Articles | Volume 17, issue 12
https://doi.org/10.5194/hess-17-4789-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/hess-17-4789-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
04 Dec 2013
Research article |
| 04 Dec 2013
Assessing the hydrologic restoration of an urbanized area via an integrated distributed hydrological model
D. H. Trinh
Department of Civil and Environmental Engineering, National University of Singapore, Singapore, Singapore
T. F. M. Chui
Department of Civil Engineering, The University of Hong Kong, Hong Kong, China
Related authors
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Mengxiang Zhang and Ting Fong May Chui
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-232, https://doi.org/10.5194/hess-2024-232, 2024
Preprint under review for HESS
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This study introduces a multiagent socio-hydrologic framework for city, inter-city, and watershed scale integrated green infrastructures (GI) and water resource management. Applied to the Upper Mississippi River Basin, it explores GI-driven water-sharing dynamics in a watershed. It identifies four city-scale water use patterns and characterizes cost and equity in broader scales, thereby enhancing comprehension of GI's role in water resource management and aiding decision-making.
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
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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.
Related subject area
Subject: Urban Hydrology | Techniques and Approaches: Modelling approaches
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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
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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
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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
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
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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
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A coupled statistical–hydrodynamic model framework is employed to quantitatively evaluate the sensitivity of compound flood hazards to the relative timing of peak storm surges and rainfall. The findings reveal that the timing difference between these two factors significantly affects flood inundation depth and extent. The most severe inundation occurs when rainfall precedes the storm surge peak by 2 h.
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 under review for HESS
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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In this work we investigate the influence rainfall and catchment scales have on hydrological response. This problem is quite relevant in urban areas, where the response is fast due to the high degree of imperviousness. We presented a new approach to classify rainfall variability in space and time and use this classification to investigate rainfall aggregation effects on urban hydrological response. This classification allows the spatial extension of the main core of the storm to be identified.
Suresh Hettiarachchi, Conrad Wasko, and Ashish Sharma
Hydrol. Earth Syst. Sci., 22, 2041–2056, https://doi.org/10.5194/hess-22-2041-2018, https://doi.org/10.5194/hess-22-2041-2018, 2018
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The study examines the impact of higher temperatures expected in a future climate on how rainfall varies with time during severe storm events. The results show that these impacts increase future flood risk in urban environments and that current design guidelines need to be adjusted so that effective adaptation measures can be implemented.
Stephanie Clark, Ashish Sharma, and Scott A. Sisson
Hydrol. Earth Syst. Sci., 22, 1793–1810, https://doi.org/10.5194/hess-22-1793-2018, https://doi.org/10.5194/hess-22-1793-2018, 2018
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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
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This paper proposes a two-step investigation to illustrate the extent of scale effects in urban hydrology. First, fractal tools are used to highlight the scale dependency observed within GIS data inputted in urban hydrological models. Then an intensive multi-scale modelling work was carried out to confirm effects on model performances. The model was implemented at 17 spatial resolutions ranging from 100 to 5 m. Results allow the understanding of scale challenges in hydrology modelling.
Per Skougaard Kaspersen, Nanna Høegh Ravn, Karsten Arnbjerg-Nielsen, Henrik Madsen, and Martin Drews
Hydrol. Earth Syst. Sci., 21, 4131–4147, https://doi.org/10.5194/hess-21-4131-2017, https://doi.org/10.5194/hess-21-4131-2017, 2017
Elena Cristiano, Marie-Claire ten Veldhuis, and Nick van de Giesen
Hydrol. Earth Syst. Sci., 21, 3859–3878, https://doi.org/10.5194/hess-21-3859-2017, https://doi.org/10.5194/hess-21-3859-2017, 2017
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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
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In developed countries, the majority of fatalities during floods occurs as a consequence of inappropriate high-risk behaviour such as walking or driving in floodwaters. This work addresses pedestrians' instability in floodwaters. It analyses both the contribution of flood and human physical characteristics in the loss of stability highlighting the key role of subject height (submergence) and flow regime. The method consists of a re-analysis of experiments and numerical modelling.
Hjalte Jomo Danielsen Sørup, Stylianos Georgiadis, Ida Bülow Gregersen, and Karsten Arnbjerg-Nielsen
Hydrol. Earth Syst. Sci., 21, 345–355, https://doi.org/10.5194/hess-21-345-2017, https://doi.org/10.5194/hess-21-345-2017, 2017
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In this study we propose a methodology changing present-day precipitation time series to reflect future changed climate. Present-day time series have a much finer resolution than what is provided by climate models and thus have a much broader application range. The proposed methodology is able to replicate most expectations of climate change precipitation. These time series can be used to run fine-scale hydrological and hydraulic models and thereby assess the influence of climate change on them.
