Estimation Of The Variation In Specific Discharge Over Large Depth Using Distributed Temperature Sensing (Dts) Measurements Of The Heat Pulse Response

The data contains measurements and derived values that are used for the manuscript "Estimation of the variation in specific discharge over large depth using Distributed Temperature Sensing (DTS) measurements of the heat pulse response, [Paper # 2018WR024171]" Currently under review at the Water Resources Research journal. The data is stored in netCDF files with xarray (Python), and should be readable with any other netCDF reader. TEMP is the measured temperature in degrees Celsius relative to the background temperature tempinfty is one of the calibration parameters. Represents the steady state temperature increase A is one of the calibration parameters. Represents the timescale in days b is one of the calibration parameters. Represents the scaled distance to the heat source err_alpha is one of the calibration parameters. Represents the autoregressive parameter TEMPmodel is the best fit temperature response in degrees Celsius relative to the background temperature Innovation is termed the noise in the article, in degrees Celsius. q is the estimated specific discharge in meters per day q_MC_XX are the confidence intervals of the estimated specific discharge calculated with Monte Carlo as presented in the article q_lmfit_XX are the confidence intervals of the estimated specific discharge calculated with LMFIT. Is a rough estimate for q_MC_XX calculated by lmfit (Python package). Time is measured in days with respect to when the heating cable is turned on. Additionally, a Jupyter notebook is supplemented to the article. It demonstrates the calibration routine and the calculation of the confidence interval for the temperature response at a single depth.