Articles | Volume 29, issue 8
https://doi.org/10.5194/hess-29-2043-2025
© Author(s) 2025. 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-29-2043-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
23 Apr 2025
Research article |
| 23 Apr 2025
| 23 Apr 2025
Pluvial and potential compound flooding in a coupled coastal modeling framework: New York City during post-tropical Cyclone Ida (2021)
Civil, Environmental & Ocean Engineering, Stevens Institute of Technology, Hoboken, NJ, 07030, USA
Philip M. Orton
Civil, Environmental & Ocean Engineering, Stevens Institute of Technology, Hoboken, NJ, 07030, USA
David K. Ralston
Applied Ocean Physics & Engineering, Woods Hole Oceanographic Institution, Woods Hole, MA, 02543, USA
John C. Warner
US Geological Survey, Woods Hole Coastal & Marine Science Center, Woods Hole, MA, 02543, USA
Related authors
No articles found.
Ziyu Chen, Philip Orton, James Booth, Thomas Wahl, Arthur DeGaetano, Joel Kaatz, and Radley Horton
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-135, https://doi.org/10.5194/hess-2024-135, 2024
Revised manuscript accepted for HESS
Short summary
Short summary
Urban flooding can be driven by rain and storm surge or the combination of the two, which is called compound flooding. In this study we analyzed hourly historical rain and surge data for New York City to provide a more detailed statistical analysis than prior studies of this topic. The analyses reveal that tropical cyclones (e.g. hurricanes) have potential for causing more extreme compound floods than other storms, while extratropical cyclones cause more frequent, lesser compound events.
Chu-En Hsu, Katherine A. Serafin, Xiao Yu, Christie A. Hegermiller, John C. Warner, and Maitane Olabarrieta
Nat. Hazards Earth Syst. Sci., 23, 3895–3912, https://doi.org/10.5194/nhess-23-3895-2023, https://doi.org/10.5194/nhess-23-3895-2023, 2023
Short summary
Short summary
Total water levels (TWLs) induced by tropical cyclones (TCs) are among the leading hazards faced by coastal communities. Using numerical models, we examined how TWL components (surge and wave runup) along the South Atlantic Bight varied during hurricanes Matthew (2016), Dorian (2019), and Isaias (2020). Peak surge and peak wave runup were dominated by wind speeds and relative positions to TCs. The exceedance time of TWLs was controlled by normalized distances to TC and TC translation speeds.
Related subject area
Subject: Urban Hydrology | Techniques and Approaches: Modelling approaches
Beyond total impervious area: a new lumped descriptor of basin-wide hydrologic connectivity for characterizing urban watersheds
INSPIRE game: integration of vulnerability into impact-based forecasting of urban floods
Exploring the driving factors of compound flood severity in coastal cities: a comprehensive analytical approach
Enhancing generalizability of data-driven urban flood models by incorporating contextual information
Simulation of spatially distributed sources, transport, and transformation of nitrogen from fertilization and septic systems in a suburban watershed
Combining statistical and hydrodynamic models to assess compound flood hazards from rainfall and storm surge: a case study of Shanghai
Analyzing past and future droughts that induce clay shrinkage in France using an index based on water budget simulation for trees
An optimized long short-term memory (LSTM)-based approach applied to early warning and forecasting of ponding in the urban drainage system
A deep-learning-technique-based data-driven model for accurate and rapid flood predictions in temporal and spatial dimensions
Impact of urban geology on model simulations of shallow groundwater levels and flow paths
Technical note: Modeling spatial fields of extreme precipitation – a hierarchical Bayesian approach
Intersecting near-real time fluvial and pluvial inundation estimates with sociodemographic vulnerability to quantify a household flood impact index
Forecasting green roof detention performance by temporal downscaling of precipitation time-series projections
Evaluating different machine learning methods to simulate runoff from extensive green roofs
Modeling and interpreting hydrological responses of sustainable urban drainage systems with explainable machine learning methods
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
Space variability impacts on hydrological responses of nature-based solutions and the resulting uncertainty: a case study of Guyancourt (France)
Urban surface water flood modelling – a comprehensive review of current models and future challenges
Resampling and ensemble techniques for improving ANN-based high-flow forecast accuracy
Event selection and two-stage approach for calibrating models of green urban drainage systems
Modeling the high-resolution dynamic exposure to flooding in a city region
Drainage area characterization for evaluating green infrastructure using the Storm Water Management Model
Critical scales to explain urban hydrological response: an application in Cranbrook, London
Increase in flood risk resulting from climate change in a developed urban watershed – the role of storm temporal patterns
Patterns and comparisons of human-induced changes in river flood impacts in cities
Scale effect challenges in urban hydrology highlighted with a distributed hydrological model
Comparison of the impacts of urban development and climate change on exposing European cities to pluvial flooding
Spatial and temporal variability of rainfall and their effects on hydrological response in urban areas – a review
Hydrodynamics of pedestrians' instability in floodwaters
Formulating and testing a method for perturbing precipitation time series to reflect anticipated climatic changes
