Articles | Volume 21, issue 3
https://doi.org/10.5194/hess-21-1359-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Special issue:
https://doi.org/10.5194/hess-21-1359-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Weather radar rainfall data in urban hydrology
Department of Civil Engineering, Aalborg University, Aalborg, 9220,
Denmark
Thomas Einfalt
hydro & meteo GmbH & Co KG, 23552 Lübeck, Germany
Patrick Willems
Department of Civil Engineering, KU Leuven, Leuven, 3001, Belgium
Jesper Ellerbæk Nielsen
Department of Civil Engineering, Aalborg University, Aalborg, 9220,
Denmark
Marie-Claire ten Veldhuis
Department of Water Management, Delft University of Technology, Delft,
2628 CN, the Netherlands
Karsten Arnbjerg-Nielsen
Department of Environmental Engineering, Technical University of
Denmark, Lyngby, 2800, Denmark
Michael R. Rasmussen
Department of Civil Engineering, Aalborg University, Aalborg, 9220,
Denmark
Peter Molnar
Institute of Environmental Engineering, ETH Zurich, Zurich, 8093,
Switzerland
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Gijsbert A. Vis, Oscar K. Hartogensis, Marie-Claire ten Veldhuis, Bas J. H. van de Wiel, and Miriam Coenders-Gerrits
EGUsphere, https://doi.org/10.5194/egusphere-2025-6125, https://doi.org/10.5194/egusphere-2025-6125, 2026
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
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Heat exchange between land and air mainly happens through turbulence. Usually turbulence is measured at one location over time, but it also happens over space. In this study, we use fiber optic cables (DTS) that can measure temperature to capture turbulence over time, as well as directly over space. If compared with conventional instruments, we found a good correlation but also an underestimation. We recommend using DTS alongside with conventional instruments so they can complement each other.
Constantijn G. B. ter Horst, Gijsbert A. Vis, Judith Jongen-Boekee, Marie-Claire ten Veldhuis, Rolf W. Hut, and Bas J. H. van de Wiel
Atmos. Meas. Tech., 18, 6853–6867, https://doi.org/10.5194/amt-18-6853-2025, https://doi.org/10.5194/amt-18-6853-2025, 2025
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We present the the Fine Resolution Adaptable Distributed Temperature Sensing (FRADTS) method, which allows for mm-resolution probing of vertical temperature profiles, using coil-based distributed temperature sensing. The method is fully open source and parametric, such that unique field setups can be generated and reproduced. The method is extensively tested within a ~10cm grass canopy in a field campaign.
Wenyue Zou, Ruidong Li, Daniel B. Wright, Jovan Blagojevic, Peter Molnar, Mohammad A. Hussain, Yue Zhu, Yongkun Li, Guangheng Ni, and Nadav Peleg
EGUsphere, https://doi.org/10.5194/egusphere-2025-4099, https://doi.org/10.5194/egusphere-2025-4099, 2025
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
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We present a framework using observed rainfall and temperature to generate realistic storms and simulate street-scale flooding for present and future climates. It integrates temperature-based rainfall scaling, storm-frequency estimation, and urban flood modeling, demonstrated in Beijing to assess changes in regional storm and flood depth, timing, and flow velocity. The workflow is data-light, physically grounded, and transferable worldwide.
Magali Ponds, Sarah Hanus, Harry Zekollari, Marie-Claire ten Veldhuis, Gerrit Schoups, Roland Kaitna, and Markus Hrachowitz
Hydrol. Earth Syst. Sci., 29, 3545–3568, https://doi.org/10.5194/hess-29-3545-2025, https://doi.org/10.5194/hess-29-3545-2025, 2025
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This research examines how future climate changes impact root zone storage, a key hydrological model parameter. Root zone storage – the soil water accessible to plants – adapts to climate but is often kept constant in models. We estimated climate-adapted storage in six Austrian Alps catchments. While storage increased, streamflow projections showed minimal change, which suggests that dynamic root zone representation is less critical in humid regions but warrants further study in arid areas.
Amber van Hamel, Peter Molnar, Joren Janzing, and Manuela Irene Brunner
Hydrol. Earth Syst. Sci., 29, 2975–2995, https://doi.org/10.5194/hess-29-2975-2025, https://doi.org/10.5194/hess-29-2975-2025, 2025
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Suspended sediment is a natural component of rivers, but extreme suspended sediment concentrations (SSCs) can have negative impacts on water use and aquatic ecosystems. We identify the main factors influencing the spatial and temporal variability of annual SSC regimes and extreme SSC events. Our analysis shows that different processes are more important for annual SSC regimes than for extreme events and that compound events driven by glacial melt and high-intensity rainfall led to the highest SSCs.
Mosisa Tujuba Wakjira, Nadav Peleg, Johan Six, and Peter Molnar
Hydrol. Earth Syst. Sci., 29, 863–886, https://doi.org/10.5194/hess-29-863-2025, https://doi.org/10.5194/hess-29-863-2025, 2025
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In this study, we implement a climate, water, and crop interaction model to evaluate current conditions and project future changes in rainwater availability and its yield potential, with the goal of informing adaptation policies and strategies in Ethiopia. Although climate change is likely to increase rainfall in Ethiopia, our findings suggest that water-scarce croplands in Ethiopia are expected to face reduced crop yields during the main growing season due to increases in temperature.
Jessica Droujko, Srividya Hariharan Sudha, Gabriel Singer, and Peter Molnar
Earth Surf. Dynam., 11, 881–897, https://doi.org/10.5194/esurf-11-881-2023, https://doi.org/10.5194/esurf-11-881-2023, 2023
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We combined data from satellite images with data measured from a kayak in order to understand the propagation of fine sediment in the Vjosa River. We were able to find some storm-activated and some permanent sources of sediment. We also estimated how much fine sediment is carried into the Adriatic Sea by the Vjosa River: approximately 2.5 Mt per year, which matches previous findings. With our work, we hope to show the potential of open-access satellite images.
Cynthia Maan, Marie-Claire ten Veldhuis, and Bas J. H. van de Wiel
Hydrol. Earth Syst. Sci., 27, 2341–2355, https://doi.org/10.5194/hess-27-2341-2023, https://doi.org/10.5194/hess-27-2341-2023, 2023
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Their flexible growth provides the plants with a strong ability to adapt and develop resilience to droughts and climate change. But this adaptability is badly included in crop and climate models. To model plant development in changing environments, we need to include the survival strategies of plants. Based on experimental data, we set up a simple model for soil-moisture-driven root growth. The model performance suggests that soil moisture is a key parameter determining root growth.
Tobias Siegfried, Aziz Ul Haq Mujahid, Beatrice Sabine Marti, Peter Molnar, Dirk Nikolaus Karger, and Andrey Yakovlev
EGUsphere, https://doi.org/10.5194/egusphere-2023-520, https://doi.org/10.5194/egusphere-2023-520, 2023
Preprint archived
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Our study investigates climate change impacts on water resources in Central Asia's high-mountain regions. Using new data and a stochastic soil moisture model, we found increased precipitation and higher temperatures in the future, leading to higher water discharge despite decreasing glacier melt contributions. These findings are crucial for understanding and preparing for climate change effects on Central Asia's water resources, with further research needed on extreme weather event impacts.
Qinggang Gao, Christian Zeman, Jesus Vergara-Temprado, Daniela C. A. Lima, Peter Molnar, and Christoph Schär
Weather Clim. Dynam., 4, 189–211, https://doi.org/10.5194/wcd-4-189-2023, https://doi.org/10.5194/wcd-4-189-2023, 2023
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We developed a vortex identification algorithm for realistic atmospheric simulations. The algorithm enabled us to obtain a climatology of vortex shedding from Madeira Island for a 10-year simulation period. This first objective climatological analysis of vortex streets shows consistency with observed atmospheric conditions. The analysis shows a pronounced annual cycle with an increasing vortex shedding rate from April to August and a sudden decrease in September.
Fabian Walter, Elias Hodel, Erik S. Mannerfelt, Kristen Cook, Michael Dietze, Livia Estermann, Michaela Wenner, Daniel Farinotti, Martin Fengler, Lukas Hammerschmidt, Flavia Hänsli, Jacob Hirschberg, Brian McArdell, and Peter Molnar
Nat. Hazards Earth Syst. Sci., 22, 4011–4018, https://doi.org/10.5194/nhess-22-4011-2022, https://doi.org/10.5194/nhess-22-4011-2022, 2022
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Debris flows are dangerous sediment–water mixtures in steep terrain. Their formation takes place in poorly accessible terrain where instrumentation cannot be installed. Here we propose to monitor such source terrain with an autonomous drone for mapping sediments which were left behind by debris flows or may contribute to future events. Short flight intervals elucidate changes of such sediments, providing important information for landscape evolution and the likelihood of future debris flows.
