Anna Kauffeldt

Imbalanced land surface water budgets in a numerical weather prediction system

There has been a significant increase in the skill and resolution of numerical weather prediction models (NWPs) in recent decades, extending the time scales of useful weather predictions. The land surface models (LSMs) of NWPs are often employed in hydrological applications, which raises the question of how hydrologically representative LSMs really are. In this paper, precipitation (P), evaporation (E), and runoff (R) from the European Centre for Medium-Range Weather Forecasts global models were evaluated against observational products. The forecasts differ substantially from observed data for key hydrological variables. In addition, imbalanced surface water budgets, mostly caused by data assimilation, were found on both global (P-E) and basin scales (P-E-R), with the latter being more important. Modeled surface fluxes should be used with care in hydrological applications, and further improvement in LSMs in terms of process descriptions, resolution, and estimation of uncertainties is needed to accurately describe the land surface water budgets.

Event and model dependent rainfall adjustments to improve discharge predictions

ABSTRACT Most conceptual rainfall–runoff models use as input spatially averaged rainfall fields which are typically associated with significant errors that affect the model outcome. In this study, it was hypothesised that a simple spatially and temporally averaged event-dependent rainfall multiplier can account for errors in the rainfall input. The potentials and limitations of this lumped multiplier approach were explored 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 and were used to identify rainfall multipliers for each of the individual events. An effect of the parameter sets on identified multipliers was found; however, it was small compared to the differences between events. Accounting for event-dependent multipliers improved the reliability of the predictions. At the cost of a small decrease in precision, the distribution of identified multipliers for past events can be used to account for possible rainfall errors when predicting future events. By using behavioural parameter sets to identify rainfall multipliers, the method offers a simple and computationally efficient way to address rainfall errors in hydrological modelling.