Using rainfall thresholds and ensemble precipitation forecasts to issue and improve urban inundation alerts
Enhancing the T-shaped learning profile when teaching hydrology using data, modeling, and visualization activities
On the sensitivity of urban hydrodynamic modelling to rainfall spatial and temporal resolution
Precipitation variability within an urban monitoring network via microcanonical cascade generators
Estimation of peak discharges of historical floods
Indirect downscaling of hourly precipitation based on atmospheric circulation and temperature
Assessing the hydrologic restoration of an urbanized area via an integrated distributed hydrological model
Using the Storm Water Management Model to predict urban headwater stream hydrological response to climate and land cover change
Evaluating scale and roughness effects in urban flood modelling using terrestrial LIDAR data
Contribution of directly connected and isolated impervious areas to urban drainage network hydrographs
Thermal management of an unconsolidated shallow urban groundwater body
Online multistep-ahead inundation depth forecasts by recurrent NARX networks
A statistical analysis of insurance damage claims related to rainfall extremes
Joint impact of rainfall and tidal level on flood risk in a coastal city with a complex river network: a case study of Fuzhou City, China
Urbanization and climate change impacts on future urban flooding in Can Tho city, Vietnam
Multi-objective optimization for combined quality–quantity urban runoff control
Development of flood probability charts for urban drainage network in coastal areas through a simplified joint assessment approach
Auto-control of pumping operations in sewerage systems by rule-based fuzzy neural networks
Coupling urban event-based and catchment continuous modelling for combined sewer overflow river impact assessment
Dynamic neural networks for real-time water level predictions of sewerage systems-covering gauged and ungauged sites
Francesco Dell'Aira and Claudio I. Meier
Hydrol. Earth Syst. Sci., 29, 1001–1032, https://doi.org/10.5194/hess-29-1001-2025, https://doi.org/10.5194/hess-29-1001-2025, 2025
Short summary
Short summary
Scientists and engineers need better indices to frame the hydrologic effects of land development. Existing approaches are not able to reflect the interactions due to the spatial arrangement of distinct land patches, which affect how much runoff is generated and how fast it can travel downstream, impacting flood response. Our novel, GIS-based modeling framework explicitly considers these aspects and is applicable to a wide range of problems, including peak-flow predictions in ungauged basins.
Akshay Singhal, Louise Crochemore, Isabelle Ruin, and Sanjeev K. Jha
Hydrol. Earth Syst. Sci., 29, 947–967, https://doi.org/10.5194/hess-29-947-2025, https://doi.org/10.5194/hess-29-947-2025, 2025
Short summary
Short summary
A serious game experiment is presented which assesses the interplay between hazard, exposure, and vulnerability in a flash flood event. The results show that participants' use of information to make decisions was based on the severity of the situation. Participants used precipitation forecast and exposure to make correct decisions in the first round, while they used precipitation forecast and vulnerability information in the second round.
Yan Liu, Ting Zhang, Yi Ding, Aiqing Kang, Xiaohui Lei, and Jianzhu Li
Hydrol. Earth Syst. Sci., 28, 5541–5555, https://doi.org/10.5194/hess-28-5541-2024, https://doi.org/10.5194/hess-28-5541-2024, 2024
Short summary
Short summary
In coastal cities, rainfall and tides contribute to compound flooding. This study quantifies the impacts of rainfall and tides on compound flooding and analyzes interactions between different flood types. Findings show that rainfall generally has a greater effect on flooding than tide levels. The interaction between fluvial and pluvial flooding amplifies the flood disaster, with tide levels having the most significant impact during the interaction phase.
Tabea Cache, Milton Salvador Gomez, Tom Beucler, Jovan Blagojevic, João Paulo Leitao, and Nadav Peleg
Hydrol. Earth Syst. Sci., 28, 5443–5458, https://doi.org/10.5194/hess-28-5443-2024, https://doi.org/10.5194/hess-28-5443-2024, 2024
Short summary
Short summary
We introduce a new deep-learning model that addresses the limitations of existing urban flood models in handling varied terrains and rainfall events. Our model subdivides a city into small patches and presents a novel approach to incorporate broader terrain information. It accurately predicts high-resolution flood maps across diverse rainfall events and cities (on minute and meter scales) that haven’t been seen by the model, which offers valuable insights for urban flood mitigation strategies.
Ruoyu Zhang, Lawrence E. Band, Peter M. Groffman, Laurence Lin, Amanda K. Suchy, Jonathan M. Duncan, and Arthur J. Gold
Hydrol. Earth Syst. Sci., 28, 4599–4621, https://doi.org/10.5194/hess-28-4599-2024, https://doi.org/10.5194/hess-28-4599-2024, 2024
Short summary
Short summary
Human-induced nitrogen (N) from fertilization and septic effluents is the primary N source in urban watersheds. We developed a model to understand how spatial and temporal patterns of these loads affect hydrologic and biogeochemical processes at the hillslope level. The comparable simulations to observations showed the ability of our model to enhance insights into current water quality conditions, identify high-N-retention locations, and plan future restorations to improve urban water quality.
Hanqing Xu, Elisa Ragno, Sebastiaan N. Jonkman, Jun Wang, Jeremy D. Bricker, Zhan Tian, and Laixiang Sun
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
Short summary
Short summary
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.
