From Dynamic Groundwater Level Measurements to Regional Aquifer Parameters— Assessing the Power of Spectral Analysis
Large‐scale groundwater models are required to estimate groundwater availability and to inform water management strategies on the national scale. However, parameterization of large‐scale groundwater models covering areas of major river basins and more is challenging due to the lack of observational...
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description | Large‐scale groundwater models are required to estimate groundwater availability and to inform water management strategies on the national scale. However, parameterization of large‐scale groundwater models covering areas of major river basins and more is challenging due to the lack of observational data and the mismatch between the scales of modeling and measurements. In this work, we propose to bridge the scale gap and derive regional hydraulic parameters by spectral analysis of groundwater level fluctuations. We hypothesize that specific locations in aquifers can reveal regional parameters of the hydraulic system. We first generate ensembles of synthetic but realistic aquifers which systematically differ in complexity. Applying Liang and Zhang’s (2013), https://doi.org/10.1016/j.jhydrol.2012.11.044, semi‐analytical solution for the spectrum of hydraulic head time series, we identify for each ensemble member and at different locations representative aquifer parameters. Next, we extend our study to investigate the use of spectral analysis in more complex numerical models and in real settings. Our analyses indicate that the variance of inferred effective transmissivity and storativity values for stochastic aquifer ensembles is small for observation points which are far away from the Dirichlet boundary. Moreover, the head time series has to cover a period which is roughly 10 times as long as the characteristic time of the aquifer. In deterministic aquifer models we infer equivalent, regionally valid parameters. A sensitivity analysis further reveals that as long as the aquifer length and the position of the groundwater measurement location is roughly known, the parameters can be robustly estimated.
Plain Language Summary
We build large‐scale (regional) computer models of the subsurface flow conditions in order to quantify the long‐term shift in groundwater storage and response on the national level under changing climatic conditions and increasing human water demands. These models must be fed with hydrogeological parameters obtained from subsurface observation wells, drilling logs, and hydraulic tests in conjunction with (hydro)geological and geostatistical methods. In some regions these wells are sparsely distributed and derived parameters are representative only for small areas. We hypothesize that groundwater level records can reveal regional aquifer information when analyzed in the spectral domain. In order to bridge that scale gap and because groundwate |
doi_str_mv | 10.1029/2021WR031289 |
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Plain Language Summary
We build large‐scale (regional) computer models of the subsurface flow conditions in order to quantify the long‐term shift in groundwater storage and response on the national level under changing climatic conditions and increasing human water demands. These models must be fed with hydrogeological parameters obtained from subsurface observation wells, drilling logs, and hydraulic tests in conjunction with (hydro)geological and geostatistical methods. In some regions these wells are sparsely distributed and derived parameters are representative only for small areas. We hypothesize that groundwater level records can reveal regional aquifer information when analyzed in the spectral domain. In order to bridge that scale gap and because groundwater level time series are generally available, we propose to infer regional parameters by analyzing the frequency content (spectrum) of long groundwater level time series. The required parameters were determined using mathematical formulations of the theoretical spectrum for simplified settings. We tested the methodology in computer models with limited complexity and found that the groundwater level time series indeed contain regional information if the time of observation is sufficiently long. Lastly, we apply the spectral analysis to real groundwater data to test the capability of the method to infer regional aquifer parameters in real aquifers.
