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.
Review article
| 
07 Mar 2017
Review article |
| 07 Mar 2017
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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A multinational assessment of radar's ability to capture heavy rain events is conducted. In total, six different radar products in Denmark, the Netherlands, Finland and Sweden were considered. Results show a fair agreement, with radar underestimating by 17 %-44 % on average compared with gauges. Despite being adjusted for bias, five of six radar products still exhibited strong conditional biases with intensities of 1–2% per mm/h. Median peak intensity bias was significantly higher, reaching 44 %–67%.
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Landslides are a natural hazard that affects alpine regions. Here we focus on rainfall-induced shallow landslides and one of the most widely used approaches for their predictions: rainfall thresholds. We design several comparisons utilizing a landslide database and rainfall records in Switzerland. We find that using daily rather than hourly rainfall might be a better option in some circumstances, and mean annual precipitation and antecedent wetness can improve predictions at the regional scale.
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Giulia Battista, Peter Molnar, and Paolo Burlando
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Suspended sediment load in rivers is highly uncertain because of spatial and temporal variability. By means of a hydrology and suspended sediment transport model, we investigated the effect of spatial variability in precipitation and surface erodibility on catchment sediment fluxes in a mesoscale river basin.
We found that sediment load depends on the spatial variability in erosion drivers, as this affects erosion rates and the location and connectivity to the channel of the erosion areas.
Marc Schleiss, Jonas Olsson, Peter Berg, Tero Niemi, Teemu Kokkonen, Søren Thorndahl, Rasmus Nielsen, Jesper Ellerbæk Nielsen, Denica Bozhinova, and Seppo Pulkkinen
Hydrol. Earth Syst. Sci., 24, 3157–3188, https://doi.org/10.5194/hess-24-3157-2020, https://doi.org/10.5194/hess-24-3157-2020, 2020
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A multinational assessment of radar's ability to capture heavy rain events is conducted. In total, six different radar products in Denmark, the Netherlands, Finland and Sweden were considered. Results show a fair agreement, with radar underestimating by 17 %-44 % on average compared with gauges. Despite being adjusted for bias, five of six radar products still exhibited strong conditional biases with intensities of 1–2% per mm/h. Median peak intensity bias was significantly higher, reaching 44 %–67%.
Roland Löwe and Karsten Arnbjerg-Nielsen
Nat. Hazards Earth Syst. Sci., 20, 981–997, https://doi.org/10.5194/nhess-20-981-2020, https://doi.org/10.5194/nhess-20-981-2020, 2020
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Nadav Peleg, Chris Skinner, Simone Fatichi, and Peter Molnar
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Extreme rainfall is expected to intensify with increasing temperatures, which will likely affect rainfall spatial structure. The spatial variability of rainfall can affect streamflow and sediment transport volumes and peaks. The sensitivity of the hydro-morphological response to changes in the structure of heavy rainfall was investigated. It was found that the morphological components are more sensitive to changes in rainfall spatial structure in comparison to the hydrological components.
Giuliano Di Baldassarre, Heidi Kreibich, Sergiy Vorogushyn, Jeroen Aerts, Karsten Arnbjerg-Nielsen, Marlies Barendrecht, Paul Bates, Marco Borga, Wouter Botzen, Philip Bubeck, Bruna De Marchi, Carmen Llasat, Maurizio Mazzoleni, Daniela Molinari, Elena Mondino, Johanna Mård, Olga Petrucci, Anna Scolobig, Alberto Viglione, and Philip J. Ward
Hydrol. Earth Syst. Sci., 22, 5629–5637, https://doi.org/10.5194/hess-22-5629-2018, https://doi.org/10.5194/hess-22-5629-2018, 2018
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One common approach to cope with floods is the implementation of structural flood protection measures, such as levees. Numerous scholars have problematized this approach and shown that increasing levels of flood protection can generate a false sense of security and attract more people to the risky areas. We briefly review the literature on this topic and then propose a research agenda to explore the unintended consequences of structural flood protection.