Sophie Barthelemy, Bertrand Bonan, Miquel Tomas-Burguera, Gilles Grandjean, Séverine Bernardie, Jean-Philippe Naulin, Patrick Le Moigne, Aaron Boone, and Jean-Christophe Calvet
EGUsphere, https://doi.org/10.5194/egusphere-2024-1079, https://doi.org/10.5194/egusphere-2024-1079, 2024
Short summary
Short summary
A drought index is developed that quantifies drought on an annual scale for deciduous broadleaf vegetation, making it applicable to monitoring clay shrinkage damage to buildings, agriculture or forestry. It is found that significant soil moisture drought events occurred in France in 2003, 2018, 2019, 2020 and 2022. Particularly high index values are observed throughout the country in 2022. It is also found that droughts will become more severe in the future.
Wen Zhu, Tao Tao, Hexiang Yan, Jieru Yan, Jiaying Wang, Shuping Li, and Kunlun Xin
Hydrol. Earth Syst. Sci., 27, 2035–2050, https://doi.org/10.5194/hess-27-2035-2023, https://doi.org/10.5194/hess-27-2035-2023, 2023
Short summary
Short summary
To provide a possibility for early warning and forecasting of ponding in the urban drainage system, an optimized long short-term memory (LSTM)-based model is proposed in this paper. It has a remarkable improvement compared to the models based on LSTM and convolutional neural network (CNN) structures. The performance of the corrected model is reliable if the number of monitoring sites is over one per hectare. Increasing the number of monitoring points further has little impact on the performance.
Qianqian Zhou, Shuai Teng, Zuxiang Situ, Xiaoting Liao, Junman Feng, Gongfa Chen, Jianliang Zhang, and Zonglei Lu
Hydrol. Earth Syst. Sci., 27, 1791–1808, https://doi.org/10.5194/hess-27-1791-2023, https://doi.org/10.5194/hess-27-1791-2023, 2023
Short summary
Short summary
A deep-learning-based data-driven model for flood predictions in temporal and spatial dimensions, with the integration of a long short-term memory network, Bayesian optimization, and transfer learning is proposed. The model accurately predicts water depths and flood time series/dynamics for hyetograph inputs, with substantial improvements in computational time. With transfer learning, the model was well applied to a new case study and showed robust compatibility and generalization ability.
Ane LaBianca, Mette H. Mortensen, Peter Sandersen, Torben O. Sonnenborg, Karsten H. Jensen, and Jacob Kidmose
Hydrol. Earth Syst. Sci., 27, 1645–1666, https://doi.org/10.5194/hess-27-1645-2023, https://doi.org/10.5194/hess-27-1645-2023, 2023
Short summary
Short summary
The study explores the effect of Anthropocene geology and the computational grid size on the simulation of shallow urban groundwater. Many cities are facing challenges with high groundwater levels close to the surface, yet urban planning and development seldom consider its impact on the groundwater resource. This study illustrates that the urban subsurface infrastructure significantly affects the groundwater flow paths and the residence time of shallow urban groundwater.
Bianca Rahill-Marier, Naresh Devineni, and Upmanu Lall
Hydrol. Earth Syst. Sci., 26, 5685–5695, https://doi.org/10.5194/hess-26-5685-2022, https://doi.org/10.5194/hess-26-5685-2022, 2022
Short summary
Short summary
We present a new approach to modeling extreme regional rainfall by considering the spatial structure of extreme events. The developed models allow a probabilistic exploration of how the regional drainage network may respond to extreme rainfall events and provide a foundation for how future risks may be better estimated.
Matthew Preisser, Paola Passalacqua, R. Patrick Bixler, and Julian Hofmann
Hydrol. Earth Syst. Sci., 26, 3941–3964, https://doi.org/10.5194/hess-26-3941-2022, https://doi.org/10.5194/hess-26-3941-2022, 2022
Short summary
Short summary
There is rising concern in numerous fields regarding the inequitable distribution of human risk to floods. The co-occurrence of river and surface flooding is largely excluded from leading flood hazard mapping services, therefore underestimating hazards. Using high-resolution elevation data and a region-specific social vulnerability index, we developed a method to estimate flood impacts at the household level in near-real time.
Vincent Pons, Rasmus Benestad, Edvard Sivertsen, Tone Merete Muthanna, and Jean-Luc Bertrand-Krajewski
Hydrol. Earth Syst. Sci., 26, 2855–2874, https://doi.org/10.5194/hess-26-2855-2022, https://doi.org/10.5194/hess-26-2855-2022, 2022
Short summary
Short summary
Different models were developed to increase the temporal resolution of precipitation time series to minutes. Their applicability under climate change and their suitability for producing input time series for green infrastructure (e.g. green roofs) modelling were evaluated. The robustness of the model was validated against a range of European climates in eight locations in France and Norway. The future hydrological performances of green roofs were evaluated in order to improve design practice.