Key Points
We successfully tested the spectral analysis of groundwater level fluctuations in numerical models and obtained regional aquifer parameters
In a sensitivity analysis of the spectral analysis using field data, the storativity and the response times could be robustly estimated
The application of the suggested methodology to the field data from a catchment in central Germany produced plausible results</description><identifier>ISSN: 0043-1397</identifier><identifier>EISSN: 1944-7973</identifier><identifier>DOI: 10.1029/2021WR031289</identifier><language>eng</language><publisher>Washington: John Wiley & Sons, Inc</publisher><subject>Analysis ; Aquifer models ; Aquifers ; Climate change ; Climatic conditions ; Complexity ; Computer models ; Dirichlet problem ; Drilling ; Exact solutions ; Frequency analysis ; Geology ; Groundwater ; Groundwater availability ; Groundwater data ; Groundwater levels ; Groundwater models ; Groundwater storage ; homogeneous, stochastic and deterministic numerical model design ; Hydraulic equipment ; Hydraulic systems ; Hydraulic tests ; Hydraulics ; Hydrogeology ; Hydrologic data ; Mathematical models ; Modelling ; Numerical models ; Observation wells ; Parameter robustness ; Parameter sensitivity ; Parameterization ; Parameters ; Piezometric head ; plausibility test with field data ; Position measurement ; proof of concept in numerical environments ; Regional analysis ; regional aquifer parameters ; River basins ; Sensitivity analysis ; sensitivity analysis with field data ; Spectral analysis ; spectral analysis of groundwater level fluctuations ; Spectrum analysis ; Stochasticity ; Subsurface flow ; Time series ; Transmissivity ; Water management</subject><ispartof>Water resources research, 2022-05, Vol.58 (5), p.n/a</ispartof><rights>2022. The Authors.</rights><rights>2022. This article is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-a2837-861034a5359658a3bd9b7dd77ba0b0f201cb128662d4a1cd27acdd9b97c7753e3</citedby><cites>FETCH-LOGICAL-a2837-861034a5359658a3bd9b7dd77ba0b0f201cb128662d4a1cd27acdd9b97c7753e3</cites><orcidid>0000-0002-7798-7080 ; 0000-0002-8189-7192 ; 0000-0002-2604-5376 ; 0000-0002-7866-5702 ; 0000-0003-2699-2354</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1029%2F2021WR031289$$EPDF$$P50$$Gwiley$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1029%2F2021WR031289$$EHTML$$P50$$Gwiley$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,1417,11514,27924,27925,45574,45575,46468,46892</link.rule.ids></links><search><creatorcontrib>Houben, Timo</creatorcontrib><creatorcontrib>Pujades, Estanislao</creatorcontrib><creatorcontrib>Kalbacher, Thomas</creatorcontrib><creatorcontrib>Dietrich, Peter</creatorcontrib><creatorcontrib>Attinger, Sabine</creatorcontrib><title>From Dynamic Groundwater Level Measurements to Regional Aquifer Parameters— Assessing the Power of Spectral Analysis</title><title>Water resources research</title><description>Large‐scale groundwater models are required to estimate groundwater availability and to inform water management strategies on the national scale. However, parameterization of large‐scale groundwater models covering areas of major river basins and more is challenging due to the lack of observational data and the mismatch between the scales of modeling and measurements. In this work, we propose to bridge the scale gap and derive regional hydraulic parameters by spectral analysis of groundwater level fluctuations. We hypothesize that specific locations in aquifers can reveal regional parameters of the hydraulic system. We first generate ensembles of synthetic but realistic aquifers which systematically differ in complexity. Applying Liang and Zhang’s (2013), https://doi.org/10.1016/j.jhydrol.2012.11.044, semi‐analytical solution for the spectrum of hydraulic head time series, we identify for each ensemble member and at different locations representative aquifer parameters. Next, we extend our study to investigate the use of spectral analysis in more complex numerical models and in real settings. Our analyses indicate that the variance of inferred effective transmissivity and storativity values for stochastic aquifer ensembles is small for observation points which are far away from the Dirichlet boundary. Moreover, the head time series has to cover a period which is roughly 10 times as long as the characteristic time of the aquifer. In deterministic aquifer models we infer equivalent, regionally valid parameters. A sensitivity analysis further reveals that as long as the aquifer length and the position of the groundwater measurement location is roughly known, the parameters can be robustly estimated.