Tsun-Hua Yang, Gong-Do Hwang, Chin-Cheng Tsai, and Jui-Yi Ho
Hydrol. Earth Syst. Sci., 20, 4731–4745, https://doi.org/10.5194/hess-20-4731-2016, https://doi.org/10.5194/hess-20-4731-2016, 2016
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Taiwan continues to suffer from floods. This study proposes the integration of rainfall thresholds and ensemble precipitation forecasts to provide probabilistic urban inundation forecasts. Utilization of ensemble precipitation forecasts can extend forecast lead times to 72 h, preceding peak flows and allowing response agencies to take necessary preparatory measures. This study also develops a hybrid of real-time observation and rainfall forecasts to improve the first 24 h inundation forecasts.
Christopher A. Sanchez, Benjamin L. Ruddell, Roy Schiesser, and Venkatesh Merwade
Hydrol. Earth Syst. Sci., 20, 1289–1299, https://doi.org/10.5194/hess-20-1289-2016, https://doi.org/10.5194/hess-20-1289-2016, 2016
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The use of authentic learning activities is especially important for place-based geosciences like hydrology, where professional breadth and technical depth are critical for practicing hydrologists. The current study found that integrating computerized learning content into the learning experience, using only a simple spreadsheet tool and readily available hydrological data, can effectively bring the "real world" into the classroom and provide an enriching educational experience.
G. Bruni, R. Reinoso, N. C. van de Giesen, F. H. L. R. Clemens, and J. A. E. ten Veldhuis
Hydrol. Earth Syst. Sci., 19, 691–709, https://doi.org/10.5194/hess-19-691-2015, https://doi.org/10.5194/hess-19-691-2015, 2015
P. Licznar, C. De Michele, and W. Adamowski
Hydrol. Earth Syst. Sci., 19, 485–506, https://doi.org/10.5194/hess-19-485-2015, https://doi.org/10.5194/hess-19-485-2015, 2015
J. Herget, T. Roggenkamp, and M. Krell
Hydrol. Earth Syst. Sci., 18, 4029–4037, https://doi.org/10.5194/hess-18-4029-2014, https://doi.org/10.5194/hess-18-4029-2014, 2014
F. Beck and A. Bárdossy
Hydrol. Earth Syst. Sci., 17, 4851–4863, https://doi.org/10.5194/hess-17-4851-2013, https://doi.org/10.5194/hess-17-4851-2013, 2013
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
Antrop, M.: Landscape change and the urbanization process in Europe, Landsc. Urban Plann., 67, 9–26, https://doi.org/10.1016/s0169-2046(03)00026-4, 2004.
Barron, O. V., Barr, A. D., and Donn, M. J.: Effect of urbanisation on the water balance of a catchment with shallow groundwater, J. Hydrol., 485, 162–0176, https://doi.org/10.1016/j.jhydrol.2012.04.027, 2013.
Bengtsson, L., Grahn, L., and Olsson, J.: Hydrological function of a thin extensive green roof in southern Sweden, Nord. Hydrol., 36, 259–268, 2005.
Bhaduri, B., Harbor, J., Engel, B., and Grove, M.: Assessing watershed-scale, long-term hydrologic impacts of land-use change using a GIS-NPS model, Environ. Manage., 26, 643–658, 2000.
Bliss, D. J., Neufeld, R. D., and Ries, R. J.: Stormwater runoff mitigation using a green roof, Environ. Eng. Sci., 26, 407–417, https://doi.org/10.1089/ees.2007.0186, 2009.
Carter, T. L. and Rasmussen, T. C.: Hydrological behavior of vegetated roofs, JAWRA J. Am. Water Resour. Assoc., 42, 1261–1274, https://doi.org/10.1111/j.1752-1688.2006.tb05299.x, 2006.
Chang, C. L., Lo, S. L., and Huang, S. M.: Optimal strategies for best management practice placement in a synthetic watershed, Environ. Monit. Assess., 153, 359–364, https://doi.org/10.1007/s10661-008-0362-y, 2009.
Chow, V. T.: Open-Channel Hydraulics, McGraw-Hill, 1959.
Collin, M. L. and Melloul, A. J.: Assessing groundwater vulnerability to pollution to promote sustainable urban and rural development, J. Cleaner Product., 11, 727–736, https://doi.org/10.1016/s0959-6526(02)00131-2, 2003.
Czemiel Berndtsson, J.: Green roof performance towards management of runoff water quantity and quality: A review., Ecol. Eng., 36, 351–360, https://doi.org/10.1016/j.ecoleng.2009.12.014, 2010.