Silvan Ragettli, Tabea Donauer, Peter Molnar, Ron Delnoije, and Tobias Siegfried
Earth Surf. Dynam., 10, 797–815, https://doi.org/10.5194/esurf-10-797-2022, https://doi.org/10.5194/esurf-10-797-2022, 2022
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This paper presents a novel methodology to identify and quantitatively analyze deposition and erosion patterns in ephemeral ponds or in perennial lakes with strong water level fluctuations. We apply this method to unravel the water and sediment balance of Lac Wégnia, a designated Ramsar site in Mali. The study can be a showcase for monitoring Sahelian lakes using remote sensing data, as it sheds light on the actual drivers of change in Sahelian lakes.
Punpim Puttaraksa Mapiam, Monton Methaprayun, Thom Bogaard, Gerrit Schoups, and Marie-Claire Ten Veldhuis
Hydrol. Earth Syst. Sci., 26, 775–794, https://doi.org/10.5194/hess-26-775-2022, https://doi.org/10.5194/hess-26-775-2022, 2022
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The density of rain gauge networks plays an important role in radar rainfall bias correction. In this work, we aimed to assess the extent to which daily rainfall observations from a dense network of citizen scientists improve the accuracy of hourly radar rainfall estimates in the Tubma Basin, Thailand. Results show that citizen rain gauges significantly enhance the performance of radar rainfall bias adjustment up to a range of about 40 km from the center of the citizen rain gauge network.
Vassilis Aschonitis, Dimos Touloumidis, Marie-Claire ten Veldhuis, and Miriam Coenders-Gerrits
Earth Syst. Sci. Data, 14, 163–177, https://doi.org/10.5194/essd-14-163-2022, https://doi.org/10.5194/essd-14-163-2022, 2022
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This work provides a global database of correction coefficients for improving the performance of the temperature-based Thornthwaite potential evapotranspiration formula and aridity indices (e.g., UNEP, Thornthwaite) that make use of this formula. The coefficients were produced using as a benchmark the ASCE-standardized reference evapotranspiration formula (formerly FAO-56) that requires temperature, solar radiation, wind speed, and relative humidity data.
Elena Leonarduzzi, Brian W. McArdell, and Peter Molnar
Hydrol. Earth Syst. Sci., 25, 5937–5950, https://doi.org/10.5194/hess-25-5937-2021, https://doi.org/10.5194/hess-25-5937-2021, 2021
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Landslides are a dangerous natural hazard affecting alpine regions, calling for effective warning systems. Here we consider different approaches for the prediction of rainfall-induced shallow landslides at the regional scale, based on open-access datasets and operational hydrological forecasting systems. We find antecedent wetness useful to improve upon the classical rainfall thresholds and the resolution of the hydrological model used for its estimate to be a critical aspect.
Jacob Hirschberg, Alexandre Badoux, Brian W. McArdell, Elena Leonarduzzi, and Peter Molnar
Nat. Hazards Earth Syst. Sci., 21, 2773–2789, https://doi.org/10.5194/nhess-21-2773-2021, https://doi.org/10.5194/nhess-21-2773-2021, 2021
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Debris-flow prediction is often based on rainfall thresholds, but uncertainty assessments are rare. We established rainfall thresholds using two approaches and find that 25 debris flows are needed for uncertainties to converge in an Alpine basin and that the suitable method differs for regional compared to local thresholds. Finally, we demonstrate the potential of a statistical learning algorithm to improve threshold performance. These findings are helpful for early warning system development.
Didier de Villiers, Marc Schleiss, Marie-Claire ten Veldhuis, Rolf Hut, and Nick van de Giesen
Atmos. Meas. Tech., 14, 5607–5623, https://doi.org/10.5194/amt-14-5607-2021, https://doi.org/10.5194/amt-14-5607-2021, 2021
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Ground-based rainfall observations across the African continent are sparse. We present a new and inexpensive rainfall measuring instrument (the intervalometer) and use it to derive reasonably accurate rainfall rates. These are dependent on a fundamental assumption that is widely used in parameterisations of the rain drop size distribution. This assumption is tested and found to not apply for most raindrops but is still useful in deriving rainfall rates. The intervalometer shows good potential.
Cited articles
Achleitner, S., Fach, S., Einfalt, T., and Rauch, W.: Nowcasting of rainfall and of combined sewage flow in urban drainage systems, Water Sci. Technol., 59, 1145–1151, https://doi.org/10.2166/wst.2009.098, 2009.
Ahm, M., Thorndahl, S., Rasmussen, M. R., and Bassø, L.: Estimating subcatchment runoff coefficients using weather radar and a downstream runoff sensor, Water Sci. Technol., 68, 1293–1299, https://doi.org/10.2166/wst.2013.371, 2013.
Anagnostou, M. N. and Anagnostou, E. N.: Precipitation: Advances in Measurement, Estimation and Prediction, edited by: Michaelides, S., Springer Berlin Heidelberg, Berlin, Heidelberg, 2008.
Anagnostou, E. N., Anagnostou, M. N., Krajewski, W. F., Kruger, A., and Miriovsky, B. J.: High-Resolution Rainfall Estimation from X-Band Polarimetric Radar Measurements, J. Hydrometeorol., 5, 110–128, https://doi.org/10.1175/1525-7541(2004)005<0110:HREFXP>2.0.CO;2, 2004.
Atencia, A., Mediero, L., Llasat, M. C., and Garrote, L.: Effect of radar rainfall time resolution on the predictive capability of a distributed hydrologic model, Hydrol. Earth Syst. Sci., 15, 3809–3827, https://doi.org/10.5194/hess-15-3809-2011, 2011.
Austin, G. L. and Austin, L. B.: The use of radar in urban hydrology, J. Hydrol., 22, 131–142, https://doi.org/10.1016/0022-1694(74)90100-0, 1974.
Austin, G. L. and Bellon, A.: The use of digital weather radar records for short-term precipitation forecasting, Q. J. Roy. Meteor. Soc., 100, 658–664, https://doi.org/10.1002/qj.49710042612, 1974.
Battan, L. J.: Radar observation of the atmosphere, University of Chicago Press, 1973.
Bell, V. A. and Moore, R. J.: A grid-based distributed flood forecasting model for use with weather radar data: Part 1. Formulation, Hydrol. Earth Syst. Sci., 2, 265–281, https://doi.org/10.5194/hess-2-265-1998, 1998.
Berenguer, M., Corral, C., Sanchez-Diezma, R., and Sempere-Torres, D.: Hydrological validation of a radar-based nowcasting technique, J. Hydrometeorol., 6, 532–549, https://doi.org/10.1175/JHM433.1, 2005.
Berenguer, M., Sempere-Torres, D., and Pegram, G. G. S.: SBMcast – An ensemble nowcasting technique to assess the uncertainty in rainfall forecasts by Lagrangian extrapolation, J. Hydrol., 404, 226–240, https://doi.org/10.1016/j.jhydrol.2011.04.033, 2011.
Berg, P., Norin, L., and Olsson, J.: Creation of a high resolution precipitation data set by merging gridded gauge data and radar observations for Sweden, J. Hydrol., 541, 6–13, https://doi.org/10.1016/j.jhydrol.2015.11.031, 2015.
Berndt, C., Rabiei, E., and Haberlandt, U.: Geostatistical merging of rain gauge and radar data for high temporal resolutions and various station density scenarios, J. Hydrol., 508, 88–101, https://doi.org/10.1016/j.jhydrol.2013.10.028, 2014.
Berne, A. and Krajewski, W. F.: Radar for hydrology: Unfulfilled promise or unrecognized potential?, Adv. Water Resour., 51, 357–366, https://doi.org/10.1016/j.advwatres.2012.05.005, 2013.
Berne, A., Delrieu, G., Creutin, J.-D., and Obled, C.: Temporal and spatial resolution of rainfall measurements required for urban hydrology, J. Hydrol., 299, 166–179, https://doi.org/10.1016/j.jhydrol.2004.08.002, 2004.
Borga, M., Anagnostou, E. N., and Frank, E.: On the use of real-time radar rainfall estimates for flood prediction in mountainous basins, J. Geophys. Res., 105, 2269–2280, https://doi.org/10.1029/1999JD900270, 2000.
Borga, M., Tonelli, F., Moore, R. J., and Andrieu, H.: Long-term assessment of bias adjustment in radar rainfall estimation, Water Resour. Res., 38, 1–10, https://doi.org/10.1029/2001WR000555, 2002.
