Articles | Volume 23, issue 8
https://doi.org/10.5194/hess-23-3353-2019
© Author(s) 2019. 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-23-3353-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
14 Aug 2019
Research article |
| 14 Aug 2019
| 14 Aug 2019
Modeling the high-resolution dynamic exposure to flooding in a city region
Xuehong Zhu
Key Laboratory of VGE of Ministry of Education, Nanjing Normal
University, Nanjing, China
Qiang Dai
CORRESPONDING AUTHOR
Key Laboratory of VGE of Ministry of Education, Nanjing Normal
University, Nanjing, China
WEMRC, Department of Civil Engineering, University of Bristol,
Bristol, UK
Jiangsu Center for Collaborative Innovation in Geographical
Information Resource Development and Application, Nanjing, China
Dawei Han
WEMRC, Department of Civil Engineering, University of Bristol,
Bristol, UK
WEMRC, Department of Civil Engineering, University of Bristol,
Bristol, UK
Shaonan Zhu
College of Geographical and Biological Information, Nanjing University of Posts and Telecommunications, Nanjing, China
Shuliang Zhang
CORRESPONDING AUTHOR
Key Laboratory of VGE of Ministry of Education, Nanjing Normal
University, Nanjing, China
Related authors
No articles found.
Cristina Prieto, Dhruvesh Patel, Dawei Han, Benjamin Dewals, Michaela Bray, and Daniela Molinari
Nat. Hazards Earth Syst. Sci., 24, 3381–3386, https://doi.org/10.5194/nhess-24-3381-2024, https://doi.org/10.5194/nhess-24-3381-2024, 2024
Yu Gao, Haipeng Lu, Yaru Zhang, Hengxu Jin, Shuai Wu, Yixuan Gao, and Shuliang Zhang
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-144, https://doi.org/10.5194/nhess-2024-144, 2024
Preprint under review for NHESS
Short summary
Short summary
This study focuses on the Yangtze River Delta Urban Agglomeration (YRDUA), where we determined flood risk assessment indices across different dimensions, including hazard, exposure, vulnerability, and resilience. We constructed a flood risk assessment model using AutoML and AHP to examine the spatial and temporal changes in flood risk in the region over the past 30 years (1990 to 2020), aiming to provide a scientific basis for flood prevention and resilience strategies in the YRDUA.
S. Zhu, H. Zhang, Y. Jiang, and X. Yang
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B4-2022, 217–222, https://doi.org/10.5194/isprs-archives-XLIII-B4-2022-217-2022, https://doi.org/10.5194/isprs-archives-XLIII-B4-2022-217-2022, 2022
Xichao Gao, Zhiyong Yang, Dawei Han, Kai Gao, and Qian Zhu
Hydrol. Earth Syst. Sci., 25, 6023–6039, https://doi.org/10.5194/hess-25-6023-2021, https://doi.org/10.5194/hess-25-6023-2021, 2021
Short summary
Short summary
We proposed a theoretical framework and conducted a laboratory experiment to understand the relationship between wind and the rainfall–runoff process in urban high-rise building areas. The runoff coefficient (relating the amount of runoff to the amount of precipitation received) found in the theoretical framework was close to that found in the laboratory experiment.
Qiang Dai, Jingxuan Zhu, Shuliang Zhang, Shaonan Zhu, Dawei Han, and Guonian Lv
Hydrol. Earth Syst. Sci., 24, 5407–5422, https://doi.org/10.5194/hess-24-5407-2020, https://doi.org/10.5194/hess-24-5407-2020, 2020
Short summary
Short summary
Rainfall is a driving force that accounts for a large proportion of soil loss around the world. Most previous studies used a fixed rainfall–energy relationship to estimate rainfall energy, ignoring the spatial and temporal changes of raindrop microphysical processes. This study proposes a novel method for large-scale and long-term rainfall energy and rainfall erosivity investigations based on rainfall microphysical parameterization schemes in the Weather Research and Forecasting (WRF) model.
