Articles | Volume 26, issue 11
https://doi.org/10.5194/hess-26-2855-2022
© Author(s) 2022. 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-26-2855-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Forecasting green roof detention performance by temporal downscaling of precipitation time-series projections
Department of Civil and Environmental Engineering, The Norwegian University of Science and Technology, Trondheim,
7031, Norway
Univ Lyon, INSA Lyon, DEEP, EA7429, 11 rue de la Physique, 69621, Villeurbanne cedex, France
Rasmus Benestad
Norwegian Meteorological Institute, Oslo, Norway
Edvard Sivertsen
SINTEF AS, S.P. Andersens veg 3, 7465 Trondheim, Norway
Tone Merete Muthanna
Department of Civil and Environmental Engineering, The Norwegian University of Science and Technology, Trondheim,
7031, Norway
Jean-Luc Bertrand-Krajewski
Univ Lyon, INSA Lyon, DEEP, EA7429, 11 rue de la Physique, 69621, Villeurbanne cedex, France
Related authors
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.
Anatoly O. Sinitsyn, Sara Bazin, Rasmus Benestad, Bernd Etzelmüller, Ketil Isaksen, Hanne Kvitsand, Julia Lutz, Andrea L. Popp, Lena Rubensdotter, and Sebastian Westermann
EGUsphere, https://doi.org/10.5194/egusphere-2023-2950, https://doi.org/10.5194/egusphere-2023-2950, 2023
Preprint archived
Short summary
Short summary
This study looked at under the ground on Svalbard, an archipelago close to the North Pole. We found something very surprising – there is water under the all year around frozen soil. This was not known before. This water could be used for drinking if we manage it carefully. This is important because getting clean drinking water is very difficult in Svalbard, and other Arctic places. Also, because the climate is getting warmer, there might be even more water underground in the future.
Kajsa Maria Parding, Rasmus Emil Benestad, Anita Verpe Dyrrdal, and Julia Lutz
Hydrol. Earth Syst. Sci., 27, 3719–3732, https://doi.org/10.5194/hess-27-3719-2023, https://doi.org/10.5194/hess-27-3719-2023, 2023
Short summary
Short summary
Intensity–duration–frequency (IDF) curves describe the likelihood of extreme rainfall and are used in hydrology and engineering, for example, for flood forecasting and water management. We develop a model to estimate IDF curves from daily meteorological observations, which are more widely available than the observations on finer timescales (minutes to hours) that are needed for IDF calculations. The method is applied to all data at once, making it efficient and robust to individual errors.
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.
M. Bazlur Rashid, Syed Shahadat Hossain, M. Abdul Mannan, Kajsa M. Parding, Hans Olav Hygen, Rasmus E. Benestad, and Abdelkader Mezghani
Adv. Sci. Res., 18, 99–114, https://doi.org/10.5194/asr-18-99-2021, https://doi.org/10.5194/asr-18-99-2021, 2021
Short summary
Short summary
This study presents estimates of the maximum temperature in Bangladesh for the 21st century for the pre-monsoon season (March–May), the hottest season in Bangladesh. The maximum temperature is important as indicator of the frequency and severity of heatwaves. Several emission scenarios were considered assuming different developments in the emission of greenhouse gases. Results show that there will likely be a heating of at least 1 to 2 degrees Celsius.
Aynalem T. Tsegaw, Marie Pontoppidan, Erle Kristvik, Knut Alfredsen, and Tone M. Muthanna
Nat. Hazards Earth Syst. Sci., 20, 2133–2155, https://doi.org/10.5194/nhess-20-2133-2020, https://doi.org/10.5194/nhess-20-2133-2020, 2020
Short summary
Short summary
Hydrological impacts of climate change are generally performed by following steps from global to regional climate modeling through data tailoring and hydrological modeling. Usually, the climate–hydrology chain primary focuses on medium to large catchments. To study impacts of climate change on small catchments, a high-resolution regional climate model and hydrological model are required. The results from high-resolution models help in proposing specific adaptation strategies for impacts.
Abdelkader Mezghani, Andreas Dobler, Jan Erik Haugen, Rasmus E. Benestad, Kajsa M. Parding, Mikołaj Piniewski, Ignacy Kardel, and Zbigniew W. Kundzewicz
Earth Syst. Sci. Data, 9, 905–925, https://doi.org/10.5194/essd-9-905-2017, https://doi.org/10.5194/essd-9-905-2017, 2017
Short summary
Short summary
Projected changes estimated from an ensemble of nine model simulations showed that annual means of temperature are expected to increase steadily by 1 °C until 2021–2050 and by 2 °C until 2071–2100 assuming the RCP4.5, which is accelerating assuming the RCP8.5 scenario and can reach up to almost 4 °C by 2071–2100. Similarly to temperature, projected changes in regional annual means of precipitation are expected to increase by 6 to 10 % and by 8 to 16 % for the two future horizons and RCPs.
