Application of time series analysis to explore the feasibility of reducing extraction rates to mitigate groundwater declines during summer droughts: a case study in the Netherlands

In recent years, meteorological droughts over Northwestern Europe caused severe declines in groundwater levels with significant damage to groundwater-dependent ecosystems and agriculture. One possible solution to reduce the declines in groundwater levels is to temporarily lower the extraction rates of nearby well fields used for drinking water production. The effectiveness of such measures depends on the magnitude and time of the response of the groundwater system to changes in groundwater extraction, which is salient information for decision makers. The response of the groundwater system is commonly quantified using numerical groundwater models that are time-consuming to develop and can be difficult to calibrate. In this research, a quick data-driven approach is proposed, based on time series analysis, that serves as a complement to more traditional groundwater modeling approaches. 

A scripted workflow was developed using Pastas, an open-source Python module for Transfer Function Noise modeling. The approach was applied to 243 monitoring wells in an area of the Netherlands, a country where summer droughts can cause serious problems, even though the country is better known for problems with too much water. For each monitoring well, the best model structure and relevant hydrological forcings (rainfall, evaporation, river stages, and extraction rates of well fields) were selected iteratively. Model selection was performed through split-sample testing and diagnostic checking. The accepted model for each monitoring well represents an independent estimate of the contribution of different hydrological forcings and processes to the groundwater response and is based exclusively on observed data. The modeled responses to the pumping rates of the well fields were used to determine the feasibility of reducing extraction rates to control heads during droughts.