20 research outputs found
Thermodynamics of a fast-moving Greenlandic outlet glacier revealed by fiber-optic distributed temperature sensing
Funding: This research was funded by the European Research Council as part of the RESPONDER project under the European Union’s Horizon 2020 research and innovation program (grant 683043). R.L. and T.R.C. were supported by Natural Environment Research Council Doctoral Training Partnership studentships (grant NE/ L002507/1). B.H. was supported by a HEFCW/Aberystwyth University Capital Equipment Grant.Measurements of ice temperature provide crucial constraints on ice viscosity and the thermodynamic processes occurring within a glacier. However, such measurements are presently limited by a small number of relatively coarse-spatial-resolution borehole records, especially for ice sheets. Here, we advance our understanding of glacier thermodynamics with an exceptionally high-vertical-resolution (~0.65 m), distributed-fiber-optic temperature-sensing profile from a 1043-m borehole drilled to the base of Sermeq Kujalleq (Store Glacier), Greenland. We report substantial but isolated strain heating within interglacial-phase ice at 208 to 242 m depth together with strongly heterogeneous ice deformation in glacial-phase ice below 889 m. We also observe a high-strain interface between glacial- and interglacial-phase ice and a 73-m-thick temperate basal layer, interpreted as locally formed and important for the glacier's fast motion. These findings demonstrate notable spatial heterogeneity, both vertically and at the catchment scale, in the conditions facilitating the fast motion of marine-terminating glaciers in Greenland.Publisher PDFPeer reviewe
Dtscalibration Python package for calibrating distributed temperature sensing measurements
A Python package to load raw Distributed Temperature Sensing (DTS) files, perform a calibration, and plot the result
Dtscalibration Python package for calibrating distributed temperature sensing measurements
A Python package to load raw Distributed Temperature Sensing (DTS) files, perform a calibration, and plot the result
Dtscalibration Python package for calibrating distributed temperature sensing measurements
A Python package to load raw Distributed Temperature Sensing (DTS) files, perform a calibration, and plot the result
Correction: des Tombe, B., et al. Estimation of Temperature and Associated Uncertainty from Fiber-Optic Raman-Spectrum Distributed Temperature Sensing. <i>Sensors</i> 2020, <i>20</i>, 2235
The authors wish to make the following two corrections to this paper [...
dtscalibration/python-dts-calibration: v0.7.0
0.7.0 (2019-11-07)
Ensure order of dimension upon initialization of DataStore. Resampling would lead to issues
Bug in section definition (reported by Robert Law)
Rewritten calibration solvers to align with article of this package
Removed old calibration solvers
New possibilities of saving and loading large DataStores saved to multiple netCDF file
Accelerating the understanding of plant response to drought stress
Climate extremes like droughts and heatwaves impact how water, energy, and carbon move through ecosystems. Soil-water-plant-energy interactions can be represented by SCOPE (vegetation photosynthesis model) and STEMMUS (soil water and heat model). SCOPE simulates the radiative transfer of incident light and thermal and fluorescence radiation emitted by soil and plants, temperatures of leaves and soil in the sun and shade, photosynthesis and turbulent heat exchange whereas STEMMUS traces soil moisture and soil heat dynamics and root water uptake. The integrated model, “STEMMUS-SCOPE”, thus links vegetation dynamics to soil moisture and soil temperature variability. This helps to simulate evaporation, transpiration and carbon fluxes better, especially under water stress conditions. With STEMMUS-SCOPE, we can model variables like moisture levels in deeper soil (root-zone-soil moisture) and the amount of carbon that is stored underground (carbon sequestration) at a global scale. However, applying STEMMUS-SCOPE across ecosystems at a global scale faces numerical problems and computational challenges, such as numerical convergency of the model, optimization issues in calibration, and expensive computational cost. To overcome the challenges, we are developing tools for efficient computing and data handling within the context of EcoExtreML project. The project aims to improve the coupling of STEMMUS and SCOPE models, approximate the integrated model by a machine learning approach, and estimate uncertain model states and parameters using data assimilation techniques. The results of STEMMUS-SCOPE are currently prepared for 170 flux tower sites representing 1040 site-years of data with a half-hour time step across most of the world’s climate zones and representative biomes
Speulderbos flux, storage, and temperature profile measurements
Measurements of sensible and latent heat fluxes, heat storages and temperature profiles in the Speulderbos forest