Graph-Based Representations in Hydrological Modeling: Comparing SWAT+ and Graph Neural Networks for Water Management Systems

Extreme hydrological events such as droughts and floods are expected to become more frequent and severe according to climate change projections, making effective water management very important to mitigate environmental and socio-economic impacts. In this context, advanced hydrological modeling tools are essential for understanding and managing water systems. The Soil and Water Assessment Tool (SWAT+), a process-based and semi-distributed eco-hydrological model, has become very popular for simulating hydrological processes and water management scenarios, especially with its improved water allocation and reservoir modules. At the same time, Graph Neural Networks (GNNs), a deep learning model, have shown potential for modeling complex relationships in networked systems. Both SWAT+'s water allocation module and GNNs use graph-like structures to model water systems. The goal of this study is to systematically compare the structural components of these two approaches and provide insights into potential integration.

Using the Upper Isar River Basin's complex water management system as a case study, we examine how SWAT+ and GNNs can be used to model it. We perform a component-wise analysis, focusing on how these models can represent nodes, edges, and attributes in a networked water management system. While this study focuses on structural rather than performance comparisons, we anticipate that our results will highlight the strengths and limitations of each approach. SWAT+ is expected to excel at incorporating domain-specific knowledge and explicitly representing management actions. GNNs could provide advantages in learning complex patterns from data and faster simulations for larger catchments.

The findings could open the way for hybrid approaches that combine traditional hydrological models' strengths with GNNs' learning capabilities. This could lead to more robust and adaptable water management tools to deal with the growing complexity of hydrological systems caused by climate change and human intervention.