Articles | Volume 25, issue 9
https://doi.org/10.5194/hess-25-4701-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-25-4701-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
31 Aug 2021
Research article |
| 31 Aug 2021
| 31 Aug 2021
The impact of the spatiotemporal structure of rainfall on flood frequency over a small urban watershed: an approach coupling stochastic storm transposition and hydrologic modeling
Zhengzheng Zhou
CORRESPONDING AUTHOR
Department of Hydraulic Engineering, Tongji University, Shanghai,
China
James A. Smith
Department of Civil and Environmental Engineering, Princeton
University, Princeton, USA
Mary Lynn Baeck
Department of Civil and Environmental Engineering, Princeton
University, Princeton, USA
Daniel B. Wright
Department of Civil and Environmental Engineering, University of
Wisconsin-Madison, Madison, USA
Brianne K. Smith
Department of Earth and Environmental Sciences, City University of New York – Brooklyn College, New York, USA
Shuguang Liu
CORRESPONDING AUTHOR
Department of Hydraulic Engineering, Tongji University, Shanghai,
China
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Hydrol. Earth Syst. Sci., 26, 5241–5267, https://doi.org/10.5194/hess-26-5241-2022, https://doi.org/10.5194/hess-26-5241-2022, 2022
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We present a new approach to estimate extreme rainfall probability and severity using the atmospheric water balance, where precipitation is the sum of water vapor components moving in and out of a storm. We apply our method to the Mississippi Basin and its five major subbasins. Our approach achieves a good fit to reference precipitation, indicating that the rainfall probability estimation can benefit from additional information from physical processes that control rainfall.
Davide Zoccatelli, Francesco Marra, Moshe Armon, Yair Rinat, James A. Smith, and Efrat Morin
Hydrol. Earth Syst. Sci., 23, 2665–2678, https://doi.org/10.5194/hess-23-2665-2019, https://doi.org/10.5194/hess-23-2665-2019, 2019
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This study presents a comparison of flood properties over multiple Mediterranean and desert catchments. While in Mediterranean areas floods are related to rainfall amount, in deserts we observed a strong connection with the characteristics of the more intense part of storms. Because of the different mechanisms involved, despite having significantly shorter and more localized storms, deserts are able to produce floods with a magnitude comparable to Mediterranean areas.
Guo Yu, Daniel B. Wright, Zhihua Zhu, Cassia Smith, and Kathleen D. Holman
Hydrol. Earth Syst. Sci., 23, 2225–2243, https://doi.org/10.5194/hess-23-2225-2019, https://doi.org/10.5194/hess-23-2225-2019, 2019
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The relationship between flood severity and probability is a key component of flood risk management, and depends on factors including rainfall, soil wetness, and watershed properties. In this study, we combine radar rainfall data and flood simulations to better understand how these factors
shapeflood frequency. We apply our method to an agricultural watershed in the Midwestern US where the flood properties are changing. Conventional methods will fail to account for these changes.
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.
Hanghui Zhang, Shuguang Liu, Jianchun Ye, and Pat J.-F. Yeh
Hydrol. Earth Syst. Sci., 21, 5339–5355, https://doi.org/10.5194/hess-21-5339-2017, https://doi.org/10.5194/hess-21-5339-2017, 2017
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The Huangpu River, an important river of the basin connecting Lake Taihu upstream and the Yangtze River estuary downstream, drains two-fifths of the entire basin. Constructing an estuary gate is considered an effective solution for flood mitigation. The main objective of this paper is to assess the potential contributions of the proposed Huangpu Gate to the flood control capacity of the basin. Results of quantitative analyses show that the Huangpu Gate is effective at evacuating floodwaters.
Qiaofeng Wu, Shuguang Liu, Yi Cai, Xinjian Li, and Yangming Jiang
Hydrol. Earth Syst. Sci., 21, 393–407, https://doi.org/10.5194/hess-21-393-2017, https://doi.org/10.5194/hess-21-393-2017, 2017
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We proposed a method to calibrate hydrological models by selecting parameter range. The results show the probability distribution can be used to determine the optimal range of a single parameter. Analysis of parameter sensitivity and correlation is helpful to obtain the optimal combination of multi-parameter ranges which contributes to a higher and more concentrated value of the objective function. The findings can provide references for enhancing the precision of hydrological process modelling.
