Data-driven modelling of hydraulic-head time series: results and lessons learned from the 2022 Groundwater Time Series Modelling Challenge

• Main Authors: Collenteur, Raoul A., Haaf, Ezra, Bakker, Mark, Liesch, Tanja, Wunsch, Andreas, Soonthornrangsan, Jenny, White, Jeremy, Martin, Nick, Hugman, Rui, de Sousa, Ed, Vanden Berghe, Didier, Fan, Xinyang, Peterson, Tim J., Bikše, Jānis, Di Ciacca, Antoine, Wang, Xinyue, Zheng, Yang, Nölscher, Maximilian, Koch, Julian, Schneider, Raphael, Benavides Höglund, Nikolas, Krishna Reddy Chidepudi, Sivarama, Henriot, Abel, Massei, Nicolas, Jardani, Abderrahim, Rudolph, Max Gustav, Rouhani, Amir, Gómez-Hernández, J. Jaime, Jomaa, Seifeddine, Pölz, Anna, Franken, Tim, Behbooei, Morteza, Lin, Jimmy, Meysami, Rojin
• Format: Article in Journal/Newspaper
• Language: English
• Published: Copernicus Publications 2024
• Subjects: ddc:910; Geography & travel; info:eu-repo/classification/ddc/910; Subarctic
• Online Access: https://publikationen.bibliothek.kit.edu/1000177525; https://publikationen.bibliothek.kit.edu/1000177525/156124703; https://doi.org/10.5445/IR/1000177525

This paper presents the results of the 2022 Groundwater Time Series Modelling Challenge, where 15 teams from different institutes applied various data-driven models to simulate hydraulic-head time series at four monitoring wells. Three of the wells were located in Europe and one was located in the USA in different hydrogeological settings in temperate, continental, or subarctic climates. Participants were provided with approximately 15 years of measured heads at (almost) regular time intervals and daily measurements of weather data starting some 10 years prior to the first head measurements and extending around 5 years after the last head measurement. The participants were asked to simulate the measured heads (the calibration period), to provide a prediction for around 5 years after the last measurement (the validation period for which weather data were provided but not head measurements), and to include an uncertainty estimate. Three different groups of models were identified among the submissions: lumped-parameter models (three teams), machine learning models (four teams), and deep learning models (eight teams). Lumped-parameter models apply relatively simple response functions with few parameters, while the artificial intelligence models used models of varying complexity, generally with more parameters and more input, including input engineered from the provided data (e.g. multi-day averages). The models were evaluated on their performance in simulating the heads in the calibration period and in predicting the heads in the validation period. Different metrics were used to assess performance, including metrics for average relative fit, average absolute fit, fit of extreme (high or low) heads, and the coverage of the uncertainty interval. For all wells, reasonable performance was obtained by at least one team from each of the three groups. However, the performance was not consistent across submissions within each group, which implies that the application of each method to individual sites requires significant ...