D
Dylan R. Harp
Researcher at Los Alamos National Laboratory
Publications - 81
Citations - 1227
Dylan R. Harp is an academic researcher from Los Alamos National Laboratory. The author has contributed to research in topics: Permafrost & Snow. The author has an hindex of 16, co-authored 73 publications receiving 757 citations. Previous affiliations of Dylan R. Harp include Government of the United States of America & University of New Mexico.
Papers
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Geologic CO2 sequestration monitoring design: A machine learning and uncertainty quantification based approach
TL;DR: A filtering-based data assimilation procedure is developed to design effective monitoring approaches and it is demonstrated that the proposed approach can be effective in developing monitoring approaches that take into consideration uncertainties.
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Thermal effects of groundwater flow through subarctic fens: A case study based on field observations and numerical modeling
Ylva Sjöberg,Ethan T. Coon,A. Britta K. Sannel,Romain Pannetier,Dylan R. Harp,Andrew Frampton,Scott L. Painter,Steve W. Lyon +7 more
TL;DR: In this paper, the authors combine field observations from a subarctic fen in the sporadic permafrost zone with numerical simulations of coupled water and thermal fluxes to quantify the influence of groundwater flow on ground temperature.
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Modeling the role of preferential snow accumulation in through talik development and hillslope groundwater flow in a transitional permafrost landscape
Elchin Jafarov,Ethan T. Coon,Dylan R. Harp,Cathy J. Wilson,Scott L. Painter,Adam L. Atchley,Vladimir E. Romanovsky +6 more
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Using field observations to inform thermal hydrology models of permafrost dynamics with ATS (v0.83)
Adam L. Atchley,Scott L. Painter,Dylan R. Harp,Ethan T. Coon,Cathy J. Wilson,Anna K. Liljedahl,Vladimir E. Romanovsky +6 more
TL;DR: In this paper, the Advanced Terrestrial Simulator (ATS) is used in combination with field measurements to achieve the goals of constructing a process-rich model based on plausible parameters and to identify fine-scale controls of active layer thickness (ALT) in ice-wedge polygon tundra in Barrow, Alaska.
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Aquifer structure identification using stochastic inversion
Dylan R. Harp,Dylan R. Harp,Zhenxue Dai,Andrew V. Wolfsberg,Jasper A. Vrugt,Bruce A. Robinson,Velimir V. Vesselinov +6 more
TL;DR: In this paper, a stochastic inverse method for aquifer structure identification using sparse geophysical and hydraulic response data is presented, based on updating structure parameters from a transition probability model to iteratively modify the aquifer structures and parameter zonation.