Andreas Dobler, Jan Erik Haugen, and Rasmus Emil Benestad
Earth Syst. Dynam. Discuss., https://doi.org/10.5194/esd-2016-27, https://doi.org/10.5194/esd-2016-27, 2016
Revised manuscript has not been submitted
Related subject area
Subject: Urban Hydrology | Techniques and Approaches: Modelling approaches
Combining statistical and hydrodynamic models to assess compound flood hazards from rainfall and storm surge: a case study of Shanghai
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
Evaluating different machine learning methods to simulate runoff from extensive green roofs
Modeling and interpreting hydrological responses of sustainable urban drainage systems with explainable machine learning methods
The impact of the spatiotemporal structure of rainfall on flood frequency over a small urban watershed: an approach coupling stochastic storm transposition and hydrologic modeling
Space variability impacts on hydrological responses of nature-based solutions and the resulting uncertainty: a case study of Guyancourt (France)
Urban surface water flood modelling – a comprehensive review of current models and future challenges
Resampling and ensemble techniques for improving ANN-based high-flow forecast accuracy
Event selection and two-stage approach for calibrating models of green urban drainage systems
Modeling the high-resolution dynamic exposure to flooding in a city region
Drainage area characterization for evaluating green infrastructure using the Storm Water Management Model
Critical scales to explain urban hydrological response: an application in Cranbrook, London
Increase in flood risk resulting from climate change in a developed urban watershed – the role of storm temporal patterns
Patterns and comparisons of human-induced changes in river flood impacts in cities
Scale effect challenges in urban hydrology highlighted with a distributed hydrological model
Comparison of the impacts of urban development and climate change on exposing European cities to pluvial flooding
Spatial and temporal variability of rainfall and their effects on hydrological response in urban areas – a review
Hydrodynamics of pedestrians' instability in floodwaters
Formulating and testing a method for perturbing precipitation time series to reflect anticipated climatic changes
Using rainfall thresholds and ensemble precipitation forecasts to issue and improve urban inundation alerts
Enhancing the T-shaped learning profile when teaching hydrology using data, modeling, and visualization activities
On the sensitivity of urban hydrodynamic modelling to rainfall spatial and temporal resolution
Precipitation variability within an urban monitoring network via microcanonical cascade generators
Estimation of peak discharges of historical floods
Indirect downscaling of hourly precipitation based on atmospheric circulation and temperature
Assessing the hydrologic restoration of an urbanized area via an integrated distributed hydrological model
Using the Storm Water Management Model to predict urban headwater stream hydrological response to climate and land cover change
Evaluating scale and roughness effects in urban flood modelling using terrestrial LIDAR data
Contribution of directly connected and isolated impervious areas to urban drainage network hydrographs
Thermal management of an unconsolidated shallow urban groundwater body
Online multistep-ahead inundation depth forecasts by recurrent NARX networks
A statistical analysis of insurance damage claims related to rainfall extremes
Joint impact of rainfall and tidal level on flood risk in a coastal city with a complex river network: a case study of Fuzhou City, China
Urbanization and climate change impacts on future urban flooding in Can Tho city, Vietnam
Multi-objective optimization for combined quality–quantity urban runoff control
Development of flood probability charts for urban drainage network in coastal areas through a simplified joint assessment approach
Auto-control of pumping operations in sewerage systems by rule-based fuzzy neural networks
Coupling urban event-based and catchment continuous modelling for combined sewer overflow river impact assessment
Dynamic neural networks for real-time water level predictions of sewerage systems-covering gauged and ungauged sites
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.
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.
Elhadi Mohsen Hassan Abdalla, Vincent Pons, Virginia Stovin, Simon De-Ville, Elizabeth Fassman-Beck, Knut Alfredsen, and Tone Merete Muthanna
Hydrol. Earth Syst. Sci., 25, 5917–5935, https://doi.org/10.5194/hess-25-5917-2021, https://doi.org/10.5194/hess-25-5917-2021, 2021
Short summary
Short summary
This study investigated the potential of using machine learning algorithms as hydrological models of green roofs across different climatic condition. The study provides comparison between conceptual and machine learning algorithms. Machine learning models were found to be accurate in simulating runoff from extensive green roofs.
Yang Yang and Ting Fong May Chui
Hydrol. Earth Syst. Sci., 25, 5839–5858, https://doi.org/10.5194/hess-25-5839-2021, https://doi.org/10.5194/hess-25-5839-2021, 2021
Short summary
Short summary
This study uses explainable machine learning methods to model and interpret the statistical correlations between rainfall and the discharge of urban catchments with sustainable urban drainage systems. The resulting models have good prediction accuracies. However, the right predictions may be made for the wrong reasons as the model cannot provide physically plausible explanations as to why a prediction is made.
