Speaker
Description
This contribution presents the ability of individual CMLs to provide relevant QPEs for rainfall-runoff simulations, specifically, the influence of CML characteristics and position on the predicted runoff. The analysis is based on a 3-year-long experimental data set from a small (1.3 km2) urban catchment located in Prague, Czech Republic. QPEs from real world CMLs are used as inputs for an urban rainfall-runoff model. The prediction performance is assessed by comparing simulated and measured stormwater discharges. The results show that model performance is related mainly to the CML sensitivity to rainfall given by their frequency and path length and by the CML position. The runoff predictions are mostly highly overestimated. The largest volume errors are associated with the shortest CMLs. On the other hand, shortest CMLs, located with or close to catchment boundaries, reproduce especially during heavy rainfal, the discharge temporal dynamics (e.g. timing of peak flows) better than longer less biased CMLs.The feasibility of CML use for runoff predictions is largely compromised by bias in CML QPEs. Therefore our future research is focused on bias reduction based on independent hydrometeorlogical observations: specifically ) adjusting by existing precipitation ground observations, ii) discharge observations using backwards-hydrology approach.
