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  <title>David Dempsey — research papers</title>
  <link href="https://ddempsey.github.io/"/>
  <link rel="self" href="https://ddempsey.github.io/feed.xml"/>
  <id>https://ddempsey.github.io/</id>
  <updated>2026-10-17T00:00:00Z</updated>
  <author><name>David Dempsey</name><uri>https://orcid.org/0000-0003-2135-5129</uri></author>
  <entry>
    <title>Surrogate-Based probabilistic assessment of hydrogen leakage through abandoned wells in depleted gas reservoirs for underground hydrogen storage</title>
    <link href="https://ddempsey.github.io/papers/2027-chang-surrogate-probabilistic-assessment-hydrogen-leakage/"/>
    <id>https://doi.org/10.1016/j.fuel.2026.140374</id>
    <updated>2027-01-01T00:00:00Z</updated>
    <summary>Chang, Qiuhao, Dempsey, David, Xu, Guilong, Zhang, Liehui, Zhao, Yulong (2027). Surrogate-Based probabilistic assessment of hydrogen leakage through abandoned wells in depleted gas reservoirs for underground hydrogen storage. Fuel, 428, 140374. https://doi.org/10.1016/j.fuel.2026.140374 We built a fast statistical approximation of reservoir simulations that predicts whether hydrogen stored in a depleted gas reservoir could leak up an abandoned well. In our models, the distance to that well and the injection rate had the largest effect on leakage probability.</summary>
  </entry>
  <entry>
    <title>A Physics‐Based, Lumped‐Parameter Model for Attributing Natural and Anthropogenic CO2 Emissions From Producing Geothermal Systems</title>
    <link href="https://ddempsey.github.io/papers/2026-dempsey-physics-lumped-parameter-model-attributing/"/>
    <id>https://doi.org/10.1029/2026wr044239</id>
    <updated>2026-09-12T00:00:00Z</updated>
    <summary>Dempsey, David (2026). A Physics‐Based, Lumped‐Parameter Model for Attributing Natural and Anthropogenic CO2 Emissions From Producing Geothermal Systems. Water Resources Research, 62(9), e2026WR044239. https://doi.org/10.1029/2026wr044239 We built a lumped-parameter physics model of CO2 in a producing geothermal reservoir and calibrated it to two New Zealand fields. At Rotokawa, we estimate that metering the power plant overcounts the human-caused change in emissions by 17–45 kt a year, because production reduces natural CO2 discharge at the surface.</summary>
  </entry>
  <entry>
    <title>Socio-economic value of data-driven eruption forecasts to balance false alarms against catastrophic loss</title>
    <link href="https://ddempsey.github.io/papers/2026-ardid-socio-economic-value-data-driven/"/>
    <id>https://doi.org/10.1038/s41467-026-77242-4</id>
    <updated>2026-08-26T00:00:00Z</updated>
    <summary>Ardid, Alberto, Dempsey, David, McDonald, Garry, Cronin, Shane (2026). Socio-economic value of data-driven eruption forecasts to balance false alarms against catastrophic loss. Nature Communications, 17(1), 9356. https://doi.org/10.1038/s41467-026-77242-4 We compared the cost of false alarms with the loss from missed eruptions for eruption forecasts at five volcanoes. In retrospective tests, with documented losses at two volcanoes and assumed losses at three, acting on the forecasts could have reduced losses by 30 to 90%, despite many false alarms.</summary>
  </entry>
  <entry>
    <title>A Machine‐Learning Classification for Torlesse Composite Terrane Petrofacies and its Application to Sediment Provenance</title>
    <link href="https://ddempsey.github.io/papers/2026-parker-machine-learning-classification-torlesse-composite/"/>
    <id>https://doi.org/10.1002/jgo2.70064</id>
    <updated>2026-06-16T00:00:00Z</updated>