Anna Costa, Daniela Anghileri, and Peter Molnar
Hydrol. Earth Syst. Sci., 22, 3421–3434, https://doi.org/10.5194/hess-22-3421-2018, https://doi.org/10.5194/hess-22-3421-2018, 2018
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We analyse the control of hydroclimatic factors – erosive rainfall, ice melt, and snowmelt – on suspended sediment concentration (SSC) of Alpine catchments regulated by hydropower, and we develop a multivariate hydroclimatic–informed rating curve. We show that while erosive rainfall determines the variability of SSC, ice melt generates the highest contribution to SSC per unit of runoff. This approach allows the exploration of climate–driven changes in fine sediment dynamics in Alpine catchments.
Emma Dybro Thomassen, Hjalte Jomo Danielsen Sørup, Marc Scheibel, Thomas Einfalt, and Karsten Arnbjerg-Nielsen
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2018-184, https://doi.org/10.5194/hess-2018-184, 2018
Revised manuscript not accepted
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This article takes the first steps in describing rainfall with spatio-temporal variations. A detailed description of rainfall will provide an improved planning tool for protecting cities against pluvial flooding. The article uses high resolution radar data from the catchment of the river Wupper, North Rhine-Westphalia, Germany. The spatio-temporal properties of extreme rain events was described with 16 variables. Three statistical methods were applied and four rainfall types were identified.
Elena Cristiano, Marie-Claire ten Veldhuis, Santiago Gaitan, Susana Ochoa Rodriguez, and Nick van de Giesen
Hydrol. Earth Syst. Sci., 22, 2425–2447, https://doi.org/10.5194/hess-22-2425-2018, https://doi.org/10.5194/hess-22-2425-2018, 2018
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In this work we investigate the influence rainfall and catchment scales have on hydrological response. This problem is quite relevant in urban areas, where the response is fast due to the high degree of imperviousness. We presented a new approach to classify rainfall variability in space and time and use this classification to investigate rainfall aggregation effects on urban hydrological response. This classification allows the spatial extension of the main core of the storm to be identified.
Anna Costa, Peter Molnar, Laura Stutenbecker, Maarten Bakker, Tiago A. Silva, Fritz Schlunegger, Stuart N. Lane, Jean-Luc Loizeau, and Stéphanie Girardclos
Hydrol. Earth Syst. Sci., 22, 509–528, https://doi.org/10.5194/hess-22-509-2018, https://doi.org/10.5194/hess-22-509-2018, 2018
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We explore the signal of a warmer climate in the suspended-sediment dynamics of a regulated and human-impacted Alpine catchment. We demonstrate that temperature-driven enhanced melting of glaciers, which occurred in the mid-1980s, played a dominant role in suspended sediment concentration rise, through increased runoff from sediment-rich proglacial areas, increased contribution of sediment-rich meltwater, and increased sediment supply in proglacial areas due to glacier recession.
Marie-Claire ten Veldhuis, Zhengzheng Zhou, Long Yang, Shuguang Liu, and James Smith
Hydrol. Earth Syst. Sci., 22, 417–436, https://doi.org/10.5194/hess-22-417-2018, https://doi.org/10.5194/hess-22-417-2018, 2018
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The effect of storm scale and movement on runoff flows in urban catchments remains poorly understood due to the complexity of urban land use and man-made infrastructure. In this study, interactions among rainfall, urbanisation and peak flows were analyzed based on 15 years of radar rainfall and flow observations. We found that flow-path networks strongly smoothed rainfall peaks. Unexpectedly, the storm position relative to impervious cover within the basins had little effect on flow peaks.
Christian Bouwens, Marie-Claire ten Veldhuis, Marc Schleiss, Xin Tian, and Jerôme Schepers
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2017-751, https://doi.org/10.5194/hess-2017-751, 2018
Revised manuscript not accepted
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Urban drainage systems are challenged by both urbanization and climate change, intensifying flooding impacts by rainfall. We performed this study to better understand and predict this process. The paper provides an approach to analyze the functioning of an urban drainage system without the need to run hydrodynamic models. Rainfall thresholds for urban flood prediction were derived, which surprisingly are only approximately half of the theoretical drainage system design capacity.
