A new approach is developed to insert fiber optic cables vertically into the ground with direct push equipment. Groundwater temperatures may be measured along the cables with high spatial and temporal resolution using a Distributed Temperature Sensing system. The cables may be inserted up to depths of tens of meters in unconsolidated sedimentary aquifers. The main advantages of the method are that the cables are in direct contact with the aquifer material, the disturbance of the aquifer is minor, and no borehole is needed. This cost-effective approach may be applied to both passive and active heat tracer experiments. An active heat tracer experiment was conducted to estimate horizontal groundwater velocities in a managed aquifer recharge system in the Netherlands. Six fiber optic cables and a separate heating cable were inserted with a 1 m spacing at the surface. The heating cable was turned on for 4 days and temperatures were measured during both heating and cooling of the aquifer. Temperature measurements at the heating cable alone were used to estimate the magnitude of the groundwater velocity and the thermal conductivity of the solids. The direction of the velocity and heat capacity of the solids were estimated by including temperature measurements at the other fiber optic cables in the analysis. The latter analysis suffered from the fact that the cables were not inserted exactly vertical. The three-dimensional position of the fiber optic cables must be measured for future active heat tracer experiments.

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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