Articles | Volume 20, issue 3
https://doi.org/10.5194/hess-20-1289-2016
© Author(s) 2016. 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-20-1289-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
31 Mar 2016
Research article |
| 31 Mar 2016
Enhancing the T-shaped learning profile when teaching hydrology using data, modeling, and visualization activities
Christopher A. Sanchez
CORRESPONDING AUTHOR
Oregon State University, Corvallis, Oregon, USA
Benjamin L. Ruddell
Arizona State University, Tempe, Arizona, USA
Roy Schiesser
Chandler-Gilbert Community College, Chandler, Arizona, USA
Venkatesh Merwade
Purdue University, West Lafayette, Indiana, USA
Related authors
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Benjamin L. Ruddell and Richard Rushforth
Hydrol. Earth Syst. Sci., 28, 1089–1106, https://doi.org/10.5194/hess-28-1089-2024, https://doi.org/10.5194/hess-28-1089-2024, 2024
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This study finds that bedroom cities show higher water productivity based on the standard efficiency benchmark of gallons per capita, but core cities that host large businesses show higher water productivity using a basket of economic values like taxes, payroll, and business revenues. Using a broader basket of water productivity benchmarks that consider more of the community’s socio-economic values and goals could inform more balanced and equitable water allocation decisions by policymakers.
Elnaz Azmi, Uwe Ehret, Steven V. Weijs, Benjamin L. Ruddell, and Rui A. P. Perdigão
Hydrol. Earth Syst. Sci., 25, 1103–1115, https://doi.org/10.5194/hess-25-1103-2021, https://doi.org/10.5194/hess-25-1103-2021, 2021
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Computer models should be as simple as possible but not simpler. Simplicity refers to the length of the model and the effort it takes the model to generate its output. Here we present a practical technique for measuring the latter by the number of memory visits during model execution by
Strace, a troubleshooting and monitoring program. The advantage of this approach is that it can be applied to any computer-based model, which facilitates model intercomparison.
Jessica A. Eisma and Venkatesh M. Merwade
Hydrol. Earth Syst. Sci., 24, 1891–1906, https://doi.org/10.5194/hess-24-1891-2020, https://doi.org/10.5194/hess-24-1891-2020, 2020
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Sand dams capture and store water for use during the dry season in rural communities. A year long field study of three sand dams in Tanzania showed that sand dams are not a suitable habitat for aquatic insects. They capture plenty of water, but most is evaporated during the first few months of the dry season. Sand dams positively impact vegetation and minimally impact erosion. Community water security can be increased by sand dams, but site characteristics and construction are important factors.
Benjamin L. Ruddell
Hydrol. Earth Syst. Sci., 22, 5551–5558, https://doi.org/10.5194/hess-22-5551-2018, https://doi.org/10.5194/hess-22-5551-2018, 2018
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We now lack sufficient empirical observations of consumptive use of water by humans and their economy, so it is worth considering what we can do with the withdrawal-based water use data we already possess. Fortunately, a wide range of applied water management and policy questions can be addressed using currently available withdrawal data. This discussion identifies important data collection problems and argues that the withdrawal data we already possess are adequate for some important purposes.
Richard R. Rushforth and Benjamin L. Ruddell
Hydrol. Earth Syst. Sci., 22, 3007–3032, https://doi.org/10.5194/hess-22-3007-2018, https://doi.org/10.5194/hess-22-3007-2018, 2018
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The National Water Economy Database is a new data resource to better understand the human economy's water use impact on the hydrosphere. NWED quantifies and maps a spatially detailed and economically complete blue water footprint for the United States, utilizing several datasets: US Geological Survey, the US Department of Agriculture, the US Energy Information Administration, the US Department of Transportation, the US Department of Energy, and the US Bureau of Labor Statistics.
Tian Guo, Margaret Gitau, Venkatesh Merwade, Jeffrey Arnold, Raghavan Srinivasan, Michael Hirschi, and Bernard Engel
Hydrol. Earth Syst. Sci., 22, 89–110, https://doi.org/10.5194/hess-22-89-2018, https://doi.org/10.5194/hess-22-89-2018, 2018
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The new tile drainage routine in the SWAT model provided more realistic and accurate simulation of tile drainage, and the new curve number retention parameter adjustment factor improved surface runoff simulation, and is suitable for surface runoff simulation in mildly sloped watersheds. This study can provide guidance for selection of tile drainage routines for tile drainage simulation, and can allow accurate simulation of hydrological systems at both field and watershed scales.