Borup, M., Grum, M., Linde, J. J., and Mikkelsen, P. S.: Dynamic gauge adjustment of high-resolution X-band radar data for convective rain storms: Model-based evaluation against measured combined sewer overflow, J. Hydrol., 539, 687–699, https://doi.org/10.1016/j.jhydrol.2016.05.002, 2016.
Bowler, N. E., Pierce, C. E., and Seed, A. W.: STEPS: A probabilistic precipitation forecasting scheme which merges an extrapolation nowcast with downscaled NWP, Q. J. Roy. Meteor. Soc., 132, 2127–2155, https://doi.org/10.1256/qj.04.100, 2006.
Brauer, C. C., Overeem, A., Leijnse, H., and Uijlenhoet, R.: The effect of differences between rainfall measurement techniques on groundwater and discharge simulations in a lowland catchment, Hydrol. Process., 30, 3885–3900, https://doi.org/10.1002/hyp.10898, 2016.
Bringi, V. N. and Chandrasekar, V.: Polarimetric Doppler Weather Radar, Cambridge University Press, 2001.
Bringi, V. N., Rico-Ramirez, M. A., and Thurai, M.: Rainfall Estimation with an Operational Polarimetric C-Band Radar in the United Kingdom: Comparison with a Gauge Network and Error Analysis, J. Hydrometeorol., 12, 935–954, https://doi.org/10.1175/JHM-D-10-05013.1, 2011.
Bruni, G., Reinoso, R., van de Giesen, N. C., Clemens, F. H. L. R., and ten Veldhuis, J. A. E.: On the sensitivity of urban hydrodynamic modelling to rainfall spatial and temporal resolution, Hydrol. Earth Syst. Sci., 19, 691–709, https://doi.org/10.5194/hess-19-691-2015, 2015.
Chumchean, S., Seed, A., and Sharma, A.: Correcting of real-time radar rainfall bias using a Kalman filtering approach, J. Hydrol., 317, 123–137, https://doi.org/10.1016/j.jhydrol.2005.05.013, 2006.
Ciach, G. J.: Local random errors in tipping-bucket rain gauge measurements, J. Atmos. Ocean. Tech., 20, 752–759, https://doi.org/10.1175/1520-0426(2003)20<752:LREITB>2.0.CO;2, 2003.
Ciach, G. J., Morrissey, M.-L., and Krajewski, W. F.: Conditional bias in radar rainfall estimation, J. Appl. Meteorol., 39, 1941–1946, https://doi.org/10.1175/1520-0450(2000)039<1941:CBIRRE>2.0.CO;2, 2000.
Ciach, G. J., Krajewski, W. F., and Villarini, G.: Product-Error-Driven Uncertainty Model for Probabilistic Quantitative Precipitation Estimation with NEXRAD Data, J. Hydrometeorol., 8, 1325–1347, https://doi.org/10.1175/2007JHM814.1, 2007.
Collier, C. G.: Applications of Weather Radar Systems: A Guide to Uses of Radar Data in Meteorology and Hydrology, 2nd ed., Wiley, Chichester, England, 1996.
Delrieu, G., Braud, I., Berne, A., Borga, M., Boudevillain, B., Fabry, F., Freer, J., Gaume, E., Nakakita, E., Seed, A., Tabary, P., and Uijlenhoet, R.: Weather radar and hydrology, Adv. Water Resour., 32, 969–974, https://doi.org/10.1016/j.advwatres.2009.03.006, 2009.
Dirckx, G.: EPIGONE: the argus on the daily operation of throttle structures, Water Practice & Technology, 8, 382–389, https://doi.org/10.2166/wpt.2013.038, 2013.
Dixon, M., Li, Z., Lean, H., Roberts, N., and Balland, S.: Impact of data assimilation on forecasting convection over the United Kingdom using a high-resolution version of the met office unified model, Mon. Weather Rev., 137, 1562–1584, https://doi.org/10.1175/2008MWR2561.1, 2009.
Dolan, B. and Rutledge, S. A.: Using CASA IP1 to Diagnose Kinematic and Microphysical Interactions in a Convective Storm, Mon. Weather Rev., 138, 1613–1634, https://doi.org/10.1175/2009MWR3016.1, 2010.
Dotto, C. B. S., Mannina, G., Kleidorfer, M., Vezzaro, L., Henrichs, M., McCarthy, D. T., Freni, G., Rauch, W., and Deletic, A.: Comparison of different uncertainty techniques in urban stormwater quantity and quality modelling, Water Res., 46, 2545–2558, https://doi.org/10.1016/j.watres.2012.02.009, 2012.
Doviak, R. J. and Zrnić, D. S.: Doppler Radar and Weather Observations, Academic San Diego Calif, 33, 562, 1993.
Duncan, A. P., Chen, A. S., Keedwell, E. C., Djordjević, S., and Savić, D. A.: RAPIDS: Early warning system for urban flooding and water quality hazards, in: Machine Learning in Water Systems – AISB Convention 2013, 25–29, 2013.
Einfalt, T. and Luers, S.: Flash Flood warning for emergency warning, in: UrbanRain15 – 10th International Workshop on Precipitation in Urban Areas “Rainfall in Urban and Natural Systems” Pontresina, Switzerland, 1–5 December, edited by: Molnar, P. and Peleg, N., ETH-Zürich, Institute of Environmental Engineering, 2015.
Einfalt, T., Denoeux, T., and Jacquet, G.: A radar rainfall forecasting method designed for hydrological purposes, J. Hydrol., 114, 229–244, https://doi.org/10.1016/0022-1694(90)90058-6, 1990.
Einfalt, T., Arnbjerg-Nielsen, K., Golz, C., Jensen, N.-E., Quirmbach, M., Vaes, G., and Vieux, B.: Towards a roadmap for use of radar rainfall data in urban drainage, J. Hydrol., 299, 186–202, https://doi.org/10.1016/j.jhydrol.2004.08.004, 2004.
Einfalt, T., Hatzfeld, F., Wagner, A., Seltmann, J., Castro, D., and Frerichs, S.: URBAS: forecasting and management of flash floods in urban areas, Urban Water J., 6, 369–374, https://doi.org/10.1080/15730620902934819, 2009.
Emmanuel, I., Andrieu, H., and Tabary, P.: Evaluation of the new French operational weather radar product for the field of urban hydrology, Atmos. Res., 103, 20–32, 2012a.
Emmanuel, I., Andrieu, H., Leblois, E., and Flahaut, B.: Temporal and spatial variability of rainfall at the urban hydrological scale, J. Hydrol., 430–431, 162–172, https://doi.org/10.1016/j.jhydrol.2012.02.013, 2012b.
Fabry, F., Bellon, A., Duncan, M. R., and Austin, G. L.: High resolution rainfall measurements by radar for very small basins: the sampling problem reexamined, J. Hydrol., 161, 415–428, https://doi.org/10.1016/0022-1694(94)90138-4, 1994.
Fang, Z., Bedient, P. B., Benavides, J., and Zimmer, A. L.: Enhanced Radar-Based Flood Alert System and Floodplain Map Library, J. Hydrol. Eng., 13, 926–938, https://doi.org/10.1061/(ASCE)1084-0699(2008)13:10(926), 2008.
Faure, D. and Auchet, P.: Real time weather radar data processing for urban hydrology in Nancy, Phys. Chem. Earth Pt. B, 24, 909–914, https://doi.org/10.1016/S1464-1909(99)00102-1, 1999.
Foresti, L., Reyniers, M., Seed, A., and Delobbe, L.: Development and verification of a real-time stochastic precipitation nowcasting system for urban hydrology in Belgium, Hydrol. Earth Syst. Sci., 20, 505–527, https://doi.org/10.5194/hess-20-505-2016, 2016.
Freni, G., Mannina, G., and Viviani, G.: Uncertainty in urban stormwater quality modelling: The effect of acceptability threshold in the GLUE methodology, Water Res., 42, 2061–2072, https://doi.org/10.1016/j.watres.2007.12.014, 2008.
Fuchs, L. and Beeneken, T.: Development and implementation of a real time control strategy for the sewer system of the city of Vienna, Water Sci. Technol., 52, 187–194, 2005.
Germann, U. and Joss, J.: Variograms of Radar Reflectivity to Describe the Spatial Continuity of Alpine Precipitation, J. Appl. Meteorol., 40, 1042–1059, https://doi.org/10.1175/1520-0450(2001)040<1042:VORRTD>2.0.CO, 2001.
Germann, U., Galli, G., Boscacci, M., and Bolliger, M.: Radar precipitation measurement in a mountainous region, Q. J. Roy. Meteor. Soc., 132, 1669–1692, https://doi.org/10.1256/qj.05.190, 2006.