Plain Language Summary
We build large‐scale (regional) computer models of the subsurface flow conditions in order to quantify the long‐term shift in groundwater storage and response on the national level under changing climatic conditions and increasing human water demands. These models must be fed with hydrogeological parameters obtained from subsurface observation wells, drilling logs, and hydraulic tests in conjunction with (hydro)geological and geostatistical methods. In some regions these wells are sparsely distributed and derived parameters are representative only for small areas. We hypothesize that groundwater level records can reveal regional aquifer information when analyzed in the spectral domain. In order to bridge that scale gap and because groundwater level time series are generally available, we propose to infer regional parameters by analyzing the frequency content (spectrum) of long groundwater level time series. The required parameters were determined using mathematical formulations of the theoretical spectrum for simplified settings. We tested the methodology in computer models with limited complexity and found that the groundwater level time series indeed contain regional information if the time of observation is sufficiently long. Lastly, we apply the spectral analysis to real groundwater data to test the capability of the method to infer regional aquifer parameters in real aquifers.
Key Points
We successfully tested the spectral analysis of groundwater level fluctuations in numerical models and obtained regional aquifer parameters
In a sensitivity analysis of the spectral analysis using field data, the storativity and the response times could be robustly estimated
The application of the suggested methodology to the field data from a catchment in central Germany produced plausible results</description><subject>Analysis</subject><subject>Aquifer models</subject><subject>Aquifers</subject><subject>Climate change</subject><subject>Climatic conditions</subject><subject>Complexity</subject><subject>Computer models</subject><subject>Dirichlet problem</subject><subject>Drilling</subject><subject>Exact solutions</subject><subject>Frequency analysis</subject><subject>Geology</subject><subject>Groundwater</subject><subject>Groundwater availability</subject><subject>Groundwater data</subject><subject>Groundwater levels</subject><subject>Groundwater models</subject><subject>Groundwater storage</subject><subject>homogeneous, stochastic and deterministic numerical model design</subject><subject>Hydraulic equipment</subject><subject>Hydraulic systems</subject><subject>Hydraulic tests</subject><subject>Hydraulics</subject><subject>Hydrogeology</subject><subject>Hydrologic data</subject><subject>Mathematical models</subject><subject>Modelling</subject><subject>Numerical models</subject><subject>Observation wells</subject><subject>Parameter robustness</subject><subject>Parameter sensitivity</subject><subject>Parameterization</subject><subject>Parameters</subject><subject>Piezometric head</subject><subject>plausibility test with field data</subject><subject>Position measurement</subject><subject>proof of concept in numerical environments</subject><subject>Regional analysis</subject><subject>regional aquifer parameters</subject><subject>River basins</subject><subject>Sensitivity analysis</subject><subject>sensitivity analysis with field data</subject><subject>Spectral analysis</subject><subject>spectral analysis of groundwater level fluctuations</subject><subject>Spectrum analysis</subject><subject>Stochasticity</subject><subject>Subsurface flow</subject><subject>Time series</subject><subject>Transmissivity</subject><subject>Water management</subject><issn>0043-1397</issn><issn>1944-7973</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>24P</sourceid><sourceid>WIN</sourceid><recordid>eNp90c1OwkAQB_CN0UREbz7AJl6t7kfb7R4JCppgJKjh2GzbKZbQLu60EG4-hE_ok7gED548zeX3n8wHIZec3XAm9K1ggs9nTHKR6CPS4zoMA6WVPCY9xkIZcKnVKTlDXDLGwyhWPbIZOVvTu11j6iqnY2e7ptiaFhydwAZW9AkMdg5qaFqkraUzWFS2MSs6-Oiq0rOpcaYGH8Dvzy86QATEqlnQ9h3o1G69sCV9WUPeun3KR3dY4Tk5Kc0K4eK39snb6P51-BBMnsePw8EkMCKRKkhizmRoIhnpOEqMzAqdqaJQKjMsY6VgPM_8snEsitDwvBDK5IU3WuVKRRJkn1wd-q6d_egA23RpO-eHwFTEsY6kUirx6vqgcmcRHZTp2lW1cbuUs3R_2fTvZT2XB76tVrD716bz2XAmIu2f8AP-7Hw5</recordid><startdate>202205</startdate><enddate>202205</enddate><creator>Houben, Timo</creator><creator>Pujades, Estanislao</creator><creator>Kalbacher, Thomas</creator><creator>Dietrich, Peter</creator><creator>Attinger, Sabine</creator><general>John Wiley & Sons, Inc</general><scope>24P</scope><scope>WIN</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QH</scope><scope>7QL</scope><scope>7T7</scope><scope>7TG</scope><scope>7U9</scope><scope>7UA</scope><scope>8FD</scope><scope>C1K</scope><scope>F1W</scope><scope>FR3</scope><scope>H94</scope><scope>H96</scope><scope>KL.