Zhengzheng Zhou, James A. Smith, Mary Lynn Baeck, Daniel B. Wright, Brianne K. Smith, and Shuguang Liu
Hydrol. Earth Syst. Sci., 25, 4701–4717, https://doi.org/10.5194/hess-25-4701-2021, https://doi.org/10.5194/hess-25-4701-2021, 2021
Short summary
Short summary
The role of rainfall space–time structure in flood response is an important research issue in urban hydrology. This study contributes to this understanding in small urban watersheds. Combining stochastically based rainfall scenarios with a hydrological model, the results show the complexities of flood response for various return periods, implying the common assumptions of spatially uniform rainfall in urban flood frequency are problematic, even for relatively small basin scales.
Yangzi Qiu, Igor da Silva Rocha Paz, Feihu Chen, Pierre-Antoine Versini, Daniel Schertzer, and Ioulia Tchiguirinskaia
Hydrol. Earth Syst. Sci., 25, 3137–3162, https://doi.org/10.5194/hess-25-3137-2021, https://doi.org/10.5194/hess-25-3137-2021, 2021
Short summary
Short summary
Our original research objective is to investigate the uncertainties of the hydrological responses of nature-based solutions (NBSs) that result from the multiscale space variability in both the rainfall and the NBS distribution. Results show that the intersection effects of spatial variability in rainfall and the spatial arrangement of NBS can generate uncertainties of peak flow and total runoff volume estimations in NBS scenarios.
Kaihua Guo, Mingfu Guan, and Dapeng Yu
Hydrol. Earth Syst. Sci., 25, 2843–2860, https://doi.org/10.5194/hess-25-2843-2021, https://doi.org/10.5194/hess-25-2843-2021, 2021
Short summary
Short summary
This study presents a comprehensive review of models and emerging approaches for predicting urban surface water flooding driven by intense rainfall. It explores the advantages and limitations of existing models and identifies major challenges. Issues of model complexities, scale effects, and computational efficiency are also analysed. The results will inform scientists, engineers, and decision-makers of the latest developments and guide the model selection based on desired objectives.
Everett Snieder, Karen Abogadil, and Usman T. Khan
Hydrol. Earth Syst. Sci., 25, 2543–2566, https://doi.org/10.5194/hess-25-2543-2021, https://doi.org/10.5194/hess-25-2543-2021, 2021
Short summary
Short summary
Flow distributions are highly skewed, resulting in low prediction accuracy of high flows when using artificial neural networks for flood forecasting. We investigate the use of resampling and ensemble techniques to address the problem of skewed datasets to improve high flow prediction. The methods are implemented both independently and in combined, hybrid techniques. This research presents the first analysis of the effects of combining these methods on high flow prediction accuracy.
Ico Broekhuizen, Günther Leonhardt, Jiri Marsalek, and Maria Viklander
Hydrol. Earth Syst. Sci., 24, 869–885, https://doi.org/10.5194/hess-24-869-2020, https://doi.org/10.5194/hess-24-869-2020, 2020
Short summary
Short summary
Urban drainage models are usually calibrated using a few events so that they accurately represent a real-world site. This paper compares 14 single- and two-stage strategies for selecting these events and found significant variation between them in terms of model performance and the obtained values of model parameters. Calibrating parameters for green and impermeable areas in two separate stages improved model performance in the validation period while making calibration easier and faster.
Xuehong Zhu, Qiang Dai, Dawei Han, Lu Zhuo, Shaonan Zhu, and Shuliang Zhang
Hydrol. Earth Syst. Sci., 23, 3353–3372, https://doi.org/10.5194/hess-23-3353-2019, https://doi.org/10.5194/hess-23-3353-2019, 2019
Short summary
Short summary
Urban flooding exposure is generally investigated with the assumption of stationary disasters and disaster-hit bodies during an event, and thus it cannot satisfy the increasingly elaborate modeling and management of urban floods. In this study, a comprehensive method was proposed to simulate dynamic exposure to urban flooding considering human mobility. Several scenarios, including diverse flooding types and various responses of residents to flooding, were considered.
Joong Gwang Lee, Christopher T. Nietch, and Srinivas Panguluri
Hydrol. Earth Syst. Sci., 22, 2615–2635, https://doi.org/10.5194/hess-22-2615-2018, https://doi.org/10.5194/hess-22-2615-2018, 2018
Short summary
Short summary
This paper demonstrates an approach to spatial discretization for analyzing green infrastructure (GI) using SWMM. Besides DCIA, pervious buffers should be identified for GI modeling. Runoff contributions from different spatial components and flow pathways would impact GI performance. The presented approach can reduce the number of calibration parameters and apply scale–independently to a watershed scale. Hydrograph separation can add insights for developing GI scenarios.
Elena Cristiano, Marie-Claire ten Veldhuis, Santiago Gaitan, Susana Ochoa Rodriguez, and Nick van de Giesen
Hydrol. Earth Syst. Sci., 22, 2425–2447, https://doi.org/10.5194/hess-22-2425-2018, https://doi.org/10.5194/hess-22-2425-2018, 2018
Short summary
Short summary
In this work we investigate the influence rainfall and catchment scales have on hydrological response. This problem is quite relevant in urban areas, where the response is fast due to the high degree of imperviousness. We presented a new approach to classify rainfall variability in space and time and use this classification to investigate rainfall aggregation effects on urban hydrological response. This classification allows the spatial extension of the main core of the storm to be identified.