Germann, U., Berenguer, M., Sempere-Torres, D., and Zappa, M.: REAL – Ensemble radar precipitation estimation for hydrology in a mountainous region, Q. J. Roy. Meteor. Soc., 135, 445–456, https://doi.org/10.1002/qj.375, 2009.
Gires, A., Onof, C., Maksimovic, C., Schertzer, D., Tchiguirinskaia, I., and Simoes, N.: Quantifying the impact of small scale unmeasured rainfall variability on urban runoff through multifractal downscaling: A case study, J. Hydrol., 442–443, 117–128, https://doi.org/10.1016/j.jhydrol.2012.04.005, 2012.
Gires, A., Tchiguirinskaia, I., Schertzer, D., and Lovejoy, S.: Multifractal analysis of a semi-distributed urban hydrological model, Urban Water J., 10, 195–208, https://doi.org/10.1080/1573062X.2012.716447, 2013.
Gires, A., Giangola-Murzyn, A., Abbes, J.-B., Tchiguirinskaia, I., Schertzer, D., and Lovejoy, S.: Impacts of small scale rainfall variability in urban areas: a case study with 1D and 1D/2D hydrological models in a multifractal framework, Urban Water J., 12, 607–617, https://doi.org/10.1080/1573062X.2014.923917, 2014a.
Gires, A., Tchiguirinskaia, I., Schertzer, D., Schellart, A., Berne, A., and Lovejoy, S.: Influence of small scale rainfall variability on standard comparison tools between radar and rain gauge data, Atmos. Res., 138, 125–138, https://doi.org/10.1016/j.atmosres.2013.11.008, 2014b.
Gjertsen, U., Sálek, M., and Michelson, D. B.: Gauge adjustment of radar-based precipitation estimates in Europe, Proceedings of ERAD Copernicus GmbH, 7–11, 2004.
Goormans, T. and Willems, P.: Using Local Weather Radar Data for Sewer System Modeling: Case Study in Flanders, Belgium, J. Hydrol. Eng., 18, 269–278, https://doi.org/10.1061/(ASCE)HE.1943-5584.0000589, 2013.
Goudenhoofdt, E. and Delobbe, L.: Evaluation of radar-gauge merging methods for quantitative precipitation estimates, Hydrol. Earth Syst. Sci., 13, 195–203, https://doi.org/10.5194/hess-13-195-2009, 2009.
Goudenhoofdt, E. and Delobbe, L.: Generation and Verification of Rainfall Estimates from 10-Yr Volumetric Weather Radar Measurements, J. Hydrometeorol., 17, 1223–1242, https://doi.org/10.1175/JHM-D-15-0166.1, 2016.
Gregersen, I. B., Madsen, H., Rosbjerg, D., and Arnbjerg-Nielsen, K.: Long term variations of extreme rainfall in Denmark and southern Sweden, Clim. Dynam., 44, 3155–3169, https://doi.org/10.1007/s00382-014-2276-4, 2014.
Haberlandt, U.: Geostatistical interpolation of hourly precipitation from rain gauges and radar for a large-scale extreme rainfall event, J. Hydrol., 332, 144–157, https://doi.org/10.1016/j.jhydrol.2006.06.028, 2007.
Haylock, M. R., Hofstra, N., Klein Tank, A. M. G., Klok, E. J., Jones, P. D., and New, M.: A European daily high-resolution gridded data set of surface temperature and precipitation for 1950–2006, J. Geophys. Res.-Atmos., 113, 1–12, https://doi.org/10.1029/2008JD010201, 2008.
He, X., Vejen, F., Stisen, S., Sonnenborg, T. O., and Jensen., K. H.: An Operational Weather Radar–Based Quantitative Precipitation Estimation and its Application in Catchment Water Resources Modeling, Vadose Zone J., 10, 8, https://doi.org/10.2136/vzj2010.0034, 2011.
Henonin, J., Russo, B., Mark, O., and Gourbesville, P.: Real-time urban flood forecasting and modelling – a state of the art, J. Hydroinform., 15, 717, https://doi.org/10.2166/hydro.2013.132, 2013.
Hossain, F., Anagnostou, E. N., Dinku, T., and Borga, M.: Hydrological model sensitivity to parameter and radar rainfall estimation uncertainty, Hydrol. Process., 18, 3277–3291, https://doi.org/10.1002/hyp.5659, 2004.
Illingworth, A.: Improved Precipitation Rates and Data Quality by Using Polarimetric Measurements, in: Weather radar, edited by: Meischner, P., 130–166, Springer, Berlin, Heidelberg, 2004.
ISO: Meteorology – Ground-based remote sensing of precipitation Weather radar, ISO 19926, 2017.
Jasper-Tönnies, A. and Jessen, M.: Improved radar QPE with temporal interpolation using an advection scheme, in: ERAD 2014 – The eighth European conference on radar in meteorology and hydrology, Garmisch, 1–5 September 2014.
Javier, J. R. N., Smith, J. A., Meierdiercks, K. L., Baeck, M. L., and Miller, A. J.: Flash Flood Forecasting for Small Urban Watersheds in the Baltimore Metropolitan Region, Weather Forecast., 22, 1331–1344, https://doi.org/10.1175/2007WAF2006036.1, 2007.
Jensen, D. G., Petersen, C., and Rasmussen, M. R.: Assimilation of radar-based nowcast into a HIRLAM NWP model, Meteorol. Appl., 494, 485–494, https://doi.org/10.1002/met.1479, 2015.
Jensen, D. G., Nielsen, J. E., Thorndahl, S., and Rasmussen, M. R.: Ensemble prediction system based on Lagrangian extrapolation of radar derived precipitation (RESEMBLE), in preparation, 2017.
Jessen, M., Einfalt, T., Stoffer, A., and Mehlig, B.: Analysis of heavy rainfall events in North Rhine–Westphalia with radar and raingauge data, Atmos. Res., 77, 337–346, https://doi.org/10.1016/j.atmosres.2004.11.031, 2005.
Johnson, D., Smith, M., Koren, V., and Finnerty, B.: Comparing Mean Areal Precipitation Estimates from NEXRAD and Rain Gauge Networks, J. Hydrol. Eng., 4, 117–124, https://doi.org/10.1061/(ASCE)1084-0699(1999)4:2(117), 1999.
Johnson, J. T., MacKeen, P. L., Witt, A., Mitchell, E. D. W., Stumpf, G. J., Eilts, M. D., and Thomas, K. W.: The Storm Cell Identification and Tracking Algorithm: An Enhanced WSR-88D Algorithm, Weather Forecast., 13, 263–276, https://doi.org/10.1175/1520-0434(1998)013<0263:TSCIAT>2.0.CO;2, 1998.
Kendon, E., Roberts, N., and Fowler, H.: Heavier summer downpours with climate change revealed by weather forecast resolution model, Nature Climate Change, 4, 1–7, https://doi.org/10.1038/NCLIMATE2258, 2014.
Korsholm, U. S., Petersen, C., Sass, B. H., Nielsen, N. W., Jensen, D. G., Olsen, B. T., Gill, R., and Vedel, H.: A new approach for assimilation of 2D radar precipitation in a high-resolution NWP model, Meteorol. Appl., 22, 48–59, https://doi.org/10.1002/met.1466, 2015.
Krajewski, W. F.: Cokriging radar-rainfall and rain gage data, J. Geophys. Res., 92, 9571, https://doi.org/10.1029/JD092iD08p09571, 1987.
Krajewski, W. F. and Smith, J. A.: Radar hydrology: Rainfall estimation, Adv. Water Resour., 25, 1387–1394, https://doi.org/10.1016/S0309-1708(02)00062-3, 2002.
Krajewski, W. F., Villarini, G., and Smith, J. A.: Radar-Rainfall Uncertainties: Where are We after Thirty Years of Effort?, B. Am. Meteorol. Soc., 91, 87–94, https://doi.org/10.1175/2009BAMS2747.1, 2010.
Krämer, S., Grum, M., Verworn, H. R., and Redder, A.: Runoff modelling using radar data and flow measurements in a stochastic state space approach, Water Sci. Technol., 52, 1–8, 2005.
Kroll, S., Dirckx, G., Donckels, B. M. R., Van Dorpe, M., Weemaes, M., and Willems, P.: Modelling real-time control of WWTP influent flow under data scarcity, Water Sci. Technol., 73, 1637–1643, https://doi.org/10.2166/wst.2015.641, 2016.
Kuichling, E.: The relation between the rainfall and the discharge of sewers in populous districts, T. Am. Soc. Civ. Eng., 20, 1–56, 1889.