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Hydrol. Earth Syst. Sci., 28, 3919–3930, https://doi.org/10.5194/hess-28-3919-2024, https://doi.org/10.5194/hess-28-3919-2024, 2024
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A coupled statistical–hydrodynamic model framework is employed to quantitatively evaluate the sensitivity of compound flood hazards to the relative timing of peak storm surges and rainfall. The findings reveal that the timing difference between these two factors significantly affects flood inundation depth and extent. The most severe inundation occurs when rainfall precedes the storm surge peak by 2 h.
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Xuehong Zhu, Qiang Dai, Dawei Han, Lu Zhuo, Shaonan Zhu, and Shuliang Zhang
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Joong Gwang Lee, Christopher T. Nietch, and Srinivas Panguluri
Hydrol. Earth Syst. Sci., 22, 2615–2635, https://doi.org/10.5194/hess-22-2615-2018, https://doi.org/10.5194/hess-22-2615-2018, 2018
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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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Suresh Hettiarachchi, Conrad Wasko, and Ashish Sharma
Hydrol. Earth Syst. Sci., 22, 2041–2056, https://doi.org/10.5194/hess-22-2041-2018, https://doi.org/10.5194/hess-22-2041-2018, 2018
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The study examines the impact of higher temperatures expected in a future climate on how rainfall varies with time during severe storm events. The results show that these impacts increase future flood risk in urban environments and that current design guidelines need to be adjusted so that effective adaptation measures can be implemented.
Stephanie Clark, Ashish Sharma, and Scott A. Sisson
Hydrol. Earth Syst. Sci., 22, 1793–1810, https://doi.org/10.5194/hess-22-1793-2018, https://doi.org/10.5194/hess-22-1793-2018, 2018
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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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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
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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Chiara Arrighi, Hocine Oumeraci, and Fabio Castelli
Hydrol. Earth Syst. Sci., 21, 515–531, https://doi.org/10.5194/hess-21-515-2017, https://doi.org/10.5194/hess-21-515-2017, 2017
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In developed countries, the majority of fatalities during floods occurs as a consequence of inappropriate high-risk behaviour such as walking or driving in floodwaters. This work addresses pedestrians' instability in floodwaters. It analyses both the contribution of flood and human physical characteristics in the loss of stability highlighting the key role of subject height (submergence) and flow regime. The method consists of a re-analysis of experiments and numerical modelling.
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.
Tsun-Hua Yang, Gong-Do Hwang, Chin-Cheng Tsai, and Jui-Yi Ho
Hydrol. Earth Syst. Sci., 20, 4731–4745, https://doi.org/10.5194/hess-20-4731-2016, https://doi.org/10.5194/hess-20-4731-2016, 2016
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Taiwan continues to suffer from floods. This study proposes the integration of rainfall thresholds and ensemble precipitation forecasts to provide probabilistic urban inundation forecasts. Utilization of ensemble precipitation forecasts can extend forecast lead times to 72 h, preceding peak flows and allowing response agencies to take necessary preparatory measures. This study also develops a hybrid of real-time observation and rainfall forecasts to improve the first 24 h inundation forecasts.
Christopher A. Sanchez, Benjamin L. Ruddell, Roy Schiesser, and Venkatesh Merwade
Hydrol. Earth Syst. Sci., 20, 1289–1299, https://doi.org/10.5194/hess-20-1289-2016, https://doi.org/10.5194/hess-20-1289-2016, 2016
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The use of authentic learning activities is especially important for place-based geosciences like hydrology, where professional breadth and technical depth are critical for practicing hydrologists. The current study found that integrating computerized learning content into the learning experience, using only a simple spreadsheet tool and readily available hydrological data, can effectively bring the "real world" into the classroom and provide an enriching educational experience.
