Incorporating insights from Time Series Analysis in groundwater modelling for the urban area of the city of Amsterdam

Abstract

As the public water authority of the city of Amsterdam and surrounding areas, Waternet makes use of both steady-state and transient groundwater models for a variety of purposes involving urban groundwater management. For instance when determining the effect of planned measures on the occurrence of groundwater levels that are too low or too high, potentially resulting in degradation of wooden pile foundations or mold in houses, respectively. One of the challenges we face in that modelling, is the discrepancy between the physical processes that play a part in the city, influencing groundwater measurements, and the extent to which we are able to quantify those processes in groundwater models. We addressed this discrepancy with automated Time Series Analysis (TSA). In the over 3000 time series of groundwater measurements that were analyzed, TSA often identified one or more disturbances, such as groundwater extractions and measurement error. Along with identifying disturbances and artifacts, TSA offers a way to structurally address these issues. Incorporating (automated) TSA in generating model observations also results in a uniform, reproducible approach, and the ability to evaluate enormous amounts of monitoring wells. We argue that this approach is preferable to visual inspection and evaluation of measurement series, and discuss ways to incorporate these insights into groundwater models.