How do rainfall multipliers affect the accuracy and precision of hydrological models?

Abstract:

Conceptual rainfall-runoff models use spatially averaged rainfall fields as input. However, these are typically associated with significant errors, e.g. due to poor representativity of the gauge network, that affect the model outcome. In this study it is hypothesised that a simple spatially and temporally averaged event-dependent rainfall multiplier can account for errors in the rainfall input and this is tested by evaluating the effects of multipliers on the accuracy and precision of the predictive distributions.

Parameter sets found to be behavioural across a range of different flood events were assumed to be a good representation of the catchment dynamics. Each parameter set was subsequently used to identify rainfall multipliers for each of the individual events from a range of multipliers estimated from rainfall gauge data. An effect of the parameter sets on identified multipliers was found, however it was small compared to the effect due to the event rainfall input. The distributions of the identified multipliers were shown to be event-dependant by statistical testing.

Accounting for input rainfall errors by using event-dependent multipliers improved the reliability of the predictions, and only at the cost of a small decrease in precision. Hence, the distribution of identified multipliers for past events can be used to account for possible rainfall errors when predicting future events and be updated as new data becomes available. The method offers a simple and computationally cheap way to address rainfall errors in hydrological modelling.