Suresh Hettiarachchi, Conrad Wasko, and Ashish Sharma
Hydrol. Earth Syst. Sci., 22, 2041–2056, https://doi.org/10.5194/hess-22-2041-2018, https://doi.org/10.5194/hess-22-2041-2018, 2018
Short summary
Short summary
The study examines the impact of higher temperatures expected in a future climate on how rainfall varies with time during severe storm events. The results show that these impacts increase future flood risk in urban environments and that current design guidelines need to be adjusted so that effective adaptation measures can be implemented.
Stephanie Clark, Ashish Sharma, and Scott A. Sisson
Hydrol. Earth Syst. Sci., 22, 1793–1810, https://doi.org/10.5194/hess-22-1793-2018, https://doi.org/10.5194/hess-22-1793-2018, 2018
Short summary
Short summary
This study investigates global patterns relating urban river flood impacts to socioeconomic development and changing hydrologic conditions, and comparisons are provided between 98 individual cities. This paper condenses and communicates large amounts of information to accelerate the understanding of relationships between local urban conditions and global processes, and to potentially motivate knowledge transfer between decision-makers facing similar circumstances.
Abdellah Ichiba, Auguste Gires, Ioulia Tchiguirinskaia, Daniel Schertzer, Philippe Bompard, and Marie-Claire Ten Veldhuis
Hydrol. Earth Syst. Sci., 22, 331–350, https://doi.org/10.5194/hess-22-331-2018, https://doi.org/10.5194/hess-22-331-2018, 2018
Short summary
Short summary
This paper proposes a two-step investigation to illustrate the extent of scale effects in urban hydrology. First, fractal tools are used to highlight the scale dependency observed within GIS data inputted in urban hydrological models. Then an intensive multi-scale modelling work was carried out to confirm effects on model performances. The model was implemented at 17 spatial resolutions ranging from 100 to 5 m. Results allow the understanding of scale challenges in hydrology modelling.
Per Skougaard Kaspersen, Nanna Høegh Ravn, Karsten Arnbjerg-Nielsen, Henrik Madsen, and Martin Drews
Hydrol. Earth Syst. Sci., 21, 4131–4147, https://doi.org/10.5194/hess-21-4131-2017, https://doi.org/10.5194/hess-21-4131-2017, 2017
Elena Cristiano, Marie-Claire ten Veldhuis, and Nick van de Giesen
Hydrol. Earth Syst. Sci., 21, 3859–3878, https://doi.org/10.5194/hess-21-3859-2017, https://doi.org/10.5194/hess-21-3859-2017, 2017
Short summary
Short summary
In the last decades, new instruments were developed to measure rainfall and hydrological processes at high resolution. Weather radars are used, for example, to measure how rainfall varies in space and time. At the same time, new models were proposed to reproduce and predict hydrological response, in order to prevent flooding in urban areas. This paper presents a review of our current knowledge of rainfall and hydrological processes in urban areas, focusing on their variability in time and space.
Chiara Arrighi, Hocine Oumeraci, and Fabio Castelli
Hydrol. Earth Syst. Sci., 21, 515–531, https://doi.org/10.5194/hess-21-515-2017, https://doi.org/10.5194/hess-21-515-2017, 2017
Short summary
Short summary
In developed countries, the majority of fatalities during floods occurs as a consequence of inappropriate high-risk behaviour such as walking or driving in floodwaters. This work addresses pedestrians' instability in floodwaters. It analyses both the contribution of flood and human physical characteristics in the loss of stability highlighting the key role of subject height (submergence) and flow regime. The method consists of a re-analysis of experiments and numerical modelling.
Hjalte Jomo Danielsen Sørup, Stylianos Georgiadis, Ida Bülow Gregersen, and Karsten Arnbjerg-Nielsen
Hydrol. Earth Syst. Sci., 21, 345–355, https://doi.org/10.5194/hess-21-345-2017, https://doi.org/10.5194/hess-21-345-2017, 2017
Short summary
Short summary
In this study we propose a methodology changing present-day precipitation time series to reflect future changed climate. Present-day time series have a much finer resolution than what is provided by climate models and thus have a much broader application range. The proposed methodology is able to replicate most expectations of climate change precipitation. These time series can be used to run fine-scale hydrological and hydraulic models and thereby assess the influence of climate change on them.
Tsun-Hua Yang, Gong-Do Hwang, Chin-Cheng Tsai, and Jui-Yi Ho
Hydrol. Earth Syst. Sci., 20, 4731–4745, https://doi.org/10.5194/hess-20-4731-2016, https://doi.org/10.5194/hess-20-4731-2016, 2016
Short summary
Short summary
Taiwan continues to suffer from floods. This study proposes the integration of rainfall thresholds and ensemble precipitation forecasts to provide probabilistic urban inundation forecasts. Utilization of ensemble precipitation forecasts can extend forecast lead times to 72 h, preceding peak flows and allowing response agencies to take necessary preparatory measures. This study also develops a hybrid of real-time observation and rainfall forecasts to improve the first 24 h inundation forecasts.
