Disaggregation of spatial rainfall fields for hydrological modelling

International audienceMeteorological models generate fields of precipitation and other climatological variables as spatial averages at the scale of the grid used for numerical solution. The grid-scale can be large, particularly for GCMs, and disaggregation is required, for example to generate appropriate spatial-temporal properties of rainfall for coupling with surface-boundary conditions or more general hydrological applications. A method is presented here which considers the generation of the wet areas and the simulation of rainfall intensities separately. For the first task, a nearest-neighbour Markov scheme, based upon a Bayesian technique used in image processing, is implemented so as to preserve the structural features of the observed rainfall. Essentially, the large-scale field and the previously disaggregated field are used as evidence in an iterative procedure which aims at selecting a realisation according to the joint posterior probability distribution. In the second task the morphological characteristics of the field of rainfall intensities are reproduced through a random sampling of intensities according to a beta distribution and their allocation to pixels chosen so that the higher intensities are more likely to be further from the dry areas. The components of the scheme are assessed for Arkansas-Red River basin radar rainfall (hourly averages) by disaggregating from 40 km x 40 km to 8 km x 8 km. The wet/dry scheme provides a good reproduction both of the number of correctly classified pixels and the coverage, while the intensitiy scheme generates fields with an adequate variance within the grid-squares, so that this scheme provides the hydrologist with a useful tool for the downscaling of meteorological model outputs. Keywords: Rainfall, disaggregation, General Circulation Model, Bayesian analysi

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

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

Stochastic evaluation of sewer inlet capacity on urban pluvial flooding

In this paper we present an innovative methodology to stochastically assess the impact of sewer inlet conditions on urban pluvial flooding. The results showed that sewer inlet capacity can have a large impact on the occurrence of urban pluvial flooding. The methodology is a useful tool for dealing with uncertainties in sewer inlet operational conditions and contribute to comprehensive assessment of urban pluvial risk assessment

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

Improving rainfall nowcasting and urban runoff forecasting through dynamic radar-raingauge rainfall adjustment

The insufficient accuracy of radar rainfall estimates is a major source of uncertainty in short-term quantitative precipitation forecasts (QPFs) and associated urban flood forecasts. This study looks at the possibility of improving QPFs and urban runoff forecasts through the dynamic adjustment of radar rainfall estimates based on raingauge measurements. Two commonly used techniques (Kriging with External Drift (KED) and mean field bias correction) were used to adjust radar rainfall estimates for a large area of the UK (250,000 km2) based on raingauge data. QPFs were produced using original radar and adjusted rainfall estimates as input to a nowcasting algorithm. Runoff forecasts were generated by feeding the different QPFs into the storm water drainage model of an urban catchment in London. The performance of the adjusted precipitation estimates and the associated forecasts was tested using local rainfall and flow records. The results show that adjustments done at too large scales cannot provide tangible improvements in rainfall estimates and associated QPFs and runoff forecasts at small scales, such as those of urban catchments. Moreover, the results suggest that the KED adjusted rainfall estimates may be unsuitable for generating QPFs, as this method damages the continuity of spatial structures between consecutive rainfall fields

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