Assessing uncertainty propagation of precipitation input in hydrometeorological ensemble forecasting systems

The aim of the thesis is the assessment of precipitation input uncertainty into hydrological response in hydrometeorological ensemble systems for flood prediction. The study has been preliminary focused on the development of a hydrometeorological system that couples a statistical precipitation downscaling model, known as STRAIN, with a fully-distributed hydrological model, known as tRIBS. In a subsequent part of the research, a rigorous method has been designed to test the consistency hypothesis (i.e. ensemble and observations are drawn from the same distribution) of the ensemble precipitation fields generated by downscaling models. The verification procedure has been tested by means of numerical experiments. Results permit us to conclude that: (i) ensemble members generated using model parameters estimated on the observed event are overdispersed; (ii) the adoption of a single calibration relation linking model parameters and coarse meteorological observable can lead to the generation of consistent ensemble members; (iii) when a single calibration relation is not able to explain observed events variability, storm-specific calibration relation should be adopted to return consistent forecasts. Finally, in the last part of the work, a rigorous method has been developed to assess consistency of ensemble streamflows produced by hydrometeorological systems. The method has been tested with numerical experiments using the prediction system designed in the preliminary phase of the study with the purpose of evaluating the propagation of uncertainty of downscaled precipitation input into hydrological response. The innovative aspects of the thesis rely on (i) the development of rigorous verification methods for ensemble outputs of hydrometeorological systems; and (ii) the application of these procedure on a great number of events in order to draw statistically significant conclusions.