</scope><scope>KR7</scope><scope>L.G</scope><scope>M7N</scope><scope>P64</scope><orcidid>https://orcid.org/0000-0002-7798-7080</orcidid><orcidid>https://orcid.org/0000-0002-8189-7192</orcidid><orcidid>https://orcid.org/0000-0002-2604-5376</orcidid><orcidid>https://orcid.org/0000-0002-7866-5702</orcidid><orcidid>https://orcid.org/0000-0003-2699-2354</orcidid></search><sort><creationdate>202205</creationdate><title>From Dynamic Groundwater Level Measurements to Regional Aquifer Parameters— Assessing the Power of Spectral Analysis</title><author>Houben, Timo ; Pujades, Estanislao ; Kalbacher, Thomas ; Dietrich, Peter ; Attinger, Sabine</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a2837-861034a5359658a3bd9b7dd77ba0b0f201cb128662d4a1cd27acdd9b97c7753e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Analysis</topic><topic>Aquifer models</topic><topic>Aquifers</topic><topic>Climate change</topic><topic>Climatic conditions</topic><topic>Complexity</topic><topic>Computer models</topic><topic>Dirichlet problem</topic><topic>Drilling</topic><topic>Exact solutions</topic><topic>Frequency analysis</topic><topic>Geology</topic><topic>Groundwater</topic><topic>Groundwater availability</topic><topic>Groundwater data</topic><topic>Groundwater levels</topic><topic>Groundwater models</topic><topic>Groundwater storage</topic><topic>homogeneous, stochastic and deterministic numerical model design</topic><topic>Hydraulic equipment</topic><topic>Hydraulic systems</topic><topic>Hydraulic tests</topic><topic>Hydraulics</topic><topic>Hydrogeology</topic><topic>Hydrologic data</topic><topic>Mathematical models</topic><topic>Modelling</topic><topic>Numerical models</topic><topic>Observation wells</topic><topic>Parameter robustness</topic><topic>Parameter sensitivity</topic><topic>Parameterization</topic><topic>Parameters</topic><topic>Piezometric head</topic><topic>plausibility test with field data</topic><topic>Position measurement</topic><topic>proof of concept in numerical environments</topic><topic>Regional analysis</topic><topic>regional aquifer parameters</topic><topic>River basins</topic><topic>Sensitivity analysis</topic><topic>sensitivity analysis with field data</topic><topic>Spectral analysis</topic><topic>spectral analysis of groundwater level fluctuations</topic><topic>Spectrum analysis</topic><topic>Stochasticity</topic><topic>Subsurface flow</topic><topic>Time series</topic><topic>Transmissivity</topic><topic>Water management</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Houben, Timo</creatorcontrib><creatorcontrib>Pujades, Estanislao</creatorcontrib><creatorcontrib>Kalbacher, Thomas</creatorcontrib><creatorcontrib>Dietrich, Peter</creatorcontrib><creatorcontrib>Attinger, Sabine</creatorcontrib><collection>Wiley-Blackwell Open Access Titles</collection><collection>Wiley Free Content</collection><collection>CrossRef</collection><collection>Aqualine</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Water Resources Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Engineering Research Database</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Civil Engineering Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><jtitle>Water resources research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Houben, Timo</au><au>Pujades, Estanislao</au><au>Kalbacher, Thomas</au><au>Dietrich, Peter</au><au>Attinger, Sabine</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>From Dynamic Groundwater Level Measurements to Regional Aquifer Parameters— Assessing the Power of Spectral Analysis</atitle><jtitle>Water resources research</jtitle><date>2022-05</date><risdate>2022</risdate><volume>58</volume><issue>5</issue><epage>n/a</epage><issn>0043-1397</issn><eissn>1944-7973</eissn><abstract>Large‐scale groundwater models are required to estimate groundwater availability and to inform water management strategies on the national scale. However, parameterization of large‐scale groundwater models covering areas of major river basins and more is challenging due to the lack of observational data and the mismatch between the scales of modeling and measurements. In this work, we propose to bridge the scale gap and derive regional hydraulic parameters by spectral analysis of groundwater level fluctuations. We hypothesize that specific locations in