed and in particular streamflow?, and 2) What percentage of new and/or cumulative wildfire disturbance is required to initiate hydrological change? We hypothesize that sub-watersheds of the RRW Mediterranean ecoregion have not burned beyond an intrinsic, and still unknown, threshold required to initiate change. Using a series of paired burned/unburned catchments nested within the larger watershed, we examined temporal and spatial patterns of pre-and-post wildfire water hydrology using a rainfall runoff hydrological model compared with data.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Model data for Flood Frequency Analysis using Stochastic Storm Transposition and an Integrated Surface-Subsurface Hydrological Model

This archived provides scripts and input files used for the implementation of a novel approach to conduct process-based Flood Frequency Analysis using a Stochastic Storm Transposition (SST) and an Integrated Surface-Subsurface Hydrological Model (ISSHM). As a proof-of-concept, this study uses the ISSHM, Advanced Terrestrial Simulator (Amanzi-ATS) model, and the SST model, RainyDay, to conduct flood frequency analysis by simulating the flood response to 5,000 annual synthetic storm events in a ~2000 km2 Southeast Texas watershed.The Watershed Workflow package is implemented in Python3. The Jupyter notebooks can be executed through multiple open-source tools, for example, Anaconda Jupyter Lab, VS Studio Code, etc. Other data files include TXT, CSV, DAT, SBATCH, SHP, TIF, NetCDF, and HDF5 files, which can be read through Python scripts. The input files for the ATS model and RainyDay model have .XML and .SST extensions, respectively, and can be edited in any commonly used text editors.This archive contains:* Scripts and data files essential for generating the ATS model input. It uses the Watershed Workflow package to produce both mesh and ATS input files. * Jupyter notebooks designated for the ATS model evaluation, covering both long-term simulations and 40 rainfall-runoff events.* Input files required to simulate SST storm events using RainyDay.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Review of Software Packages for Local Intense Precipitation Flood Modeling

This report documents the review of the hydrologic and hydraulic modeling approaches used to estimate local intense precipitation (LIP) flooding and their implementation in readily available simulation software packages. The review focused on a few representative simulation software packages because of the similarity of their underlying mathematical bases and the rainfall-runoff and hydraulic processes included. Their availability for use in estimating LIP flood hazards and for U.S. Nuclear Regulatory Commission staff review of these estimates at the nuclear power plant (NPP) site scale were also considered.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

A Prefire Approach for Probabilistic Assessments of Postfire Debris-Flow Inundation

Increases in wildfire activity and rainfall intensification are driving more postfire debris flows (PFDF) in many regions around the world. PFDFs are most common in the first postfire year and may even occur before a fire is fully controlled. This underscores the importance of assessing postfire hazards before a fire starts. Evaluation of PFDF hazards prior to fire can help strategize interventions lessening the negative effects of future fires. However, debris-flow runout and inundation analyses are not routine in PFDF hazard assessments, partially due to time constraints and substantial uncertainties in boundary conditions. Here, we propose a prefire PFDF inundation assessment framework using a debris-flow runout model based on the Herschel-Bulkley (HB) rheology (HEC-RAS v6.1). We constrain model inputs and parameters using Bayesian posterior analysis, rainfall-runoff simulations, and a debris-flow volume model. We use observations from recent PFDF incidents in northern Arizona, USA, to calibrate model components and then apply our prefire inundation assessment framework in a nearby unburned area. Specifically, we (a) identify yield stress as the most influential factor on inundation extent and arrival time in a HB model, (b) establish posterior distributions for model parameters suitable for forward modeling by leveraging uncertainties in field observations, and (c) implement a predictive forward analysis in an area that has not burned recently to evaluate PFDF inundation under several future fire scenarios. This study improves our ability to assess postfire debris-flow hazards before a fire begins and provides guidance for future applications of single-phase rheological models when assessing PFDF hazards.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1973

Investigation of use of space data in watershed hydrology

The author has identified the following significant results. Ground truth data have been compiled and 256 rainfall runoff events have been used to detemine coefficients for two storm runoff equations that apply to the 20 study watersheds. Two scenes have been processed using CCT data. Data pertinent to each of the study watersheds have been excerpted from the tapes. A computer program and display technique has been developed to retrieve the data from irregular shaped areas and store it in a format that allows future display of only the area desired. Means, standard deviations, and distributions have been determined for extreme dry and extreme wet conditions in the dormant season. Mean digital values show some variation between watersheds when dry, but no significant differences when wet. Standard deviations of digital values diminish drastically under wet dormant conditions. Thus the standard deviations may be a good indicator of influence of moisture. Distributions of seven bit values show that odd numbers are prevalent and irregular distribution may influence some classification schemes now in use.

Blanchard, B.↗
NASA NTRS1974

Application of remote sensing techniques in land-use planning: Flood-plain delineation

The delineation of areas subject to inundation by means of remotely-sensed data acquisition represents a considerable saving in personnel time. Repeated input from aerial sensor sources provides the planner with a potent tool for the formation of a data base and for the monitoring of land use patterns over a period of time. The primary output of this project was a set of base map overlays at a scale of 1:62,500 delineating areas which require special regulations when proposed for land use involving human habitation or certain classes of storage. A secondary product of the study was county-wide maps of watershed configurations and of soil hydrologic groups. Further research is anticipated to extend the mapping of watershed areas outside the political boundaries of Cochise County, which will provide data for subsequent rainfall-runoff relationship studies in the area. All of the data provided will be incorporated into the Cochise County composite computer mapping project now operational. Results of this project have improved the pool of information available to the planning staff of Cochise County.