Davis, A.: Field performance of bioretention: Hydrology impacts, J. Hydrol. Eng., 13, 90–95, https://doi.org/10.1061/(ASCE)1084-0699(2008)13:2(90), 2008.
DeFries, R. and Eshleman, K. N.: Land-use change and hydrologic processes: a major focus for the future, Hydrol. Process., 18, 2183–2186, https://doi.org/10.1002/hyp.5584, 2004.
Dietz, M. E. and Clausen, J. C.: Stormwater runoff and export changes with development in a traditional and low impact subdivision, J. Environ. Manage., 87, 560–566, https://doi.org/10.1016/j.jenvman.2007.03.026, 2008.
Dow, C. L. and DeWalle, D. R.: Trends in evaporation and Bowen ratio on urbanizing watersheds in eastern United States, Water Resour. Res., 36, 1835–1843, 2000.
Du, J., Qian, L., Rui, H., Zuo, T., Zheng, D., Xu, Y., and Xu, C. Y.: Assessing the effects of urbanization on annual runoff and flood events using an integrated hydrological modeling system for Qinhuai River basin, China, J. Hydrol., 464–465, 127–139, https://doi.org/10.1016/j.jhydrol.2012.06.057, 2012.
Elliott, A., Trowsdale, S., and Wadhwa, S.: Effect of aggregation of on-site stormwater control devices in an urban catchment model, J. Hydrol. Eng., 14, 975–983, https://doi.org/10.1061/(ASCE)HE.1943-5584.0000064, 2009.
Endreny, T. A.: Evaporation, in: Water Encyclopedia, John Wiley & Sons, Inc, 2005.
Freeze, R. A. and Cherry, J. A.: Groundwater, Englewood Cliffs, New Jersey, Prentice Hall, 1979.
Göbel, P., Stubbe, H., Weinert, M., Zimmermann, J., Fach, S., Dierkes, C., Kories, H., Messer, J., Mertsch, V., Geiger, W. F., and Coldewey, W. G.: Near-natural stormwater management and its effects on the water budget and groundwater surface in urban areas taking account of the hydrogeological conditions, J. Hydrol., 299, 267–283, https://doi.org/10.1016/j.jhydrol.2004.08.013, 2004.
Haase, D.: Effects of urbanisation on the water balance – A long-term trajectory, Environ. Impact Assess. Rev., 29, 211–219, https://doi.org/10.1016/j.eiar.2009.01.002, 2009.
Hathaway, A., Hunt, W. F., and Jennings, G.: A field study of green roof hydrologic and water quality performance, Trans. ASABE, 51, 37–43, 2008.
Holman-Dodds, J. K., Bradley, A. A., and Potter, K. W.: Evaluation of hydrologic benefits of infiltration based urban stormwater management, J. Am. Water Resour. Assoc. (JAWRA), 39, 205–215, https://doi.org/10.1111/j.1752-1688.2003.tb01572.x, 2003.
Hutchinson, D., Abrams, P., Retzlaff, R., and Liptan, T.: Stormwater monitoring two ecorrofs in Portland, Oregon, USA, in: Greening Rooftops for Sustainable Communites, Chicago, 2003.
Ives, D. W.: Soil of the Republic of Singapore, in: New Zealand Soil Bureau Scientific Report, Soil Bureau, Lower Hutt, New Zealand, 1977.
James, M. and Dymond, R.: Bioretention hydrologic performance in an urban stormwater network, J. Hydrol. Eng., 17, 431–436, https://doi.org/10.1061/(ASCE)HE.1943-5584.0000448, 2012.
Klöcking, B. and Haberlandt, U.: Impact of land use changes on water dynamics––a case study in temperate meso and macroscale river basins, Phys. Chem. Earth, Parts A/B/C, 27, 619–629, https://doi.org/10.1016/s1474-7065(02)00046-3, 2002.
Konrad, C. P. and Booth, D. B.: Hydrologic changes in urban streams and their ecological significance, in: American Fisheries Society Symposium, Alaska, 157–177, 2005.
Lerner, D. N.: Groundwater recharge in urban areas, in: Hydrological Processes and Water Management in Urban Areas, IAHS, 1990.
Lu, X. X., Wong, P. P., and Chou, L. M.: Singapore's Biophysical Environment, Singapore, The McGraw Hill, 2005.
Mentens, J., Raes, D., and Hermy, M.: Green roofs as a tool for solving the rainwater runoff problem in the urbanized 21st century?, Landsc. Urban Plann., 77, 217–226, https://doi.org/10.1016/j.landurbplan.2005.02.010, 2006.
Moran, A., Hunt, B., and Jennings, G.: A north Carolina field study to evaluate greenroof runoff quantity, runoff quality, and plant growth, in: 2nd Greening Rooftops for Sustainable Communities Conference, Portland, 446–460, 2004.