Xichao Gao, Zhiyong Yang, Dawei Han, Guoru Huang, and Qian Zhu
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2020-367, https://doi.org/10.5194/hess-2020-367, 2020
Manuscript not accepted for further review
Short summary
Short summary
Input errors and parameter errors are two main sources of uncertainties in hydrological model calibration. We developed a new Bayesian framework for automatic calibration of the Storm Water Management Model (SWMM), simultaneously considering parameter and input uncertainties and verified the framework with a case study. The results shows that calibration considering both parameter and input uncertainties captures peak flow much better that only considering parameter uncertainty.
Lu Zhuo, Qiang Dai, Binru Zhao, and Dawei Han
Hydrol. Earth Syst. Sci., 24, 2577–2591, https://doi.org/10.5194/hess-24-2577-2020, https://doi.org/10.5194/hess-24-2577-2020, 2020
Short summary
Short summary
Soil moisture plays an important role in hydrological modelling. However, most existing in situ observation networks rarely provide sufficient coverage to capture soil moisture variations. Clearly, there is a need to develop a systematic approach, so that with the minimal number of sensors the soil moisture information could be captured accurately. In this study, a simple and low-data requirement method is proposed (WRF, PCA, CA), which can provide very efficient soil moisture estimations.
Cristina Prieto, Dhruvesh Patel, and Dawei Han
Nat. Hazards Earth Syst. Sci., 20, 1045–1048, https://doi.org/10.5194/nhess-20-1045-2020, https://doi.org/10.5194/nhess-20-1045-2020, 2020
Lu Zhuo, Qiang Dai, Dawei Han, Ningsheng Chen, and Binru Zhao
Hydrol. Earth Syst. Sci., 23, 4199–4218, https://doi.org/10.5194/hess-23-4199-2019, https://doi.org/10.5194/hess-23-4199-2019, 2019
Short summary
Short summary
This study assesses the usability of WRF model-simulated soil moisture for landslide monitoring in northern Italy. In particular, three advanced land surface model schemes (Noah, Noah-MP, and CLM4) are used to provide multi-layer soil moisture data. The results have shown Noah-MP can provide the best landslide monitoring performance. It is also demonstrated that a single soil moisture sensor located in plain area has a high correlation with a significant proportion of the study area.
Binru Zhao, Qiang Dai, Dawei Han, Huichao Dai, Jingqiao Mao, and Lu Zhuo
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2019-150, https://doi.org/10.5194/hess-2019-150, 2019
Revised manuscript not accepted
Qi Chu, Zongxue Xu, Yiheng Chen, and Dawei Han
Hydrol. Earth Syst. Sci., 22, 3391–3407, https://doi.org/10.5194/hess-22-3391-2018, https://doi.org/10.5194/hess-22-3391-2018, 2018
Short summary
Short summary
The effects of WRF domain configurations and spin-up time on rainfall were evaluated at high temporal and spatial scales for simulating an extreme sub-daily heavy rainfall (SDHR) event. Both objective verification metrics and subjective verification were used to identify the likely best set of the configurations. Results show that re-evaluation of these WRF settings is of great importance in improving the accuracy and reliability of the rainfall simulations in the regional SDHR applications.
S. Zhu, Q. Yang, and J. Shao
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 2599–2601, https://doi.org/10.5194/isprs-archives-XLII-3-2599-2018, https://doi.org/10.5194/isprs-archives-XLII-3-2599-2018, 2018
Dong-Ik Kim, Hyun-Han Kwon, and Dawei Han
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2018-36, https://doi.org/10.5194/hess-2018-36, 2018
Manuscript not accepted for further review
Short summary
Short summary
This study introduces a new QM approach based on a composite distribution of a generalized Pareto distribution for the upper tail and a gamma distribution for the interior part of the distribution. The proposed composite distributions provide a significant reduction of the biases compared with that of the conventional method for the extremes. The proposed approach can provide a useful alternative for the bias correction of a regional-scale modeled data with a limited network of rain gauges.
Binru Zhao, Huichao Dai, Dawei Han, and Guiwen Rong
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2017-396, https://doi.org/10.5194/hess-2017-396, 2017
Revised manuscript not accepted
Short summary
Short summary
This study compared the hydrological model performance of different sub-annual calibration schemes, which take into account intra-annual variations of climate. Two methods recognizing climatic patterns were applied to partition sub-periods with hydroclimatic similarities. The effect of time scales on sub-annual calibration schemes was also studied. Results indicate when using sub-annual calibration schemes, the selection of partitioning method and time scale is important to model performances.
