Evaluating the Performance of Transfer Function Models for Identifying Groundwater Disturbances in Infrastructure Projects

Abstract

Hydrogeology is inherently challenged by the difficulty of making direct observa tions. To address this, numerical groundwater models are commonly developed to simulate hydrogeological conditions at specific study sites. However, such models require extensive input data and are often time- and resource-intensive. As a result, alternative, data-driven modeling techniques are of growing interest. This study evaluates the performance of a data-driven Transfer Function model in identifying and analyzing groundwater head disturbances caused by underground excavation activities. The transfer function model used for this project is the open-source Python package Pastas. Focusing on the Haga Station site within the Västlänken infrastructure project in Gothenburg, Sweden, the research compares transfer function models with a numerical benchmark model developed using MOD FLOW. Synthetic and constructed disturbance scenarios in the form of excavation shafts were modeled in the benchmark model to generate time series of groundwater head, leakage and infiltration. These time series were then used to test the transfer function model’s ability to detect controlled disturbances. To validate the results, a qualitative assessment was also performed using observed field data. The findings demonstrate that transfer function models developed using Pastas can replicate general groundwater trends and detect some disturbance signals. However, the models showed significant variation between different well simulations, with in consistencies in how disturbances were interpreted. Additional limitations include the overestimation of recharge and the underestimation of leakage into shafts fol lowing transfer function model calibration. Despite these challenges, the transfer function approach shows promise due to its low data requirements and its ability to model complex hydrogeologic study sites. This work contributes to a better under standing of transfer function model capabilities and their potential application in hydrogeological assessments of infrastructure projects.