Abdellah Ichiba, Auguste Gires, Ioulia Tchiguirinskaia, Daniel Schertzer, Philippe Bompard, and Marie-Claire Ten Veldhuis
Hydrol. Earth Syst. Sci., 22, 331–350, https://doi.org/10.5194/hess-22-331-2018, https://doi.org/10.5194/hess-22-331-2018, 2018
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This paper proposes a two-step investigation to illustrate the extent of scale effects in urban hydrology. First, fractal tools are used to highlight the scale dependency observed within GIS data inputted in urban hydrological models. Then an intensive multi-scale modelling work was carried out to confirm effects on model performances. The model was implemented at 17 spatial resolutions ranging from 100 to 5 m. Results allow the understanding of scale challenges in hydrology modelling.
Søren Thorndahl, Aske Korup Andersen, and Anders Badsberg Larsen
Hydrol. Earth Syst. Sci., 21, 4433–4448, https://doi.org/10.5194/hess-21-4433-2017, https://doi.org/10.5194/hess-21-4433-2017, 2017
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Time series of rainfall are developed in order to represent future climate conditions. These series can be used in design of, for example, drainage systems where future rainfall loads are important to account for. The climate projections are evaluated on a number of key statistical parameters of rainfall such as yearly and seasonal precipitation amounts, number of extreme events and rainfall intensities, specific duration, and return periods.
Per Skougaard Kaspersen, Nanna Høegh Ravn, Karsten Arnbjerg-Nielsen, Henrik Madsen, and Martin Drews
Hydrol. Earth Syst. Sci., 21, 4131–4147, https://doi.org/10.5194/hess-21-4131-2017, https://doi.org/10.5194/hess-21-4131-2017, 2017
Matthieu Spekkers, Viktor Rözer, Annegret Thieken, Marie-Claire ten Veldhuis, and Heidi Kreibich
Nat. Hazards Earth Syst. Sci., 17, 1337–1355, https://doi.org/10.5194/nhess-17-1337-2017, https://doi.org/10.5194/nhess-17-1337-2017, 2017
Elena Cristiano, Marie-Claire ten Veldhuis, and Nick van de Giesen
Hydrol. Earth Syst. Sci., 21, 3859–3878, https://doi.org/10.5194/hess-21-3859-2017, https://doi.org/10.5194/hess-21-3859-2017, 2017
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In the last decades, new instruments were developed to measure rainfall and hydrological processes at high resolution. Weather radars are used, for example, to measure how rainfall varies in space and time. At the same time, new models were proposed to reproduce and predict hydrological response, in order to prevent flooding in urban areas. This paper presents a review of our current knowledge of rainfall and hydrological processes in urban areas, focusing on their variability in time and space.
Anna Costa, Daniela Anghileri, and Peter Molnar
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2017-419, https://doi.org/10.5194/hess-2017-419, 2017
Manuscript not accepted for further review
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We develop a novel rating curve to simulate suspended sediment concentration (SSC) in Alpine catchments (Process-Based Rating Curve, PBRC). Instead of relating SSC to discharge, as in traditional approaches, we model SSC by differentiating the potential contributions of the main erosional and transport processes of Alpine environments: erosive rainfall, snowmelt, and icemelt. We show that PBRC significantly improves predictions of SSC, especially when analysing climate-induced changes.
Auguste Gires, Ioulia Tchiguirinskaia, Daniel Schertzer, Susana Ochoa-Rodriguez, Patrick Willems, Abdellah Ichiba, Li-Pen Wang, Rui Pina, Johan Van Assel, Guendalina Bruni, Damian Murla Tuyls, and Marie-Claire ten Veldhuis
Hydrol. Earth Syst. Sci., 21, 2361–2375, https://doi.org/10.5194/hess-21-2361-2017, https://doi.org/10.5194/hess-21-2361-2017, 2017
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Data from 10 urban or peri-urban catchments located in five EU countries are used to analyze the imperviousness distribution and sewer network geometry. Consistent scale invariant features are retrieved for both (fractal dimensions can be defined), which enables to define a level of urbanization. Imperviousness representation in operational model is also found to exhibit scale-invariant features (even multifractality). The research was carried out as part of the UE INTERREG IV RainGain project.
