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.

Heads vary as a result of a variety of stresses on the aquifer, including rainfall, evaporation, pumping, and variations in surface water levels. One of the common objectives of groundwater models is to untangle measured groundwater head variations into contributions of these different stresses, for example to determine if lower heads are caused by a reduction in rainfall, an increase in pumping, or the construction of a new drainage system. Time series analysis with response functions is a relatively new groundwater modeling technique to model groundwater dynamics at observation wells. The method is fully data-driven. It requires measured time series of the head in an observation well and of the stresses on the aquifer. No other knowledge, including knowledge of aquifer parameters, is required. Two of the main difficulties in application of this method are the choice of the response function and the approach to simulate the movement of recharge through the unsaturated zone. Response functions are often based on analytic solutions to simple groundwater flow problems. Movement of recharge through the unsaturated zone is modeled numerically with a reservoir-type model. In this presentation, examples will be given of untangling head responses using the newly developed Pastas software. Pastas is free and open-source software, and is available from https://github.com/pastas/pastas.