Lu Zhuo and Dawei Han
Hydrol. Earth Syst. Sci., 21, 3267–3285, https://doi.org/10.5194/hess-21-3267-2017, https://doi.org/10.5194/hess-21-3267-2017, 2017
Short summary
Short summary
Reliable estimation of hydrological soil moisture state is of critical importance in operational hydrology to improve the flood prediction and hydrological cycle description. This paper attempts for the first time to build a soil moisture product directly applicable to hydrology using multiple data sources retrieved from remote sensing and land surface modelling. The result shows a significant improvement of the soil moisture state accuracy; the method can be easily applied in other catchments.
Jun Zhang, Dawei Han, Yang Song, and Qiang Dai
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2017-289, https://doi.org/10.5194/hess-2017-289, 2017
Preprint retracted
Short summary
Short summary
We explore unit hydrograph (UH) affected by geomorphology that could be used in ungauged catchments. Virtual catchments approach (VCA) is used instead of gauged catchments in runoff modelling. Catchment shape is newly introduced and the agreement of the results with the hydrological principles verifies the reliability of VCA. With the robust VCA, a large amount of catchments can be created with desirable features to explore a more comprehensive equation that can be used in ungauged catchments.
Remko Nijzink, Christopher Hutton, Ilias Pechlivanidis, René Capell, Berit Arheimer, Jim Freer, Dawei Han, Thorsten Wagener, Kevin McGuire, Hubert Savenije, and Markus Hrachowitz
Hydrol. Earth Syst. Sci., 20, 4775–4799, https://doi.org/10.5194/hess-20-4775-2016, https://doi.org/10.5194/hess-20-4775-2016, 2016
Short summary
Short summary
The core component of many hydrological systems, the moisture storage capacity available to vegetation, is typically treated as a calibration parameter in hydrological models and often considered to remain constant in time. In this paper we test the potential of a recently introduced method to robustly estimate catchment-scale root-zone storage capacities exclusively based on climate data to reproduce the temporal evolution of root-zone storage under change (deforestation).
Kue Bum Kim, Hyun-Han Kwon, and Dawei Han
Hydrol. Earth Syst. Sci., 20, 2019–2034, https://doi.org/10.5194/hess-20-2019-2016, https://doi.org/10.5194/hess-20-2019-2016, 2016
Short summary
Short summary
A primary advantage of using model ensembles for climate change impact studies is to represent the uncertainties associated with models through the ensemble spread. Currently, most of the conventional bias correction methods adjust all the ensemble members to one reference observation. As a result, the ensemble spread is degraded during bias correction. However the proposed method is able to correct the bias and conform to the ensemble spread so that the ensemble information can be better used.
S. Ceola, B. Arheimer, E. Baratti, G. Blöschl, R. Capell, A. Castellarin, J. Freer, D. Han, M. Hrachowitz, Y. Hundecha, C. Hutton, G. Lindström, A. Montanari, R. Nijzink, J. Parajka, E. Toth, A. Viglione, and T. Wagener
Hydrol. Earth Syst. Sci., 19, 2101–2117, https://doi.org/10.5194/hess-19-2101-2015, https://doi.org/10.5194/hess-19-2101-2015, 2015
Short summary
Short summary
We present the outcomes of a collaborative hydrological experiment undertaken by five different international research groups in a virtual laboratory. Moving from the definition of accurate protocols, a rainfall-runoff model was independently applied by the research groups, which then engaged in a comparative discussion. The results revealed that sharing protocols and running the experiment within a controlled environment is fundamental for ensuring experiment repeatability and reproducibility.
J. Liu and D. Han
Hydrol. Earth Syst. Sci., 17, 3639–3659, https://doi.org/10.5194/hess-17-3639-2013, https://doi.org/10.5194/hess-17-3639-2013, 2013
J. Liu, M. Bray, and D. Han
Hydrol. Earth Syst. Sci., 17, 3095–3110, https://doi.org/10.5194/hess-17-3095-2013, https://doi.org/10.5194/hess-17-3095-2013, 2013
Related subject area
Subject: Urban Hydrology | Techniques and Approaches: Modelling approaches
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
Beyond Total Impervious Area: A New Lumped Descriptor of Basin-Wide Hydrologic Connectivity for Characterizing Urban Watersheds
INSPIRE Game: Integration of vulnerability in 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
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
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
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.
