Capacity of semi-parametric regression models to predict extreme-event water quality in the Northeastern US

Abstract This study assessed the capacity of semi-parametric regression models to predict riverine solute concentrations during extreme high-flow hydrologic events, when such events are absent from the models' calibration data. Using a large dataset from 459 monitoring stations across the US Northeast, the models showed a tendency to overpredict extreme-event concentrations, with increasing bias and variance for increasingly extreme hydrologic conditions. The validation framework in this study effectively compared model performance across disparate hydrologic regimes and constituents, yet can be used to estimate individual model performance under an unobserved extreme-flow condition, regardless of whether any extreme-flow data are available for that model. The validation procedure can further be generalized to explore model performance in an arbitrarily defined extreme condition for a broad range of model types. Despite an overall increase in uncertainty for extreme-event concentration estimates, estimates under extreme hydrologic conditions could be improved by taking into account the observed bias in the aggregated regional database.