Enhancing short-lead flood forecasting by integrated modeling of surface and groundwater

Groundwater plays an important role in flood formation yet, flood forecasting in coastal basins is often limited by inadequate representation of surface and groundwater interactions. In this study, we use a Graph Neural Network (GNN) to evaluate the added value of incorporating hourly groundwater information for short-term flood forecasting. Harris County, Texas is considered as a case study. The region is monitored by an extensive network of rainfall and channel-level sensors, supplemented by United States Geological Survey (USGS) wells providing hourly groundwater level data. Within the GNN framework, the sensor network is represented as a graph, where nodes correspond to monitoring areas and edges represent learned hydrological influence paths. Node inputs include recent precipitation, recent streamflow level changes, and normalized groundwater hydraulic load anomalies derived from Harvey Hurricane (2017) and post-Harvey flood events from 2018 to 2023. Results show that including a single groundwater-based prediction variable improves prediction ability by approximately 20% compared to precipitation and level-based reference models. This gain is strongest in areas with continuous groundwater withdrawal and accelerated recharge, where enhanced hydraulic gradients can intensify coastal storage exchange and enhance hydrogeological memory. The learned graph also provides an interpretable, directed interaction structure that supports data-driven causal hypotheses about network connectivity. Furthermore, we estimated the time delay dependency associated with the lag between two stations in our study area, which form a head-to-tail pair. The learned delay between these two stations is sub-daily, with a magnitude of ~0.5 to 0.7 days, corresponding to roughly 12 to 17 hours. This information can guide the parameterization of lag in rainfall-runoff modeling workflows. The results indicate that shallow groundwater dynamics can act as an important regulator of short-term urban flood response in coastal basins. When designing next-generation warning systems for Harris County and similar regions, groundwater levels and rainfall effects should be considered together.