Marie-Claire ten Veldhuis and Marc Schleiss
Hydrol. Earth Syst. Sci., 21, 1991–2013, https://doi.org/10.5194/hess-21-1991-2017, https://doi.org/10.5194/hess-21-1991-2017, 2017
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In this paper we analysed flow measurements from 17 watersheds in a (semi-)urban region, to characterise flow patterns according to basin features. Instead of sampling flows at fixed time intervals, we looked at how fast given amounts of flow were accumulated. By doing so, we could identify patterns of flow regulation in urban streams and quantify flashiness of hydrological response. We were able to show that in this region, higher urbanisation was clearly associated with lower basin flashiness.
Nadav Peleg, Frank Blumensaat, Peter Molnar, Simone Fatichi, and Paolo Burlando
Hydrol. Earth Syst. Sci., 21, 1559–1572, https://doi.org/10.5194/hess-21-1559-2017, https://doi.org/10.5194/hess-21-1559-2017, 2017
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We investigated the relative contribution of the spatial versus climatic rainfall variability for flow peaks by applying an advanced stochastic rainfall generator to simulate rainfall for a small urban catchment and simulate flow dynamics in the sewer system. We found that the main contribution to the total flow variability originates from the natural climate variability. The contribution of spatial rainfall variability to the total flow variability was found to increase with return periods.
Hjalte Jomo Danielsen Sørup, Stylianos Georgiadis, Ida Bülow Gregersen, and Karsten Arnbjerg-Nielsen
Hydrol. Earth Syst. Sci., 21, 345–355, https://doi.org/10.5194/hess-21-345-2017, https://doi.org/10.5194/hess-21-345-2017, 2017
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In this study we propose a methodology changing present-day precipitation time series to reflect future changed climate. Present-day time series have a much finer resolution than what is provided by climate models and thus have a much broader application range. The proposed methodology is able to replicate most expectations of climate change precipitation. These time series can be used to run fine-scale hydrological and hydraulic models and thereby assess the influence of climate change on them.
Claudio I. Meier, Jorge Sebastián Moraga, Geri Pranzini, and Peter Molnar
Hydrol. Earth Syst. Sci., 20, 4177–4190, https://doi.org/10.5194/hess-20-4177-2016, https://doi.org/10.5194/hess-20-4177-2016, 2016
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We show that the derived distribution approach is able to characterize the interannual variability of precipitation much better than fitting a probabilistic model to annual rainfall totals, as long as continuously gauged data are available. The method is a useful tool for describing temporal changes in the distribution of annual rainfall, as it works for records as short as 5 years, and therefore does not require any stationarity assumption over long periods.
Bahareh Kianfar, Simone Fatichi, Athansios Paschalis, Max Maurer, and Peter Molnar
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2016-536, https://doi.org/10.5194/hess-2016-536, 2016
Revised manuscript has not been submitted
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Raingauge observations show a large variability in extreme rainfall depths in the current climate. Climate model predictions of extreme rainfall in the future have to be compared with this natural variability. Our work shows that predictions of future extreme rainfall often lie within the range of natural variability of present-day climate, and therefore predictions of change are highly uncertain. We demonstrate this by using stochastic rainfall models and 10-min rainfall data in Switzerland.
Matteo Saletti, Peter Molnar, Marwan A. Hassan, and Paolo Burlando
Earth Surf. Dynam., 4, 549–566, https://doi.org/10.5194/esurf-4-549-2016, https://doi.org/10.5194/esurf-4-549-2016, 2016
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This study presents a new reduced-complexity model with few parameters linked to basic physical processes, which aims to reproduce the transport of sediment as bed load and the formation and stability of channel morphology in steep mountain streams. The model is able to simulate the formation and stability of steps, bed structures commonly encountered in steep channels, by assuming that their formation is due to intense sediment transport during high flows causing jamming of particles.
