Estimation of critical rainfall for flood early warning systems using hydrologic and hydrodynamic modeling

Floods rank among the most significant natural disaster risks worldwide. They can develop rapidly, within hours or even minutes, often moving with high flow velocities and causing widespread devastation with little to no warning. Early flood warning is crucial for mitigating the associated risks and relies on understanding the critical rainfall thresholds that can trigger floods, often serving as a key "warning index". Estimating critical rainfall is particularly challenging due to the small spatial and temporal scales of such phenomena, especially in data-scarce regions where high-resolution weather models and advanced monitoring networks are lacking. In this study, a methodology for estimating critical rainfall, with application to floods, is presented based on an integrated hydrologic-hydrodynamic model. The model is applied in the Lilantas River catchment in Evia, Greece, for a relatively large number of rainfall-soil moisture condition scenario combinations in order to (1) determine inflow hydrographs used as boundary conditions for the hydrodynamic model and (2) calculate the distribution of “critical hazard” across the cells of the two-dimensional (2D) computational field, which is defined by combining the main hydrodynamic characteristics of flow depth and velocity. Finally, based on the calculated critical hazard, estimates of critical rainfall values for the selected study area are provided, along with an example of the flood warning system’s operation.