Bardossy, A.

Kunstmann, H.

Marx, A.

Seltmann, J.

KITopen

Estimation of a Z/R-relationship to compute rainfall intensities                    from radar data using inverse hydrological modellingDistributed HydrologicalModel WaSiM-ETH• Radar Data from                                Meteorological Observatory            Hohenpeißenberg (MOHp)• 3-min. precipitation scan• ~ 65000 DX-scenes June-August 2001• Standard Processing of Radar data (Z=256R1.42) and simplified after                                            RADOLAN (2004): (Radar Online Adjustment), German Weather Service, Department for Hydrometeorology,http://www.dwd.de/de/wir/Geschaeftsfelder/Hydrometeorologie/a_href_pages/RADOLAN/radolan-online.htmcontact: Andreas Marx, Forschungszentrum Karlsruhe, 82467 Garmisch-Partenkirchen, Germany andreas.marx@imk.fzk.deUniversitätStuttgartHigh-Tech-OffensiveZukunftBayernMeteorologischesObservatoriumHohenpeißenbergA. Marx (1), Kunstmann, H. (1), Bárdossy, A. (2), Seltmann, J. (3)(1)Karlsruhe Research Center, IMK-IFU, 82467 Garmisch-Partenkirchen, Germany(2) University of Stuttgart, Institute of Hydraulic Engineering, 70550 Stuttgart, Germany(3) German Weather Service, Meteorological Observatory Hohenpeißenberg, Germany( )( ) max)],,,,([)],,,,([1!1221,1221,,⇒−−−∑∑==NijjisimobsNijjisimiobsccbaZRQQccbaZRQQCALIBRATION of the HYDROLOGICAL MODELRADARDA T AOP T  I M I  Z  A T  I ONINTRODUCTIONThe quality of hydrological modeling is limited due to the availability of high resolution temporal and spatial input data. Rain gauge measurements give accurate information at a single point while radar measurements provide good spatial information. On the other hand, it is difficult to estimate areal precipitation from rain gauge measurements and absolute rainfall intensities from radar data. In this study, a method to calibrate a Z/R-relationship using observed river discharge data is presented. River gauge measurements from five subcatchments with sizes around 100 km2 are used to estimate Q-Z/R-relationships using the calibrated hydrological model WASIM-ETH and the resulting spatially differentiated precipitation. 1.91.61.4b77200125a> 4436.5 … 44< 36.5dBZFig. 4: Total precipitation sum June-August 2001 [mm] from radar data (Z=256R1.42); black lines show the  location of the Ammer catchment.This study was performed in the Ammer catchment (Fig.1) in southern Bavaria, Germany. The simulated catchment size is 601 km². Due to the complex orography (Fig. 2), it faces very short reaction times of the river gauges to rainfall events. Fig.1: Location of the Ammer catchment, ~45 km southwest of Munich; black outlines show simulated subcatchments. Background:      LANDSAT TM (7-5-3), 2001-06-19Fig.2: DEM of the Ammer catchment and position of the river discharge measurements Tab. 1. Subbasincharacteristics in the Ammer catchment.601Weilheim294Peissenberg117Oberhausen51.5Obernach43.5Halbammer114OberammergauArea [km2]River gaugeFig.6: Simulation results using  the calibrated  Q-Z/R-relationship, blue  background denotes radar data gap.Tab. 4: Performance of river runoff simulations (Nash-Sutcliffe efficiency) using different radar rainfall estimates and stationdata for summer 2001.RESULTSTab. 2: Simplified split three-part Z/R relationship after RADOLAN (2004) which is used as input data for optimization process; Crossovers between parts depend on reflectivity values.Aim of the optimization     process is to find a robust Q-Z/R-relationship for hydrological modeling in the Ammer catchment.It is  applied in a way that the Nash-Sutcliffe-efficiency is maximized over a three-month period (over five subcatchments):Qobs observed river discharge dataQsim simulated discharge data i        subcatchmentc       crossover between relationship    parts1.631.571.43B73131104a>44.536 … 44.5<36dBZTab. 3: Resulting Q-Z/R-relationship for the Ammer catchment based on river discharge data.• after Schulla, J. & Jasper, K. (2000): Model description WaSiM-ETH. Institute for Geography, ETH Zürich, 167 pp.• calculation of the vertical water fluxes in the unsaturated zone is done by the discrete Richards Equation. • soil moisture content is parameterized considering suction head and hydraulic conductivity • interflow is generated in defined different soil layers depending on drainable water content, suction, the hydraulic conductivity and gradient. TopographyLand UseSub-catchmentsSoil data• surface runoff is routed using a subdivision of the basin into flow time zones. • evapotranspiration is calculated following the approach of Penman-Monteith• contains a simple 2D groundwater model which is dynamically coupled to the unsaturated zone.Adjustment of a robust Q-Z/R-relationship for                                     hydrological modeling using observed river discharge dataFig.7: Difference in rainfall sum [mm] over three months in Summer 2001 using  the  calibrated  Q-Z/R-relationship (left) and interpolated (inverse distance weight ) station data (right side).Fig.8: Validation of Q-Z/R-relationship was performed using 14 rain gauge measurements [mm/7h]. A comparison of differentially estimated radar rainfall showed good results although high rainfall intensities are still underestimated with the new method.0.9690.9560.4710.6590.710.582Q-Z/R   0.9670.9670.350.5880.6190.561simpl. RADOLAN0.9620.943-0.2040.4140.5360.538Z=256R1.420.920.90.180.680.580.2Station dataheimberghausennachammerammergauWeil-PeißenOber-Ober-Halb-Ober-Crossover2Crossover 1Fig. 5: Calibrating subcatchment Obernach: Optimization process was done iteratively calibra-ting always two parameters at the same time   Fig.3: Results from the hydrological simulations using interpolated rain gauge measurements for the calibration period in Summer 2001 (June-August).

Adjustment of a robust Q-Z/R-relationship for hydrological modeling using observed river discharge data

https://publikationen.bibliothek.kit.edu/220069924/3815098

Adjustment of a robust Q-Z/R-relationship for hydrological modeling using observed river discharge data

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