Leijnse, H., Uijlenhoet, R., van de Beek, C. Z., Overeem, A., Otto, T., Unal, C. M. H., Dufournet, Y., Russchenberg, H. W. J., Figueras i Ventura, J., Klein Baltink, H., and Holleman, I.: Precipitation Measurement at CESAR, the Netherlands, J. Hydrometeorol., 11, 1322–1329, https://doi.org/10.1175/2010JHM1245.1, 2010.
Lenderink, G.: Exploring metrics of extreme daily precipitation in a large ensemble of regional climate model simulations, Clim. Res., 44, 151–166, https://doi.org/10.3354/cr00946, 2010.
Lengfeld, K., Clemens, M., Münster, H., and Ament, F.: Performance of high-resolution X-band weather radar networks – the PATTERN example, Atmos. Meas. Tech., 7, 4151–4166, https://doi.org/10.5194/amt-7-4151-2014, 2014.
Leonhardt, G., Sun, S., Rauch, W., and Bertrand-Krajewski, J.-L.: Comparison of two model based approaches for areal rainfall estimation in urban hydrology, J. Hydrol., 511, 880–890, https://doi.org/10.1016/j.jhydrol.2014.02.048, 2014.
Li, L., Schmid, W., and Joss, J.: Nowcasting of Motion and Growth of Precipitation with Radar over a Complex Orography, J. Appl. Meteorol., 34, 1286–1300, https://doi.org/0.1175/1520-0450(1995)034<1286:NOMAGO>2.0.CO;2, 1995.
Liguori, S. and Rico-Ramirez, M. A.: Quantitative assessment of short-term rainfall forecasts from radar nowcasts and MM5 forecasts, Hydrol. Process., 26, 3842–3857, https://doi.org/10.1002/hyp.8415, 2012.
Liguori, S. and Rico-Ramirez, M. A.: A review of current approaches to radar-based quantitative precipitation forecasts, International Journal of River Basin Management, 12, 391–402, https://doi.org/10.1080/15715124.2013.848872, 2013.
Liguori, S., Rico-Ramirez, M. A., Schellart, A. N. A., and Saul, A. J.: Using probabilistic radar rainfall nowcasts and NWP forecasts for flow prediction in urban catchments, Atmos. Res., 103, 80–95, https://doi.org/10.1016/j.atmosres.2011.05.004, 2012.
Lobligeois, F., Andréassian, V., Perrin, C., Tabary, P., and Loumagne, C.: When does higher spatial resolution rainfall information improve streamflow simulation? An evaluation using 3620 flood events, Hydrol. Earth Syst. Sci., 18, 575–594, https://doi.org/10.5194/hess-18-575-2014, 2014.
Löwe, R., Thorndahl, S., Mikkelsen, P. S., Rasmussen, M. R., and Madsen, H.: Probabilistic online runoff forecasting for urban catchments using inputs from rain gauges as well as statically and dynamically adjusted weather radar, J. Hydrol., 512, 397–407, https://doi.org/10.1016/j.jhydrol.2014.03.027, 2014.
Löwe, R., Vezzaro, L., Mikkelsen, P. S., Grum, M., and Madsen, H.: Probabilistic runoff volume forecasting in risk-based optimization for RTC of urban drainage systems, Environ. Modell. Softw., 80, 143–158, https://doi.org/10.1016/j.envsoft.2016.02.027, 2016.
Madsen, H., Arnbjerg-Nielsen, K., and Mikkelsen, P. S.: Update of regional intensity-duration-frequency curves in Denmark: Tendency towards increased storm intensities, Atmos. Res., 92, 343–349, https://doi.org/10.1016/j.atmosres.2009.01.013, 2009.
Marra, F. and Morin, E.: Use of radar QPE for the derivation of Intensity-Duration-Frequency curves in a range of climatic regimes, J. Hydrol., 531, 427–440, https://doi.org/10.1016/j.jhydrol.2015.08.064, 2015.
Marshall, J. S. and Palmer, W. M.: The distribution of raindrops with size, J. Meteor., 5, 165–166, 1945.
McKee, J. L. and Binns, A. D.: A review of gauge–radar merging methods for quantitative precipitation estimation in hydrology, Can. Water Resour. J., 41, 186–203, https://doi.org/10.1080/07011784.2015.1064786, 2016.
Mecklenburg, S., Joss, J., and Schmid, W.: Improving the nowcasting of precipitation in an Alpine region with an enhanced radar echo tracking algorithm, J. Hydrol., 239, 46–68, https://doi.org/10.1016/S0022-1694(00)00352-8, 2000.
Meischner, P.: Weather Radar Principles and Advanced Applications, Springer-Verlag Berlin Heidelberg, 2004.
Michaelides, S.: Precipitation: Advances in measurement, estimation and prediction, Springer Berlin Heidelberg, Berlin, Heidelberg, 2008.
Michelson, D., Einfalt, T., Holleman, I., Gjertsen, U., Friedrich, K., Haase, G., Lindskog, M., and Jurczyk, A.: Weather radar data quality in Europe – quality control and characterization, Review, COST Action 717, Luxembourg, ISBN-10: 92-898-0018-6, 2005.
Mishra, K. V., Krajewski, W. F., Goska, R., Ceynar, D., Seo, B.-C., Kruger, A., Niemeier, J. J., Galvez, M. B., Thurai, M., Bringi, V. N., Tolstoy, L., Kucera, P. A., Petersen, W. A., Grazioli, J., and Pazmany, A. L.: Deployment and Performance Analyses of High-Resolution Iowa XPOL Radar System during the NASA IFloodS Campaign, J. Hydrometeorol., 17, 455–479, https://doi.org/10.1175/JHM-D-15-0029.1, 2016.
Mounce, S. R., Shepherd, W., Sailor, G., Shucksmith, J., and Saul, A. J.: Predicting combined sewer overflows chamber depth using artificial neural networks with rainfall radar data, Water Sci. Technol., 69, 1326–1333, https://doi.org/10.2166/wst.2014.024, 2014.
Muñoz, C., Wang, L.-P., and Willems, P.: A stochastic spatial-temporal rainfall generator for urban hydrological applications, in: UrbanRain15 – 10th International Workshop on Precipitation in Urban Areas “Rainfall in Urban and Natural Systems” Pontresina, Switzerland, 1–5 December, edited by: Molnar, P. and Peleg, N., ETH-Zürich, Institute of Environmental Engineering, 2015.
Nielsen, J. E., Rasmussen, M. R., and Thorndahl, S.: What is a proper resolution of weather radar precipitation estimates for urban drainage modelling, IAHS-AISH Publication, 351, 601–606, 2012.
Nielsen, J. E., Jensen, N. E., and Rasmussen, M. R.: Calibrating LAWR weather radar using laser disdrometers, Atmos. Res., 122, 165–173, https://doi.org/10.1016/j.atmosres.2012.10.017, 2013.
Nielsen, J. E., Thorndahl, S., and Rasmussen, M. R.: A numerical method to generate high temporal resolution precipitation time series by combining weather radar measurements with a nowcast model, Atmos. Res., 138, 1–12, https://doi.org/10.1016/j.atmosres.2013.10.015, 2014a.
Nielsen, J. E., Beven, K., Thorndahl, S., and Rasmussen, M. R.: GLUE based marine X-band weather radar data calibration and uncertainty estimation, Urban Water J., 12, 283–294, https://doi.org/10.1080/1573062X.2013.871044, 2014b.
Nielsen, J. E., Thorndahl, S., and Rasmussen, M. R.: Improving weather radar precipitation estimates by combining two types of radars, Atmos. Res., 139, 36–45, https://doi.org/10.1016/j.atmosres.2013.12.013, 2014c.
Nielsen, J. E., Thorndahl, S., and Rasmussen, M. R.: Intercomparison of rainfall measurements from three different types of weather radars covering the same urban area, Proceedings of the 10th International Workshop on Precipitation in Urban Areas (UrbanRain15), 143–144, ETH-Zürich, Institute of Environmental Engineering, https://doi.org/10.3929/ethz-a-010549004, 2015.
Ntegeka, V. and Willems, P.: Trends and multidecadal oscillations in rainfall extremes, based on a more than 100-year time series of 10 min rainfall intensities at Uccle, Belgium, Water Resour. Res., 44, 1–15, https://doi.org/10.1029/2007WR006471, 2008.
Ntegeka, V., Murla Tuyls, D., Wang, L.-P., Foresti, L., Reyniers, M., Delobbe, L., Van Herck, K., Van Ootegem, L., and Willems, P.: Probabilistic urban inundation nowcasting, in: UrbanRain15 – 10th International Workshop on Precipitation in Urban Areas “Rainfall in Urban and Natural Systems” Pontresina, Switzerland, 1–5 December, edited by: Molnar, P. and Peleg, N., ETH-Zürich, Institute of Environmental Engineering, 2015.