Christopher A. Sanchez, Benjamin L. Ruddell, Roy Schiesser, and Venkatesh Merwade
Hydrol. Earth Syst. Sci., 20, 1289–1299, https://doi.org/10.5194/hess-20-1289-2016, https://doi.org/10.5194/hess-20-1289-2016, 2016
Short summary
Short summary
The use of authentic learning activities is especially important for place-based geosciences like hydrology, where professional breadth and technical depth are critical for practicing hydrologists. The current study found that integrating computerized learning content into the learning experience, using only a simple spreadsheet tool and readily available hydrological data, can effectively bring the "real world" into the classroom and provide an enriching educational experience.
G. Bruni, R. Reinoso, N. C. van de Giesen, F. H. L. R. Clemens, and J. A. E. ten Veldhuis
Hydrol. Earth Syst. Sci., 19, 691–709, https://doi.org/10.5194/hess-19-691-2015, https://doi.org/10.5194/hess-19-691-2015, 2015
P. Licznar, C. De Michele, and W. Adamowski
Hydrol. Earth Syst. Sci., 19, 485–506, https://doi.org/10.5194/hess-19-485-2015, https://doi.org/10.5194/hess-19-485-2015, 2015
J. Herget, T. Roggenkamp, and M. Krell
Hydrol. Earth Syst. Sci., 18, 4029–4037, https://doi.org/10.5194/hess-18-4029-2014, https://doi.org/10.5194/hess-18-4029-2014, 2014
F. Beck and A. Bárdossy
Hydrol. Earth Syst. Sci., 17, 4851–4863, https://doi.org/10.5194/hess-17-4851-2013, https://doi.org/10.5194/hess-17-4851-2013, 2013
D. H. Trinh and T. F. M. Chui
Hydrol. Earth Syst. Sci., 17, 4789–4801, https://doi.org/10.5194/hess-17-4789-2013, https://doi.org/10.5194/hess-17-4789-2013, 2013
J. Y. Wu, J. R. Thompson, R. K. Kolka, K. J. Franz, and T. W. Stewart
Hydrol. Earth Syst. Sci., 17, 4743–4758, https://doi.org/10.5194/hess-17-4743-2013, https://doi.org/10.5194/hess-17-4743-2013, 2013
H. Ozdemir, C. C. Sampson, G. A. M. de Almeida, and P. D. Bates
Hydrol. Earth Syst. Sci., 17, 4015–4030, https://doi.org/10.5194/hess-17-4015-2013, https://doi.org/10.5194/hess-17-4015-2013, 2013
Y. Seo, N.-J. Choi, and A. R. Schmidt
Hydrol. Earth Syst. Sci., 17, 3473–3483, https://doi.org/10.5194/hess-17-3473-2013, https://doi.org/10.5194/hess-17-3473-2013, 2013
J. Epting, F. Händel, and P. Huggenberger
Hydrol. Earth Syst. Sci., 17, 1851–1869, https://doi.org/10.5194/hess-17-1851-2013, https://doi.org/10.5194/hess-17-1851-2013, 2013
H.-Y. Shen and L.-C. Chang
Hydrol. Earth Syst. Sci., 17, 935–945, https://doi.org/10.5194/hess-17-935-2013, https://doi.org/10.5194/hess-17-935-2013, 2013
M. H. Spekkers, M. Kok, F. H. L. R. Clemens, and J. A. E. ten Veldhuis
Hydrol. Earth Syst. Sci., 17, 913–922, https://doi.org/10.5194/hess-17-913-2013, https://doi.org/10.5194/hess-17-913-2013, 2013
J. J. Lian, K. Xu, and C. Ma
Hydrol. Earth Syst. Sci., 17, 679–689, https://doi.org/10.5194/hess-17-679-2013, https://doi.org/10.5194/hess-17-679-2013, 2013
H. T. L. Huong and A. Pathirana
Hydrol. Earth Syst. Sci., 17, 379–394, https://doi.org/10.5194/hess-17-379-2013, https://doi.org/10.5194/hess-17-379-2013, 2013
S. Oraei Zare, B. Saghafian, and A. Shamsai
Hydrol. Earth Syst. Sci., 16, 4531–4542, https://doi.org/10.5194/hess-16-4531-2012, https://doi.org/10.5194/hess-16-4531-2012, 2012
R. Archetti, A. Bolognesi, A. Casadio, and M. Maglionico
Hydrol. Earth Syst. Sci., 15, 3115–3122, https://doi.org/10.5194/hess-15-3115-2011, https://doi.org/10.5194/hess-15-3115-2011, 2011
Y.-M. Chiang, L.-C. Chang, M.-J. Tsai, Y.-F. Wang, and F.-J. Chang
Hydrol. Earth Syst. Sci., 15, 185–196, https://doi.org/10.5194/hess-15-185-2011, https://doi.org/10.5194/hess-15-185-2011, 2011
I. Andrés-Doménech, J. C. Múnera, F. Francés, and J. B. Marco
Hydrol. Earth Syst. Sci., 14, 2057–2072, https://doi.org/10.5194/hess-14-2057-2010, https://doi.org/10.5194/hess-14-2057-2010, 2010
