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Your search keyword '"Schneidewind, U."' showing total 4 results
Start Over Author "Schneidewind, U." Journal water resources research
4 results on '"Schneidewind, U."'

1. iFLOW: A Framework and GUI to Quantify Effective Thermal Diffusivity and Advection in Permeable Materials From Temperature Time Series.

Author

Bertagnoli, A., Luce, C., van Kampen, R., Schneidewind, U., van Berkel, M., Tranmer, A. W., Vandersteen, G., Krause, S., and Tonina, D.

Subjects
TIME series analysis, THERMAL diffusivity, GRAPHICAL user interfaces, THERMAL properties, HEAT flux
Abstract

iFLOW is a free, open‐source, and python‐based framework and graphical user interface to visualize and analyze temperature time series, and extract one dimensional thermal velocity, vT, and bulk effective thermal diffusivity, ke. Information of thermal properties of the sediment‐water mixture (bulk) and water allows quantifying the one‐dimensional Darcian flux, q, and seepage velocity, v, from vT. Available software packages were developed to quantify q and ke only based on a specific mathematical model or focused on specific data processing or parameter estimation techniques, and all these steps were lumped together preventing users to identify potential source of errors. iFLOW proposes a novel organizational philosophy with a modular framework that parses the analysis process into three fundamental steps: (a) the mathematical model, (b) signal processing, and (c) parameter estimation. iFLOW houses a suite of models and analysis techniques. This suite can be readily added to and expanded through its modular framework. iFLOW contains a wizard to guide users through the selection process with respect to the three fundamental steps. Users can analyze and visualize intermediate results to identify problematic issues in the time series data and improve data interpretation. Here, we present iFLOW and summarize its performance using a set of one‐dimensional synthetic heat transport simulations. Plain Language Summary: iFLOW is a free, open‐source computer application to help users visualize and analyze temperature data. It calculates the water velocity within sediment along with thermal diffusivity from measured temperature data. Unlike other applications, iFLOW is flexible, allowing users to check for errors by breaking down the analysis into three parts: mathematical model selection, signal processing, and estimating parameters. It includes various models and methods that users can easily expand on. iFLOW also guides users through the analysis and in choosing options for these three analysis stages. We introduce iFLOW through a set of examples with simulated heat transport data. Key Points: iFLOW is a novel framework for temperature time series analysisiFLOW helps users estimate advective flux and effective thermal diffusivity with their uncertaintyThe analysis is parsed into the mathematical model selection, signal processing, and parameter estimation steps [ABSTRACT FROM AUTHOR]

Published
2024
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2. LPMLE3 METHOD

Author

Schneidewind, U., van Berkel, M., Anibas, C., Vandersteen, G., Schmidt, C., Joris, I., Seuntjens, P., Batelaan, O., Zwart, H.J., Control Systems Technology, Dynamics and Control, Hydrology and Hydraulic Engineering, Earth System Sciences, Electricity, and IR Academic Unit

Subjects
Agriculture and Food Sciences, FLUXES, EWI-27415, groundwater-surface water interaction, THERMAL-DIFFUSIVITY, SPATIAL VARIABILITY, TEMPERATURE TIME-SERIES, heat tracer, FLUID-FLOW, TRANSIENT, frequency domain, Chemistry, hyporheic zone, maximum-likelihood estimator, IR-102211, SURFACE-WATER, 2023 OA procedure, RIVER, METIS-319477, HETEROGENEITY, INDUCED HYPORHEIC EXCHANGE, Biology
Abstract

We introduce LPMLE3, a new 1D approach to quantify vertical water flow components at streambeds using temperature data collected in different depths. LPMLE3 solves the partial differential equation for coupled water flow and heat transport in the frequency domain. Unlike other 1D approaches it does not assume a semi-infinite halfspace with the location of the lower boundary condition approaching infinity. Instead it uses local upper and lower boundary conditions. As such, the streambed can be divided into finite sub-domains bound at the top and bottom by a temperature-time series. Information from a third temperature sensor within each sub-domain is then used for parameter estimation. LPMLE3 applies a low order local polynomial to separate periodic and transient parts (including the noise contributions) of a temperature-time series and calculates the frequency response of each sub-domain to a known temperature input at the streambed top. A maximum likelihood estimator is used to estimate the vertical component of water flow, thermal diffusivity and their uncertainties for each streambed sub-domain and provides information regarding model quality. We tested the method on synthetic temperature data generated with the numerical model STRIVE and demonstrate how the vertical flow component can be quantified for field data collected in a Belgian stream. We show that by using the results in additional analyses, non-vertical flow components could be identified and by making certain assumptions they could be quantified for each sub-domain. LPMLE3 performed well on both simulated and field data and can be considered a valuable addition to the existing 1D methods. This article is protected by copyright. All rights reserved.

Published
2016

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