Hjalte Jomo Danielsen Sørup, Ole Bøssing Christensen, Karsten Arnbjerg-Nielsen, and Peter Steen Mikkelsen
Hydrol. Earth Syst. Sci., 20, 1387–1403, https://doi.org/10.5194/hess-20-1387-2016, https://doi.org/10.5194/hess-20-1387-2016, 2016
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Fine-resolution spatio-temporal precipitation data are important as input to urban hydrological models to assess performance issues under all possible conditions. In the present study synthetic data at very fine spatial and temporal resolution are generated using a stochastic model. Data are generated for both present and future climate conditions. The results show that it is possible to generate spatially distributed data at resolutions relevant for urban hydrology.
P. Skougaard Kaspersen, N. Høegh Ravn, K. Arnbjerg-Nielsen, H. Madsen, and M. Drews
Proc. IAHS, 370, 21–27, https://doi.org/10.5194/piahs-370-21-2015, https://doi.org/10.5194/piahs-370-21-2015, 2015
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A combined remote sensing and hydrological modelling approach is developed to examine the influence of urban land cover changes and climate change for the exposure of cities towards flooding. Results show that the past 30 years of urban development has increased the exposure to pluvial flooding by 6-26%. Corresponding estimates for a medium and high climate change scenario (2071-2100) are 40% and 100%, indicating that urban land cover changes are central for the exposure of cities to flooding.
S. Gaitan and J. A. E. ten Veldhuis
Proc. IAHS, 370, 9–14, https://doi.org/10.5194/piahs-370-9-2015, https://doi.org/10.5194/piahs-370-9-2015, 2015
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The objective of this paper is to outline opportunities for multivariate analysis of open spatial datasets to characterize urban flooding risks. To that end, a cluster analysis is performed. Results indicate that incidence of
rainfall-related impacts is higher in areas characterized by older infrastructure and higher population density.
J. Hall, B. Arheimer, G. T. Aronica, A. Bilibashi, M. Boháč, O. Bonacci, M. Borga, P. Burlando, A. Castellarin, G. B. Chirico, P. Claps, K. Fiala, L. Gaál, L. Gorbachova, A. Gül, J. Hannaford, A. Kiss, T. Kjeldsen, S. Kohnová, J. J. Koskela, N. Macdonald, M. Mavrova-Guirguinova, O. Ledvinka, L. Mediero, B. Merz, R. Merz, P. Molnar, A. Montanari, M. Osuch, J. Parajka, R. A. P. Perdigão, I. Radevski, B. Renard, M. Rogger, J. L. Salinas, E. Sauquet, M. Šraj, J. Szolgay, A. Viglione, E. Volpi, D. Wilson, K. Zaimi, and G. Blöschl
Proc. IAHS, 370, 89–95, https://doi.org/10.5194/piahs-370-89-2015, https://doi.org/10.5194/piahs-370-89-2015, 2015
P. Molnar, S. Fatichi, L. Gaál, J. Szolgay, and P. Burlando
Hydrol. Earth Syst. Sci., 19, 1753–1766, https://doi.org/10.5194/hess-19-1753-2015, https://doi.org/10.5194/hess-19-1753-2015, 2015
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We present an empirical study of the rates of increase in precipitation intensity with air temperature using high-resolution 10 min precipitation records in Switzerland. We estimated the scaling rates for lightning (convective) and non-lightning event subsets and show that scaling rates are between 7 and 14%/C for convective rain and that mixing of storm types exaggerates the relations to air temperature. Doubled CC rates reported by other studies are an exception in our data set.
M. H. Spekkers, F. H. L. R. Clemens, and J. A. E. ten Veldhuis
Nat. Hazards Earth Syst. Sci., 15, 261–272, https://doi.org/10.5194/nhess-15-261-2015, https://doi.org/10.5194/nhess-15-261-2015, 2015
G. Bruni, R. Reinoso, N. C. van de Giesen, F. H. L. R. Clemens, and J. A. E. ten Veldhuis
Hydrol. Earth Syst. Sci., 19, 691–709, https://doi.org/10.5194/hess-19-691-2015, https://doi.org/10.5194/hess-19-691-2015, 2015
K. Džubáková, P. Molnar, K. Schindler, and M. Trizna
Hydrol. Earth Syst. Sci., 19, 195–208, https://doi.org/10.5194/hess-19-195-2015, https://doi.org/10.5194/hess-19-195-2015, 2015
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We use a high-resolution ground-based camera system with near-infrared sensitivity to quantify the response of riparian vegetation in an Alpine river to floods with the use of vegetation indices. The vegetation showed both damage and enhancement within 1 week following floods, with a selective impact determined by pre-flood vegetation vigour, morphological setting and intensity of flood forcing. The tested vegetation indices differed in the direction of predicted change in the range 0.7-35.8%.