Francesco Dell'Aira and Claudio I. Meier
EGUsphere, https://doi.org/10.5194/egusphere-2024-1956, https://doi.org/10.5194/egusphere-2024-1956, 2024
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 Jha
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-116, https://doi.org/10.5194/hess-2024-116, 2024
Revised manuscript accepted for HESS
Short summary
Short summary
The study presents a serious game experiment based on the real event of flash flood on 26–27th July, 2005 in Mumbai, India. The aim is to examine different combinations of hazard, exposure and vulnerability information and identify the most effective information for making emergency decisions. Results show that the efficacy of information depends upon the severity of the situation. Qualitative information of rainfall is more preferable than the quantitative for making decisions.
Yan Liu, Ting Zhang, Yi Ding, Aiqing Kang, Xiaohui Lei, and Jianzhu Li
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-100, https://doi.org/10.5194/hess-2024-100, 2024
Revised manuscript accepted for HESS
Short summary
Short summary
In coastal cities, rainfall and storm surges cause compound flooding. This study quantifies the contributions of rainfall and tides to compound flooding and analyzes interactions between different flood types. Findings show rainfall has a greater effect on flooding compared to tidal levels. The interaction between fluvial and pluvial flooding exacerbates the flood disaster. Notably, tidal levels have the most significant impact during the interaction phase of these flood types.
Tabea Cache, Milton Salvador Gomez, Tom Beucler, Jovan Blagojevic, João Paulo Leitao, and Nadav Peleg
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-63, https://doi.org/10.5194/hess-2024-63, 2024
Revised manuscript accepted for HESS
Short summary
Short summary
We introduce a new deep-learning model that addresses limitations of existing urban flood models in handling varied terrains and rainfall events. Our model subdivides the city into small patches and presents a novel approach to incorporate broader spatial information. It accurately predicts high-resolution flood maps across diverse rainfall events and cities (on a minutes and meters scale) that haven’t been seen by the model, which offers valuable insights for urban flood mitigation strategies.
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.
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
Abt, S., Wittier, R., Taylor, A., and Love, D.: Human Stability In A High Flood Hazard Zone, J. Am. Water Resour. Assoc., 25, 881–890,
https://doi.org/10.1111/j.1752-1688.1989.tb05404.x, 1989.
Bates, P. D. and De Roo, A. P. J.: A simple raster-based model for flood
inundation simulation, J. Hydrol., 236, 54–77, https://doi.org/10.1016/S0022-1694(00)00278-X, 2000.
Bates, P., Trigg, M., Neal, J., and Dabrowa, A.: LISFLOOD-FP User manual, Code release 5.9.6, School of Geographical Sciences, University of Bristol, Bristol, UK, available at:
https://www.bristol.ac.uk/media-library/sites/geography/migrated/documents/lisflood-manual-v5.9.6.pdf
(last access: March 2019), 2013.
Bekhor, S., Ben-Akiva, M. E., and Ramming, M. S.: Evaluation of choice set
generation algorithms for route choice models, Ann. Operat. Res., 144, 235–247, https://doi.org/10.1007/s10479-006-0009-8, 2006.
Brunner, G. W.: HEC-RAS River Analysis System User's Manual Version 4.0,
Report CPD-68,, US Army Corps of Engineers, Hydrologic Engineering Center, USA, 2008.
Cen, G., Shen, J., and Fan, R.: Research on rainfall pattern of urban design
storm, Adv. Water Sci., 9, 41–46, https://doi.org/10.14042/j.cnki.32.1309.1998.01.007, 1998.
Charley, W., Pabst, A., and Peters, J.: The Hydrologic Modeling System (HEC-HMS): Design and Development Issues, Technical Paper No. 149, Hydrological Engineering Center, US Army Corps of Engineers, USA, 1995.
