Application of Time Series Analysis to Estimate Drawdown From Multiple Well Fields

Abstract

In 2018–2020, meteorological droughts over Northwestern Europe caused severe declines in groundwater heads with significant damage to groundwater-dependent ecosystems and agriculture. The response of the groundwater system to different hydrological stresses is valuable information for decision-makers. In this paper, a reproducible, data-driven approach using open-source software is proposed to quantify the effects of different hydrological stresses on heads. A scripted workflow was developed using the open-source Pastas software for time series modeling of heads. For each head time series, the best model structure and relevant hydrological stresses (rainfall, evaporation, river stages, and pumping at one or more well fields) were selected iteratively. A new method was applied to model multiple well fields with a single response function, where the response was scaled by the distances between the pumping and observation wells. Selection of the best model structure was performed through reliability checking based on four criteria. The time series model of each observation well represents an independent estimate of the contribution of different hydrological stresses to the head and is based exclusively on observed data. The approach was applied to estimate the drawdown caused by nearby well fields to 250 observed head time series measured at 122 locations in the eastern part of the Netherlands, a country where summer droughts can cause problems, even though the country is better known for problems with too much water. Reliable models were obtained for 126 head time series of which 78 contain one or more well fields as a contributing stress. The spatial variation of the modeled responses to pumping at the well fields show the expected decline with distance from the well field, even though all responses were modeled independently. An example application at one well field showed how the head response to pumping varies per aquifer. Time series analysis was used to determine the feasibility of reducing pumping rates to mitigate large drawdowns during droughts, which depends on the magnitude and response time of the groundwater system to changes in pumping. This is salient information for decision-makers. This article is part of the special issue “Rapid, Reproducible, and Robust Environmental Modeling for Decision Support: Worked Examples and Open-Source Software Tools”.

Analysis of nationwide groundwater monitoring networks using lumped-parameter models

“…The finding that for these five monitoring wells there are multiple model structures that perform almost equally well to simulate the heads, only came to light after this additional analysis. A multi-model approach, as for exam-ple is taken in Brakenhoff et al (2022) as well as in this study for the snow model, would have revealed this issue at an earlier stage and is therefore recommended for future studies. This way, additional criteria can be used to make a more informed decision and to determine which model is best and used for further analysis.…”

“…( 9)). Such an approach has also been applied in other studies to determine if a stress is influencing the heads or not, for example for pumping wells (Collenteur et al, 2019;Brakenhoff et al, 2022). For five monitoring wells (Ermensee, Oberglatt, Poschiavo, Visp, and Wila), the parameter for the gain of the response to river stage changes (A riv ) was estimated at the upper boundary value of A riv = 1.…”

“…The computational demand could be reduced substantially, however, by checking for autocorrelation directly after model calibration, and decreasing the temporal resolution only if there is still significant autocorrelation present. Alternatively and much more efficiently, a fixed temporal resolution may be used, as is for example done in Brakenhoff et al (2022). An individual approach to select the temporal resolution may, however, allow the use of more data for model cali-bration, which potentially allows the identification of more complex models.…”

Spatial-Temporal Graph Neural Networks for Groundwater Data

“…The shallow subsurface is characterized by a low-permeable phreatic layer, underlain by two aquifers separated by an aquitard. The groundwater is relatively shallow, with the depth to the water table varying between 0.8 and 4.2 m. For a more comprehensive understanding of the hydrological system, the reader is referred to the work of 7 .…”

“…Our study focuses on leveraging ST-GNNs to predict groundwater levels in the Overbetuwe area, Netherlands. The selection of this area is driven by the availability of data 7 .…”