    <summary>Parker, Matthew O., Dempsey, David E., Sagar, Matthew W., Bassett, Kari N., Nichols, Alexander R. L., Browne, Greg H. (2026). A Machine‐Learning Classification for Torlesse Composite Terrane Petrofacies and its Application to Sediment Provenance. New Zealand Journal of Geology and Geophysics, 69(2), e70064. https://doi.org/10.1002/jgo2.70064 We trained four machine-learning algorithms to assign sandstone clasts to source regions of the Torlesse Composite Terrane, including the newly recognised Kaweka Terrane. Combined by equal vote, they reached a balanced accuracy of 80% on unseen samples, counting a two-two split as correct if either group was right.</summary>
  </entry>
  <entry>
    <title>From forecast skill to economic value: sub-hourly wildfire potential forecasting across Australian regions</title>
    <link href="https://ddempsey.github.io/papers/2026-ardid-forecast-skill-economic-value-sub/"/>
    <id>https://doi.org/10.1071/wf25221</id>
    <updated>2026-03-25T00:00:00Z</updated>
    <summary>Ardid, Alberto, Power, Anthony, Valencia, Andres, Pearce, H. Grant, Gross, Shana, Dempsey, David, Boer, Matthias (2026). From forecast skill to economic value: sub-hourly wildfire potential forecasting across Australian regions. International Journal of Wildland Fire, 35(4), WF25221. https://doi.org/10.1071/wf25221 We tested a machine learning forecast of fire potential, updated every 30 minutes, in three Australian regions. On the Sunshine Coast it detected twice as many pre-fire periods as the Fire Behaviour Index, at a similar false alarm rate. Projected savings were nearly three times higher. Both are upper bounds.</summary>
  </entry>
  <entry>
    <title>Impacts of reservoir permeability anisotropy on hydrogen migration and recovery efficiency in geological storage</title>
    <link href="https://ddempsey.github.io/papers/2026-liu-impacts-reservoir-permeability-anisotropy-hydrogen/"/>
    <id>https://doi.org/10.1016/j.fuel.2025.137596</id>
    <updated>2026-03-01T00:00:00Z</updated>
    <summary>Liu, Jinjiang, Dempsey, David, Nicol, Andy, Parker, Matt, Titus, Karan (2026). Impacts of reservoir permeability anisotropy on hydrogen migration and recovery efficiency in geological storage. Fuel, 408, 137596. https://doi.org/10.1016/j.fuel.2025.137596 We simulated hydrogen storage in a depleted gas reservoir to test how direction-dependent permeability affects how much stored hydrogen can be withdrawn again. In our models, lower vertical permeability raised recovery for most well layouts, and the orientation of horizontal wells changed it by up to 8 percentage points.</summary>
  </entry>
  <entry>
    <title>Effects of reservoir interlayers on hydrogen migration and distribution in underground hydrogen storage</title>
    <link href="https://ddempsey.github.io/papers/2026-liu-effects-reservoir-interlayers-hydrogen-migration/"/>
    <id>https://doi.org/10.1016/j.ijhydene.2026.153704</id>
    <updated>2026-02-01T00:00:00Z</updated>
    <summary>Liu, Jinjiang, Dempsey, David, Nicol, Andy, Parker, Matt, Passmore, John (2026). Effects of reservoir interlayers on hydrogen migration and distribution in underground hydrogen storage. International Journal of Hydrogen Energy, 211, 153704. https://doi.org/10.1016/j.ijhydene.2026.153704 In our models of hydrogen storage in a layered gas reservoir with the well open to every layer, thin low-permeability layers (interlayers) slowed the rise of hydrogen and raised the share recovered. At low permeability, a model with diffusion predicted more hydrogen in these layers than one without.</summary>
  </entry>
  <entry>
    <title>Determination of interfacial tension for H2-H2S-water and CH4-H2S-water mixtures under different geological conditions for underground hydrogen storage</title>