Chen, Y., Zhou, H., Zhang, H., Du, G., and Zhou, J.: Urban flood risk warning under rapid urbanization, Environ. Res., 139, 3–10, https://doi.org/10.1016/j.envres.2015.02.028, 2015.
Cools, M., Moons, E., Creemers, L., and Wets, G.: Changes in travel behavior
in response to weather conditions: do type of weather and trip purpose matter?, Transport. Res. Rec.: J. Transport. Res. Board, 2157, 22–28, https://doi.org/10.3141/2157-03, 2010.
Dai, Q., Han, D., Rico-Ramirez, M. A., and Srivastava, P. K.: Multivariate
Distributed Ensemble Generator: A new scheme for ensemble radar precipitation estimation over temperate maritime climate, J. Hydrol., 511, 17–27, 2014.
Dai, Q., Rico-Ramirez, M. A., Han, D., Islam, T., and Liguori S.: Probabilistic radar rainfall nowcasts using empirical and theoretical
uncertainty models, Hydrol. Process., 29, 66–79, 2015.
Dankers, R. and Feyen, L.: Climate change impact on flood hazard in Europe: An assessment based on high-resolution climate simulations, J. Geophys. Res.-Atmos., 113, D19105, https://doi.org/10.1029/2007JD009719, 2008.
Dawson, R. J., Peppe, R., and Wang, M.: An agent-based model for risk-based
flood incident management, Nat. Hazards, 59, 167–189, https://doi.org/10.1007/s11069-011-9745-4, 2011.
DEFRA and Environment Agency: Flood risks to people phase 1: R & D Technical Report FD2317, DEFRA, London, 2003.
DHI – Danish Hydraulic Institute: MIKE SHE Water movement user manual, DHI
Water & Environment, Denmark, 2000.
Dijkstra, E. W.: A note on two problems in connexion with graphs, Numer. Math., 1, 269–271, 1959.
Drobot, S. D., Benight, C., and Gruntfest, E. C.: Risk factors for driving into flooded roads, Environ. Hazards, 7, 227–234, https://doi.org/10.1016/j.envhaz.2007.07.003, 2007.
Gain, A. K., Mojtahed, V., Biscaro, C., Balbi, S., and Giupponi, C.: An
integrated approach of flood risk assessment in the eastern part of Dhaka
City, Nat. Hazards, 79, 1499–1530, https://doi.org/10.1007/s11069-015-1911-7, 2015.
Geopandas: GeoPandas 0.5.1 – GeoPandas 0.5.1 documentation, available at:
http://geopandas.org/ (last access: March 2019), 2018.
Guo, E., Zhang, J., Ren, X., Zhang, Q., and Sun, Z.: Integrated risk assessment of flood disaster based on improved set pair analysis and the
variable fuzzy set theory in central Liaoning Province, China, Nat. Hazards, 74, 947–965, https://doi.org/10.1007/s11069-014-1238-9, 2014.
Hammond, M. J., Chen, A. S., Djordjević, S., Butler, D., and Mark, O.:
Urban flood impact assessment: A state-of-the-art review, Urban Water J., 12, 14–29, https://doi.org/10.1080/1573062X.2013.857421, 2015.
Havnø, K., Madsen, M. N., and Dørge, J.: MIKE 11 – a generalized river
modelling package, in: Computer models of watershed hydrology, Water Resources Publications, Colorado, 733–782, 1995.
Horritt, M. S. and Bates, P. D.: Evaluation of 1D and 2D numerical models for predicting river flood inundation, J. Hydrol., 268, 87–99, https://doi.org/10.1016/S0022-1694(02)00121-X, 2002.
Huang, H., Fan, Y., Yang, S., Li, W., Guo, X., Lai W., and Wang H.: A
multi-agent based theoretical model for dynamic flood disaster risk assessment, Geogr. Res., 34, 1875–1886, 2015.
IPCC.: Summary for Policymakers. I, ie Change Adaptation. A Special Report of
Working Groups I and II of the Intergovernmental Panel on Climate Change,
Cambridge University Press, Cambridge, UK, and New York, NY, USA, 3–21, 2012.
Johnstone, M. A.: Life safety modelling framework and performance measures to assess community protection systems: application to tsunami emergency preparedness and dam safety management, PhD thesis, University of British
Columbia, British Columbia, 2012.