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
P. Licznar, C. De Michele, and W. Adamowski
Hydrol. Earth Syst. Sci., 19, 485–506, https://doi.org/10.5194/hess-19-485-2015, https://doi.org/10.5194/hess-19-485-2015, 2015
J. Herget, T. Roggenkamp, and M. Krell
Hydrol. Earth Syst. Sci., 18, 4029–4037, https://doi.org/10.5194/hess-18-4029-2014, https://doi.org/10.5194/hess-18-4029-2014, 2014
F. Beck and A. Bárdossy
Hydrol. Earth Syst. Sci., 17, 4851–4863, https://doi.org/10.5194/hess-17-4851-2013, https://doi.org/10.5194/hess-17-4851-2013, 2013
D. H. Trinh and T. F. M. Chui
Hydrol. Earth Syst. Sci., 17, 4789–4801, https://doi.org/10.5194/hess-17-4789-2013, https://doi.org/10.5194/hess-17-4789-2013, 2013
J. Y. Wu, J. R. Thompson, R. K. Kolka, K. J. Franz, and T. W. Stewart
Hydrol. Earth Syst. Sci., 17, 4743–4758, https://doi.org/10.5194/hess-17-4743-2013, https://doi.org/10.5194/hess-17-4743-2013, 2013
H. Ozdemir, C. C. Sampson, G. A. M. de Almeida, and P. D. Bates
Hydrol. Earth Syst. Sci., 17, 4015–4030, https://doi.org/10.5194/hess-17-4015-2013, https://doi.org/10.5194/hess-17-4015-2013, 2013
Y. Seo, N.-J. Choi, and A. R. Schmidt
Hydrol. Earth Syst. Sci., 17, 3473–3483, https://doi.org/10.5194/hess-17-3473-2013, https://doi.org/10.5194/hess-17-3473-2013, 2013
J. Epting, F. Händel, and P. Huggenberger
Hydrol. Earth Syst. Sci., 17, 1851–1869, https://doi.org/10.5194/hess-17-1851-2013, https://doi.org/10.5194/hess-17-1851-2013, 2013
H.-Y. Shen and L.-C. Chang
Hydrol. Earth Syst. Sci., 17, 935–945, https://doi.org/10.5194/hess-17-935-2013, https://doi.org/10.5194/hess-17-935-2013, 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
J. J. Lian, K. Xu, and C. Ma
Hydrol. Earth Syst. Sci., 17, 679–689, https://doi.org/10.5194/hess-17-679-2013, https://doi.org/10.5194/hess-17-679-2013, 2013
H. T. L. Huong and A. Pathirana
Hydrol. Earth Syst. Sci., 17, 379–394, https://doi.org/10.5194/hess-17-379-2013, https://doi.org/10.5194/hess-17-379-2013, 2013
S. Oraei Zare, B. Saghafian, and A. Shamsai
Hydrol. Earth Syst. Sci., 16, 4531–4542, https://doi.org/10.5194/hess-16-4531-2012, https://doi.org/10.5194/hess-16-4531-2012, 2012
R. Archetti, A. Bolognesi, A. Casadio, and M. Maglionico
Hydrol. Earth Syst. Sci., 15, 3115–3122, https://doi.org/10.5194/hess-15-3115-2011, https://doi.org/10.5194/hess-15-3115-2011, 2011
Y.-M. Chiang, L.-C. Chang, M.-J. Tsai, Y.-F. Wang, and F.-J. Chang
Hydrol. Earth Syst. Sci., 15, 185–196, https://doi.org/10.5194/hess-15-185-2011, https://doi.org/10.5194/hess-15-185-2011, 2011
I. Andrés-Doménech, J. C. Múnera, F. Francés, and J. B. Marco
Hydrol. Earth Syst. Sci., 14, 2057–2072, https://doi.org/10.5194/hess-14-2057-2010, https://doi.org/10.5194/hess-14-2057-2010, 2010
Yen-Ming Chiang, Li-Chiu Chang, Meng-Jung Tsai, Yi-Fung Wang, and Fi-John Chang
Hydrol. Earth Syst. Sci., 14, 1309–1319, https://doi.org/10.5194/hess-14-1309-2010, https://doi.org/10.5194/hess-14-1309-2010, 2010
Cited articles
Achen, C. H.: What Does “Explained Variance” Explain?: Reply, Polit. Anal., 2, 173–184, https://doi.org/10.1093/pan/2.1.173, 2017.
Adams, R., Western, A. W., and Seed, A. W.: An analysis of the impact of
spatial variability in rainfall on runoff and sediment predictions from a
distributed model, Hydrol. Process., 26, 3263–3280, https://doi.org/10.1002/hyp.8435, 2012.
Beighley, R. E. and Moglen, G. E.: Trend Assessment in Rainfall-Runoff Behavior in Urbanizing Watersheds, J. Hydrol. Eng., 7, 27–34, https://doi.org/10.1061/(ASCE)1084-0699(2002)7:1(27), 2002.