    <link href="https://ddempsey.github.io/papers/2026-chang-determination-interfacial-tension-h2s-water/"/>
    <id>https://doi.org/10.1016/j.est.2025.119683</id>
    <updated>2026-01-01T00:00:00Z</updated>
    <summary>Chang, Qiuhao, Dempsey, David, Huang, Liangliang (2026). Determination of interfacial tension for H2-H2S-water and CH4-H2S-water mixtures under different geological conditions for underground hydrogen storage. Journal of Energy Storage, 143, 119683. https://doi.org/10.1016/j.est.2025.119683 Hydrogen stored in depleted oil and gas reservoirs may mix with hydrogen sulfide (H2S). In our molecular simulations, H2S lowered the interfacial tension between the gas and pore water more than pressure or temperature did. We fitted a linear formula in H2S content, pressure and temperature to predict it.</summary>
  </entry>
  <entry>
    <title>Preferential seawater intrusion in a heterogeneous alluvial aquifer that extends offshore: Insights from numerical modeling</title>
    <link href="https://ddempsey.github.io/papers/2025-cleary-preferential-seawater-intrusion-heterogeneous-alluvial/"/>
    <id>https://doi.org/10.1016/j.jhydrol.2025.133455</id>
    <updated>2025-11-01T00:00:00Z</updated>
    <summary>Cleary, Connor A., Sarris, Theo S., Morgan, Leanne K., Dempsey, David E. (2025). Preferential seawater intrusion in a heterogeneous alluvial aquifer that extends offshore: Insights from numerical modeling. Journal of Hydrology, 661, 133455. https://doi.org/10.1016/j.jhydrol.2025.133455 We modelled seawater moving inland through a gravel aquifer beneath the Canterbury coast. In our model, seawater moved preferentially through high-conductivity gravel. In places, the salt/fresh water interface moved more than five times faster than a uniform model with dispersion predicts.</summary>
  </entry>
  <entry>
    <title>Advanced Hydrogeochemical Coupling in Carbon Sequestration: A 4D Spatiotemporal Fourier Neural Operator–Driven Modeling Framework</title>
    <link href="https://ddempsey.github.io/papers/2025-rui-advanced-hydrogeochemical-coupling-carbon-sequestration/"/>
    <id>https://doi.org/10.2118/228397-pa</id>
    <updated>2025-07-21T00:00:00Z</updated>
    <summary>Rui, Zhenhua, Zhou, Kaihu, Hu, Ting, Dempsey, David, Du, Kai, Chen, Siwei (2025). Advanced Hydrogeochemical Coupling in Carbon Sequestration: A 4D Spatiotemporal Fourier Neural Operator–Driven Modeling Framework. SPE Journal, 30(09), 5858-5880. https://doi.org/10.2118/228397-pa</summary>
  </entry>
  <entry>
    <title>The Research and Impact of the Works by Don Nield</title>
    <link href="https://ddempsey.github.io/papers/2025-becker-research-impact-works-don-nield/"/>
    <id>https://doi.org/10.1007/s11242-025-02199-9</id>
    <updated>2025-07-16T00:00:00Z</updated>
    <summary>Becker, Sid, Dempsey, David, Zhao, Dan (2025). The Research and Impact of the Works by Don Nield. Transport in Porous Media, 152(9), 61. https://doi.org/10.1007/s11242-025-02199-9 We review the published work of Don Nield, a University of Auckland mathematician who studied convection and flow in porous media for more than 50 years. Scopus indexes 249 of his works, with 22,480 citations in total.</summary>
  </entry>
  <entry>
    <title>Quantifying Interannual Variations in Groundwater Fluxes Within a Braidplain Aquifer Using Active‐Distributed Temperature Sensing</title>
    <link href="https://ddempsey.github.io/papers/2025-sai-louie-quantifying-interannual-variations-groundwater-fluxes/"/>
    <id>https://doi.org/10.1029/2024wr039328</id>
    <updated>2025-07-15T00:00:00Z</updated>