M. H. Spekkers, M. Kok, F. H. L. R. Clemens, and J. A. E. ten Veldhuis
Nat. Hazards Earth Syst. Sci., 14, 2531–2547, https://doi.org/10.5194/nhess-14-2531-2014, https://doi.org/10.5194/nhess-14-2531-2014, 2014
J. Hall, B. Arheimer, M. Borga, R. Brázdil, P. Claps, A. Kiss, T. R. Kjeldsen, J. Kriaučiūnienė, Z. W. Kundzewicz, M. Lang, M. C. Llasat, N. Macdonald, N. McIntyre, L. Mediero, B. Merz, R. Merz, P. Molnar, A. Montanari, C. Neuhold, J. Parajka, R. A. P. Perdigão, L. Plavcová, M. Rogger, J. L. Salinas, E. Sauquet, C. Schär, J. Szolgay, A. Viglione, and G. Blöschl
Hydrol. Earth Syst. Sci., 18, 2735–2772, https://doi.org/10.5194/hess-18-2735-2014, https://doi.org/10.5194/hess-18-2735-2014, 2014
B. Merz, J. Aerts, K. Arnbjerg-Nielsen, M. Baldi, A. Becker, A. Bichet, G. Blöschl, L. M. Bouwer, A. Brauer, F. Cioffi, J. M. Delgado, M. Gocht, F. Guzzetti, S. Harrigan, K. Hirschboeck, C. Kilsby, W. Kron, H.-H. Kwon, U. Lall, R. Merz, K. Nissen, P. Salvatti, T. Swierczynski, U. Ulbrich, A. Viglione, P. J. Ward, M. Weiler, B. Wilhelm, and M. Nied
Nat. Hazards Earth Syst. Sci., 14, 1921–1942, https://doi.org/10.5194/nhess-14-1921-2014, https://doi.org/10.5194/nhess-14-1921-2014, 2014
L. Gaál, P. Molnar, and J. Szolgay
Hydrol. Earth Syst. Sci., 18, 1561–1573, https://doi.org/10.5194/hess-18-1561-2014, https://doi.org/10.5194/hess-18-1561-2014, 2014
M. A. Sunyer, H. J. D. Sørup, O. B. Christensen, H. Madsen, D. Rosbjerg, P. S. Mikkelsen, and K. Arnbjerg-Nielsen
Hydrol. Earth Syst. Sci., 17, 4323–4337, https://doi.org/10.5194/hess-17-4323-2013, https://doi.org/10.5194/hess-17-4323-2013, 2013
M. H. Spekkers, M. Kok, F. H. L. R. Clemens, and J. A. E. ten Veldhuis
Hydrol. Earth Syst. Sci., 17, 913–922, https://doi.org/10.5194/hess-17-913-2013, https://doi.org/10.5194/hess-17-913-2013, 2013
Related subject area
Subject: Urban Hydrology | Techniques and Approaches: Instruments and observation techniques
A Bayesian updating framework for calibrating the hydrological parameters of road networks using taxi GPS data
Assessing specific differential phase (KDP)-based quantitative precipitation estimation for the record- breaking rainfall over Zhengzhou city on 20 July 2021
Sources and pathways of biocides and their transformation products in urban storm water infrastructure of a 2 ha urban district
Assessing different imaging velocimetry techniques to measure shallow runoff velocities during rain events using an urban drainage physical model
Using soil water isotopes to infer the influence of contrasting urban green space on ecohydrological partitioning
Reconstituting past flood events: the contribution of citizen science
Scalable flood level trend monitoring with surveillance cameras using a deep convolutional neural network
Technical note: Laboratory modelling of urban flooding: strengths and challenges of distorted scale models
The potential of urban rainfall monitoring with crowdsourced automatic weather stations in Amsterdam
Gauge-adjusted rainfall estimates from commercial microwave links
Improving the precipitation accumulation analysis using lightning measurements and different integration periods
Local nutrient regimes determine site-specific environmental triggers of cyanobacterial and microcystin variability in urban lakes
Variability of drainage and solute leaching in heterogeneous urban vegetation environs
Technical note on measuring run-off dynamics from pavements using a new device: the weighable tipping bucket
Xiangfu Kong, Jiawen Yang, Ke Xu, Bo Dong, and Shan Jiang
Hydrol. Earth Syst. Sci., 27, 3803–3822, https://doi.org/10.5194/hess-27-3803-2023, https://doi.org/10.5194/hess-27-3803-2023, 2023
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To solve the issue of sparsity of field-observed runoff data, we propose a methodology that leverages taxi GPS data to support hydrological parameter calibration for road networks. Novel to this study is that a new kind of data source, namely floating car data, is introduced to tackle the ungauged catchment problem, providing alternative flooding early warning supports for cities that have little runoff data but rich taxi data.