aquifers can reveal regional parameters of the hydraulic system. We first generate ensembles of synthetic but realistic aquifers which systematically differ in complexity. Applying Liang and Zhang’s (2013), https://doi.org/10.1016/j.jhydrol.2012.11.044, semi‐analytical solution for the spectrum of hydraulic head time series, we identify for each ensemble member and at different locations representative aquifer parameters. Next, we extend our study to investigate the use of spectral analysis in more complex numerical models and in real settings. Our analyses indicate that the variance of inferred effective transmissivity and storativity values for stochastic aquifer ensembles is small for observation points which are far away from the Dirichlet boundary. Moreover, the head time series has to cover a period which is roughly 10 times as long as the characteristic time of the aquifer. In deterministic aquifer models we infer equivalent, regionally valid parameters. A sensitivity analysis further reveals that as long as the aquifer length and the position of the groundwater measurement location is roughly known, the parameters can be robustly estimated.
Plain Language Summary
We build large‐scale (regional) computer models of the subsurface flow conditions in order to quantify the long‐term shift in groundwater storage and response on the national level under changing climatic conditions and increasing human water demands. These models must be fed with hydrogeological parameters obtained from subsurface observation wells, drilling logs, and hydraulic tests in conjunction with (hydro)geological and geostatistical methods. In some regions these wells are sparsely distributed and derived parameters are representative only for small areas. We hypothesize that groundwater level records can reveal regional aquifer information when analyzed in the spectral domain. In order to bridge that scale gap and because groundwater level time series are generally available, we propose to infer regional parameters by analyzing the frequency content (spectrum) of long groundwater level time series. The required parameters were determined using mathematical formulations of the theoretical spectrum for simplified settings. We tested the methodology in computer models with limited complexity and found that the groundwater level time series indeed contain regional information if the time of observation is sufficiently long. Lastly, we apply the spectral analysis to real groundwater data to test the capability of the method to infer regional aquifer parameters in real aquifers.
Key Points
We successfully tested the spectral analysis of groundwater level fluctuations in numerical models and obtained regional aquifer parameters
In a sensitivity analysis of the spectral analysis using field data, the storativity and the response times could be robustly estimated
The application of the suggested methodology to the field data from a catchment in central Germany produced plausible results</abstract><cop>Washington</cop><pub>John Wiley & Sons, Inc</pub><doi>10.1029/2021WR031289</doi><tpages>22</tpages><orcidid>https://orcid.org/0000-0002-7798-7080</orcidid><orcidid>https://orcid.org/0000-0002-8189-7192</orcidid><orcidid>https://orcid.org/0000-0002-2604-5376</orcidid><orcidid>https://orcid.org/0000-0002-7866-5702</orcidid><orcidid>https://orcid.org/0000-0003-2699-2354</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Analysis Aquifer models Aquifers Climate change Climatic conditions Complexity Computer models Dirichlet problem Drilling Exact solutions Frequency analysis Geology Groundwater Groundwater availability Groundwater data Groundwater levels Groundwater models Groundwater storage homogeneous, stochastic and deterministic numerical model design Hydraulic equipment Hydraulic systems Hydraulic tests Hydraulics Hydrogeology Hydrologic data Mathematical models Modelling Numerical models Observation wells Parameter robustness Parameter sensitivity Parameterization Parameters Piezometric head plausibility test with field data Position measurement proof of concept in numerical environments Regional analysis regional aquifer parameters River basins Sensitivity analysis sensitivity analysis with field data Spectral analysis spectral analysis of groundwater level fluctuations Spectrum analysis Stochasticity Subsurface flow Time series Transmissivity Water management |
title | From Dynamic Groundwater Level Measurements to Regional Aquifer Parameters— Assessing the Power of Spectral Analysis |
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