A fast flood inundation model with groundwater interactions and hydraulic structures

To efficiently predict flooding caused by intense rainfall (pluvial flooding), many physics-based flood inundation models adopt simplistic parameterizations of infiltration such as the Kostiakov, Horton, Soil Conservation Service and Green-Ampt methods. However, these methods are not explicitly dependent on soil moisture (or the groundwater table height), which is known to strongly influence the amount of runoff generated by rainfall. Models that fully couple surface and groundwater flow equations offer an alternative approach, but require larger amounts of input data and greater computational effort. Here we present a fast flood inundation model that couples two-dimensional shallow-water equations for surface flow with a zero-dimensional, time-dependent groundwater equation to capture sensitivity to groundwater. The model is also configured to account for storm drains, pumping and gates so human influences on flooding can be resolved, and is implemented with a dual-grid finite-volume scheme and with OpenACC directives for execution on graphical processing units (GPUs). With a 1.5 m resolution application across a 1,000 km${}^2$ area in Miami, Florida, where pluvial flooding is sensitive to depth to groundwater and simulation models that accurately reproduce observed flooding are needed to explore and plan response options, we first show that hourly water levels are predicted with a Mean Absolute Error of 8–16 cm across six canal gaging stations where flows are affected by tides, pumping, gate operations, and rainfall runoff. Second, we show high sensitivity of flooding to antecedent groundwater levels: flood extent is predicted to vary by a factor of six when initial depth to groundwater is varied between 10 and 200 cm, an amount that aligns with seasonal changes across the area. And third, we show that the model runs 30 times faster than real time (i.e., model speed = 30) using an NVIDIA V100 GPU. Furthermore, using a 3 m resolution model of Houston, Texas, we benchmark model speeds greater than 20 and 100 for domain sizes of 10,000 or 1,000 km${}^2$, respectively. The importance of model speed is discussed in the context of flood risk management and adaptation.