Haoran Li, Dmitri Moisseev, Yali Luo, Liping Liu, Zheng Ruan, Liman Cui, and Xinghua Bao
Hydrol. Earth Syst. Sci., 27, 1033–1046, https://doi.org/10.5194/hess-27-1033-2023, https://doi.org/10.5194/hess-27-1033-2023, 2023
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A rainfall event that occurred at Zhengzhou on 20 July 2021 caused tremendous loss of life and property. This study compares different KDP estimation methods as well as the resulting QPE outcomes. The results show that the selection of the KDP estimation method has minimal impact on QPE, whereas the inadequate assumption of rain microphysics and unquantified vertical air motion may explain the underestimated 201.9 mm h−1 record.
Felicia Linke, Oliver Olsson, Frank Preusser, Klaus Kümmerer, Lena Schnarr, Marcus Bork, and Jens Lange
Hydrol. Earth Syst. Sci., 25, 4495–4512, https://doi.org/10.5194/hess-25-4495-2021, https://doi.org/10.5194/hess-25-4495-2021, 2021
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We used a two-step approach with limited sampling effort in existing storm water infrastructure to illustrate the risk of biocide emission in a 2 ha urban area 13 years after construction had ended. First samples at a swale confirmed the overall relevance of biocide pollution. Then we identified sources where biocides were used for film protection and pathways where transformation products were formed. Our results suggest that biocide pollution is a also continuous risk in aging urban areas.
Juan Naves, Juan T. García, Jerónimo Puertas, and Jose Anta
Hydrol. Earth Syst. Sci., 25, 885–900, https://doi.org/10.5194/hess-25-885-2021, https://doi.org/10.5194/hess-25-885-2021, 2021
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Surface water velocities are key in the calibration of physically based urban drainage models, but the shallow depths developed during non-extreme rainfall and the risks during floods limit the availability of this type of data. This study proves the potential of different imaging velocimetry techniques to measure water runoff velocities in urban catchments during rain events, highlighting the importance of considering rain properties to interpret and assess the results obtained.
Lena-Marie Kuhlemann, Doerthe Tetzlaff, Aaron Smith, Birgit Kleinschmit, and Chris Soulsby
Hydrol. Earth Syst. Sci., 25, 927–943, https://doi.org/10.5194/hess-25-927-2021, https://doi.org/10.5194/hess-25-927-2021, 2021
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We studied water partitioning under urban grassland, shrub and trees during a warm and dry growing season in Berlin, Germany. Soil evaporation was highest under grass, but total green water fluxes and turnover time of soil water were greater under trees. Lowest evapotranspiration losses under shrub indicate potential higher drought resilience. Knowledge of water partitioning and requirements of urban green will be essential for better adaptive management of urban water and irrigation strategies.