Yen-Ming Chiang, Li-Chiu Chang, Meng-Jung Tsai, Yi-Fung Wang, and Fi-John Chang
Hydrol. Earth Syst. Sci., 14, 1309–1319, https://doi.org/10.5194/hess-14-1309-2010, https://doi.org/10.5194/hess-14-1309-2010, 2010
Cited articles
Alfieri, L., Laio, F., and Claps, P.: A simulation experiment for optimal
design hyetograph selection, Hydrol. Process., 22, 813–820,
https://doi.org/10.1002/hyp.6646, 2008. a
Benestad, R.: Downscaling Climate Information, in: Oxford Research
Encyclopedia of Climate Science, Oxford University Press, https://doi.org/10.1093/acrefore/9780190228620.013.27,
2016. a
Berg, P., Moseley, C., and Haerter, J. O.:
Strong increase in convective precipitation in response to higher temperatures, Nat. Geosci., 6, 181–185, https://doi.org/10.1038/ngeo1731, 2013. a
Bürger, G., Heistermann, M., and Bronstert, A.: Towards subdaily
rainfall disaggregation via clausius-clapeyron, J. Hydrometeorol., 15, 1303–1311,
https://doi.org/10.1175/JHM-D-13-0161.1, 2014. a
Bürger, G., Pfister, A., and Bronstert, A.: Temperature-driven rise in
extreme sub-hourly rainfall, J. Climate, 32, 7597–7609,
https://doi.org/10.1175/JCLI-D-19-0136.1, 2019. a, b
Dyrrdal, A. V. and Førland, E. J.: Klimapåslag for korttidsnedbør-Anbefalte verdier for Norge, NCCS report 5, https://klimaservicesenter.no/kss/rapporter/rapporter-og-publikasjoner_2 (last access: 26 May 2022),
2019. a
Dyrrdal, A. V., Stordal, F., and Lussana, C.: Evaluation of summer
precipitation from EURO-CORDEX fine-scale RCM simulations over Norway,
Int. J. Climatol., 38, 1661–1677, https://doi.org/10.1002/joc.5287, 2018. a, b
Gaur, A. and Lacasse, M.: Multisite multivariate disaggregation of climate
parameters using multiplicative random cascades, Urban Climate,
https://doi.org/10.1016/j.uclim.2018.08.010, 2018. a
Gires, A., Tchiguirinskaia, I., and Schertzer, D.: Blunt extension of discrete
universal multifractal cascades: development and application to downscaling,
Hydrolog. Sci. J., 65, 1204–1220,
https://doi.org/10.1080/02626667.2020.1736297, 2020. a
Glasbey, C. A., Cooper, G., and McGechan, M. B.: Disaggregation of daily
rainfall by conditional simulation from a point-process model, J.
Hydrol., 165, 1–9, https://doi.org/10.1016/0022-1694(94)02598-6, 1995. a
Greater Lyon council: Règlement du service public de l'assainissement
collectif [Stormwater and wastewater service regulation], City guideline,
Greater Lyon, https://www.grandlyon.com/services/gestion-des-eaux-de-pluie.html (last access: 10 November /2021), 2020. a
Gupta, V. K. and Waymire, E. C.: A Statistical Analysis of Mesoscale Rainfall
as a Random Cascade, J. Appl. Meteorol. Clim., 32, 251–267, https://doi.org/10.1175/1520-0450(1993)032<0251:ASAOMR>2.0.CO;2, 1993. a
Hamouz, V. and Muthanna, T. M.: Hydrological modelling of green and grey roofs
in cold climate with the SWMM model, J. Environ. Manage.,
249, 109350, https://doi.org/10.1016/j.jenvman.2019.109350, 2019. a
Jacob, D., Petersen, J., Eggert, B., Alias, A., Christensen, O. B., Bouwer,
L. M., Braun, A., Colette, A., Déqué, M., Georgievski, G.,
Georgopoulou, E., Gobiet, A., Menut, L., Nikulin, G., Haensler, A.,
Hempelmann, N., Jones, C., Keuler, K., Kovats, S., Kröner, N., Kotlarski,
S., Kriegsmann, A., Martin, E., van Meijgaard, E., Moseley, C., Pfeifer, S.,
Preuschmann, S., Radermacher, C., Radtke, K., Rechid, D., Rounsevell, M.,
Samuelsson, P., Somot, S., Soussana, J.-F., Teichmann, C., Valentini, R.,
Vautard, R., Weber, B., and Yiou, P.: Erratum to: EURO-CORDEX: new
high-resolution climate change projections for European impact research,
Reg. Environ. Change, 14, 579–581, https://doi.org/10.1007/s10113-014-0587-y,
2014. a
Johannessen, B. G., Hanslin, H. M., and Muthanna, T. M.: Green roof