Elhadi Mohsen Hassan Abdalla, Vincent Pons, Virginia Stovin, Simon De-Ville, Elizabeth Fassman-Beck, Knut Alfredsen, and Tone Merete Muthanna
Hydrol. Earth Syst. Sci., 25, 5917–5935, https://doi.org/10.5194/hess-25-5917-2021, https://doi.org/10.5194/hess-25-5917-2021, 2021
Short summary
Short summary
This study investigated the potential of using machine learning algorithms as hydrological models of green roofs across different climatic condition. The study provides comparison between conceptual and machine learning algorithms. Machine learning models were found to be accurate in simulating runoff from extensive green roofs.
Yang Yang and Ting Fong May Chui
Hydrol. Earth Syst. Sci., 25, 5839–5858, https://doi.org/10.5194/hess-25-5839-2021, https://doi.org/10.5194/hess-25-5839-2021, 2021
Short summary
Short summary
This study uses explainable machine learning methods to model and interpret the statistical correlations between rainfall and the discharge of urban catchments with sustainable urban drainage systems. The resulting models have good prediction accuracies. However, the right predictions may be made for the wrong reasons as the model cannot provide physically plausible explanations as to why a prediction is made.
Zhengzheng Zhou, James A. Smith, Mary Lynn Baeck, Daniel B. Wright, Brianne K. Smith, and Shuguang Liu
Hydrol. Earth Syst. Sci., 25, 4701–4717, https://doi.org/10.5194/hess-25-4701-2021, https://doi.org/10.5194/hess-25-4701-2021, 2021
Short summary
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.
Yangzi Qiu, Igor da Silva Rocha Paz, Feihu Chen, Pierre-Antoine Versini, Daniel Schertzer, and Ioulia Tchiguirinskaia
Hydrol. Earth Syst. Sci., 25, 3137–3162, https://doi.org/10.5194/hess-25-3137-2021, https://doi.org/10.5194/hess-25-3137-2021, 2021
Short summary
Short summary
Our original research objective is to investigate the uncertainties of the hydrological responses of nature-based solutions (NBSs) that result from the multiscale space variability in both the rainfall and the NBS distribution. Results show that the intersection effects of spatial variability in rainfall and the spatial arrangement of NBS can generate uncertainties of peak flow and total runoff volume estimations in NBS scenarios.
Kaihua Guo, Mingfu Guan, and Dapeng Yu
Hydrol. Earth Syst. Sci., 25, 2843–2860, https://doi.org/10.5194/hess-25-2843-2021, https://doi.org/10.5194/hess-25-2843-2021, 2021
Short summary
Short summary
This study presents a comprehensive review of models and emerging approaches for predicting urban surface water flooding driven by intense rainfall. It explores the advantages and limitations of existing models and identifies major challenges. Issues of model complexities, scale effects, and computational efficiency are also analysed. The results will inform scientists, engineers, and decision-makers of the latest developments and guide the model selection based on desired objectives.
Everett Snieder, Karen Abogadil, and Usman T. Khan
Hydrol. Earth Syst. Sci., 25, 2543–2566, https://doi.org/10.5194/hess-25-2543-2021, https://doi.org/10.5194/hess-25-2543-2021, 2021
Short summary
Short summary
Flow distributions are highly skewed, resulting in low prediction accuracy of high flows when using artificial neural networks for flood forecasting. We investigate the use of resampling and ensemble techniques to address the problem of skewed datasets to improve high flow prediction. The methods are implemented both independently and in combined, hybrid techniques. This research presents the first analysis of the effects of combining these methods on high flow prediction accuracy.
Ico Broekhuizen, Günther Leonhardt, Jiri Marsalek, and Maria Viklander
Hydrol. Earth Syst. Sci., 24, 869–885, https://doi.org/10.5194/hess-24-869-2020, https://doi.org/10.5194/hess-24-869-2020, 2020
Short summary
Short summary
Urban drainage models are usually calibrated using a few events so that they accurately represent a real-world site. This paper compares 14 single- and two-stage strategies for selecting these events and found significant variation between them in terms of model performance and the obtained values of model parameters. Calibrating parameters for green and impermeable areas in two separate stages improved model performance in the validation period while making calibration easier and faster.
Xuehong Zhu, Qiang Dai, Dawei Han, Lu Zhuo, Shaonan Zhu, and Shuliang Zhang
Hydrol. Earth Syst. Sci., 23, 3353–3372, https://doi.org/10.5194/hess-23-3353-2019, https://doi.org/10.5194/hess-23-3353-2019, 2019
Short summary
Short summary
Urban flooding exposure is generally investigated with the assumption of stationary disasters and disaster-hit bodies during an event, and thus it cannot satisfy the increasingly elaborate modeling and management of urban floods. In this study, a comprehensive method was proposed to simulate dynamic exposure to urban flooding considering human mobility. Several scenarios, including diverse flooding types and various responses of residents to flooding, were considered.