Altenstadter, J.↗
NASA NTRS1980

Radar target for remotely sensing hydrological phenomena

An apparatus for remotely measuring and accessing water status relative to snow and glacial melt, surface runoff, rainfall, evaporation, flow rate, and soil moisture is described. A radar target located at a selected location on the surface of the Earth is designed to collect water and render its cross sectional area variable as a function of the height of the water level within the target. The target is remotely monitored by an orbiting or airborne synthetic aperature radar. The target appears as a bright spot embedded within the radar image. The target brightness is indicative of the height of the water level within the ground located target.

Sivertson, W. E., Jr.↗
NASA NTRS2010

Combining Hydrological Modeling and Remote Sensing Observations to Enable Data-Driven Decision Making for Devils Lake Flood Mitigation in a Changing Climate

This slide presentation reviews work to combine the hydrological models and remote sensing observations to monitor Devils Lake in North Dakota, to assist in flood damage mitigation. This reports on the use of a distributed rainfall-runoff model, HEC-HMS, to simulate the hydro-dynamics of the lake watershed, and used NASA's remote sensing data, including the TRMM Multi-Satellite Precipitation Analysis (TMPA) and AIRS surface air temperature, to drive the model.

Zhang, Xiaodong↗
NASA NTRS2009

Hydrological Regimes of Small Catchments in the High Tatra Mountains Before and After Extraordinary Wind-Induced Deforestation

The paper presents the results of rainfall-runoff data analysis for small catchments of the upper Poprad River affected by wind-induced deforestation in November 2004. Before-event and afterevent measured data were compared in order to assess the impact of deforestation on hydrological regimes. Several characteristics were used including water balance, minimum and maximum runoff, runoff thresholds, number of runoff events, selected characteristics of events, runoff coefficients, and flashiness indices. Despite increased spring runoff minima, which in one catchment (Velick Creek) exceeded previously observed values after deforestation took place, it can be generally concluded that the impact of the deforestation was not clearly manifested in the analyzed hydrological data.

Holko, Ladislav↗
NASA NTRS2013

State of the Art in Large-Scale Soil Moisture Monitoring

Soil moisture is an essential climate variable influencing land atmosphere interactions, an essential hydrologic variable impacting rainfall runoff processes, an essential ecological variable regulating net ecosystem exchange, and an essential agricultural variable constraining food security. Large-scale soil moisture monitoring has advanced in recent years creating opportunities to transform scientific understanding of soil moisture and related processes. These advances are being driven by researchers from a broad range of disciplines, but this complicates collaboration and communication. For some applications, the science required to utilize large-scale soil moisture data is poorly developed. In this review, we describe the state of the art in large-scale soil moisture monitoring and identify some critical needs for research to optimize the use of increasingly available soil moisture data. We review representative examples of 1) emerging in situ and proximal sensing techniques, 2) dedicated soil moisture remote sensing missions, 3) soil moisture monitoring networks, and 4) applications of large-scale soil moisture measurements. Significant near-term progress seems possible in the use of large-scale soil moisture data for drought monitoring. Assimilation of soil moisture data for meteorological or hydrologic forecasting also shows promise, but significant challenges related to model structures and model errors remain. Little progress has been made yet in the use of large-scale soil moisture observations within the context of ecological or agricultural modeling. Opportunities abound to advance the science and practice of large-scale soil moisture monitoring for the sake of improved Earth system monitoring, modeling, and forecasting.

Review↗
NASA NTRS2015

Early Mars Climate Modeling and the Faint Young Sun Paradox.

Today Mars is a cold, dry, desert planet. Liquid water is not stable on its surface. There are no lakes, seas, or oceans, and precipitation falls as snowfall. Yet early in its history during the Noachian epoch, there is geological and mineralogical evidence that liquid water from rainfall flowed on its surface creating drainage systems, lakes, and - possibly - seas and oceans. More recent observations by Curiosity in Gale crater hint that such conditions may have persited into the Hesperian. The implication is that early Mars had a wamer climate than it does today as a result of a thicker atmosphere with a more powerful greenhouse effect capable of producing an active hydrological cycle with rainfall, runoff, and evaporation. Since Mariner 9 began accumulating such evidence, researchers have been trying to understand what kind of a climate system could have created greenhouse conditions favorable for liquid water. Unfortunately, the problem is not yet solved.

greenhouse gases↗
NASA NTRS2017

GC51D-0831: A Study of the Impact of Dams on Sediment Retention in the Mekong River Basin

Dam construction in the Mekong Basin has many cascading effects on the ecology, economy, and hydrology of the surrounding region. The focus of this study is to utilize the Soil Water Assessment Tool (SWAT), developed at Texas A & M, a rainfall-runoff hydrologic model to determine change in sedimentation in the Mekong Basin after the construction of dams. This study uses land cover land use and reservoir datasets created by the NASA SERVIR-Mekong Regional Land Cover Monitoring System and Dam Inundation Mapping Tool as inputs into the model. The study also builds on the capabilities of the SWAT model by using the sediment trapping efficiency (STE) equation from Brune (1953), rewritten by Kummu & Varis (2007), to calculate STE of dams and estimate change in sediment concentration downstream. The outputs from this study can be used to inform dam operation policies, study the correlation between dams and delta subsidence, and study the impact of dams on river fisheries, which are all pressing issues in the Mekong region.

Earth scienc↗