Andersen, Christoffer Bang

Duus, Lene Bassø

Kraglund, Lene Lykke

Simonsen, Preben Dam

Thorndahl, Søren Liedtke

Wright, Daniel B.

English

    Aalborg UniversitetApplying Stochastic Storm Transposition in Design of Urban Drainage SystemsAndersen, Christoffer Bang; Wright, Daniel B.; Thorndahl, Søren Liedtke; Simonsen, PrebenDam; Kraglund, Lene Lykke ; Duus, Lene BassøPublication date:2021Link to publication from Aalborg UniversityCitation for published version (APA):Andersen, C. B., Wright, D. B., Thorndahl, S. L., Simonsen, P. D., Kraglund, L. L., & Duus, L. B. (2021).Applying Stochastic Storm Transposition in Design of Urban Drainage Systems. 1. Poster presented at IWADigital World Water Congress, .General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright ownersand it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.            ? Users may download and print one copy of any publication from the public portal for the purpose of private study or research.            ? You may not further distribute the material or use it for any profit-making activity or commercial gain            ? You may freely distribute the URL identifying the publication in the public portal ?Take down policyIf you believe that this document breaches copyright please contact us at vbn@aub.aau.dk providing details, and we will remove access tothe work immediately and investigate your claim.Downloaded from vbn.aau.dk on: August 24, 2021www.postersession.comDesign of urban drainage systems is traditionally based on eitherIntensity-Duration-Frequency curves (IDF), “Chicago DesignStorms” (CDS), which are derived from IDF-curves or other kindof design storms. These design storms allows for an intuitiveunderstanding of the relation between the average rainfall intensity,over a specified duration, and the return period of the storm. Usingthese types of design storms, and point measured rainfall timeseries, do neglect both the spatial and temporal dynamics, whichare driving factors in rainfall runoff response.Lack of information about the spatial variability in rainfall fieldsare considered one of the greatest sources of errors in hydrologicaland hydrodynamic modelling (Thorndahl et. al, 2017). To includethe spatial factor “Areal Reductions Factors” (ARF) have beenintroduced; a relation between peak intensity of a rainfall event andtotal area coverage at peak. These factors can be derived directlyfrom a network of rain gauges or from radar data. While these, justlike the IDF relations, enable an intuitive understanding of thespatio-temporal dynamics of rainfall ARF’s are only applicable fordesign storms and not useable for temporally dynamic rainfall. Astochastic framework, known as “Stochastic Storm Transposition”(SST) has shown promising results using radar rainfall data toderive IDF-curves from a relative short period of observation.In this study we apply the SST procedure to a high-resolution radarrainfall dataset and compare the IDF estimates with ones derivedfrom a long record of rainfall gauges. Furthermore, the spatialstructure of the areal rain is examined to enable an understandingof the relation between storm duration, return level and spatialvariability.DataConclusionsApplying Stochastic Storm Transposition in Design of Urban Drainage Systems Christoffer Bang Andersen1, Daniel B. Wright2, Søren Thorndahl1, Preben Dam Simonsen3, Lene Lykke Kraglund3, Lene Bassø Duus41Aalborg University, Denmark; 2UW-Madison, USA; 3Niras, Denmark; 4Aarhus Vand, DenmarkBibliography1. Wright, D.B., Mantilla, R. & Peters-Lidard, C.D. 2016 A remote sensing-based tool for assessing rainfall-driven hazards. Environmental Modelling & Software. 90, 34–54.2. Wright, D.B., Smith, J.A., Villarini, G. & Baeck, M.L. 2013 Estimating the frequency of extreme rainfall using weather radar and stochastic storm transposition. Journal of Hydrology. 488, 150–165. 3. Thorndahl, S., Nielsen, J.E. & Rasmussen, M.R. 2014 Bias adjustment and advection interpolation of long-term high resolution radar rainfall series. Journal of Hydrology. 508. 214-226.4. Thorndahl, S., Einfalt, T., Willems, P., Nielsen, J.E., Veldhuis, M.T., Arnbjerg-Nielsen, K., Rasmussen, M.R. & Molnar, P. 2017 Weather radar rainfall data in urban hydrology. Hydrol. Earth Syst. Sci. 21. 13595. WPC, 2014. Opdaterede klimafaktorer og dimensionsgivende regnintensiteter, Skrift nr. 30 (Updated climate factors and rain intensities for design, Guideline no. 30). Water Pollut. Comm. Soc. Danish Eng.IDF recreationFigure 3 displays the SST procedure ability to recreate long-termextreme rainfall statistics. In general, the SST procedure slightlyunderestimates in relation to the Danish regional model. This canbe explained by sub-pixel variability from radar to rain gauge. Therecreated IDF’s falls within the range of uncertainty of the regionalmodel at all durations validating the method.Max-mean analysisThe max-mean analysis relates the largest pixel value (R_max) inthe areal storms, generated with the SST approach, with the meanvalue of the areal storm (R_mean). Large values indicates