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
Short summary
Short summary
This paper demonstrates an approach to spatial discretization for analyzing green infrastructure (GI) using SWMM. Besides DCIA, pervious buffers should be identified for GI modeling. Runoff contributions from different spatial components and flow pathways would impact GI performance. The presented approach can reduce the number of calibration parameters and apply scale–independently to a watershed scale. Hydrograph separation can add insights for developing GI scenarios.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
This study investigates global patterns relating urban river flood impacts to socioeconomic development and changing hydrologic conditions, and comparisons are provided between 98 individual cities. This paper condenses and communicates large amounts of information to accelerate the understanding of relationships between local urban conditions and global processes, and to potentially motivate knowledge transfer between decision-makers facing similar circumstances.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Bao, D., Xue, Z. G., Warner, J. C., Moulton, M., Yin, D., Hegermiller, C. A., Zambon, J. B., and He, R.: A numerical investigation of Hurricane Florence-induced compound flooding in the Cape Fear Estuary using a dynamically coupled hydrological-ocean model, J. Adv. Model. Earth Sy., 14, e2022MS003131, https://doi.org/10.1029/2022MS003131, 2022.
Bates, P. D., Quinn, N., Sampson, C., Smith, A., Wing, O., Sosa, J., Savage, J., Olcese, G., Neal, J., and Schumann, G.: Combined modeling of US fluvial, pluvial, and coastal flood hazard under current and future climates, Water Resour. Res., 57, e2020WR028673, https://doi.org/10.1029/2020WR028673, 2021.
Beven, J., Hagen, A., and Berg, R.: Tropical Cyclone Report Hurricane Ida, National Hurricane Center, https://www.nhc.noaa.gov/data/tcr/AL092021_Ida.pdf (last access: 1 August 2023), 2022.
Bulti, D. T. and Abebe, B. G.: A review of flood modeling methods for urban pluvial flood application, Modeling Earth Systems and Environment, 6, 1293–1302, https://doi.org/10.1007/s40808-020-00803-z, 2020.
Capurso, W. D., Simonson, A., Noll, M. L., Busciolano, R., and Finkelstein, J.: High-Water Marks in the Five Boroughs of New York City from Flash Flooding Caused by the Remnants of Hurricane Ida, September 1, 2021,U.S. Geological Survey, [data set], https://doi.org/10.5066/P9OMBJPQ, 2023.
Center Environmental Modeling: North American Mesoscale (NAM) analysis and forecast system characteristics, Environmental Modeling Center, https://www.ncei.noaa.gov/products/weather-climate-models/north-american-mesoscale (last access: 15 August 2023), 2017.
Chassignet, E. P., Arango, H., Dietrich, D., Ezer, T., Ghil, M., Haidvogel, D. B., Ma, C.-C., Mehra, A., Paiva, A. M., and Sirkes, Z.: DAMEE-NAB: the base experiments, Dynam. Atmos. Oceans, 32, 155–183, 2000.
Chen, Z., Orton, P., Booth, J., Wahl, T., DeGaetano, A., Kaatz, J., and Horton, R.: Influence of Storm Type on Compound Flood Hazard of a Mid-Latitude Coastal-Urban Environment, Hydrol. Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/hess-2024-135, in review, 2024.
City of New York, Department of Small Business Services (SBS) New York: Sandy Inundation Zone (11/12/2015), City of New York, Department of Small Business Services (SBS) New York [data set], https://data.cityofnewyork.us/Environment/Sandy-Inundation-Zone/uyj8-7rv5 (last access: 1 August 2023), 2015.
Cooperative Institute for Research in Environmental Sciences (CIRES) at the University of Colorado, Boulder: Continuously Updated Digital Elevation Model (CUDEM) –
Cooperative Institute for Research in Environmental Sciences (CIRES) at the University of Colorado, Boulder: Continuously Updated Digital Elevation Model (CUDEM) –
Cornell University, Northeast Regional Climate Center (NRCC): New York State IDF Projections, https://ny-idf-projections.nrcc.cornell.edu/, last access: 15 August 2024.
Cronshey, R.: Urban hydrology for small watersheds, 55, US Department of Agriculture, Soil Conservation Service, Engineering Division, https://www.nrc.gov/docs/ML1421/ML14219A437.pdf (last access: 1 January 2024), 1986.
Department of City Planning (DCP): Dwn-Pluto and MapPluto, https://www.nyc.gov/site/planning/data-maps/open-data/dwn-pluto-mappluto.page, last access: 15 August 2024.
Dresback, K. M., Szpilka, C. M., Kolar, R. L., Moghimi, S., and Myers, E. P.: Development and validation of accumulation term (Distributed and/or Point Source) in a Finite Element Hydrodynamic model, J. Mar. Sci. Eng., 11, 248, https://doi.org/10.3390/jmse11020248, 2023.
FEMA: Reducing the Effects of Urban Flooding in New York City: Hurricane Ida NYC MAT Technical Report 3, Federal Emergency Management Agency, 2–3, 2023.
Feng, D., Tan, Z., Engwirda, D., Wolfe, J. D., Xu, D., Liao, C., Bisht, G., Benedict, J. J., Zhou, T., and Li, H. Y.: Simulation of compound flooding using river-ocean two-way coupled E3SM ensemble on variable-resolution meshes, J. Adv. Model. Earth Sy., 16, e2023MS004054, https://doi.org/10.1029/2023MS004054, 2024.
