Results from the 2022 Groundwater Time Series Modeling Challenge

At the general assembly of the European Geophysical Union in 2022, the “Groundwater Time Series Modeling Challenge” was launched (Haaf et al., 2022). We challenged our colleagues in the field to model five time series of hydraulic heads measured in groundwater observations wells around Europe and North America. Part of the head data was not made available to the participants and held back as independent evaluation data. In this presentation, we will share and summarize the results from the challenge. The challenge attracted submissions from 17 teams using a variety of modeling techniques (https://github.com/gwmodeling/challenge). The models used in the submissions ranged from machine and deep learning models to empirical and bucket-type models. Many of the participants devoted notable attention to the uncertainty quantification, providing not only the results of the best-fit model but also uncertainty intervals for their models. The time to set up each model ranged from a couple of minutes to a couple of hours, indicating that models are generally set up in a limited amount of time. For most models, the majority of the time was used for the training and/or uncertainty quantification. The time spent on training showed larger differences between the models, ranging from a few minutes to more than a day for a single model. The data used to model the time series varied per model, with empirical models using less information than the machine learning models. A preliminary analysis of the modeling results showed that most of the models performed well (as measured by goodness-of-fit metrics such as NSE, MAE, KGE) in both the training and evaluation period. For one of the time series, none of the models showed a good fit with the data in the evaluation period and we suspect that a systematic change in the groundwater system may have occurred. The best-performing model differed between observation wells; none of the models outperformed all other models for all time series. In the coming months up to the EGU 2023 General Assembly we will further analyze and synthesize the results.Haaf, E., Collenteur, R., Liesch, T., & Bakker, M. (2022). Presenting the Groundwater Time Series Modeling Challenge(No. EGU22-12580). Copernicus Meetings.