Optimal design of pipes in series: An explicit approximation

This paper introduces a new methodology for the optimum design of pipes in series, named Optimum Hydraulic Grade Line (OHGL). This methodology is explicit and is based on the knowledge of the series topology and the geometrical distribution of water demands on nodes, i.e. the way in which the pipe in series delivers water mass as function of the distance from the entrance. OHGL consists in the pre-determination of that hydraulic grade line which gives the minimum construction cost, in an explicit way. Once this line has been established, calculation of the pipe’s continuous diameters is direct; after a round up to commercial diameters is developed. To validate the proposed methodology, several pipes in series were designed both using GA and OHGL. Four hundred series were used in total, each with different topological characteristics and demands. Keywords: Pipe in series, optimum design, genetic algorithms, optimum hydraulic grade line

Singularity-sensitive gauge-based radar rainfall adjustment methods for urban hydrological applications

Gauge-based radar rainfall adjustment techniques have been widely used to improve the applicability of radar rainfall estimates to large-scale hydrological modelling. However, their use for urban hydrological applications is limited as they were mostly developed based upon Gaussian approximations and therefore tend to smooth off so-called "singularities" (features of a non-Gaussian field) that can be observed in the fine-scale rainfall structure. Overlooking the singularities could be critical, given that their distribution is highly consistent with that of local extreme magnitudes. This deficiency may cause large errors in the subsequent urban hydrological modelling. To address this limitation and improve the applicability of adjustment techniques at urban scales, a method is proposed herein which incorporates a local singularity analysis into existing adjustment techniques and allows the preservation of the singularity structures throughout the adjustment process. In this paper the proposed singularity analysis is incorporated into the Bayesian merging technique and the performance of the resulting singularity-sensitive method is compared with that of the original Bayesian (non singularity-sensitive) technique and the commonly used mean field bias adjustment. This test is conducted using as case study four storm events observed in the Portobello catchment (53 km2) (Edinburgh, UK) during 2011 and for which radar estimates, dense rain gauge and sewer flow records, as well as a recently calibrated urban drainage model were available. The results suggest that, in general, the proposed singularity-sensitive method can effectively preserve the non-normality in local rainfall structure, while retaining the ability of the original adjustment techniques to generate nearly unbiased estimates. Moreover, the ability of the singularity-sensitive technique to preserve the non-normality in rainfall estimates often leads to better reproduction of the urban drainage system's dynamics, particularly of peak runoff flows

On the possibility of calibrating urban storm-water drainage models using gauge-based adjusted radar rainfall estimates

Traditionally, urban storm water drainage models have been calibrated using only raingauge data, which may result in overly conservative models due to the lack of spatial description of rainfall. With the advent of weather radars, radar rainfall estimates with higher temporal and spatial resolution have become increasingly available and have started to be used operationally for urban storm water model calibration and real time operation. Nonetheless, the insufficient accuracy of radar rainfall estimates has proven problematic and has hindered its widespread practical use. This work explores the possibility of improving the applicability of radar rainfall estimates to the calibration of urban storm-water drainage models by employing gauge-based radar rainfall adjustment techniques. Four different types of rainfall estimates were used as input to the recently verified urban storm water drainage models of the Beddington catchment in South London; these included: raingauge, block-kriged raingauge, radar (UK Met Office Nimrod) and the adjusted (or merged) radar rainfall estimates. The performance of the simulated flow and water depths was assessed using measurements from 78 gauges. Results suggest that a better calibration could be achieved by using the block-kriged raingauge and the adjusted radar estimates as input, as compared to using only radar or raingauge estimates

Surface water flood warnings in England: overview, Assessment and recommendations based on survey responses and workshops

Following extensive surface water flooding (SWF) in England in summer 2007, progress has been made in improving the management and prediction of this type of flooding. A rainfall threshold-based extreme rainfall alert (ERA) service was launched in 2009 and superseded in 2011 by the surface water flood risk assessment (SWFRA). Through survey responses from local authorities (LAs) and the outcome of workshops with a range of flood professionals, this paper examines the understanding, benefits, limitations and ways to improve the current SWF warning service. The current SWFRA alerts are perceived as useful by district and county LAs, although their understanding of them is limited. The majority of LAs take action upon receipt of SWFRA alerts, and their reactiveness to alerts appears to have increased over the years and as SWFRA superseded ERA. This is a positive development towards increased resilience to SWF. The main drawback of the current service is its broad spatial resolution. Alternatives for providing localised SWF forecast and warnings were analysed, and a two-tier national-local approach, with pre-simulated scenario-based local SWF forecasting and warning systems, was deemed most appropriate by flood professionals given current monetary, human and technological resources

Improving the applicability of radar rainfall estimates for urban pluvial flood modelling and forecasting

This work explores the possibility of improving the applicability of radar rainfall estimates (whose accuracy is generally insufficient) to the verification and operation of urban storm-water drainage models by employing a number of local gauge-based radar rainfall adjustment techniques. The adjustment techniques tested in this work include a simple mean-field bias (MFB) adjustment, as well as a more complex Bayesian radar-raingauge data merging method which aims at better preserving the spatial structure of rainfall fields. In addition, a novel technique (namely, local singularity analysis) is introduced and shown to improve the Bayesian method by better capturing and reproducing storm patterns and peaks. Two urban catchments were used as case studies in this work: the Cranbrook catchment (9 km2) in North-East London, and the Portobello catchment (53 km2) in the East of Edinburgh. In the former, the potential benefits of gauge-based adjusted radar rainfall estimates in an operational context were analysed, whereas in the latter the potential benefits of adjusted estimates for model verification purposes were explored. Different rainfall inputs, including raingauge, original radar and the aforementioned merged estimates were fed into the urban drainage models of the two catchments. The hydraulic outputs were compared against available flow and depth records. On the whole, the tested adjustment techniques proved to improve the applicability of radar rainfall estimates to urban hydrological applications, with the Bayesian-based methods, in particular the singularity sensitive one, providing more realistic and accurate rainfall fields which result in better reproduction of the urban drainage system’s dynamics. Further testing is still necessary in order to better assess the benefits of these adjustment methods, identify their shortcomings and improve them accordingly

On the use of semi-distributed and fully-distributed urban stormwater models

Urban stormwater models comprise four main components: rainfall, rainfall-runoff, overland flow and sewer flow modules. They can be considered semi-distributed (SD) or fully distributed (FD) according to the rainfall-runoff module definition. SD models are based on sub-catchments units through which rainfall is applied to the model and at which runoff volumes are estimated. In FD models, the runoff volumes are estimated and applied directly on every element of a twodimensional (2D) model of the surface. This poster presents a comparison of SD and FD models based on two case studies: Zona Central catchment at Coimbra, Portugal, and Cranbrook catchment at London, UK. SD and FD modelling results are compared against water depth and flow records in sewers, and photographic records of a flood event. In general, FD models are theoretically more realistic and physically-based, but the results of this study suggest that the implementation of these models requires higher resolution (more detailed) elevation, land use and sewer network data than is normally used in the implementation of SD models. Failing to use higher resolution data for the implementation of FD models could result in poor-performing models. In cases when high resolution data are not available, the use of SD models could be a better choice