Finkelstein, J., Gazoorian, C. L., and Capurso, W. D.: Geospatial Datasets of Water Surface Elevation and Water Depth in New York City, NY Associated with the Remnants of Hurricane Ida – September 1, 2021, U.S. Geological Survey (USGS) [data set], https://doi.org/10.5066/P9JF4OWB, 2023.
Flood, R.: High-Resolution bathymetric and backscatter mapping in Jamaica Bay, Final Report to the National Park Service, State University of New York at Stony Brook, Stony Brook, NY, 2011.
Genest, C. and Favre, A.-C.: Everything you always wanted to know about copula modeling but were afraid to ask, J. Hydrol. Eng., 12, 347–368, 2007.
Gold, A., Anarde, K., Grimley, L., Neve, R., Srebnik, E. R., Thelen, T., Whipple, A., and Hino, M.: Data from the drain: a sensor framework that captures multiple drivers of chronic coastal floods, Water Resour. Res., 59, e2022WR032392, https://doi.org/10.1029/2022WR032392, 2023.
Green, J., Haigh, I. D., Quinn, N., Neal, J., Wahl, T., Wood, M., Eilander, D., de Ruiter, M., Ward, P., and Camus, P.: A comprehensive review of coastal compound flooding literature, arXiv [preprint], https://doi.org/10.48550/arXiv.2404.01321, 2024.
Haidvogel, D. B., Arango, H. G., Hedstrom, K., Beckmann, A., Malanotte-Rizzoli, P., and Shchepetkin, A. F.: Model evaluation experiments in the North Atlantic Basin: simulations in nonlinear terrain-following coordinates, Dynam. Atmos. Oceans, 32, 239–281, 2000.
Haidvogel, D. B., Arango, H., Budgell, W. P., Cornuelle, B. D., Curchitser, E., Di Lorenzo, E., Fennel, K., Geyer, W. R., Hermann, A. J., and Lanerolle, L.: Ocean forecasting in terrain-following coordinates: Formulation and skill assessment of the Regional Ocean Modeling System, J. Comput. Phys., 227, 3595–3624, 2008.
Jane, R., Wahl, T., Santos, V. M., Misra, S. K., and White, K. D.: Assessing the potential for compound storm surge and extreme river discharge events at the catchment scale with statistical models: sensitivity analysis and recommendations for best practice, J. Hydrol. Eng., 27, 04022001, https://doi.org/10.1061/(ASCE)HE.1943-5584.0002154, 2022.
Kasaei, S. and Orton, P.: Model data, Mendeley [data set], https://doi.org/10.17632/3tz69prrwv.1, 2025.
Kasaei, S., Orton, P., Ralston, D., and Warner, J.: Post-tropical cyclone Ida (2021) flood map for New York City's Jamaica Bay watershed, Mendeley [data set], https://doi.org/10.17632/hs2zt6ngwd.1, 2024.
Kim, H., Villarini, G., Jane, R., Wahl, T., Misra, S., and Michalek, A.: On the generation of high-resolution design events capturing the joint occurrence of rainfall and storm surge in coastal basins, Front. Hydrol., 2022, 224-002, https://ui.adsabs.harvard.edu/abs/2022frhy.conf22402K (last access: 15 April 2025), 2022.
Marchesiello, P., McWilliams, J. C., and Shchepetkin, A.: Open boundary conditions for long-term integration of regional oceanic models, Ocean Model., 3, 1–20, 2001.
Mattocks, C. and Forbes, C.: A real-time, event-triggered storm surge forecasting system for the state of North Carolina, Ocean Model., 25, 95–119, https://doi.org/10.1016/j.ocemod.2008.06.008, 2008.
Mita, K. S., Orton, P., Montalto, F., Saleh, F., and Rockwell, J.: Sea Level Rise-Induced Transition from Rare Fluvial Extremes to Chronic and Compound Floods, Water, 15, 2671, https://doi.org/10.3390/w15142671, 2023.
Mukai, A. Y., Westerink, J. J., Luettich, R. A., and Mark, D. J.: Eastcoast 2001, a tidal constituent database for the western North Atlantic, Gulf of Mexico, and Caribbean Sea, https://apps.dtic.mil/sti/tr/pdf/ADA408733.pdf (last access: 10 September 2023), 2002.
MyCoast New York: High Water Reports, MyCoast [data set], https://mycoast.org/search-reports?state=ny&fwp_categories=highwater, last access: 1 August 2023.
Mydlarz, C., Sai Venkat Challagonda, P., Steers, B., Rucker, J., Brain, T., Branco, B., Burnett, H. E., Kaur, A., Fischman, R., and Graziano, K.: FloodNet: Low-Cost Ultrasonic Sensors for Real-Time Measurement of Hyperlocal, Street-Level Floods in New York City, Water Resour. Res., 60, e2023WR036806, https://doi.org/10.1029/2023WR036806, 2024.
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.
National Severe Storms Laboratory: Warn-on-Forecast: Tornado Outbreak & Historic Flash Flood with Post-Tropical Cyclone Ida, 1–2 September, 2021, https://www.nssl.noaa.gov/projects/wof/casestudies/hurricane-ida-sep2021/ (last access: 15 August 2024), 2021.