    <summary>Sai Louie, A. J., Morgan, L. K., Dempsey, D., Wilson, S., Banks, E. W. (2025). Quantifying Interannual Variations in Groundwater Fluxes Within a Braidplain Aquifer Using Active‐Distributed Temperature Sensing. Water Resources Research, 61(7), e2024WR039328. https://doi.org/10.1029/2024wr039328 Over nearly three years we used heated fibre optic cables to measure groundwater flow beneath a braided river in Canterbury, New Zealand. River flow varied about tenfold between surveys, but average groundwater flux remained between 2.4 and 5.9 m/day. Riverbed changes after a flood likely had the largest effect.</summary>
  </entry>
  <entry>
    <title>Impact of operational parameters on water invasion in underground hydrogen storage: A numerical investigation of the Ahuroa field, New Zealand</title>
    <link href="https://ddempsey.github.io/papers/2025-liu-impact-operational-parameters-water-invasion/"/>
    <id>https://doi.org/10.1016/j.ijhydene.2025.150236</id>
    <updated>2025-07-01T00:00:00Z</updated>
    <summary>Liu, Jinjiang, Dempsey, David, Nicol, Andy, Parker, Matt, Passmore, John (2025). Impact of operational parameters on water invasion in underground hydrogen storage: A numerical investigation of the Ahuroa field, New Zealand. International Journal of Hydrogen Energy, 153, 150236. https://doi.org/10.1016/j.ijhydene.2025.150236 We modelled hydrogen storage in the depleted Ahuroa gas field, New Zealand, to evaluate how operating choices affect water rising toward the wells. In our model, longer or faster withdrawal and a smaller cushion of gas produced the most water.</summary>
  </entry>
  <entry>
    <title>Forecasting Eruptions at Steamboat Geyser: Time Scales, Differentiability, and Detectability of Seismic Precursors Through Data‐Driven Methods</title>
    <link href="https://ddempsey.github.io/papers/2025-ardid-forecasting-eruptions-steamboat-geyser-time/"/>
    <id>https://doi.org/10.1029/2024jh000569</id>
    <updated>2025-04-12T00:00:00Z</updated>
    <summary>Ardid, Alberto, Barth, Anna, Dempsey, David, Manga, Michael, Cronin, Shane J. (2025). Forecasting Eruptions at Steamboat Geyser: Time Scales, Differentiability, and Detectability of Seismic Precursors Through Data‐Driven Methods. Journal of Geophysical Research: Machine Learning and Computation, 2(2), e2024JH000569. https://doi.org/10.1029/2024jh000569 A machine learning model trained on signals in the 2018 seismic record of Steamboat Geyser showed a fair ability to separate pre-eruption periods from other times in later data. Its eruption probability peaked at 12.6% within 18 hours of an eruption, compared with 8.1% from eruption intervals alone.</summary>
  </entry>
  <entry>
    <title>Ergodic seismic precursors and transfer learning for short term eruption forecasting at data scarce volcanoes</title>
    <link href="https://ddempsey.github.io/papers/2025-ardid-ergodic-seismic-precursors-transfer-learning/"/>
    <id>https://doi.org/10.1038/s41467-025-56689-x</id>
    <updated>2025-02-25T00:00:00Z</updated>
    <summary>Ardid, Alberto, Dempsey, David, Caudron, Corentin, Cronin, Shane, Kennedy, Ben, Girona, Társilo, Roman, Diana, Miller, Craig, Potter, Sally, Lamb, Oliver D., Martanto, Anto, Cubuk-Sabuncu, Yesim, Cabrera, Leoncio, Ruiz, Sergio, Contreras, Rodrigo, Pacheco, Javier, Mora, Mauricio M., De Angelis, Silvio (2025). Ergodic seismic precursors and transfer learning for short term eruption forecasting at data scarce volcanoes. Nature Communications, 16(1), 1758. https://doi.org/10.1038/s41467-025-56689-x We trained forecasting models on seismic data from 41 eruptions at 24 volcanoes and tested each on a volcano left out of training. They scored better than a seismic amplitude benchmark and, at three volcanoes, close to models trained on that volcano alone. This suggests precursors are shared between volcanoes.</summary>
