Quantifying radar-rainfall uncertainties in urban drainage flow modelling

AbstractThis work presents the results of the implementation of a probabilistic system to model the uncertainty associated to radar rainfall (RR) estimates and the way this uncertainty propagates through the sewer system of an urban area located in the North of England. The spatial and temporal correlations of the RR errors as well as the error covariance matrix were computed to build a RR error model able to generate RR ensembles that reproduce the uncertainty associated with the measured rainfall. The results showed that the RR ensembles provide important information about the uncertainty in the rainfall measurement that can be propagated in the urban sewer system. The results showed that the measured flow peaks and flow volumes are often bounded within the uncertainty area produced by the RR ensembles. In 55% of the simulated events, the uncertainties in RR measurements can explain the uncertainties observed in the simulated flow volumes. However, there are also some events where the RR uncertainty cannot explain the whole uncertainty observed in the simulated flow volumes indicating that there are additional sources of uncertainty that must be considered such as the uncertainty in the urban drainage model structure, the uncertainty in the urban drainage model calibrated parameters, and the uncertainty in the measured sewer flows

Exploration of an adaptive merging scheme for optimal precipitation estimation over ungauged urban catchment

Merging rain gauge and radar data improves the accuracy of precipitation estimation for urban areas. Since the rain gauge network around the ungauged urban catchment is fixed, the relevant question relates to the optimal merging area that produces the best rainfall estimation inside the catchment. Thus, an incremental radar-gauge merging was performed by gradually increasing the distance from the centre of the study area, the number of merging gauges around it and the radar domain. The proposed adaptive merging scheme is applied to a small urban catchment in west Yorkshire, Northern England, for 118 extreme events from 2007 to 2009. The performance of the scheme is assessed using four experimental rain gauges installed inside the study area. The result shows that there is indeed an optimum radar-gauge merging area and consequently there is an optimum number of rain gauges that produce the best merged rainfall data inside the study area. Different merging methods produce different results for both classified and unclassified rainfall types. Although the scheme was applied on daily data, it is applicable to other temporal resolutions. This study has importance for other studies such as urban flooding analysis, since it provides improved rainfall estimation for ungauged urban catchments.</jats:p

Analysis Of Combined Effects Of Input Uncertainty And Parameter Uncertainty In Hydrological Modelling

Uncertainty of a hydrological model mainly stems from a lack of understanding and knowledge about the real hydrological process. Input uncertainty and parameter uncertainty are considered to be the two major uncertainty sources of hydrological model. Until now, enormous studies have aimed at calibrating model parameters and estimating model uncertainty. However, these studies mainly ascribe the model output uncertainty to the unknown non-physical parameters. In fact, rainfall, especially of weather radar rainfall, is widely recognized as a main error source. There are seldom studies that aim to explicitly describe model input and parameter uncertainty simultaneously. For this reason, in this study, we investigate the combined effects of radar rainfall uncertainty and parameter uncertainty on the model output. A radar probabilistic quantitative rainfall scheme (Multivariate Distributed Ensemble Generator, MDEG) is integrated with a rainfall-runoff model (Probability Distributed Model, PDM) to calibrate model parameters and estimate the model uncertainty. Finally, the simulated flows, together with their uncertainty bands are compared with the observed flows to evaluate the proposed scheme