New York City Government: Hurricane Ida – Community Development Block Grant Disaster Recovery: https://www.nyc.gov/site/cdbgdr/hurricane-ida/hurricane-ida.page, last access: 10 August 2024.
NOAA National Centers for Environmental Information: North American Mesoscale (NAM) Analysis Data, NOAA NCEI [data set], https://www.ncei.noaa.gov/thredds/catalog/model-nam218/202108/20210828/catalog.html (last access: 1 August 2023), 2023.
National Oceanic and Atmospheric Administration: Coastal Change Analysis Program (C-CAP) Regional Land Cover, National Oceanic and Atmospheric Administration [data set], https://coast.noaa.gov/digitalcoast/data/ccapregional.html (last access: 15 August 2023), 2016.
National Oceanic and Atmospheric Administration: What percentage of the American population lives near the coast?: https://oceanservice.noaa.gov/facts/population.html (last access: 2 June 2024), 2018.
National Weather Service: Precipitation Frequency Data Server (PFDS), https://hdsc.nws.noaa.gov/pfds/pfds_gis.html, last access: 10 August 2024.
NYC-DEP: Jamaica Bay Watershed Protection Plan Update, NYC Department of Environmental Protection, https://a860-gpp.nyc.gov/concern/parent/tb09j798b/file_sets/pz50gz44d (last access: 30 August 2023), 2018.
NYC-MOCEJ: New York City Stormwater Resiliency Plan, Report, NYC Department of Environmental Protection, https://www.nyc.gov/assets/orr/pdf/publications/stormwater-resiliency-plan.pdf (last access: 1 December 2023), 2023.
Orton, P., Georgas, N., Blumberg, A., and Pullen, J.: Detailed modeling of recent severe storm tides in estuaries of the New York City region, J. Geophys. Res.-Oceans, 117, C09030, https://doi.org/10.1029/2012JC008220, 2012.
Orton, P., Vinogradov, S., Georgas, N., Blumberg, A., Lin, N., Gornitz, V., Little, C., Jacob, K., and Horton, R.: New York City panel on climate change 2015 report chapter 4: dynamic coastal flood modeling, Ann. NY Acad. Sci., 1336, 56–66, 2015.
Orton, P. M., Sanderson, E. W., Talke, S. A., Giampieri, M., and MacManus, K.: Storm tide amplification and habitat changes due to urbanization of a lagoonal estuary, Nat. Hazards Earth Syst. Sci., 20, 2415–2432, https://doi.org/10.5194/nhess-20-2415-2020, 2020a.
Orton, P. M., Conticello, F. R., Cioffi, F., Hall, T. M., Georgas, N., Lall, U., Blumberg, A. F., and MacManus, K.: Flood hazard assessment from storm tides, rain and sea level rise for a tidal river estuary, Nat. Hazards, 102, 729–757, https://doi.org/10.1007/s11069-018-3251-x, 2020b.
Peters, J. C.: HEC-HMS Hydrologic Modeling System: User's Manual, US Army Corps of Engineers, Hydrologic Engineering Center, 1998.
Ralston, D. K.: Impacts of storm surge barriers on drag, mixing, and exchange flow in a partially mixed estuary, J. Geophys. Res.-Oceans, 127, e2021JC018246, https://doi.org/10.1029/2021JC018246, 2022.
Rosenzweig, B., Herreros Cantis, P., Kim, Y., Cohn, A., Grove, K., Brock, J., Yesuf, J., Mistry, P., Welty, C., and McPhearson, T.: The value of urban flood modeling, Earth's Future, 9, e2020EF001739, https://doi.org/10.1029/2020EF001739, 2021.
Rossman, L. A. and Huber, W. C.: Storm water management model reference manual Volume I – Hydrology (Revised), US Environmental Protection Agency: Cincinnati, OH, USA, http://nepis.epa.gov/Exe/ZyPDF.cgi?Dockey=P100NYRA.txt (last access: 1 February 2024), 2016.
Santiago-Collazo, F. L., Bilskie, M. V., and Hagen, S. C.: A comprehensive review of compound inundation models in low-gradient coastal watersheds, Environ. Modell. Softw., 119, 166–181, https://doi.org/10.1016/j.envsoft.2019.06.002, 2019.
Santiago-Collazo, F. L., Bilskie, M. V., Bacopoulos, P., and Hagen, S. C.: Compound inundation modeling of a 1-D idealized coastal watershed using a reduced-physics approach, Water Resour. Res., 60, e2023WR035718, https://doi.org/10.1029/2023WR035718, 2024.
Schroeder, D. W., Tsegaye, S., Singleton, T. L., and Albrecht, K. K.: GIS-and ICPR-Based Approach to Sustainable Urban Drainage Practices: Case Study of a Development Site in Florida, Water, 14, 1557, https://doi.org/10.3390/w14101557, 2022.
Sebastian, A., Bader, D., Nederhoff, C., Leijnse, T., Bricker, J., and Aarninkhof, S.: Hindcast of pluvial, fluvial, and coastal flood damage in Houston, Texas during Hurricane Harvey (2017) using SFINCS, Nat. Hazards, 109, 2343–2362, 2021.
