Presenting the Groundwater Time Series Modeling Challenge

Groundwater level time series are the most common source of information on the dynamics of subsurface water resources. The modeling of such time series is of crucial importance to increase our understanding of the system and make predictions on future changes. This becomes ever more important with global challenges such as climate change and human over-exploitation of groundwater resources. Different types of models can be applied to model groundwater level time series, ranging from purely statistical models (black-box), through lumped conceptual models (grey-box), to physically based models (white-box). Traditionally, physically based, distributed models are predominantly used to solve groundwater problems. In recent years, the use of grey- and black-box models has been receiving increased attention. In this poster we will present the “Groundwater Time Series Modeling Challenge”, which aims to systematically compare different methodologies to model groundwater level time series. We challenge researchers to model a predefined set of groundwater time series observed in unconsolidated aquifers worldwide, set in a variety of physiographic and climatic conditions. Participating groups are free to use the model of their choice, but are required to use the same forcing data and periods for calibration. Model performance will be centrally assessed by the organizers using non-public validation data set, which will be made public after the challenge. A more detailed description of the rules for this challenge and all groundwater and forcing data is available in a GitHub repository at https://github.com/gwmodeling/challenge. The results of the challenge will be presented at the General Assembly of the EGU in 2023 and reported in a peer-reviewed paper. With this challenge, we hope to increase the awareness in the groundwater community about all the different types of models that are available to model groundwater level time series.