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Distinguishing high and low flow domains in urban drainage systems 2days ahead using numerical weather prediction ensembles
- Courdent, Vianney, Grum, Morten, Mikkelsen, Peter Steen
- Journal of hydrology 2018 v.556 pp. 1013-1025
- drainage systems, hydrologic models, peroxidase, prediction, probability, radar, rain, runoff, wastewater, watersheds, weight-of-evidence
- Precipitation constitutes a major contribution to the flow in urban storm- and wastewater systems. Forecasts of the anticipated runoff flows, created from radar extrapolation and/or numerical weather predictions, can potentially be used to optimize operation in both wet and dry weather periods. However, flow forecasts are inevitably uncertain and their use will ultimately require a trade-off between the value of knowing what will happen in the future and the probability and consequence of being wrong.In this study we examine how ensemble forecasts from the HIRLAM-DMI-S05 numerical weather prediction (NWP) model subject to three different ensemble post-processing approaches can be used to forecast flow exceedance in a combined sewer for a wide range of ratios between the probability of detection (POD) and the probability of false detection (POFD). We use a hydrological rainfall-runoff model to transform the forecasted rainfall into forecasted flow series and evaluate three different approaches to establishing the relative operating characteristics (ROC) diagram of the forecast, which is a plot of POD against POFD for each fraction of concordant ensemble members and can be used to select the weight of evidence that matches the desired trade-off between POD and POFD. In the first approach, the rainfall input to the model is calculated for each of 25 ensemble members as a weighted average of rainfall from the NWP cells over the catchment where the weights are proportional to the areal intersection between the catchment and the NWP cells. In the second approach, a total of 2825 flow ensembles are generated using rainfall input from the neighbouring NWP cells up to approximately 6 cells in all directions from the catchment. In the third approach, the first approach is extended spatially by successively increasing the area covered and for each spatial increase and each time step selecting only the cell with the highest intensity resulting in a total of 175 ensemble members. While the first and second approaches have the disadvantage of not covering the full range of the ROC diagram and being computationally heavy, respectively, the third approach leads to both a broad coverage of the ROC diagram range at a relatively low computational cost. A broad coverage of the ROC diagram offers a larger selection of prediction skill to choose from to best match to the prediction purpose.The study distinguishes itself from earlier research in being the first application to urban hydrology, with fast runoff and small catchments that are highly sensitive to local extremes. Furthermore, no earlier reference has been found on the highly efficient third approach using only neighbouring cells with the highest threat to expand the range of the ROC diagram. This study provides an efficient and robust approach to using ensemble rainfall forecasts affected by bias and misplacement errors for predicting flow threshold exceedance in urban drainage systems.