Exploring reservoir network structures for rainfall–runoff modelling

Catchment hydrology relies on models with complementary strengths and limitations in terms of physical interpretability, data requirements, structural flexibility, and predictive performance. Conceptual process-based models provide parsimony and physical meaning, but often sacrifice predictive performance. Data-driven approaches offer greater flexibility and predictive skill, yet often sacrifice interpretability and physical consistency. Hybrid strategies seek to combine these strengths, but many existing implementations rely on loosely constrained components that obscure process understanding.

In this study, we explore a structured reservoir network architecture for rainfall–runoff modelling as an intermediate approach between conceptual hydrological models and fully data-driven approaches. Rather than framing the method as a neural network, we focus on assembling physically interpretable reservoir elements into a network structure that can be calibrated using gradient-based optimization while preserving hydrological meaning.

The proposed model represents runoff generation through multiple parallel reservoir chains, each governed by a conceptual soil moisture balance with physically interpretable parameters. Excess rainfall from each chain is routed using convolution with a gamma transfer function to represent delayed runoff response. The routed contributions are combined through convex weighting, enabling a transparent and controlled aggregation of parallel runoff pathways. The overall architecture remains mass-conservative and avoids black-box recurrent components.

The approach is evaluated across multiple catchments within the Moselle basin (27,100 km²), which exhibits substantial heterogeneity in elevation and land use. The model is driven by daily precipitation and potential evapotranspiration and evaluated against observed discharge. Performance is assessed using the Nash–Sutcliffe Efficiency and hydrological signatures, and results are compared to the GR4J conceptual model under identical calibration conditions.

Results indicate that the proposed reservoir network achieves performance comparable to or slightly better than GR4J, with a mean validation NSE of 0.70 compared to 0.67. Improvements are particularly evident for low-flow metrics and flow-duration curve characteristics. Beyond predictive performance, the model enables interpretation of the relative contributions and temporal dynamics of parallel runoff generation pathways.

Overall, this work demonstrates the potential of reservoir network architectures as a transparent and flexible modelling framework for rainfall–runoff simulation and process exploration. Future work will focus on incorporating additional catchment information, such as permeability and physiographic descriptors, and on extending the approach toward regional-scale applications across heterogeneous catchments.