  </entry>
  <entry>
    <title>Sub-hourly forecasting of fire potential using machine learning on time series of surface weather variables</title>
    <link href="https://ddempsey.github.io/papers/2025-ardid-sub-hourly-forecasting-fire-potential/"/>
    <id>https://doi.org/10.1071/wf24113</id>
    <updated>2025-01-30T00:00:00Z</updated>
    <summary>Ardid, Alberto, Valencia, Andres, Power, Anthony, Boer, Matthias M., Katurji, Marwan, Gross, Shana, Dempsey, David (2025). Sub-hourly forecasting of fire potential using machine learning on time series of surface weather variables. International Journal of Wildland Fire, 34(1), WF24113. https://doi.org/10.1071/wf24113 We trained machine learning models on 15 years of half-hourly records from one weather station on the Sunshine Coast, Australia, to recognise the weather in the days before fires. Tested on fires withheld from training, 11 of the 15 fires occurred during a warning from our 10-day model.</summary>
  </entry>
  <entry>
    <title>Techno-economic analysis of geothermal combined with direct and biomass-based carbon dioxide removal for high-temperature hydrothermal systems</title>
    <link href="https://ddempsey.github.io/papers/2025-titus-techno-economic-analysis-geothermal-combined/"/>
    <id>https://doi.org/10.1016/j.geothermics.2024.103159</id>
    <updated>2025-01-01T00:00:00Z</updated>
    <summary>Titus, K.A., Dempsey, David, Peer, Rebecca A.M., Archer, Rosalind (2025). Techno-economic analysis of geothermal combined with direct and biomass-based carbon dioxide removal for high-temperature hydrothermal systems. Geothermics, 125, 103159. https://doi.org/10.1016/j.geothermics.2024.103159 We modelled geothermal power plants that also remove CO2 from the atmosphere, by burning forestry residues or by capturing CO2 from air, and store it dissolved in reinjected water. At USD 100 per tonne of CO2, the biomass design had the lowest median costs of electricity and of carbon abatement.</summary>
  </entry>
  <entry>
    <title>Influence of hydrogen sulfide on gas-water interface in underground hydrogen storage: A molecular dynamics study</title>
    <link href="https://ddempsey.github.io/papers/2024-chang-influence-hydrogen-sulfide-gas-water/"/>
    <id>https://doi.org/10.1016/j.est.2024.112766</id>
    <updated>2024-09-01T00:00:00Z</updated>
    <summary>Chang, Qiuhao, Huang, Liangliang, McKenzie, Kelsey, Carere, Carlo, Stott, Matthew, Nicol, Andy, Dempsey, David (2024). Influence of hydrogen sulfide on gas-water interface in underground hydrogen storage: A molecular dynamics study. Journal of Energy Storage, 97, 112766. https://doi.org/10.1016/j.est.2024.112766 We used molecular dynamics simulations to test how hydrogen sulfide, a gas that microbes can produce in underground hydrogen stores, changes the tension between stored gas and pore water. At 343 K and 14.5 MPa, 5% hydrogen sulfide in the gas lowered it by 12% when mixed with hydrogen and by 6% when mixed with methane.</summary>
  </entry>
  <entry>
    <title>Earthquakes Trigger Rapid Flash Boiling Front at Optimal Geologic Conditions</title>
    <link href="https://ddempsey.github.io/papers/2024-sanchez-alfaro-earthquakes-trigger-rapid-flash-boiling/"/>
    <id>https://doi.org/10.1029/2023gl108109</id>
    <updated>2024-08-21T00:00:00Z</updated>
