Improving the statistical reliability of river model predictions via simple state adjustments

A major challenge in hydrologic modelling is producing reliable uncertainty estimates outside of calibration periods. One obvious strategy is to improve model structures utilising advancements in process knowledge and observations. Another statistically relevant approach is to develop improved error models so that the uncertainty can be more reliably estimated. The recently introduced river bed/bank storage (RBS) model is an improved representation of transmission losses/gains within basin-scale river system models. The novelty of the current research is the way the RBS model is combined with State and Parameter Uncertainty Estimation (SPUE) to produce more statistically reliable predictions. Using the RBS model with SPUE resulted in better matched predictive distributions in 13 out of 16 test cases, and higher proportions of observed values within the predictive ranges for all cases. The paper demonstrates that improving model structures combined with better characterisation of state error can alleviate issues of overfitting in predictions.

Automated summary

A major challenge in hydrologic modelling is producing reliable uncertainty estimates outside of calibration periods. This research addresses the challenge by improving model structures and error models to more reliably estimate uncertainty. The combination of the RBS model and SPUE produces statistically reliable predictions and shows better matching performance in tests.