performance potential in cold and wet regions, Ecol. Eng., 106, 436–447,
https://doi.org/10.1016/j.ecoleng.2017.06.011, 2017. a, b
Johannessen, B. G., Muthanna, T. M., and Braskerud, B. C.: Detention and
Retention Behavior of Four Extensive Green Roofs in Three Nordic Climate
Zones, Water, 10, 671, https://doi.org/10.3390/w10060671, 2018. a
Kalra, A. and Ahmad, S.: Evaluating changes and estimating seasonal
precipitation for the Colorado River Basin using a stochastic nonparametric
disaggregation technique, Water Resour. Res., 47, W05555,
https://doi.org/10.1029/2010WR009118, 2011. a
Koutsoyiannis, D. and Langousis, A.: 2.02 – Precipitation, in: Treatise on
Water Science, edited by: Wilderer, P., 27–77, Elsevier, Oxford,
https://doi.org/10.1016/B978-0-444-53199-5.00027-0, 2011. a, b
Kristvik, E., Johannessen, B. G., and Muthanna, T. M.: Temporal Downscaling of
IDF Curves Applied to Future Performance of Local Stormwater Measures,
Sustainability, 11, 1231, https://doi.org/10.3390/su11051231, 2019. a, b, c
Laloy, E. and Vrugt, J. A.: High-dimensional posterior exploration of
hydrologic models using multiple-try DREAM (ZS) and high-performance
computing, Water Resour. Res., 48, W01526, https://doi.org/10.1029/2011WR010608, 2012. a
Li, X., Meshgi, A., Wang, X., Zhang, J., Tay, S. H. X., Pijcke, G., Manocha,
N., Ong, M., Nguyen, M. T., and Babovic, V.: Three resampling approaches
based on method of fragments for daily-to-subdaily precipitation
disaggregation, Int. J. Climatol., 38, e1119–e1138,
https://doi.org/10.1002/joc.5438, 2018. a
Lombardo, F., Volpi, E., and Koutsoyiannis, D.: Rainfall downscaling in time:
theoretical and empirical comparison between multifractal and
Hurst-Kolmogorov discrete random cascades, Hydrolog. Sci. J., 57, 1052–1066,
https://doi.org/10.1080/02626667.2012.695872, 2012. a
Lu, Y., Qin, X. S., and Mandapaka, P. V.: A combined weather generator and
K-nearest-neighbour approach for assessing climate change impact on regional
rainfall extremes, Int. J. Climatol., 35, 4493–4508,
https://doi.org/10.1002/joc.4301, 2015. a
Lutz, J., Grinde, L., and Dyrrdal, A. V.: Estimating rainfall design values
for the City of Oslo, Norway-comparison of methods and quantification of
uncertainty, Water (Switzerland), 12, 1735, https://doi.org/10.3390/W12061735, 2020. a
McIntyre, N., Shi, M., and Onof, C.: Incorporating parameter dependencies into
temporal downscaling of extreme rainfall using a random cascade approach,
J. Hydrol., 542, 896–912, https://doi.org/10.1016/j.jhydrol.2016.09.057, 2016. a, b
Müller-Thomy, H.: Temporal rainfall disaggregation using a micro-canonical cascade model: possibilities to improve the autocorrelation, Hydrol. Earth Syst. Sci., 24, 169–188, https://doi.org/10.5194/hess-24-169-2020, 2020. a
Olsson, J.: Evaluation of a scaling cascade model for temporal rain- fall disaggregation, Hydrol. Earth Syst. Sci., 2, 19–30, https://doi.org/10.5194/hess-2-19-1998, 1998. a
Onof, C., Chandler, R. E., Kakou, A., Northrop, P., Wheater, H. S., and Isham,
V.: Rainfall modelling using poisson-cluster processes: A review of
developments, Stoch. Env. Res. Risk A., 14, 384–411,
https://doi.org/10.1007/s004770000043, 2000. a
Oudin, L., Hervieu, F., Michel, C., Perrin, C., Andréassian, V., Anctil,
F., and Loumagne, C.: Which potential evapotranspiration input for a lumped
rainfall-runoff model? Part 2 – Towards a simple and efficient potential
evapotranspiration model for rainfall-runoff modelling, J. Hydrol., 303, 290–306, https://doi.org/10.1016/j.jhydrol.2004.08.026, 2005. a
Paschalis, A., Molnar, P., and Burlando, P.: Temporal dependence structure in
weights in a multiplicative cascade model for precipitation, Water Resour.