Ochoa-Rodriguez, S., Wang, L. P., Gires, A., Pina, R. D., Reinoso-Rondinel, R., Bruni, G., Ichiba, A., Gaitan, S., Cristiano, E., van Assel, J., Kroll, S., Murlà-Tuyls, D., Tisserand, B., Schertzer, D., Tchiguirinskaia, I., Onof, C., Willems, P., and ten Veldhuis, M. C.: Impact of spatial and temporal resolution of rainfall inputs on urban hydrodynamic modelling outputs: A multi-catchment investigation, J. Hydrol., 531, 389–407, https://doi.org/10.1016/j.jhydrol.2015.05.035, 2015.
Ochoa Rodriguez, S., Sandford, C., Norman, K., Wang, L., Jewell, S., Akerboom, M., and Onof, C.: Evaluation of the Met Office super-resolution C-band radar rainfall product over London, in: AMS 37th Conference on Radar Meteorology, 14–18 September 2015, Norman, OK, 2015.
Otto, T. and Russchenberg, H. W. J.: Estimation of specific differential phase and differential backscatter phase from polarimetric weather radar measurements of rain, IEEE Geosci. Remote S., 8, 988–992, https://doi.org/10.1109/LGRS.2011.2145354, 2011.
Overeem, A., Buishand, T. A., and Holleman, I.: Derivation of a 10-Year Radar-Based Climatology of Rainfall, J. Appl. Meteorol. Climatol., 48, 1448–1463, https://doi.org/10.1175/2009JAMC1954.1, 2009a.
Overeem, A., Buishand, T. A., and Holleman, I.: Extreme rainfall analysis and estimation of depth-duration-frequency curves using weather radar, Water Resour. Res., 45, W10424, https://doi.org/10.1029/2009WR007869, 2009b.
Overeem, A., Buishand, T. A., Holleman, I., and Uijlenhoet, R.: Extreme value modeling of areal rainfall from weather radar, Water Resour. Res., 46, 1–10, https://doi.org/10.1029/2009WR008517, 2010.
Paixao, E., Monirul Qader Mirza, M., Shephard, M. W., Auld, H., Klaassen, J., and Smith, G.: An integrated approach for identifying homogeneous regions of extreme rainfall events and estimating IDF curves in Southern Ontario, Canada: Incorporating radar observations, J. Hydrol., 528, 734–750, https://doi.org/10.1016/j.jhydrol.2015.06.015, 2015.
Pedersen, L., Jensen, N. E., and Madsen, H.: Calibration of Local Area Weather Radar – Identifying significant factors affecting the calibration, Atmos. Res., 97, 129–143, https://doi.org/10.1016/j.atmosres.2010.03.016, 2010a.
Pedersen, L., Jensen, N. E., Christensen, L. E., and Madsen, H.: Quantification of the spatial variability of rainfall based on a dense network of rain gauges, Atmos. Res., 95, 441–454, https://doi.org/10.1016/j.atmosres.2009.11.007, 2010b.
Pegram, G., Llort, X., and Sempere-Torres, D.: Radar rainfall: Separating signal and noise fields to generate meaningful ensembles, Atmos. Res., 100, 226–236, https://doi.org/10.1016/j.atmosres.2010.11.018, 2011.
Peleg, N., Blumensaat, F., Molnar, P., Fatichi, S., and Burlando, P.: Partitioning spatial and temporal rainfall variability in urban drainage modelling, Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2016-530, in review, 2016a.
Peleg, N., Marra, F., Fatichi, S., Paschalis, A., Molnar, P., and Burlando, P.: Spatial variability of extreme rainfall at radar subpixel scale, J. Hydrol., https://doi.org/10.1016/j.jhydrol.2016.05.033, in press, 2016b.
Pfister, A. and Cassar, A.: Use and benefit of radar rainfall data in an urban real time control project, Phys. Chem. Earth Pt. B, 24, 903–908, https://doi.org/10.1016/S1464-1909(99)00101-X, 1999.
Quirmbach, M. and Schultz, G. A.: Use of weather radar for combined control of an urban drainage system and a sewage treatment plant, Urban Water, 259, 245–250, 1999.
Quirmbach, M. and Schultz, G. A.: Comparison of rain gauge and radar data as input to an urban rainfall-runoff model, Water Sci. Technol., 45, 27–33, 2002.
Rabiei, E. and Haberlandt, U.: Applying bias correction for merging rain gauge and radar data, J. Hydrol., 522, 544–557, https://doi.org/10.1016/j.jhydrol.2015.01.020, 2015.
Rafieeinasab, A., Norouzi, A., Kim, S., Habibi, H., Nazari, B., Seo, D. J., Lee, H., Cosgrove, B., and Cui, Z.: Toward high-resolution flash flood prediction in large urban areas – Analysis of sensitivity to spatiotemporal resolution of rainfall input and hydrologic modeling, J. Hydrol., 531, 370–388, https://doi.org/10.1016/j.jhydrol.2015.08.045, 2015.
Raut, B. A., De La Fuente, L., Seed, A. W., Jakob, C., and Reeder, M. J.: Application of a space-time stochastic model for downscaling future rainfall projections, in: Proceedings of the 34th Hydrology and Water Resources Symposium, HWRS, 19–22 November 2012, 579–586, 2012.
Rico-Ramirez, M. A., Liguori, S., and Schellart, A. N. A.: Quantifying radar-rainfall uncertainties in urban drainage flow modelling, J. Hydrol., 528, 17–28, https://doi.org/10.1016/j.jhydrol.2015.05.057, 2015.
Rinehart, R. E.: Radar for Meteorologists, 5th ed., Rinehart Publications, 2010.
Rinehart, R. E. and Garvey, E. T.: Three-dimensional storm motion detection by conventional weather radar, Nature, 273, 287–289, https://doi.org/10.1038/273287a0, 1978.
Scarchilli, G., Goroucci, E., Chandrasekar, V., and Seliga, T. A.: Rainfall Estimation Using Polarimetric Techniques at C-Band Frequencies, J. Appl. Meteorol., 32, 1150–1160, https://doi.org/10.1175/1520-0450(1993)032<1150:REUPTA>2.0.CO;2, 1993.
Schellart, A., Liguori, S., Krämer, S., Saul, A., and Rico-Ramirez, M.: Analysis of different quantitative precipitation forecast methods for runoff and flow prediction in a small urban area, IAHS-AISH Publication, 351, 614–619, 2012a.
Schellart, A. N. A., Shepherd, W. J., and Saul, A. J.: Influence of rainfall estimation error and spatial variability on sewer flow prediction at a small urban scale, Adv. Water Resour., 45, 65–75, https://doi.org/10.1016/j.advwatres.2011.10.012, 2012b.
Schellart, A., Liguori, S., Krämer, S., Saul, A., and Rico-Ramirez, M.: Comparing quantitative precipitation forecast methods for prediction of sewer flows in a small urban area, Hydrolog. Sci. J., 59, 1418–1436, https://doi.org/10.1080/02626667.2014.920505, 2014.
Schilling, W.: Rainfall data for urban hydrology: what do we need?, Atmos. Res., 27, 5–21, https://doi.org/10.1016/0169-8095(91)90003-F, 1991.
Schütze, M., Campisano, A., Colas, H., Schilling, W., and Vanrolleghem, P. A.: Real time control of urban wastewater systems – Where do we stand today?, J. Hydrol., 299, 335–348, https://doi.org/10.1016/j.jhydrol.2004.08.010, 2004.
Seo, D.-J. and Breidenbach, J. P.: Real-Time Correction of Spatially Nonuniform Bias in Radar Rainfall Data Using Rain Gauge Measurements, J. Hydrometeorol., 3, 93–111, https://doi.org/10.1175/1525-7541(2002)003<0093:RTCOSN>2.0.CO;2, 2002.
Seo, B.-C. and Krajewski, W. F.: Scale Dependence of Radar Rainfall Uncertainty: Initial Evaluation of NEXRAD's New Super-Resolution Data for Hydrologic Applications, J. Hydrometeorol., 11, 1191–1198, https://doi.org/10.1175/2010JHM1265.1, 2010.
Seo, B. C. and Krajewski, W. F.: Correcting temporal sampling error in radar-rainfall: Effect of advection parameters and rain storm characteristics on the correction accuracy, J. Hydrol., 531, 272–283, https://doi.org/10.1016/j.jhydrol.2015.04.018, 2015.
