Abstract

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.

Highlights

  • Over the past few decades, time series analysis has become an accepted and frequently applied methodology in the field of groundwater hydrology

  • 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

  • The first transfer function noise (TFN) models used in hydrogeology were autoregressive-moving average models, which originate from econometrics

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Summary

Introduction

Over the past few decades, time series analysis has become an accepted and frequently applied methodology in the field of groundwater hydrology. Von Asmuth et al (2002) introduced a new type of TFN models based on the principles of convolution and predefined impulse response functions. This type of model has been applied in a variety of studies including the decomposition of hydrological stresses (von Asmuth and Knotters 2004; von Asmuth et al 2008; Shapoori et al 2015b), the estimation of aquifer parameters (e.g., Obergfell et al 2013; Shapoori et al 2015a), the statistical interpolation of groundwater time series (Peterson et al 2018), the analysis of nation-wide groundwater monitoring networks (Zaadnoordijk et al 2018), and the estimation of recharge (Obergfell et al 2019)

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