    <summary>Sanchez‐Alfaro, P., Wallis, I., Iturrieta, P., Rowland, J., Dempsey, D., O’Sullivan, J., Reich, M., Cembrano, J. (2024). Earthquakes Trigger Rapid Flash Boiling Front at Optimal Geologic Conditions. Geophysical Research Letters, 51(16), e2023GL108109. https://doi.org/10.1029/2023gl108109 An earthquake can make hot water boil in the fracture between two faults. In our models this flashing was limited, and complete flashing is highly unlikely. Opening was largest where the faults were separated by about 10% of their length and overlapped by about 2.5%.</summary>
  </entry>
  <entry>
    <title>From carbon neutral to carbon negative: a theoretical bioenergy and CO2 removal retrofit at Ngāwhā geothermal power station</title>
    <link href="https://ddempsey.github.io/papers/2024-titus-carbon-neutral-carbon-negative-theoretical/"/>
    <id>https://doi.org/10.1080/03036758.2024.2385807</id>
    <updated>2024-08-11T00:00:00Z</updated>
    <summary>Titus, Karan, Dempsey, David, Peer, Rebecca, Hanik, Fabian (2024). From carbon neutral to carbon negative: a theoretical bioenergy and CO2 removal retrofit at Ngāwhā geothermal power station. Journal of the Royal Society of New Zealand, 55(4), 893-911. https://doi.org/10.1080/03036758.2024.2385807 We modelled a biomass boiler retrofit that adds 1 MWe to Station-1 at the Ngāwhā geothermal power station. Dissolving the biomass CO2 in reinjected fluid could remove up to 15,900 tonnes of CO2 a year. The design that also sells its surplus CO2 had the lowest costs in our model.</summary>
  </entry>
  <entry>
    <title>Multitimescale Template Matching: Discovering Eruption Precursors across Diverse Volcanic Settings</title>
    <link href="https://ddempsey.github.io/papers/2024-ardid-multitimescale-template-matching-discovering-eruption/"/>
    <id>https://doi.org/10.1785/0220240012</id>
    <updated>2024-06-10T00:00:00Z</updated>
    <summary>Ardid, Alberto, Dempsey, David, Corry, Josh, Garrett, Owen, Lamb, Oliver D., Cronin, Shane (2024). Multitimescale Template Matching: Discovering Eruption Precursors across Diverse Volcanic Settings. Seismological Research Letters, 95(5), 2611-2621. https://doi.org/10.1785/0220240012</summary>
  </entry>
  <entry>
    <title>Prospectivity analysis for underground hydrogen storage, Taranaki basin, Aotearoa New Zealand: A multi-criteria decision-making approach</title>
    <link href="https://ddempsey.github.io/papers/2024-higgs-prospectivity-analysis-underground-hydrogen-storage/"/>
    <id>https://doi.org/10.1016/j.ijhydene.2024.05.098</id>
    <updated>2024-06-01T00:00:00Z</updated>
    <summary>Higgs, Karen E., Strogen, Dominic P., Nicol, Andrew, Dempsey, David, Leith, Kerry, Bassett, Kari, Reid, Catherine, Yates, Edward, Parker, Matthew, Bischoff, Alan, Adam, Ludmila, Rowe, Michael (2024). Prospectivity analysis for underground hydrogen storage, Taranaki basin, Aotearoa New Zealand: A multi-criteria decision-making approach. International Journal of Hydrogen Energy, 71, 1468-1485. https://doi.org/10.1016/j.ijhydene.2024.05.098 We ranked 40 possible sites for storing hydrogen underground in Taranaki Basin, New Zealand. Depleted Paleogene gas and gas-cap reservoirs ranked highest. Our results suggest that one or two of the best sites could hold more than the country&#39;s projected 2050 storage need.</summary>
  </entry>
  <entry>
    <title>Eruption Forecasting Model for Copahue Volcano (Southern Andes) Using Seismic Data and Machine Learning: A Joint Interpretation with Geodetic Data (GNSS and InSAR)</title>
    <link href="https://ddempsey.github.io/papers/2024-cabrera-eruption-forecasting-model-copahue-volcano/"/>
    <id>https://doi.org/10.1785/0220240022</id>
    <updated>2024-05-29T00:00:00Z</updated>