Seo, D.-J., Breidenbach, J., and Johnson, E.: Real-time estimation of mean field bias in radar rainfall data, J. Hydrol., 223, 131–147, https://doi.org/10.1016/S0022-1694(99)00106-7, 1999.
Sharif, H. O. and Ogden, F. L.: Mass-Conserving Remapping of Radar Data onto Two-Dimensional Cartesian Coordinates for Hydrologic Applications, J. Hydrometeorol., 15, 2190–2202, https://doi.org/10.1175/JHM-D-14-0058.1, 2014.
Sharif, H. O., Yates, D., Roberts, R., and Mueller, C.: The Use of an Automated Nowcasting System to Forecast Flash Floods in an Urban Watershed, J. Hydrometeorol., 7, 190–202, https://doi.org/10.1175/JHM482.1, 2006.
Sideris, I. V., Gabella, M., Erdin, R., and Germann, U.: Real-time radar-rain-gauge merging using spatio-temporal co-kriging with external drift in the alpine terrain of Switzerland, Q. J. Roy. Meteor. Soc., 140, 1097–1111, https://doi.org/10.1002/qj.2188, 2014.
Sinclair, S. and Pegram, G.: Combining radar and rain gauge rainfall estimates using conditional merging, Atmos. Sci. Lett., 6, 19–22, https://doi.org/10.1002/asl.85, 2005.
Sivapalan, M. and Blöschl, G.: Transformation of point rainfall to areal rainfall: Intensity-duration-frequency curves, J. Hydrol., 204, 150–167, https://doi.org/10.1016/S0022-1694(97)00117-0, 1998.
Smith, B. K., Smith, J. A., Baeck, M. L., Villarini, G., and Wright, D. B.: Spectrum of storm event hydrologic response in urban watersheds, Water Resour. Res., 49, 2649–2663, https://doi.org/10.1002/wrcr.20223, 2013.
Smith, J. A. and Krajewski, W. F.: Estimation of the mean field bias of radar rainfall estimates, J. Appl. Meteorol., 30, 397–412, 1991.
Smith, J. A., Baeck, M. L., Morrison, J. E., Sturdevant-Rees, P., Turner-Gillespie, D. F., and Bates, P. D.: The Regional Hydrology of Extreme Floods in an Urbanizing Drainage Basin, J. Hydrometeorol., 3, 267–282, https://doi.org/10.1175/1525-7541(2002)003<0267:TRHOEF>2.0.CO;2, 2002.
Smith, J. A., Baeck, M. L., Meierdiercks, K. L., Miller, A. J., and Krajewski, W. F.: Radar rainfall estimation for flash flood forecasting in small urban watersheds, Adv. Water Resour., 30, 2087–2097, https://doi.org/10.1016/j.advwatres.2006.09.007, 2007.
Sørup, H. J. D., Christensen, O. B., Arnbjerg-Nielsen, K., and Mikkelsen, P. S.: Downscaling future precipitation extremes to urban hydrology scales using a spatio-temporal Neyman-cott weather generator, Hydrol. Earth Syst. Sci., 20, 1387–1403, https://doi.org/10.5194/hess-20-1387-2016, 2016.
Stephan, K., Klink, S., and Schraff, C.: Assimilation of radar-derived rain rates into the convective-scale model COSMO-DE at DWD, Q. J. Roy. Meteor. Soc., 134, 1315–1326, https://doi.org/10.1002/qj.269, 2008.
Tabari, H., De Troch, R., Giot, O., Hamdi, R., Termonia, P., Saeed, S., Brisson, E., Van Lipzig, N., and Willems, P.: Local impact analysis of climate change on precipitation extremes: are high-resolution climate models needed for realistic simulations?, Hydrol. Earth Syst. Sci., 20, 3843–3857, https://doi.org/10.5194/hess-20-3843-2016, 2016.
Thorndahl, S. and Rasmussen, M. R.: Marine X-band weather radar data calibration, Atmos. Res., 103, 33–44, https://doi.org/10.1016/j.atmosres.2011.04.023, 2012.
Thorndahl, S. and Rasmussen, M. R.: Short-term forecasting of urban storm water runoff in real-time using extrapolated radar rainfall data, J. Hydroinform., 15, 897–912, 2013.
Thorndahl, S. and Willems, P.: Probabilistic modelling of overflow, surcharge and flooding in urban drainage using the first-order reliability method and parameterization of local rain series, Water Res., 42, 455–466, https://doi.org/10.1016/j.watres.2007.07.038, 2008.
Thorndahl, S., Johansen, C., and Schaarup-Jensen, K.: Assessment of runoff contributing catchment areas in rainfall runoff modelling, Water Sci. Technol., 54, 49–56, https://doi.org/10.2166/wst.2006.621, 2006.
Thorndahl, S., Beven, K. J., Jensen, J. B., and Schaarup-Jensen, K.: Event based uncertainty assessment in urban drainage modelling, applying the GLUE methodology, J. Hydrol., 357, 421–437, https://doi.org/10.1016/j.jhydrol.2008.05.027, 2008.
Thorndahl, S., Poulsen, T. S., Bøvith, T., Borup, M., Ahm, M., Nielsen, J. E., Grum, M., Rasmussen, M. R., Gill, R., and Mikkelsen, P. S.: Comparison of short-term rainfall forecasts for modelbased flow prediction in urban drainage systems, Water Sci. Technol., 68, 472–478, https://doi.org/10.2166/wst.2013.274, 2013.
Thorndahl, S., Smith, J. A., Baeck, M. L., and Krajewski, W. F.: Analyses of the temporal and spatial structures of heavy rainfall from a catalog of high-resolution radar rainfall fields, Atmos. Res., 144, 111–125, https://doi.org/10.1016/j.atmosres.2014.03.013, 2014a.
Thorndahl, S., Nielsen, J. E., and Rasmussen, M. R.: Bias adjustment and advection interpolation of long-term high resolution radar rainfall series, J. Hydrol., 508, 214–226, https://doi.org/10.1016/j.jhydrol.2013.10.056, 2014b.
Thorndahl, S., Nielsen, J. E., and Jensen, D. G.: Urban pluvial flood prediction: a case study evaluating radar rainfall nowcasts and numerical weather prediction models as model inputs, Water Sci. Technol., 74, 2599–2610, https://doi.org/10.2166/wst.2016.474, 2016.
Tilford, K. A., Fox, N. I., and Collier, C. G.: Application of weather radar data for urban hydrology, Meteorol. Appl., 9, 95–104, https://doi.org/10.1017/S135048270200110X, 2002.
Todini, E.: A Bayesian technique for conditioning radar precipitation estimates to rain-gauge measurements, Hydrol. Earth Syst. Sci., 5, 187–199, https://doi.org/10.5194/hess-5-187-2001, 2001.
Turner, B. J., Zawadzki, I., and Germann, U.: Predictability of precipitation from continental radar images. Part III: Operational nowcasting implementation (MAPLE), J. Appl. Meteorol., 43, 231–248, https://doi.org/10.1175/1520-0450(2004)043<0231:POPFCR>2.0.CO;2, 2004.
Uijlenhoet, R.: Raindrop size distributions and radar reflectivity-rain rate relationships for radar hydrology, Hydrol. Earth Syst. Sci., 5, 615–628, https://doi.org/10.5194/hess-5-615-2001, 2001.
Vaes, G., Willems, P., and Berlamont, J.: Areal rainfall correction coefficients for small urban catchments, Atmos. Res., 77, 48–59, https://doi.org/10.1016/j.atmosres.2004.10.015, 2005.
van de Beek, C. Z., Leijnse, H., Stricker, J. N. M., Uijlenhoet, R., and Russchenberg, H. W. J.: Performance of high-resolution X-band radar for rainfall measurement in The Netherlands, Hydrol. Earth Syst. Sci., 14, 205–221, https://doi.org/10.5194/hess-14-205-2010, 2010.
van de Beek, C. Z., Leijnse, H., Torfs, P. J. J. F., and Uijlenhoet, R.: Seasonal semi-variance of Dutch rainfall at hourly to daily scales, Adv. Water Resour., 45, 76–85, https://doi.org/10.1016/j.advwatres.2012.03.023, 2012.
Van Ootegem, L., Van Herck, K., Creten, T., Verhofstadt, E., Foresti, L., Goudenhoofdt, E., Reyniers, M., Delobbe, L., Murla Tuyls, D., and Willems, P.: Exploring the potential of multivariate depth-damage and rainfall-damage models, Journal of Flood Risk Management, https://doi.org/10.1111/jfr3.12284, in press, 2017.