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.
Breiman, L.: Random Forests, Mach. Learn., 45, 5–32, https://doi.org/10.1023/a:1010933404324, 2001.
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.
Cristiano, E., ten Veldhuis, M. C., and van de Giesen, N.: Spatial and temporal variability of rainfall and their effects on hydrological response in urban areas – a review, Hydrol. Earth Syst. Sci., 21, 3859–3878, https://doi.org/10.5194/hess-21-3859-2017, 2017.
Cristiano, E., ten Veldhuis, M.-C., Wright, D. B., Smith, J. A., and van de
Giesen, N.: The Influence of Rainfall and Catchment Critical Scales on Urban
Hydrological Response Sensitivity, Water Resour. Res., 55, 3375–3390,
https://doi.org/10.1029/2018WR024143, 2019.
Cristiano, E., ten Veldhuis, M. C., Gaitan, S., Ochoa Rodriguez, S., and van de Giesen, N.: Critical scales to explain urban hydrological response: an
application in Cranbrook, London, Hydrol. Earth Syst. Sci., 22, 2425–2447,
https://doi.org/10.5194/hess-22-2425-2018, 2018.
Downer, C. W. and Ogden, F. L.: GSSHA: Model to simulate diverse stream flow
producing processes, J. Hydrol. Eng., 9, 161–174, https://doi.org/10.1061/(ASCE)1084-0699(2004)9:3(161), 2004.
Downer, C. W. and Ogden, F. L.: Gridded Surface Subsurface Hydrologic
Analysis (GSSHA) User's Manual, Version 1.43 for Watershed Modeling System 6.1, Engineer Research And Development Center, Coastal And Hydraulics Lab, Vicksburg, MS, 2006.
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, 2012.
Emmanuel, I., Andrieu, H., Leblois, E., Janey, N., and Payrastre, O.: Influence of rainfall spatial variability on rainfall–runoff modelling:
Benefit of a simulation approach?, J. Hydrol., 531, 337–348,
https://doi.org/10.1016/j.jhydrol.2015.04.058, 2015.
Faurès, J.-M., Goodrich, D. C., Woolhiser, D. A., and Sorooshian, S.:
Impact of small-scale spatial rainfall variability on runoff modeling, J. Hydrol., 173, 309–326, https://doi.org/10.1016/0022-1694(95)02704-S, 1995.
Fulton, R. A., Breidenbach, J. P., Seo, D.-J., Miller, D. A., and O'Bannon,
T.: The WSR-88D rainfall algorithm, Weather Forecast., 13, 377–395,
https://doi.org/10.1175/1520-0434(1998)013<0377:TWRA>2.0.CO;2, 1998.
Galster, J. C., Pazzaglia, F. J., Hargreaves, B. R., Morris, D. P., Peters,
S. C., and Weisman, R. N.: Effects of urbanization on watershed hydrology: The scaling of discharge with drainage area, Geology, 34, 713–716,
https://doi.org/10.1130/g22633.1, 2006.
Gebremichael, M. and Krajewski, W. F.: Assessment of the statistical characterization of small-scale rainfall variability from radar: Analysis of
TRMM ground validation datasets, J. Appl. Meteorol., 43, 1180–1199, https://doi.org/10.1175/1520-0450(2004)043<1180:AOTSCO>2.0.CO;2, 2004.
Gesch, D. B., Oimoen, M. J., Greenlee, S. K., Nelson, C. A., Steuck, M. J.,
and Tyler, D. J.: The national elevation data set, Photogram. Eng. Remote Sens., 68, 5–11, 2002.
Gourley, J. J., Flamig, Z. L., Vergara, H., Kirstetter, P.-E., Clark, R. A.,
Argyle, E., Arthur, A., Martinaitis, S., Terti, G., Erlingis, J. M., Hong, Y., and Howard, K. W.: The FLASH Project: Improving the Tools for Flash Flood Monitoring and Prediction across the United States, B. Am. Meteorol. Soc., 98, 361–372, https://doi.org/10.1175/bams-d-15-00247.1, 2017.
Krajewski, W. and Smith, J.: Radar hydrology: rainfall estimation, Adv. Water
Resour., 25, 1387–1394, https://doi.org/10.1016/S0309-1708(02)00062-3, 2002.