Jonkman, S. N. and Kelman, I.: An analysis of the causes and circumstances of flood disaster deaths, Disasters, 29, 75–97, https://doi.org/10.1111/j.0361-3666.2005.00275.x, 2005.
Jonkman, S. N. and Penning-Rowsell, E.: Human Instability in Flood Flows 1, J. Am. Water Resour. Assoc., 44, 1208–1218, https://doi.org/10.1111/j.1752-1688.2008.00217.x, 2008.
Karvonen, R. A., Hepojoki, A., Huhta, H. K., and Louhio, A.: The use of physical models in dam-break analysis, RESCDAM Final Report, Helsinki University of Technology, Helsinki, Finland, 2000.
Liang, Y., Wen, J., Du, S., Xu, H., and Yan J.: Spatial-temporal
Distribution Modeling of Population and its Applications in Disaster and
Risk Management, J. Catastrophol., 30, 220–228,
https://doi.org/10.3969/j.issn.1000-811X.2015.04.038, 2015.
Lind, N., Hartford, D., and Assaf, H.: Hydrodynamic models of human stability in a flood, J. Am. Water Resour. Assoc., 40, 89–96,
https://doi.org/10.1111/j.1752-1688.2004.tb01012.x, 2004.
Lindberg, S., Nielsen, J. B., and Carr, R.: An integrated PC-modelling system for hydraulic analysis of drainage systems, in: Watercomp'89: The First Australasian Conference on Technical Computing in the Water Industry, Institution of Engineers, Australia, p. 127, 1989.
Lishui Municipal Statistics Bureau and Survey Office of the National Bureau of Statistics of China in Lishui: Lishui Statistical Yearbool, China Statistics Press, available at: http://tjj.lishui.gov.cn (last access: March 2019), 2014.
Lü, G., Batty, M., Strobl, J., Lin, H., Zhu, A. X., and Chen, M.:
Reflections and speculations on the progress in Geographic Information
Systems (GIS): a geographic perspective, Int. J. Geogr. Inform. Sci., 33, 346–367, https://doi.org/10.1080/13658816.2018.1533136, 2018.
Mahe, G., Paturel, J. E., Servat, E., Conway, D., and Dezetter, A.: The impact of land use change on soil water holding capacity and river flow
modelling in the Nakambe River, Burkina-Faso, J. Hydrol., 300, 33–43, https://doi.org/10.1016/j.jhydrol.2004.04.028, 2005.
Mansur, A. V., Brondízio, E. S., Roy, S., Hetrick, S., Vogt, N. D., and
Newton, A.: An assessment of urban vulnerability in the Amazon Delta and
Estuary: a multi-criterion index of flood exposure, socio-economic conditions and infrastructure, Sustainabil. Sci., 11, 625–643, https://doi.org/10.1007/s11625-016-0355-7, 2016.
Matplotlib: Matplotlib: Python plotting – Matplotlib 3.1.1 documentation, available at: https://matplotlib.org/ (last access: March 2019), 2018.
Moel, H. D., Aerts, J. C., and Koomen, E.: Development of flood exposure in
the Netherlands during the 20th and 21st century, Global Environ. Change, 21, 620–627, https://doi.org/10.1016/j.gloenvcha.2010.12.005, 2011.
National Earth System Science Data Sharing Infrastructure and National Science & Technology Infrastructure of China: 1 km grid population data, availablea at: http://www.geodata.cn (last access: March 2019), 2010
Papinski, D., Scott, D. M., and Doherty, S. T.: Exploring the route choice decision-making process: A comparison of planned and observed routes obtained using person-based GPS, Transport. Res. Pt. F, 12, 347–358, https://doi.org/10.1016/j.trf.2009.04.001, 2009.
Parker, D., Fordham, M., Tunstall, S., and Ketteridge, A. M.: Flood warning
systems under stress in the United Kingdom, Disaster Prevent. Manage.: Int. J., 4, 32–42, https://doi.org/10.1108/09653569510088050, 1995.
Python: Welcome to Python.org, available at: https://www.python.org/ (last access: March 2019), 2018.
Qt: Qt | Cross platform software development for embedded & desktop, available at: https://www.qt.io/ (last access: March 2019), 2018.