Bocar Sy, Corine Frischknecht, Hy Dao, David Consuegra, and Gregory Giuliani
Hydrol. Earth Syst. Sci., 24, 61–74, https://doi.org/10.5194/hess-24-61-2020, https://doi.org/10.5194/hess-24-61-2020, 2020
Matthew Moy de Vitry, Simon Kramer, Jan Dirk Wegner, and João P. Leitão
Hydrol. Earth Syst. Sci., 23, 4621–4634, https://doi.org/10.5194/hess-23-4621-2019, https://doi.org/10.5194/hess-23-4621-2019, 2019
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This work demonstrates a new approach to obtain flood level trend information from surveillance footage with minimal prior information. A neural network trained to detect flood water is applied to video frames to create a qualitative flooding metric (namely, SOFI). The correlation between the real water trend and SOFI was found to be 75 % on average (based on six videos of flooding under various circumstances). SOFI could be used for flood model calibration, to increase model reliability.
Xuefang Li, Sébastien Erpicum, Martin Bruwier, Emmanuel Mignot, Pascal Finaud-Guyot, Pierre Archambeau, Michel Pirotton, and Benjamin Dewals
Hydrol. Earth Syst. Sci., 23, 1567–1580, https://doi.org/10.5194/hess-23-1567-2019, https://doi.org/10.5194/hess-23-1567-2019, 2019
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With a growing urban flood risk worldwide, flood risk management tools need to be validated against reference data. Field and remote-sensing observations provide valuable data on inundation extent and depth but virtually no information on flow velocity. Laboratory scale models have the potential to deliver complementary data, provided that the model scaling is performed carefully. In this paper, we reanalyse existing laboratory data to discuss challenges related to the scaling of urban floods.
Lotte de Vos, Hidde Leijnse, Aart Overeem, and Remko Uijlenhoet
Hydrol. Earth Syst. Sci., 21, 765–777, https://doi.org/10.5194/hess-21-765-2017, https://doi.org/10.5194/hess-21-765-2017, 2017
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Recent developments have made it possible to easily crowdsource meteorological measurements from automatic personal weather stations worldwide. This has offered free access to rainfall ground measurements at spatial and temporal resolutions far exceeding those of national operational sensor networks, especially in cities. This paper is the first step to make optimal use of this promising source of rainfall measurements and identify challenges for future implementation for urban applications.
Martin Fencl, Michal Dohnal, Jörg Rieckermann, and Vojtěch Bareš
Hydrol. Earth Syst. Sci., 21, 617–634, https://doi.org/10.5194/hess-21-617-2017, https://doi.org/10.5194/hess-21-617-2017, 2017
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Commercial microwave links (CMLs) can provide rainfall observations with high space–time resolution. Unfortunately, CML rainfall estimates are often biased because we lack detailed information on the processes that attenuate the transmitted microwaves. We suggest removing the bias by continuously adjusting CMLs to cumulative data from rain gauges (RGs), which can be remote from the CMLs. Our approach practically eliminates the bias, which we demonstrate on unique data from several CMLs and RGs.
Erik Gregow, Antti Pessi, Antti Mäkelä, and Elena Saltikoff
Hydrol. Earth Syst. Sci., 21, 267–279, https://doi.org/10.5194/hess-21-267-2017, https://doi.org/10.5194/hess-21-267-2017, 2017
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A new lightning data assimilation method has been implemented and validated within the Finnish Meteorological Institute – Local Analysis and Prediction System. Lightning data do improve the analysis when no radars are available, and even with radar data, lightning data have a positive impact on the results.
We also investigate the usage of different time integration intervals: 1, 6, 12, 24 h and 7 days, where the 1 h integration time length gives the best results.
S. C. Sinang, E. S. Reichwaldt, and A. Ghadouani
Hydrol. Earth Syst. Sci., 19, 2179–2195, https://doi.org/10.5194/hess-19-2179-2015, https://doi.org/10.5194/hess-19-2179-2015, 2015
H. Nouri, S. Beecham, A. M. Hassanli, and G. Ingleton
Hydrol. Earth Syst. Sci., 17, 4339–4347, https://doi.org/10.5194/hess-17-4339-2013, https://doi.org/10.5194/hess-17-4339-2013, 2013
T. Nehls, Y. Nam Rim, and G. Wessolek
Hydrol. Earth Syst. Sci., 15, 1379–1386, https://doi.org/10.5194/hess-15-1379-2011, https://doi.org/10.5194/hess-15-1379-2011, 2011
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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