Radar-raingauge data combination techniques: a revision and analysis of their suitability for urban hydrology.

The applicability of the operational radar and raingauge networks for urban hydrology is insufficient. Radar rainfall estimates provide a good description of the spatiotemporal variability of rainfall; however, their accuracy is in general insufficient. It is therefore necessary to adjust radar measurements using raingauge data, which provide accurate point rainfall information. Several gauge-based radar rainfall adjustment techniques have been developed and mainly applied at coarser spatial and temporal scales; however, their suitability for small-scale urban hydrology is seldom explored. In this paper a review of gauge-based adjustment techniques is first provided. After that, two techniques, respectively based upon the ideas of mean bias reduction and error variance minimisation, were selected and tested using as case study an urban catchment (∼8.65 km(2)) in North-East London. The radar rainfall estimates of four historical events (2010-2012) were adjusted using in situ raingauge estimates and the adjusted rainfall fields were applied to the hydraulic model of the study area. The results show that both techniques can effectively reduce mean bias; however, the technique based upon error variance minimisation can in general better reproduce the spatial and temporal variability of rainfall, which proved to have a significant impact on the subsequent hydraulic outputs. This suggests that error variance minimisation based methods may be more appropriate for urban-scale hydrological applications.

A spatial-temporal rainfall generator for urban drainage design.

The work presented here is a contribution to the Thames Water project of improving the Counters Creek catchment sewerage system in London. An increase in the number of floods affecting basements in the area has indicated the need for improvements to the system. The cost of such improvements could be very high, and as such it is important to determine whether the traditional approach of applying 30-year spatially uniform design storms results in substantial overestimation. The first step in this is to generate simulations of spatially distributed rainfall events, from which 30-year storms can be extracted. Storms are modelled as clusters of Gaussian rainfall cells, extending the earlier Willems method to radar rainfall data. The parameters describing the cells and their motion are sampled from probability distributions derived from parameter estimates gained from 45 historical storm events within the catchment for the period 2000-2011. This spatial-temporal stochastic rainfall generator produces a two-dimensional time series of simulated storm events, from which events of given return period can be identified.