Rahman, A.-U.: Disaster risk management, Flood Perspective, VDM Verlag Publishing Co. Ltd, Germany, 2014.
Ramming, M. S.: Network knowledge and route choice, Unpublished PhD Thesis, Massachusetts Institute of Technology, Massachusetts, 2001.
Rossman, L. A.: Storm water management model user's manual Version 5.1
EPA-600/R-14/413b[z], National Risk Management Laboratory Laboratory Office
of Research and Development US Environmental Protection Agency, available at: https://nepis.epa.gov/Exe/ZyPDF.cgi/P100N3J6.PDF?Dockey=P100N3J6.PDF
(last access: March 2019), 2015.
Röthlisberger, V., Zischg, A. P., and Keiler, M.: Identifying spatial
clusters of flood exposure to support decision making in risk management, Sci. Total Environ., 598, 593–603, https://doi.org/10.1016/j.scitotenv.2017.03.216, 2017.
Ruin, I., Gaillard, J. C., and Lutoff, C.: How to get there? Assessing
motorists' flash flood risk perception on daily itineraries, Environ. Hazards, 7, 235–244, https://doi.org/10.1016/j.envhaz.2007.07.005, 2007.
Shabou, S., Ruin, I., Lutoff, C., Debionne, S., Anquetin, S., Creutin, J. D., and Beaufils, X.: MobRISK: a model for assessing the exposure of road users to flash flood events, Nat. Hazards Earth Syst. Sci., 17, 1631–1651, https://doi.org/10.5194/nhess-17-1631-2017, 2017.
Shi, P.: Theory and practice of disaster study, J. Nat. Disasters, 4, 8–19, 1996.
Terti, G., Ruin, I., Anquetin, S., and Gourley, J. J.: Dynamic vulnerability
factors for impact-based flash flood prediction, Nat. Hazards, 79, 1481–1497, https://doi.org/10.1007/s11069-015-1910-8, 2015.
Visual Studio Code: Visual Studio Code – Code Editing, Redefined, available at: https://code.visualstudio.com/ (last access: March 2019), 2018.
Wan, H. and Wang, J.: Analysis of Public Adaptive Behaviors to Drought and
Flood Disasters in Middle Reaches of Weihe River: A Case Study on Qishan County of Shaanxi Province, Acta Agricult. Jiangxi, 29, 107–111, https://doi.org/10.19386/j.cnki.jxnyxb.2017.05.21, 2017.
Weis, S. W. M., Agostini, V. N., Roth, L. M., Gilmer, B., Schill, S. R.,
Knowles, J. E., and Blyther, R.: Assessing vulnerability: an integrated
approach for mapping adaptive capacity, sensitivity, and exposure, Climatic
Change, 136, 615–629, https://doi.org/10.1007/s10584-016-1642-0, 2016.
Werren, G., Reynard, E., Lane, S. N., and Balin, D.: Flood hazard assessment
and mapping in semi-arid piedmont areas: a case study in Beni Mellal, Morocco, Nat. Hazards, 81, 481–511, https://doi.org/10.1007/s11069-015-2092-0, 2016.
Yang, X., Yue, W., and Gao, D.: Spatial improvement of human population
distribution based on multi-sensor remote-sensing data: an input for exposure assessment, Int. J. Remote Sens., 34, 5569–5583, https://doi.org/10.1080/01431161.2013.792970, 2013.
Yin, J., Yu, D., and Wilby, R.: Modelling the impact of land subsidence on
urban pluvial flooding: A case study of downtown Shanghai, China, Sci. Total Environ., 544, 744–753, https://doi.org/10.1016/j.scitotenv.2015.11.159, 2016.
Yin, W., Yu, H., Cui, S., and Wang, J.: Review on methods for estimating the
loss of life induced by heavy rain and floods, Prog. Geogr., 35, 148–158, https://doi.org/10.18306/dlkxjz.2016.02.002, 2016.
Yin, Z.: Research of urban natural disaster risk assessment and case study,
PhD thesis, East China Normal University, Shanghai, China, 2009.
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.
Urban flooding exposure is generally investigated with the assumption of stationary disasters...
