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Pastas: Open Source Software for the Analysis of Groundwater Time Series

Time series analysis is an increasingly popular method to analyze heads measured in an observation well. Common applications include the quantification of the effect of different stresses (rainfall, pumping, etc.), and the detection of trends and outliers. Pastas is a new and open source Python pack...

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Autores principales: Collenteur, Raoul A., Bakker, Mark, Caljé, Ruben, Klop, Stijn A., Schaars, Frans
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Blackwell Publishing Ltd 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6899905/
https://www.ncbi.nlm.nih.gov/pubmed/31347164
http://dx.doi.org/10.1111/gwat.12925
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author Collenteur, Raoul A.
Bakker, Mark
Caljé, Ruben
Klop, Stijn A.
Schaars, Frans
author_facet Collenteur, Raoul A.
Bakker, Mark
Caljé, Ruben
Klop, Stijn A.
Schaars, Frans
author_sort Collenteur, Raoul A.
collection PubMed
description Time series analysis is an increasingly popular method to analyze heads measured in an observation well. Common applications include the quantification of the effect of different stresses (rainfall, pumping, etc.), and the detection of trends and outliers. Pastas is a new and open source Python package for the analysis of hydrogeological time series. The objective of Pastas is twofold: to provide a scientific framework to develop and test new methods, and to provide a reliable ready‐to‐use software tool for groundwater practitioners. Transfer function noise modeling is applied using predefined response functions. For example, the head response to rainfall is simulated through the convolution of measured rainfall with a Gamma response function. Pastas models are created and analyzed through scripts, ensuring reproducibility and providing a transparent report of the entire modeling process. A Pastas model can be constructed in seven simple steps: import Pastas, read the time series, create a model, specify the stresses and the types of response functions, estimate the model parameters, visualize output, and analyze the results. These seven steps, including the corresponding Python code, are applied to investigate how rainfall and reference evaporation can explain measured heads in an observation well in Kingstown, Rhode Island, USA. The second example demonstrates the use of scripts to analyze a large number of observation wells in batch to estimate the extent of the drawdown caused by a well field in the Netherlands. Pastas is free and open source software available under the MIT‐license at http://github.com/pastas/pastas.
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spelling pubmed-68999052019-12-19 Pastas: Open Source Software for the Analysis of Groundwater Time Series Collenteur, Raoul A. Bakker, Mark Caljé, Ruben Klop, Stijn A. Schaars, Frans Ground Water Special Section: Time Series Analysis Guest Editor: Mark Bakker, Ph.D./ Time series analysis is an increasingly popular method to analyze heads measured in an observation well. Common applications include the quantification of the effect of different stresses (rainfall, pumping, etc.), and the detection of trends and outliers. Pastas is a new and open source Python package for the analysis of hydrogeological time series. The objective of Pastas is twofold: to provide a scientific framework to develop and test new methods, and to provide a reliable ready‐to‐use software tool for groundwater practitioners. Transfer function noise modeling is applied using predefined response functions. For example, the head response to rainfall is simulated through the convolution of measured rainfall with a Gamma response function. Pastas models are created and analyzed through scripts, ensuring reproducibility and providing a transparent report of the entire modeling process. A Pastas model can be constructed in seven simple steps: import Pastas, read the time series, create a model, specify the stresses and the types of response functions, estimate the model parameters, visualize output, and analyze the results. These seven steps, including the corresponding Python code, are applied to investigate how rainfall and reference evaporation can explain measured heads in an observation well in Kingstown, Rhode Island, USA. The second example demonstrates the use of scripts to analyze a large number of observation wells in batch to estimate the extent of the drawdown caused by a well field in the Netherlands. Pastas is free and open source software available under the MIT‐license at http://github.com/pastas/pastas. Blackwell Publishing Ltd 2019-08-24 2019 /pmc/articles/PMC6899905/ /pubmed/31347164 http://dx.doi.org/10.1111/gwat.12925 Text en © 2019 The Authors. Groundwater published by Wiley Periodicals, Inc. on behalf of National Ground Water Association. This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Special Section: Time Series Analysis Guest Editor: Mark Bakker, Ph.D./
Collenteur, Raoul A.
Bakker, Mark
Caljé, Ruben
Klop, Stijn A.
Schaars, Frans
Pastas: Open Source Software for the Analysis of Groundwater Time Series
title Pastas: Open Source Software for the Analysis of Groundwater Time Series
title_full Pastas: Open Source Software for the Analysis of Groundwater Time Series
title_fullStr Pastas: Open Source Software for the Analysis of Groundwater Time Series
title_full_unstemmed Pastas: Open Source Software for the Analysis of Groundwater Time Series
title_short Pastas: Open Source Software for the Analysis of Groundwater Time Series
title_sort pastas: open source software for the analysis of groundwater time series
topic Special Section: Time Series Analysis Guest Editor: Mark Bakker, Ph.D./
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6899905/
https://www.ncbi.nlm.nih.gov/pubmed/31347164
http://dx.doi.org/10.1111/gwat.12925
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