Res., 48, W01501, https://doi.org/10.1029/2011WR010679, 2012. a, b
Peel, M. C., Finlayson, B. L., and McMahon, T. A.: Updated world map of the Köppen-Geiger climate classification, Hydrol. Earth Syst. Sci., 11, 1633–1644, https://doi.org/10.5194/hess-11-1633-2007, 2007. a
Rupp, D. E., Keim, R. F., Ossiander, M., Brugnach, M., and Selker, J. S.: Time
scale and intensity dependency in multiplicative cascades for temporal
rainfall disaggregation, Water Resour. Res., 45, W07409,
https://doi.org/10.1029/2008WR007321, 2009. a, b, c
Rupp, D. E., Licznar, P., Adamowski, W., and Leśniewski, M.: Multiplicative cascade models for fine spatial downscaling of rainfall: parameterization with rain gauge data, Hydrol. Earth Syst. Sci., 16, 671–684, https://doi.org/10.5194/hess-16-671-2012, 2012. a
Sanchez, C., Williams, K. D., and Collins, M.: Improved stochastic physics
schemes for global weather and climate models, Q. J. Roy.
Meteor. Soc., 142, 147–159, https://doi.org/10.1002/qj.2640,
2016. a
Schertzer, D. and Lovejoy, S.: Physical modeling and analysis of rain and
clouds by anisotropic scaling multiplicative processes, J.
Geophys. Res.-Atmos., 92, 9693–9714,
https://doi.org/10.1029/JD092iD08p09693, 1987. a
Schilling, W.: Rainfall data for urban hydrology: what do we need?,
Atmos. Res., 27, 5–21,
https://doi.org/10.1016/0169-8095(91)90003-F, 1991. a
Serinaldi, F.: Multifractality, imperfect scaling and hydrological properties of rainfall time series simulated by continuous universal multifractal and discrete random cascade models, Nonlin. Processes Geophys., 17, 697–714, https://doi.org/10.5194/npg-17-697-2010, 2010. a, b
Stovin, V.: The potential of green roofs to manage Urban Stormwater, Water
Environ. J., 24, 192–199,
https://doi.org/10.1111/j.1747-6593.2009.00174.x, 2010. a
Stovin, V., Poë, S., and Berretta, C.: A modelling study of long term green
roof retention performance, J. Environ. Manage., 131,
206–215, https://doi.org/10.1016/j.jenvman.2013.09.026, 2013. a
Stovin, V., Vesuviano, G., and De-Ville, S.: Defining green roof detention
performance, Urban Water J., 14, 574–588,
https://doi.org/10.1080/1573062X.2015.1049279, 2017. a
Thober, S., Mai, J., Zink, M., and Samaniego, L.: Stochastic temporal
disaggregation of monthly precipitation for regional gridded data sets,
Water Resour. Res., 50, 8714–8735, https://doi.org/10.1002/2014WR015930, 2014. a
Thorndahl, S. and Andersen, C. B.: CLIMACS: A method for stochastic generation
of continuous climate projected point rainfall for urban drainage design,
J. Hydrol., 602, 126776,
https://doi.org/10.1016/j.jhydrol.2021.126776, 2021. a
Trondheim Kommune: VA-norm. Vedlegg 5. Beregning av overvannsmengde
Dimensjonering av ledning og fordrøyningsvolum [Water and Wastewater Norm.
Attachment 5. Calculation of stormwater flows. Design of pipes and detention
basins], City guideline, The City of Trondheim,
https://www.va-norm.no/trondheim/ (last access: 10 November 2021), 2015. a
Veneziano, D., Furcolo, P., and Iacobellis, V.: Imperfect scaling of time and
space–time rainfall, J. Hydrol., 322, 105–119,
https://doi.org/10.1016/j.jhydrol.2005.02.044, 2006. a, b
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T.,
Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., van
der Walt, S. J., Brett, M., Wilson, J., Millman, K. J., Mayorov, N., Nelson,
A. R. J., Jones, E., Kern, R., Larson, E., Carey, C. J., Polat, İ., Feng,
Y., Moore, E. W., VanderPlas, J., Laxalde, D., Perktold, J., Cimrman, R.,
Henriksen, I., Quintero, E. A., Harris, C. R., Archibald, A. M., Ribeiro,
A. H., Pedregosa, F., van Mulbregt, P., and SciPy 1.0 Contributors:
SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python,
Nat. Methods, 17, 261–272, https://doi.org/10.1038/s41592-019-0686-2, 2020. a
Walker, W. E., Haasnoot, M., and Kwakkel, J. H.: Adapt or Perish: A Review of
Planning Approaches for Adaptation under Deep Uncertainty, Sustainability, 5,
955–979, https://doi.org/10.3390/su5030955, 2013.
a
Zhang, Q., Li, J., Singh, V. P., and Xiao, M.: Spatio-temporal relations
between temperature and precipitation regimes: Implications for
temperature-induced changes in the hydrological cycle, Global Planet.
Change, 111, 57–76, https://doi.org/10.1016/j.gloplacha.2013.08.012,
2013. a
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.
Different models were developed to increase the temporal resolution of precipitation time series...