highspatial variability and values close to 1 indicates uniformity.Semi-variogram analysisBy fitting a semi-variogram model to the areal rainfall storms it ispossible to examine the relation between storm duration, returnperiod and the range parameter. The range parameter indicates thespatial limit of autocorrelation. A low value will indicate weakautocorrelation in the storm which can be interpreted as highvariability. This analysis is limited to the total extent of the area ofinterest which means that the range parameter at maximum canshow a value of ~20km which indicates strong autocorrelation andtherefore can be interpreted as a uniform rainfall field. The resultsare presented on figure 4.The aim of this study was to review the validity of the SSTprocedure on data from a single, non-continuous, C-band radardataset. The procedure in general underestimates in relation to theregional model derived from rain gauge data. This difference isoften seen when comparing rain gauge data with radar data andthus the difference is considered insignificant since the results fallswithin the uncertainty band of the regional model.The spatial structure of the SST generated storms was examined toform an understanding for how large an ensemble is needed toinclude all uncertainty in urban drainage modelling. The firstmeasure was a simple analysis of the relation between largest pixelin the generated storm versus the mean of the storm field. Thisrevealed that the variability is higher at the shorter duration storms.To further investigate this a semi-variogram analysis wasperformed on the generated storms which relies on the rangeparameter of the variogram that depicts the spatial limit ofautocorrelation. This analysis further concluded that storms ofshort duration in general have a more chaotic and less organizedrainfall field than those with longer durations.Looking at the results of max-mean analysis and the semi-variogram with urban drainage design, and modelling time, inmind one could infer that a large ensemble would be needed forthe short duration storms to include all uncertainties.Introduction ResultsVUDPProject: KLIMAKSStochastic Storm TranspositionA general limitation when performing frequency analysis on anytimeseries data is the observation period. SST works in a stochasticframework where the radar dataset is virtually extended by re-sampling storm events not only in time but also in space. Aprincipal sketch of the procedure is presented on figure 1.By identifying a climatological homogeneous spatial domain (alsoknown as transposition domain) it can be assumed that any stormwould have equal likelihood occurring anywhere on the identifieddomain. The selected transposition domain can be viewed onfigure 2. Homogeneity was assed through several parameters suchas: Annual average rainfall, cloud to ground lightning strikes, daysabove 20mm. From the SST procedure rainfall events at point levelwill be generated and compared with the regional model. Arealstorms will be generated to investigate the spatial structure of thestorms through a max/mean analysis of the radar pixels and a semi-variance analysis with the range parameter as result.1 3 4 5MethodRadar dataThis study uses a 14-year (2002-2016) 10-min 500x500m radar-estimated rainfall dataset, from a single weather radar, located50km south of the city of Copenhagen. The dataset is biascorrected using daily mean field bias using nearby rain gauges(Thorndahl et al. 2014) to increase the quality of the precipitationproduct. Detailed information about technical specification andpost/pre-processing of the dataset is presented in Thorndahl et al.(2017).Regional ModelThe regional model is an extreme value analysis of the WaterPollution Committee (WPC) network of rain gauges. This modelwill be used as validation for the SST procedure. This model isbased on the WPC, (2014) recommendations.Study AreaThe study area will be focused on the Danish island Zealand andthe Copenhagen area have been picked out as area of interest, aspresented on figure 2.Results2Figure #1: Principal sketch of SST procedure.Black circles indicate original storm motion, dashedcircle transposed storm. Blue dashed line indicatetransposition domain and red box indicates area ofinterestFigure #2: Selected transposition domain for thestudy and area of interest.Figure #3: The danish regional model compared toIDF estimates from SST. Shaded area indicates95% spread. WPC are IDF-curves extracted fromthe regional model. T indicates return level.Figure #3: Box-plots of the max-mean analysis.Median values are indicated by lines through theboxes. The edges of the box indicates 25% to 75%percentile and whiskers shows max values.Figure #4: Range value for each of the stormdurations. Black bars indicate uncertainty intervalof 95 percentile.6

Applying Stochastic Storm Transposition in Design of Urban Drainage Systems

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Applying Stochastic Storm Transposition in Design of Urban Drainage Systems

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