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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.

Abstract. The estimation of groundwater recharge is of paramount importance to assess the sustainability of groundwater use in aquifers around the world. Estimation of the recharge flux, however, remains notoriously difficult. In this study the application of nonlinear transfer function noise (TFN) models using impulse response functions is explored to simulate groundwater levels and estimate groundwater recharge. A nonlinear root zone model that simulates recharge is developed and implemented in a TFN model and is compared to a more commonly used linear recharge model. An additional novel aspect of this study is the use of an autoregressive–moving-average noise model so that the remaining noise fulfills the statistical conditions to reliably estimate parameter uncertainties and compute the confidence intervals of the recharge estimates. The models are calibrated on groundwater-level data observed at the Wagna hydrological research station in the southeastern part of Austria. The nonlinear model improves the simulation of groundwater levels compared to the linear model. The annual recharge rates estimated with the nonlinear model are comparable to the average seepage rates observed with two lysimeters. The recharges estimates from the nonlinear model are also in reasonably good agreement with the lysimeter data at the smaller timescale of recharge per 10 d. This is an improvement over previous studies that used comparable methods but only reported annual recharge rates. The presented framework requires limited input data (precipitation, potential evaporation, and groundwater levels) and can easily be extended to support applications in different hydrogeological settings than those presented here.

<p>HYDRUS-1D is a popular software suite for one-dimensional modeling of flow and transport through the vadose zone [1]. Models can be handled through the Graphical User Interface (GUI), made freely available by the original authors (https://www.pc-progress.com/). As the program is file-based, the HYDRUS-1D GUI already ensures a certain degree of reproducibility, as these files contain all information about a model. The original FORTRAN code of the HYDRUS-1D model is also made available and is used in many publications to perform more complicated analysis of flow and transport through the unsaturated zone. For each of these publications new code was programmed to change the input files and perform a specific analysis. Being a popular hydrological model, it seems only logical to start reusing such code and structurally develop its capabilities. In the presentation, we introduce Phydrus, an open source Python package to create, optimize and visualize HYDRUS-1D models. Python scripts or Jupyter Notebooks are used for all steps of the modeling process, documenting the entire workflow and ensuring reproducibility of the analysis. Connecting HYRDUS-1D to Python makes it easier to perform repetitive tasks on models, and potentially opens up a whole new set of possibilities and applications. While introducing Phydrus, this presentation will also focus on the process of creating the Python Package and why we think it is worthwhile for the hydrologic community to interface existing (older) code with newer programming languages popular in the hydrological scientific community.</p><p><strong>References<br></strong>[1] Šimůnek, J. and M. Th. van Genuchten (2008) Modeling nonequilibrium flow and transport with HYDRUS, Vadose Zone Journal.</p>

Weighted goodness-of-fit metrics may help to the evaluate model fit for head time series with irregular time steps.