Delineating modelling uncertainty in river flow indicators with representative parameter sets

Abstract

For mitigating negative effects of floods and droughts, estimates of flow indicators and their uncertainties are essential. The recently introduced concept of the representative parameter sets (RPSs) enables modelling uncertainty to be represented in the flow frequency space at low computational cost, using a small subset of pre-selected model parameter sets. This concept is here adapted to assess hazards of three flow indicators: annual maximal flow, annual 7-day-average low flow, and annual mean flow. An additional in-depth analysis assesses the RPS transferability to other flow indicators and to hydrological signatures. RPS-based simulations are benchmarked with a random selection of parameter sets. The results show that i) RPSs can be successfully transferred between flow indicators with only a small drop in model performance; and ii) RPSs can be used to represent modelling uncertainty in hydrological signatures. The RPS concept has thus great potential for delineating modelling uncertainty of any environmental model.