Search NASA⌕ Search

SEARCH · Search NASA

Results for “Glacier”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

112 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Improved representation of laminar and turbulent sheet flow in subglacial drainage models

Subglacial hydrology models struggle to reproduce seasonal drainage patterns that are consistent with observed subglacial water pressures and surface velocities. We modify the standard sheet-flow parameterization within a coupled sheet–channel subglacial drainage model to smoothly transition between laminar and turbulent flow based on the locally computed Reynolds number in a physically consistent way (the ‘transition’ model). We compare the transition model to standard laminar and turbulent models to assess the role of the sheet-flow parameterization in reconciling observed and modelled water pressures under idealized and realistic forcing. Relative to the turbulent model, the laminar and transition models improve seasonal simulations by increasing winter water pressure and producing a more prominent late-summer water pressure minimum. In contrast to the laminar model, the transition model remains consistent with its own internal assumptions across all flow regimes. Based on the internal consistency of the transition model and its improved performance relative to the standard turbulent model, we recommend its use for transient simulations of subglacial drainage.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Emulator-based Bayesian calibration of a subglacial drainage model

Subglacial drainage models, often motivated by the relationship between hydrology and ice flow, sensitively depend on numerous unconstrained parameters. We explore using borehole water-pressure time series to calibrate the uncertain parameters of a popular subglacial drainage model, taking a Bayesian perspective to quantify the uncertainty in parameter estimates and in the calibrated model predictions. To reduce the computation time associated with Markov Chain Monte Carlo sampling, we construct a fast Gaussian process emulator to stand in for the subglacial drainage model. We first carry out a calibration experiment using synthetic observations consisting of model simulations with hidden parameter values as a demonstration of the method. Using real borehole water pressures measured in western Greenland, we find meaningful constraints on four of the eight model parameters and a factor-of-three reduction in uncertainty of the calibrated model predictions. These experiments illustrate Gaussian process-based Bayesian inference as a useful tool for calibration and uncertainty quantification of complex glaciological models using field data. However, significant differences between the calibrated model and the borehole data suggest that structural limitations of the model, rather than poorly constrained parameters or computational cost, remain the most important constraint on subglacial drainage modelling.

58 GEOSCIENCES↗
DOE OSTI2022

Export of Ice Sheet Meltwater from Upernavik Fjord, West Greenland

Meltwater from Greenland is an important freshwater source for the North Atlantic Ocean, released into the ocean at the head of fjords in the form of runoff, submarine melt, and icebergs. The meltwater release gives rise to complex in-fjord transformations that result in its dilution through mixing with other water masses. The transformed waters, which contain the meltwater, are exported from the fjords as a new water mass Glacially Modified Water (GMW). Here we use summer hydrographic data collected from 2013 to 2019 in Upernavik, a major glacial fjord in northwest Greenland, to describe the water masses that flow into the fjord from the shelf and the exported GMWs. Using an optimum multi-parameter technique across multiple years we then show that GMW is composed of 57.8% ± 8.1% Atlantic Water (AW), 41.0% ± 8.3% Polar Water (PW), 1.0% ± 0.1% subglacial discharge, and 0.2% ± 0.2% submarine meltwater. We show that the GMW fractional composition cannot be described by buoyant plume theory alone since it includes lateral mixing within the upper layers of the fjord not accounted for by buoyant plume dynamics. Consistent with its composition, we find that changes in GMW properties reflect changes in the AW and PW source waters. Using the obtained dilution ratios, this study suggests that the exchange across the fjord mouth during summer is on the order of 50 mSv (1 Sv ≡ 10 6 m 3 s –1 ) (compared to a freshwater input of 0.5 mSv). This study provides a first-order parameterization for the exchange at the mouth of glacial fjords for large-scale ocean models.

58 GEOSCIENCES↗
DOE OSTI2025

The Global Spectra-Trait Initiative: A database of paired leaf spectroscopy and functional traits associated with leaf photosynthetic capacity (v1.0.0)

The Global Spectra-Trait Initiative (GSTI) aims to generate generalizable spectra trait models using reflectance data to predict leaf traits associated with the photosynthesis capacity of leaves. It comprises a synthesized dataset of leaf trait data, input datasets and code. Leaf traits include the maximum carboxylation rate of rubisco (Vcmax), the maximum electron transport rate (Jmax), the dark respiration, as well as the prediction of leaf nitrogen, leaf mass per area (LMA), and leaf water content (LWC). The dataset comprises >7500 paired observations from around 400 species from a broad range of biomes. This dataset comprises a zip file of the GSTI GitHub repository (https://github.com/plantphys/gsti), the synthesized database (.csv) and database metadata files. This dataset was updated on 2025-12-12 with minor edits to mirror the accepted manuscript version and GitHub release (Version 1.0.0 (ESSD accepted version)). Edits included minor changes to the project documentation on GitHub and removal of 12 duplicate entries from the database.

54 ENVIRONMENTAL SCIENCES↗