Krajewski, W. F., Kruger, A., Smith, J. A., Lawrence, R., Gunyon, C., Goska,
R., Seo, B.-C., Domaszczynski, P., Baeck, M. L., and Ramamurthy, M. K.: Towards better utilization of NEXRAD data in hydrology: an overview of
Hydro-NEXRAD, J. Hydroinform., 13, 255–266, https://doi.org/10.2166/hydro.2010.056, 2011.
Lin, N., Smith, J. A., Villarini, G., Marchok, T. P., and Baeck, M. L.: Modeling extreme rainfall, winds, and surge from Hurricane Isabel (2003),
Weather Forecast., 25, 1342–1361, https://doi.org/10.1175/2010WAF2222349.1, 2010.
Lindner, G. A. and Miller, A. J.: Numerical Modeling of Stage-Discharge
Relationships in Urban Streams, J. Hydrol. Eng., 17, 590–596, https://doi.org/10.1061/(ASCE)HE.1943-5584.0000459, 2012.
Lu, P., Smith, J. A., and Lin, N.: Spatial Characterization of Flood Magnitudes over the Drainage Network of the Delaware River Basin, J. Hydrometeorol., 18, 957–976, https://doi.org/10.1175/jhm-d-16-0071.1, 2017.
Maryland, S. o.: Department of the Environment: Water management, stormwater management, Off. of the Secretary of State, Div. of State Documents, Baltimore, MD, 1982.
Meierdiercks, K. L., Smith, J. A., Baeck, M. L., and Miller, A. J.: Analyses of urban drainage network structure and its impact on hydrologic response, J. Am. Water Resour. Assoc., 46, 932–943, 2010.
Miller, A. J., Welty, C., Duncan, J. M., Baeck, M. L., and Smith, J. A.:
Assessing urban rainfall-runoff response to stormwater management extent,
Hydrol. Process., 35, e14287, https://doi.org/10.1002/hyp.14287, 2021.
Moreau, E., Testud, J., and Le Bouar, E.: Rainfall spatial variability observed by X-band weather radar and its implication for the accuracy of
rainfall estimates, Adv. Water Resour., 32, 1011–1019,
https://doi.org/10.1016/j.advwatres.2008.11.007, 2009.
Morin, E., Goodrich, D. C., Maddox, R. A., Gao, X., Gupta, H. V., and Sorooshian, S.: Spatial patterns in thunderstorm rainfall events and their
coupling with watershed hydrological response, Adv. Water Resour., 29, 843–860, 2006.
Nash, J. E. and Sutcliffe, J. V.: River flow forecasting through conceptual
models part I – A discussion of principles, J. Hydrol., 10, 282–290, https://doi.org/10.1016/0022-1694(70)90255-6, 1970.
Nelson, P. A., Smith, J. A., and Miller, A. J.: Evolution of channel morphology and hydrologic response in an urbanizing drainage basin, Earth Surf. Proc. Land., 31, 1063–1079, https://doi.org/10.1002/esp.1308, 2006.
Nikolopoulos, E. I., Borga, M., Zoccatelli, D., and Anagnostou, E. N.: Catchment-scale storm velocity: Quantification, scale dependence and effect on flood response, Hydrolog. Sci. J., 59, 1363–1376,
https://doi.org/10.1080/02626667.2014.923889, 2014.
Ntelekos, A. A., Smith, J. A., Baeck, M. L., Krajewski, W. F., Miller, A. J., and Goska, R.: Extreme hydrometeorological events and the urban environment: Dissecting the 7 July 2004 thunderstorm over the Baltimore MD Metropolitan Region, Water Resour. Res., 44, 1–19, https://doi.org/10.1029/2007WR006346, 2008.
Ochoa-Rodriguez, S., Wang, L.-P., Gires, A., Pina, R. D., Reinoso-Rondinel,
R., Bruni, G., Ichiba, A., Gaitan, S., Cristiano, E., and van Assel, J.:
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.
Ogden, F. L., Raj Pradhan, N., Downer, C. W., and Zahner, J. A.: Relative
importance of impervious area, drainage density, width function, and subsurface storm drainage on flood runoff from an urbanized catchment, Water
Resour. Res., 47, W12503, https://doi.org/10.1029/2011wr010550, 2011.
Paschalis, A., Fatichi, S., Molnar, P., Rimkus, S., and Burlando, P.: On the
effects of small scale space–time variability of rainfall on basin flood
response, J. Hydrol., 514, 313–327, https://doi.org/10.1016/j.jhydrol.2014.04.014, 2014.