VDI: Environmental meteorology – Ground-based remote sensing of precipitation – Weather radar, VDI 3786 Part 20, Beuth Verlag, Berlin, 2014.
Velasco-Forero, C. A., Sempere-Torres, D., Cassiraga, E. F., and Jaime Gómez-Hernández, J.: A non-parametric automatic blending methodology to estimate rainfall fields from rain gauge and radar data, Adv. Water Resour., 32, 986–1002, https://doi.org/10.1016/j.advwatres.2008.10.004, 2009.
Vezzaro, L. and Grum, M.: A generalised Dynamic Overflow Risk Assessment (DORA) for Real Time Control of urban drainage systems, J. Hydrol., 515, 292–303, https://doi.org/10.1016/j.jhydrol.2014.05.019, 2014.
Vieux, B. E. and Bedient, P. B.: Assessing urban hydrologic prediction accuracy through event reconstruction, J. Hydrol., 299, 217–236, https://doi.org/10.1016/j.jhydrol.2004.08.005, 2004a.
Vieux, B. E. and Bedient, P. B.: Evaluation of urban hydrologic prediction accuracy for real-time forecasting using radar-rainfall, in: Bulletin of the American Meteorological Society, Combined Preprints: 84th American Meteorological Society (AMS) Annual Meeting, Seattle, WA, USA, 11–15 January 2004, Code 62939, 587–592, 2004b.
Vieux, B. E. and Imgarten, J. M.: On the scale-dependent propagation of hydrologic uncertainty using high-resolution X-band radar rainfall estimates, Atmos. Res., 103, 96–105, https://doi.org/10.1016/j.atmosres.2011.06.009, 2012.
Vieux, B. E., Imgarten, J. M., Looper, J. P., and Bedient, P. B.: Radar-Based Flood Forecasting: Quantifying Hydrologic Prediction Uncertainty in Urban-Scale Catchments for CASA Radar Deployment, in World Environmental and Water Resources Congress 2008, 316, 1–10, American Society of Civil Engineers, Reston, VA, 2008.
Villarini, G. and Krajewski, W. F.: Review of the different sources of uncertainty in single polarization radar-based estimates of rainfall, Surv. Geophys., 31, 107–129, https://doi.org/10.1007/s10712-009-9079-x, 2010.
Villarini, G., Serinaldi, F., and Krajewski, W. F.: Modeling radar-rainfall estimation uncertainties using parametric and non-parametric approaches, Adv. Water Resour., 31, 1674–1686, https://doi.org/10.1016/j.advwatres.2008.08.002, 2008a.
Villarini, G., Mandapaka, P. V., Krajewski, W. F., and Moore, R. J.: Rainfall and sampling uncertainties: A rain gauge perspective, J. Geophys. Res.-Atmos., 113, 1–12, https://doi.org/10.1029/2007JD009214, 2008b.
Villarini, G., Smith, J. A., Lynn Baeck, M., Sturdevant-Rees, P., and Krajewski, W. F.: Radar analyses of extreme rainfall and flooding in urban drainage basins, J. Hydrol., 381, 266–286, https://doi.org/10.1016/j.jhydrol.2009.11.048, 2010.
Villarini, G., Seo, B. C., Serinaldi, F., and Krajewski, W. F.: Spatial and temporal modeling of radar rainfall uncertainties, Atmos. Res., 135–136, 91–101, https://doi.org/10.1016/j.atmosres.2013.09.007, 2014.
Wang, L.-P., Ochoa-Rodríguez, S., Simões, N. E., Onof, C., and Maksimović, C.: Radar-raingauge data combination techniques: a revision and analysis of their suitability for urban hydrology, Water Sci. Technol., 68, 737–747, https://doi.org/10.2166/wst.2013.300, 2013.
Wang, L.-P., Ochoa-Rodríguez, S., Van Assel, J., Pina, R. D., Pessemier, M., Kroll, S., Willems, P., and Onof, C.: Enhancement of radar rainfall estimates for urban hydrology through optical flow temporal interpolation and Bayesian gauge-based adjustment, J. Hydrol., 531, 408–426, https://doi.org/10.1016/j.jhydrol.2015.05.049, 2015a.
Wang, L.-P., Ochoa-Rodríguez, S., Onof, C., and Willems, P.: Singularity-sensitive gauge-based radar rainfall adjustment methods for urban hydrological applications, Hydrol. Earth Syst. Sci., 19, 4001–4021, https://doi.org/10.5194/hess-19-4001-2015, 2015b.
Wang, L. P., Simões, N., Rico-Ramirez, M., Ochoa, S., Leitão, J., and Maksimovič, Č.: Radar-based pluvial flood forecasting over urban areas: Redbridge case study, IAHS-AISH Publication, 351, 632–637, 2012.
Willems, P.: Stochastic description of the rainfall input errors in lumped hydrological models, Stoch. Env. Res. Risk A., 15, 132–152, https://doi.org/10.1007/s004770000063, 2001.
Willems, P.: Quantification and relative comparison of different types of uncertainties in sewer water quality modeling, Water Res., 42, 3539–3551, https://doi.org/10.1016/j.watres.2008.05.006, 2008.
Willems, P.: Multidecadal oscillatory behaviour of rainfall extremes in Europe, Climatic Change, 120, 931–944, https://doi.org/10.1007/s10584-013-0837-x, 2013a.
Willems, P.: Revision of urban drainage design rules after assessment of climate change impacts on precipitation extremes at Uccle, Belgium, J. Hydrol., 496, 166–177, https://doi.org/10.1016/j.jhydrol.2013.05.037, 2013b.
Willems, P. and Berlamont, J.: Probabilistic modelling of sewer system overflow emissions, Water Sci. Technol., 39, 47–54, 1999.
Wilson, J. W. and Brandes, E. A.: Radar measurement of rainfall – a summary, B. Am. Meteorol. Soc., 60, 1048–1058, 1979.
WMO: The Guide to Hydrological Practices. Volume I Hydrology – From Measurement to Hydrological Information WMO-No. 168, World Meteorological Organization, 2008.
Wolfs, V. and Willems, P.: Modular Conceptual Modelling Approach and Software for Sewer Hydraulic Computations, Water Resour. Manag., 31, 283–298, https://doi.org/10.1007/s11269-016-1524-2, 2017.
Wright, D. B., Smith, J. A., Villarini, G., and Baeck, M. L.: Hydroclimatology of flash flooding in Atlanta, Water Resour. Res., 48, 1–14, https://doi.org/10.1029/2011WR011371, 2012.
Wright, D. B., Smith, J. A., and Baeck, M. L.: Critical Examination of Area Reduction Factors, J. Hydrol. Eng., 19, 769–776, https://doi.org/10.1061/(ASCE)HE.1943-5584.0000855, 2014a.
Wright, D. B., Smith, J. A., and Baeck, M. L.: Flood frequency analysis using radar rainfall fields and stochastic storm transposition, Water Resour. Res., 50, 1592–1615, https://doi.org/10.1002/2013WR014224, 2014b.
Wright, D. B., Smith, J. A., Villarini, G., and Baeck, M. L.: Long-Term High-Resolution Radar Rainfall Fields for Urban Hydrology, J. Am. Water Resour. As., 50, 713–734, https://doi.org/10.1111/jawr.12139, 2014c.
Yang, L., Smith, J. A., Wright, D. B., Baeck, M. L., Villarini, G., Tian, F., and Hu, H.: Urbanization and Climate Change: An Examination of Nonstationarities in Urban Flooding, J. Hydrometeorol., 14, 1791–1809, https://doi.org/10.1175/JHM-D-12-095.1, 2013.
Yuan, J. M., Tilford, K. A., Jiang, H. Y., and Cluckie, I. D.: Real-time urban drainage system modelling using weather radar rainfall data, Phys. Chem. Earth Pt. B, 24, 915–919, https://doi.org/10.1016/S1464-1909(99)00103-3, 1999.
Zhou, Q., Mikkelsen, P. S., Halsnæs, K., and Arnbjerg-Nielsen, K.: Framework for economic pluvial flood risk assessment considering climate change effects and adaptation benefits, J. Hydrol., 414–415, 539–549, https://doi.org/10.1016/j.jhydrol.2011.11.031, 2012.
Short summary
This paper reviews how weather radar data can be used in urban hydrological applications. It focuses on three areas of research: (1) temporal and spatial resolution of rainfall data, (2) rainfall estimation, radar data adjustment and data quality, and (3) nowcasting of radar rainfall and real-time applications. Moreover, the paper provides examples of urban hydrological applications which can benefit from radar rainfall data in comparison to tradition rain gauge measurements of rainfall.
This paper reviews how weather radar data can be used in urban hydrological applications. It...
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