Ngo, K. M., Turner, B. L., Muller-Landau, H. C., Davies, S. J., Larjavaara, M., Nik Hassan, N. F. b., and Lum, S.: Carbon stocks in primary and secondary tropical forests in Singapore, Forest Ecol. Manage., 296, 81–89, https://doi.org/10.1016/j.foreco.2013.02.004, 2013.
NPark: Greenery provision for roadside, N. P. Board (N. P. Board), Singapore, 2011.
Oberndorfer, E., Lundholm, J., Bass, B., Coffman, R. R., Doshi, H., Dunnett, N., Gaffin, S., KÖHler, M., Liu, K. K. Y., and Rowe, B.: Green roofs as urban ecosystems: Ecological structures, functions, and services, BioScience, 57, 823–833, 2007.
Ott, B. and Uhlenbrook, S.: Quantifying the impact of land-use changes at the event and seasonal time scale using a process-oriented catchment model, Hydrol. Earth Syst. Sci., 8, 62–78, https://doi.org/10.5194/hess-8-62-2004, 2004.
Palla, A., Gnecco, I., and Lanza, L. G.: Hydrologic restoration in the urban environment using green roofs, Water, 2, 140–154, 2010.
Palla, A., Gnecco, I., and Lanza, L. G.: Compared performance of a conceptual and a mechanistic hydrologic models of a green roof, Hydrol. Process., 26, 73–84, https://doi.org/10.1002/hyp.8112, 2012.
PUB: ABC waters master plan for the central catchment, in: ABC Waters Master Plan, Public Utility Board (Water), Singapore, 2007.
PUB: Design guidelines Public Utilities Board, Singapore, 2011.
Rezaur, R. B., Rahardjo, H., Leong, E. C., and Lee,, T. T.: Hydrologic behavior of residual soil slopes in Singapore, J. Hydrol. Eng., 8, 133–144, 2003.
Rose, S. and Peters, N. E.: Effects of urbanization on streamflow in the Atlanta area (Georgia, USA): a comparative hydrological approach, Hydrol. Process., 15, 1441–1457, https://doi.org/10.1002/hyp.218, 2001.
Rowe, D., Rugh, C., VanWoert, N., Monterusso, M., and Russell, D.: Green roof slope, substrate depth, and vegetation influence runoff, in: 1st North American Green Roof Conference: Greening Rooftops for Sustainalbe Communities, Chicago, 354–362, 2003.
Sanders, R. A.: Urban vegetation impacts on the hydrology of Dayton, Ohio, Urban Ecol., 9, 361–376, https://doi.org/10.1016/0304-4009(86)90009-4, 1986.
Schoonover, J. E., Lockaby, B. G., and Helms, B. S.: Impacts of land cover on stream hydrology in the West Georgia Piedmont, USA, J. Environ. Qual., 35, 2123–2131, 2006.
Štofko, P.: Relationships between the parameters of aboveground parts and the parameters of root plates in Norway spruce with respect to soil drainage, J. FOR. SCI, 56, 353–360, 2010.
URA: Singapore Master Plan in 2003 Urban Redevelopment Authority Singapore, 2003.
USGS: USGS Global Visualization Viewer USGS Global Visualization Viewer Virginia, 2010.
VanWoert, N. D., Rowe, D. B., Andresen, J. A., Rugh, C. L., Fernandez, R. T., and Xiao, L.: Green roof stormwater retention: Effects of roof surface, slope and media depth, J. Environ. Qual., 34, 1036–1044, https://doi.org/10.2134/jeq2004.0364, 2005.
Villarreal, E. L., Semadeni-Davies, A., and Bengtsson, L.: Inner city stormwater control using a combination of best management practices, Ecol. Eng., 22, 279–298, https://doi.org/10.1016/j.ecoleng.2004.06.007, 2004.
Wang, Y., Choi, W., and Deal, B. M.: Long-term impacts of land-use change on non-point source pollutant loads for the St. Louis Metropolitan Area, USA, Environ. Manage., 35, 194–205, 2005.
Wells, N.: The role of soils in the utilisation of sewage sludge in Singapore, in: New Zealand Soil Bureau Scientific Report, Soil Bureau, Lower Hutt, New Zealand, 1977.
Xiao, Q. and McPherson, E. G.: Performance of engineered soil and trees in a parking lot bioswale, Urban Water J., 8, 241–253, https://doi.org/10.1080/1573062x.2011.596213, 2011.
Younger, P. L.: Simple generalized methods for estimating aquifer storage parameters, Q. J. Eng. Geol. Hydrogeol. 26, 127–135, 1993.