    <summary>Cabrera, Leoncio, Ardid, Alberto, Melchor, Ivan, Ruiz, Sergio, Symmes-Lopetegui, Blanca, Carlos Báez, Juan, Delgado, Francisco, Martinez-Yáñez, Pablo, Dempsey, David, Cronin, Shane (2024). Eruption Forecasting Model for Copahue Volcano (Southern Andes) Using Seismic Data and Machine Learning: A Joint Interpretation with Geodetic Data (GNSS and InSAR). Seismological Research Letters, 95(5), 2595-2610. https://doi.org/10.1785/0220240022</summary>
  </entry>
  <entry>
    <title>Testing the reproducibility of active-distributed temperature sensing for measuring groundwater specific discharge beneath a braided river</title>
    <link href="https://ddempsey.github.io/papers/2024-sai-louie-testing-reproducibility-active-distributed-temperature/"/>
    <id>https://doi.org/10.1016/j.jhydrol.2024.130877</id>
    <updated>2024-04-01T00:00:00Z</updated>
    <summary>Sai Louie, Alice J., Morgan, Leanne K., Banks, Eddie W., Dempsey, David, Wilson, Scott (2024). Testing the reproducibility of active-distributed temperature sensing for measuring groundwater specific discharge beneath a braided river. Journal of Hydrology, 633, 130877. https://doi.org/10.1016/j.jhydrol.2024.130877 We heated a 100 m fibre optic cable beneath New Zealand&#39;s Waikirikiri-Selwyn River twelve times in 24 hours. The median groundwater flow of each survey lay between 3.5 and 4.0 m per day, within the measurement error of the method. Repeat surveys could therefore measure seasonal changes in river loss.</summary>
  </entry>
  <entry>
    <title>Molecular dynamics insights into gas-water interfacial tension: Optimizing hydrogen storage in subsurface conditions</title>
    <link href="https://ddempsey.github.io/papers/2024-chang-molecular-dynamics-insights-gas-water/"/>
    <id>https://doi.org/10.1016/j.ijhydene.2024.03.341</id>
    <updated>2024-04-01T00:00:00Z</updated>
    <summary>Chang, Qiuhao, Dempsey, David, Zhang, Liehui, Zhao, Yulong, Huang, Liangliang (2024). Molecular dynamics insights into gas-water interfacial tension: Optimizing hydrogen storage in subsurface conditions. International Journal of Hydrogen Energy, 64, 896-905. https://doi.org/10.1016/j.ijhydene.2024.03.341 In molecular simulations at underground hydrogen storage conditions, mixing a cushion gas into hydrogen lowered the tension at the boundary between gas and pore water. Carbon dioxide lowered it substantially up to a 40% share, and at a lower rate above that. Methane lowered it at a steady rate.</summary>
  </entry>
  <entry>
    <title>Active-distributed temperature sensing dataset beneath a braided river</title>
    <link href="https://ddempsey.github.io/papers/2023-sai-louie-active-distributed-temperature-sensing-dataset/"/>
    <id>https://doi.org/10.1016/j.dib.2023.109756</id>
    <updated>2023-12-01T00:00:00Z</updated>
    <summary>Sai Louie, Alice J., Morgan, Leanne K., Banks, Eddie W., Dempsey, David, Wilson, Scott (2023). Active-distributed temperature sensing dataset beneath a braided river. Data in Brief, 51, 109756. https://doi.org/10.1016/j.dib.2023.109756</summary>
  </entry>
  <entry>
    <title>Shallow groundwater characterisation and hydrograph classification in the coastal city of Ōtautahi/Christchurch, New Zealand</title>
    <link href="https://ddempsey.github.io/papers/2023-bosserelle-shallow-groundwater-characterisation-hydrograph-classification/"/>
    <id>https://doi.org/10.1007/s10040-023-02745-z</id>
    <updated>2023-11-15T00:00:00Z</updated>
