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

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.

Estimating groundwater recharge from groundwater levels using non-linear transfer function noise models and comparison to lysimeter data

“…The non-linear root zone model presented in this study is only one of many similar alternatives. Comparable results were obtained using a similar root zone model developed by Berendrecht et al (2006), which is also available in the Pastas software (Collenteur et al, 2019). It is expected that other comparable non-linear model setups (e.g.…”

“…To make the methods applicable in other hydrogeological settings than those presented here, additional hydrological processes (e.g., snow melt, surface runoff) and variables (e.g., pumping, river levels) may be included in the model. In the current framework, it is relatively easy to account for other variables causing groundwater level fluctuations (e.g., von Asmuth et al, 2008;Collenteur et al, 2019). This would allow for the estimation of recharge in hydrogeological systems where the groundwater level fluctuations are (possibly) not exclusively the result of recharge.…”

Solving Groundwater Flow Problems with Time Series Analysis: You May Not Even Need Another Model

“…There are several tools for time series analysis of heads, including the commercial Menyanthes (Von Asmuth et al 2012), the open source matlab code Hydrosight (Peterson and Western 2014), and the open source Python code Pastas (Collenteur et al, 2019). Methods for time series analysis are actively being developed to enhance its capabilities in the future.…”

Application of soil moisture information for operational water management

“…To set up the TFN models, we use the open source library Pastas (Collenteur et al, 2019b), which is a Python 3 implementation of the TFN modelling approach described in section 4.2.1. A description of Pastas can be found in Collenteur et al (2019a). In order to solve equation 4.1, we use a least squares optimization approach to t the parameters of the IR functions in Pastas (equations 4.5 and 4.6).…”

“…where ( ) is white noise resulting from a random process for time step [ 3 −3 ], is a decay parameter [ ], and Δ is the time step [ ] (Von Asmuth and Bierkens, 2005;Collenteur et al, 2019a). The subscript indicates the day.…”