Using a hybrid hydrological modelling approach to simulate drying patterns in 3 non-perennial European river networks

Abstract

Intermittent and ephemeral streams account for more than half of the world’s river channels, yet their hydrological functioning remains understudied. Modelling non-perennial river systems can help understanding the spatio-temporal patterns of drying and rewetting, but is challenging due to limited monitoring in intermittent river networks. This study is part of the EU-funded project DRYvER, which aims to understand the repercussions of drying river networks for biodiversity, functional integrity, and ecosystem services (Datry et al. 2021). Here we propose a novel hydrological modelling approach using the J2000 distributed hydrological model (Krause et al. 2006), coupled with a Random Forest classification model, to predict daily and spatially distributed flow conditions (flowing or dry). The hybrid flow intermittence model is trained using observed flow condition data from diverse sources, such as water level measurements, photo traps, remote sensing, and citizen science applications (Mimeau et al, 2023). We evaluate the model's performance in three European River Networks in Finland, France, and Spain. Results show that the hybrid flow intermittence model accurately predicts the drying events, with a probability of prediction of a drying event above 0.9 for the French and Finnish study cases. The spatio-temporal patterns of flow intermittence are contrasted among the 3 study cases: while the model simulates a few drying episodes during the summer season in the Finnish case study, mainly in the small upstream tributaries, it also simulates more complex drying patterns in the French and Spanish case studies, with drying episodes occurring throughout the year and drying events in the main river sections. Additionally, we provide insights on the role of the observed data used to train the model on the simulated flow intermittence patterns. Results indicate that the quantity of observed data, as well as their temporal distribution, their spatial location in the river network, and the representativeness of the observed flow condition can have a significant impact on the simulation performance of flow intermittence. This study shows that combining different sources of observed flow condition data can help to reduce the uncertainty in predicting flow intermittence.   Datry et al. (2021) Securing Biodiversity, Functional Integrity, and Ecosystem Services in Drying River Networks (DRYvER). Research Ideas and Outcomes. https://doi.org/10.3897/rio.7.e77750. Krause et al. (2006) Multiscale investigations in a mesoscale catchment: hydrological modelling in the Gera catchment. Advances in Geosciences. https://doi.org/10.5194/adgeo-9-53-2006. Mimeau et al. (2023) Flow intermittence prediction using a hybrid hydrological modelling approach: influence of observed intermittence data on the training of a random forest model, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2023-1322.