Peleg, N., Blumensaat, F., Molnar, P., Fatichi, S., and Burlando, P.: Partitioning the impacts of spatial and climatological rainfall variability in urban drainage modeling, Hydrol. Earth Syst. Sci., 21, 1559–1572, https://doi.org/10.5194/hess-21-1559-2017, 2017.
Perez, G., Mantilla, R., Krajewski, W. F., and Wright, D. B.: Using Physically Based Synthetic Peak Flows to Assess Local and Regional Flood Frequency Analysis Methods, Water Resour. Res., 55, 8384–8403,
https://doi.org/10.1029/2019WR024827, 2019.
Pickett, S. T. A. and Cadenasso, M. L.: Advancing urban ecological studies:
Frameworks, concepts, and results from the Baltimore Ecosystem Study, Aust. Ecol., 31, 114–125, https://doi.org/10.1111/j.1442-9993.2006.01586.x, 2006.
Probst, P., Wright, M. N., and Boulesteix, A. L.: Hyperparameters and tuning
strategies for random forest, Wiley Interdisciplin. Rev.: Data Min. Knowl. Discov., 9, e1301, https://doi.org/10.1002/widm.1301, 2019.
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.
Rahman, A., Weinmann, P. E., Hoang, T. M. T., and Laurenson, E. M.: Monte
Carlo simulation of flood frequency curves from rainfall, J. Hydrol., 256, 196–210, https://doi.org/10.1016/S0022-1694(01)00533-9, 2002.
Saghafian, B., Julien, P. Y., and Ogden, F. L.: Similarity in catchment
response: 1. Stationary rainstorms, Water Resour. Res., 31, 1533–1541,
https://doi.org/10.1029/95WR00518, 1995.
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, 2012.
Seo, B.-C., Krajewski, W. F., Kruger, A., Domaszczynski, P., Smith, J. A., and Steiner, M.: Radar-rainfall estimation algorithms of Hydro-NEXRAD, J. Hydroinform., 13, 277–291, https://doi.org/10.2166/hydro.2010.003, 2011.
Sharif, H. O., Hassan, A. A., Bin-Shafique, S., Xie, H., and Zeitler, J.:
Hydrologic modeling of an extreme flood in the Guadalupe River in Texas, J. Am. Water Resour. Assoc., 46, 881–891, https://doi.org/10.1111/j.1752-1688.2010.00459.x, 2010.
Sharif, H. O., Chintalapudi, S., Hassan, A. A., Xie, H., and Zeitler, J.:
Physically Based Hydrological Modeling of the 2002 Floods in San Antonio, Texas, J. Hydrol. Eng., 18, 228–236, https://doi.org/10.1061/(ASCE)HE.1943-5584.0000475, 2013.
Smith, J. A. and Baeck, M. L.: Baltimore MD HydroNEXRAD (KLW) radar rainfall, available at: http://arks.princeton.edu/ark:/88435/dsp01q524jr55d (last access: 27 August 2021), 2019.
Smith, B., Smith, J., Baeck, M., and Miller, A.: Exploring storage and runoff generation processes for urban flooding through a physically based watershed model, Water Resour. Res., 51, 1552–1569, https://doi.org/10.1002/2014WR016085, 2015.
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., Nelson, P. A., Miller, A. J., and Holland, E. J.: Field studies of the storm event hydrologic response in an urbanizing watershed, Water Resour. Res., 41, W10413,
https://doi.org/10.1029/2004wr003712, 2005a.
Smith, J. A., Miller, A. J., Baeck, M. L., Nelson, P. A., Fisher, G. T., and
Meierdiercks, K. L.: Extraordinary Flood Response of a Small Urban Watershed
to Short-Duration Convective Rainfall, J. Hydrometeorol., 6, 599–617, https://doi.org/10.1175/JHM426.1, 2005b.
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.
Smith, J. A., Baeck, M. L., Villarini, G., Welty, C., Miller, A. J., and Krajewski, W. F.: Analyses of a long-term, high-resolution radar rainfall
data set for the Baltimore metropolitan region, Water Resour. Res., 48, 1–14, https://doi.org/10.1029/2011wr010641, 2012.
ten Veldhuis, M. C., Zhou, Z., Yang, L., Liu, S., and Smith, J.: The role of
storm scale, position and movement in controlling urban flood response, Hydrol. Earth Syst. Sci., 22, 417–436, https://doi.org/10.5194/hess-22-417-2018, 2018.
