Abstract. The geothermal reservoir at Waiwera has been subject to active exploitation for a long time. It is located below the village on the Northern Island of New Zealand and has been used commercially since 1863. The continuous production of geothermal water, to supply hotels and spas, had a negative impact on the reservoir. So far, the physical relation between abstraction rates and water level change of the hydrogeological system is only fairly understood. The aim of this work was to link the influence of rates to the measured data to derive reservoir properties. For this purpose, the daily abstraction history was investigated by means of a variable production rate well test analysis. For the analysis, a modified deconvolution algorithm was implemented. The algorithm derives the reservoir response function by solving a least square problem with the unique feature of imposing only implicit constraints on the solution space. To further investigate the theoretical performance of the algorithm a simulation with synthetic data was conducted for three possible reservoir scenarios. Results throughout all years indicate radial flow during middle-time behaviour and a leaky flow boundary during late-time behaviour. For middle-time behaviour, the findings agree very well with prior results of a pumping test. For the future, a more extensive investigation of different flow conditions under different parametrisations should be conducted.

The conduit flow process (CFP) for MODFLOW's groundwater flow model is an advanced approach for investigating complex groundwater systems, such as karst, with coupled discrete‐continuum models. CFP represents laminar and turbulent flow in a discrete pipe network coupled to a matrix continuum. However, the preprocessing demand is comparatively high to generate the conduit network and is usually performed with graphical user interfaces. To overcome this limitation and allow a scalable, reproducible, and comprehensive workflow, existing and new routines were aggregated to a Python package named CFPy, to allow script‐based modeling that harmonizes well with the available and widely used FloPy package. CFPy allows information about the location and geometry of the conduit network to be considered by user‐specific approaches or by sophisticated methods such as stochastic conduit network generators. The latter allows the automatic generation of many model variants with differing conduit networks for advanced investigations like multi‐model approaches in combination with automatic parameter estimation. Additional postprocessing routines provide powerful control and valuable insights for CFP applications. In this methods note, a general technical description of the approach is complemented with two examples that guide users and demonstrate the main capabilities of CFPy.