Breaking the Limits: Stress Testing Hydrological Models Beyond Observed Extremes

How far can hydrological models be pushed before they break down? This study explores that question by exposing a range of modelling approaches—simple conceptual models such as the Direct Runoff Model, more complex conceptual models like HBV-Light and LARSIM, as well as a data-driven model based on LSTM networks —to both real and hypothetical rainfall extremes. Beyond reproducing extreme rainfall events that caused historical floods (Ahr, Münster, and Elbe), the models are subjected to deliberately exaggerated and synthetic rainfall scenarios that challenge the physical and conceptual limits of model design. These “stress runs” reveal how each model responds when rainfall becomes exceptionally intense, prolonged, or short but extreme — conditions that are increasingly relevant under a changing climate.
As a case study, results for the Ahr catchment at an hourly resolution are presented, including analyses of different initial states and calibration periods. By examining model robustness—the ability to produce physically plausible runoff across a wide spectrum of extreme conditions—and identifying failure modes, the study uncovers hidden sensitivities, structural biases, and nonlinear behaviors that standard validation approaches may overlook. The goal is to rethink how robustness is assessed and to advance hydrological models capable of withstanding the extremes of the future.