Wright, D. B., Smith, J. A., Villarini, G., and Baeck, M. L.: Hydroclimatology of flash flooding in Atlanta, Water Resour. Res., 48,
W04524, https://doi.org/10.1029/2011wr011371, 2012.
Wright, D. B., Smith, J. A., Villarini, G., and Baeck, M. L.: Estimating the
frequency of extreme rainfall using weather radar and stochastic storm
transposition, J. Hydrol., 488, 150–165, https://doi.org/10.1016/j.jhydrol.2013.03.003, 2013.
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, 2014a.
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. Assoc., 50, 713–734, https://doi.org/10.1111/jawr.12139, 2014b.
Wright, D. B., Mantilla, R., and Peters-Lidard, C. D.: A remote sensing-based tool for assessing rainfall-driven hazards, Environ. Model. Softw., 90, 34–54, https://doi.org/10.1016/j.envsoft.2016.12.006, 2017.
Wright, D. B., Yu, G., and England, J. F.: Six decades of rainfall and flood
frequency analysis using stochastic storm transposition: Review, progress,
and prospects, J. Hydrol., 585, 124816, https://doi.org/10.1016/j.jhydrol.2020.124816, 2020.
Yang, L., Smith, J. A., Baeck, M. L., and Zhang, Y.: Flash flooding in small
urban watersheds: Storm event hydrologic response, Water Resour. Res.,
52, 4571–4589, https://doi.org/10.1002/2015WR018326, 2016.
Yang, Y., Sun, L., Li, R., Yin, J., and Yu, D.: Linking a Storm Water Management Model to a Novel Two-Dimensional Model for Urban Pluvial Flood
Modeling, Int. J. Disast. Risk Sci., 11, 508–518, https://doi.org/10.1007/s13753-020-00278-7, 2020.
Yin, J., Yu, D., Yin, Z., Liu, M., and He, Q.: Evaluating the impact and risk of pluvial flash flood on intra-urban road network: A case study in the city center of Shanghai, China, J. Hydrol., 537, 138–145, https://doi.org/10.1016/j.jhydrol.2016.03.037, 2016.
Yu, G., Wright, D. B., Zhu, Z., Smith, C., and Holman, K. D.: Process-based
flood frequency analysis in an agricultural watershed exhibiting nonstationary flood seasonality, Hydrol. Earth Syst. Sci., 23, 2225–2243,
https://doi.org/10.5194/hess-23-2225-2019, 2019.
Zhou, Z., Smith, J. A., Yang, L., Baeck, M. L., Chaney, M., Ten Veldhuis, M.-C., Deng, H., and Liu, S.: The complexities of urban flood response:
Flood frequency analyses for the Charlotte Metropolitan Region, Water
Resour. Res., 53, 7401–7425, https://doi.org/10.1002/2016WR019997, 2017.
Zhou, Z., Smith, J. A., Wright, D. B., Baeck, M. L., and Liu, S.: Storm
catalog-based analysis of rainfall heterogeneity and frequency in a complex
terrain, Water Resour. Res., 55, 1871–1889, https://doi.org/10.1029/2018WR023567, 2019.
Zhu, Z., Wright, D. B., and Yu, G.: The Impact of Rainfall Space-Time Structure in Flood Frequency Analysis, Water Resour. Res., 54, 8983–8998, https://doi.org/10.1029/2018wr023550, 2018.
Zoccatelli, D., Borga, M., Viglione, A., Chirico, G. B., and Blöschl,
G.: Spatial moments of catchment rainfall: rainfall spatial organisation,
basin morphology, and flood response, Hydrol. Earth Syst. Sci., 15, 3767–3783, https://doi.org/10.5194/hess-15-3767-2011, 2011.
Short summary
The role of rainfall space–time structure in flood response is an important research issue in urban hydrology. This study contributes to this understanding in small urban watersheds. Combining stochastically based rainfall scenarios with a hydrological model, the results show the complexities of flood response for various return periods, implying the common assumptions of spatially uniform rainfall in urban flood frequency are problematic, even for relatively small basin scales.
The role of rainfall space–time structure in flood response is an important research issue in...