Shchepetkin, A. F. and McWilliams, J. C.: The regional oceanic modeling system (ROMS): a split-explicit, free-surface, topography-following-coordinate oceanic model, Ocean Model., 9, 347–404, 2005.
Slater, L. J. and Villarini, G.: Recent trends in US flood risk, Geophys. Res. Lett., 43, 12428–412436, 2016.
Smith, J. A., Baeck, M. L., Su, Y., Liu, M., and Vecchi, G. A.: Strange storms: Rainfall extremes from the remnants of Hurricane Ida (2021) in the northeastern US, Water Resour. Res., 59, e2022WR033934, https://doi.org/10.1029/2022WR033934, 2023.
Swanson, L., Dorsch, M., Giampieri, M., Orton, P., Parris, A. S., and Sanderson, E. W.: Dynamics of the biophysical systems of Jamaica Bay, Prospects for Resilience: Insights from New York City's Jamaica Bay, 65–89, https://link.springer.com/chapter/10.5822/978-1-61091-734-6_4 (last access: 15 February 2024), 2016.
Trenberth, K. E.: Changes in precipitation with climate change, Clim. Res., 47, 123–138, 2011.
USACE: New York-New Jersey Harbor and Tributaries Coastal Storm Risk Management Feasibility Study: Draft Integrated Feasibility Report and Tier 1 Environmental Impact Statement, New York, NY, https://www.nan.usace.army.mil/Portals/37/NYNJHATS Draft Integrated Feasibility Report Tier 1 EIS.pdf (last access: 15 June 2024), 2022.
U.S. Department of Agriculture, Natural Resources Conservation Service: Web Soil Survey, https://websoilsurvey.nrcs.usda.gov, last access: 15 September 2024.
U.S. Geological Survey: National Water Information System (NWIS) Mapper, U.S. Geological Survey [data set], https://maps.waterdata.usgs.gov/mapper/index.html (last access: 1 August 2023), 2019.
U.S. Geological Survey: Short-Term Network: Flood Event Viewer for Hurricane Ida, U.S. Geological Survey [data set], https://stn.wim.usgs.gov/FEV/#2021Ida (last access: 1 August 2023), 2021.
USGS: COAWST, USGS [code], https://code.usgs.gov/coawstmodel/COAWST, last access: 1 April 2025.
Wahl, T., Jain, S., Bender, J., Meyers, S. D., and Luther, M. E.: Increasing risk of compound flooding from storm surge and rainfall for major US cities, Nat. Clim. Change, 5, 1093–1097, 2015.
Warner, J. C., Armstrong, B., He, R., and Zambon, J. B.: Development of a coupled ocean–atmosphere–wave–sediment transport (COAWST) modeling system, Ocean Model., 35, 230–244, 2010.
Warner, J. C., Defne, Z., Haas, K., and Arango, H. G.: A wetting and drying scheme for ROMS, Comput. Geosci., 58, 54–61, 2013.
Xu, D., Bisht, G., Engwirda, D., Feng, D., Tan, Z., and Ivanov, V. Y.: Uncertainties in simulating flooding during Hurricane Harvey using 2D shallow water equations, Water Resour. Res., 61, e2024WR038032, https://doi.org/10.1029/2024WR038032, 2025.
Ye, F., Zhang, Y. J., Yu, H., Sun, W., Moghimi, S., Myers, E., Nunez, K., Zhang, R., Wang, H. V., and Roland, A.: Simulating storm surge and compound flooding events with a creek-to-ocean model: Importance of baroclinic effects, Ocean Model., 145, 101526, https://doi.org/10.1016/j.ocemod.2019.101526, 2020.
Zellou, B. and Rahali, H.: Assessment of the joint impact of extreme rainfall and storm surge on the risk of flooding in a coastal area, J. Hydrol., 569, 647–665, 2019.
Zhang, J., Howard, K., Langston, C., Kaney, B., Qi, Y., Tang, L., Grams, H., Wang, Y., Cocks, S., and Martinaitis, S.: Multi-Radar Multi-Sensor (MRMS) quantitative precipitation estimation: Initial operating capabilities, B. Am. Meteorol. Soc., 97, 621–638, 2016.
Zhu, J.: Impact of Climate Change on Extreme Rainfall across the United States, J. Hydrol. Eng., 18, 1301–1309, https://doi.org/10.1061/(ASCE)HE.1943-5584.0000725, 2013.
Zinda, J. A., Williams, L. B., Kay, D. L., and Alexander, S. M.: Flood risk perception and responses among urban residents in the northeastern United States, Int. J. Disast. Risk Re., 64, 102528, https://doi.org/10.1016/j.ijdrr.2021.102528, 2021.
Short summary
Coastal urban areas are highly prone to flooding from rainfall, storm surge, and the combination of both. We improve a coastal model and use it to quantify flooding from Hurricane Ida in the Jamaica Bay watershed of New York City (NYC), creating a flood map and flooded area estimation. Experiments with shifted storm tracks and rainfall timing at high tide show that Ida, already the worst rainfall in NYC, could have been worse. This highlights the area's vulnerability and the need for thorough flood risk analysis.
Coastal urban areas are highly prone to flooding from rainfall, storm surge, and the combination...
