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An approach is presented to determine groundwater flow in unconsolidated aquifers with a heat pulse response test using a heating cable and a fiber-optic cable. The cables are installed together using direct push so that the cables are in direct contact with the aquifer. The temperature response is measured for multiple days along the fiber-optic cable with Distributed Temperature Sensing (DTS). The new approach fits a two-dimensional analytical solution to the temperature measurements, so that the specific discharge can be estimated without knowledge of the position of the fiber-optic cable relative to the heating cable. Two case studies are presented. The first case study is at a managed aquifer recharge system where fiber-optic cables are inserted 15 m deep at various locations to test the fitting procedure. Similar and relatively large specific discharges are found at the different locations with little vertical variation (0.4–0.6 m/day). The second case study is at a polder, where the water level is maintained 2 m below the surrounding lakes, resulting in significant groundwater flow. The heating and fiber-optic cables are inserted to a depth of 45 m. The specific discharge varies 0.07–0.1 m/day and is significantly larger in a thin layer at 30-m depth. It is shown with numerical experiments that the estimated specific discharge is smoother than in reality due to vertical conduction, but the peak specific discharge is estimated correctly for layers thicker than ∼1.5 m.

High-resolution observations from a 1043-m-deep borehole show highly variable ice properties and heterogeneous deformation., 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.

Distributed temperature sensing (DTS) systems can be used to estimate the temperature along optic fibers of several kilometers at a sub-meter interval. DTS systems function by shooting laser pulses through a fiber and measuring its backscatter intensity at two distinct wavelengths in the Raman spectrum. The scattering-loss coefficients for these wavelengths are temperature-dependent, so that the temperature along the fiber can be estimated using calibration to fiber sections with a known temperature. A new calibration approach is developed that allows for an estimate of the uncertainty of the estimated temperature, which varies along the fiber and with time. The uncertainty is a result of the noise from the detectors and the uncertainty in the calibrated parameters that relate the backscatter intensity to temperature. Estimation of the confidence interval of the temperature requires an estimate of the distribution of the noise from the detectors and an estimate of the multi-variate distribution of the parameters. Both distributions are propagated with Monte Carlo sampling to approximate the probability density function of the estimated temperature, which is different at each point along the fiber and varies over time. Various summarizing statistics are computed from the approximate probability density function, such as the confidence intervals and the standard uncertainty (the estimated standard deviation) of the estimated temperature. An example is presented to demonstrate the approach and to assess the reasonableness of the estimated confidence intervals. The approach is implemented in the open-source Python package &ldquo, dtscalibration&rdquo

Distributed Temperature Sensing (DTS) using fiber optic cables is a promising technique capable of filling in critical gaps between point observations and remote sensing. While DTS only directly measures the fiber temperature, it has been used to make spatially distributed observations of air temperature, wet bulb temperature, wind speed, and more, on the scales of centimeters to kilometers at temporal resolutions as fine as a second. Of particular interest for the flux community, the spatially distributed nature of DTS allows us to place point observations within a spatial context, highlighting missing physics and linking processes across scales.However, DTS is not without its drawbacks. It is not a push button operation – each DTS array is unique, requiring an exceptional investment in time for the deployment and for turning DTS observations into physically-meaningful results. Characteristics of DTS observations change with the DTS device used, but also with, e.g., the type of the fiber, the layout of the fiber optic array, and properties of the reference sections used in calibration. These issues create two main challenges in processing DTS data: 1) the need for a robust calibration and 2) management of data that can exceed a terabyte, especially with large or long-term installations. To address these challenges and simplify the use of this powerful technique we present two tools, which can be used both standalone and in conjunction with each other.First is ‘python-dts-calibration’, a Python package which is aimed at performing thorough calibration of DTS data, as calibration by DTS devices is often lacking in quality. It is able to perform a more robust calibration than the device default, and provides confidence intervals for the calibrated temperature. The confidence intervals vary along the fiber and over time and are different for every setup. The second tool, ‘pyfocs’, is a Python package meant for managing larger, long term installations. This tool automates the workflow including checking data integrity, calibration, and physically mapping the data. pyfocs incorporates ‘python-dts-calibration’ at its core, allowing the tool to robustly calibrate any DTS configuration. Lastly, the package provides the option for calculating other parameters, such as wind speed.Both tools are open-source and hosted on GitHub[1][2], allowing for everyone to check the code and suggest changes. By sharing our tools, we hope to make the use of fiber optic DTS in geosciences easier and open the door of this new technology to non-specialists. [1] https://github.com/dtscalibration/python-dts-calibration[2] https://github.com/klapo/pyfocs

An approach is presented to determine the seasonal variations in travel time in a bank filtration system using a passive heat tracer test. The temperature in the aquifer varies seasonally because of temperature variations of the infiltrating surface water and at the soil surface. Temperature was measured with distributed temperature sensing along fiber optic cables that were inserted vertically into the aquifer with direct push equipment. The approach was applied to a bank filtration system consisting of a sequence of alternating, elongated recharge basins and rows of recovery wells. A SEAWAT model was developed to simulate coupled flow and heat transport. The model of a two-dimensional vertical cross section is able to simulate the temperature of the water at the well and the measured vertical temperature profiles reasonably well. MODPATH was used to compute flowpaths and the travel time distribution. At the study site, temporal variation of the pumping discharge was the dominant factor influencing the travel time distribution. For an equivalent system with a constant pumping rate, variations in the travel time distribution are caused by variations in the temperature-dependent viscosity. As a result, travel times increase in the winter, when a larger fraction of the water travels through the warmer, lower part of the aquifer, and decrease in the summer, when the upper part of the aquifer is warmer.

Regular aquifer storage recovery, ASR, is often not feasible for small-scale storage in brackish or saline aquifers because fresh water floats to the top of the aquifer where it is unrecoverable. Flow barriers that partially penetrate a brackish or saline aquifer prevent a stored volume of fresh water from expanding sideways, thus increasing the recovery efficiency. In this paper, the groundwater flow and mixing is studied during injection, storage, and recovery of fresh water in a brackish or saline aquifer in a flow-tank experiment and by numerical modeling to investigate the effect of density difference, hydraulic conductivity, pumping rate, cyclic operation, and flow barrier settings. Two injection and recovery methods are investigated: constant flux and constant head. Fresh water recovery rates on the order of 65% in the first cycle climbing to as much as 90% in the following cycles were achievable for the studied configurations with constant flux whereas the recovery efficiency was somewhat lower for constant head. The spatial variation in flow velocity over the width of the storage zone influences the recovery efficiency, because it induces leakage of fresh water underneath the barriers during injection and upconing of salt water during recovery.

Targeted provisional session N°8.01 The seasonal variation in residence times is determined in a managed aquifer recharge system using a passive heat tracer test. The managed aquifer recharge system consists of a sequence of alternating elongated recharge basins and rows of recovery wells. The temperature of both the water in the recharge basin and the surface influence the temperature in the aquifer. The flow field changes when the temperature changes, as the hydraulic conductivity is a function of the temperature. Fiber optic cables were inserted up to a depth of 20 meters with direct push equipment to measure vertical temperature profiles with DTS. In this fashion, the fiber optic cables are in direct contact with the aquifer and the disturbance of the aquifer is minimal. The measured spatial and temporal temperature variations in the subsurface were modeled with SEAWAT, a coupled flow and heat transport model. MODPATH was used to compute flow paths and residence times. During the winter, a larger fraction of the water moves through the warmer lower part of the aquifer, thereby increasing the residence time. The opposite happens during the summer, when most of the water moves through the warmer upper part of the aquifer, resulting in shorter residence times.