    <summary>Bosserelle, Amandine L., Morgan, Leanne K., Dempsey, David E., Setiawan, Irene (2023). Shallow groundwater characterisation and hydrograph classification in the coastal city of Ōtautahi/Christchurch, New Zealand. Hydrogeology Journal, 32(2), 577-600. https://doi.org/10.1007/s10040-023-02745-z Records from 265 monitoring wells in Christchurch, New Zealand, show that from 2016 to 2020 the median depth to the water table was 1 m or less below about 62% of the mapped city. In a preliminary one-week comparison, wells nearer tidal rivers followed the ocean tide more closely.</summary>
  </entry>
  <entry>
    <title>Using Template Matching to Detect Hidden Fluid Release Episodes Beneath Crater Lakes in Ruapehu, Copahue, and Kawah Ijen Volcanoes</title>
    <link href="https://ddempsey.github.io/papers/2023-ardid-template-matching-detect-hidden-fluid/"/>
    <id>https://doi.org/10.1029/2023jb026729</id>
    <updated>2023-10-17T00:00:00Z</updated>
    <summary>Ardid, A., Dempsey, D., Caudron, C., Cronin, S. J., Miller, C. A., Melchor, I., Syahbana, D., Kennedy, B. (2023). Using Template Matching to Detect Hidden Fluid Release Episodes Beneath Crater Lakes in Ruapehu, Copahue, and Kawah Ijen Volcanoes. Journal of Geophysical Research: Solid Earth, 128(10), e2023JB026729. https://doi.org/10.1029/2023jb026729 We searched Ruapehu seismic data from 2006 to 2021 for a tremor pattern seen before the 2019 Whakaari eruption. We then looked for a tremor peak and exponential decay after each match. Six events passed, including a 2009 event that we classify as a fluid release into the crater lake.</summary>
  </entry>
  <entry>
    <title>Association Between Injection and Microseismicity in Geothermal Fields With Multiple Wells: Data‐Driven Modeling of Rotokawa, New Zealand, and Húsmúli, Iceland</title>
    <link href="https://ddempsey.github.io/papers/2023-yu-association-injection-microseismicity-geothermal-fields/"/>
    <id>https://doi.org/10.1029/2022jb025952</id>
    <updated>2023-04-16T00:00:00Z</updated>
    <summary>Yu, Pengliang, Dempsey, David, Rinaldi, Antonio Pio, Calibugan, Aimee, Ritz, Vanille A., Archer, Rosalind (2023). Association Between Injection and Microseismicity in Geothermal Fields With Multiple Wells: Data‐Driven Modeling of Rotokawa, New Zealand, and Húsmúli, Iceland. Journal of Geophysical Research: Solid Earth, 128(4), e2022JB025952. https://doi.org/10.1029/2022jb025952 We used data-driven methods to find which injection wells are associated with small earthquakes at the Rotokawa (New Zealand) and Húsmúli (Iceland) fields. Injection was more closely associated with earthquake rates averaged over weeks than over days. At Rotokawa, we found no evidence that rapid injection changes trigger earthquakes.</summary>
  </entry>
  <entry>
    <title>Identification of Seismo‐Volcanic Regimes at Whakaari/White Island (New Zealand) Via Systematic Tuning of an Unsupervised Classifier</title>
    <link href="https://ddempsey.github.io/papers/2023-steinke-identification-seismo-volcanic-regimes-whakaari/"/>
    <id>https://doi.org/10.1029/2022jb026221</id>
    <updated>2023-03-14T00:00:00Z</updated>
    <summary>Steinke, B., Jolly, A. D., Carniel, R., Dempsey, D. E., Cronin, S. J. (2023). Identification of Seismo‐Volcanic Regimes at Whakaari/White Island (New Zealand) Via Systematic Tuning of an Unsupervised Classifier. Journal of Geophysical Research: Solid Earth, 128(3), e2022JB026221. https://doi.org/10.1029/2022jb026221 We tuned an unsupervised classifier on 12.5 years of volcanic tremor at Whakaari. It separated background from unrest. In a retrospective analysis, its output changed to unrest regimes 6 weeks before the August 2012 eruption and about 3.5 months before the December 2019 eruption.</summary>
  </entry>
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