Related subject area
Subject: Urban Hydrology | Techniques and Approaches: Modelling approaches
An optimized long short-term memory (LSTM)-based approach applied to early warning and forecasting of ponding in the urban drainage system
A deep-learning-technique-based data-driven model for accurate and rapid flood predictions in temporal and spatial dimensions
Impact of urban geology on model simulations of shallow groundwater levels and flow paths
Technical note: Modeling spatial fields of extreme precipitation – a hierarchical Bayesian approach
Intersecting near-real time fluvial and pluvial inundation estimates with sociodemographic vulnerability to quantify a household flood impact index
Forecasting green roof detention performance by temporal downscaling of precipitation time-series projections
Evaluating different machine learning methods to simulate runoff from extensive green roofs
Modeling and interpreting hydrological responses of sustainable urban drainage systems with explainable machine learning methods
The impact of the spatiotemporal structure of rainfall on flood frequency over a small urban watershed: an approach coupling stochastic storm transposition and hydrologic modeling
Space variability impacts on hydrological responses of nature-based solutions and the resulting uncertainty: a case study of Guyancourt (France)
Urban surface water flood modelling – a comprehensive review of current models and future challenges
Resampling and ensemble techniques for improving ANN-based high-flow forecast accuracy
Event selection and two-stage approach for calibrating models of green urban drainage systems
Modeling the high-resolution dynamic exposure to flooding in a city region
Drainage area characterization for evaluating green infrastructure using the Storm Water Management Model
Critical scales to explain urban hydrological response: an application in Cranbrook, London
Increase in flood risk resulting from climate change in a developed urban watershed – the role of storm temporal patterns
Patterns and comparisons of human-induced changes in river flood impacts in cities
Scale effect challenges in urban hydrology highlighted with a distributed hydrological model
Comparison of the impacts of urban development and climate change on exposing European cities to pluvial flooding
Spatial and temporal variability of rainfall and their effects on hydrological response in urban areas – a review
Hydrodynamics of pedestrians' instability in floodwaters
Formulating and testing a method for perturbing precipitation time series to reflect anticipated climatic changes
Using rainfall thresholds and ensemble precipitation forecasts to issue and improve urban inundation alerts
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
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
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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.
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., Brooks, P. J., Aldrich, N. J., and Tenenbaum, H. R.: Does discovery-based instruction enhance learning?, J. Educ. Psychol., 103, 1–18, 2011.
ASCE: Perspectives on water resources education and training, J. Water Res. Pl. Manag., 116, 99–133, 1990.
Bransford, J. D., Brown, A. L., and Cocking, R. R.: How people learn: Brain, mind, experience, and school. National Academy Press, Washington, DC, 1999.
Brown, J. S., Collins, A., and Duguid, P.: Situated cognition and the culture of learning, Educ. Res., 18, 32–42, 1989.
Cap-Net: Economics in sustainable water management training manual and facilitators' guide, http://www.cap-net.org/training-material/economics-in-sustainable-water-management-english/ (last access: 22 May 2013), 2008.
Cognition and Technology Group at Vanderbilt: Emerging technologies, ISD, and learning environments: critical perspectives, Educ. Tech. Res., 40, 65–80, 1992.
Collins, A.: Cognitive apprenticeship and instructional technology, in: Educational Values and Cognitive Instruction: Implications for Reform, edited by: Idol, L. and Jones, B. F., Hillsdale, NJ, Lawrence Erlbaum Associates, 121–138, 1991.
De Jong, T. and Van Joolingen, W. R.: Scientific discovery learning with computer simulations of conceptual domains, Rev. Educ. Res., 68, 179–201, 1998.
Duch, B. J., Groh, S. E., and Allen, D. E.: Why problem-based learning, A case study of institutional change in undergraduate education, edited by: Duch, B. J., Groh, S. E., and Allen, D. E., Sterling, Virginia, The Power of Problem-Based Learning, 3–11, 2001 (in Duch).
Duderstadt, J. J.: Engineering for a changing world: A roadmap to the future engineering practice, research, and education, Ann Arbor, MI, The University of Michigan, 2008.
Eagleson, P. S., Brutsaert, W. H., Colbeck, S. C., Cummins, K. W., Dozier, J., Dunne, T., and Zipser, E. J.: Opportunities in the hydrologic sciences, Natl. Acad. Press, Washington, DC, 1991.
Felder, R. M., Felder, G. N., and Dietz, E. J.: A longitudinal study of engineering student performance and retention, V. comparisons with traditionally-taught students, J. Eng. Educ., 87, 469–480, 1998.
Gleeson, T., Allen, D. M., and Ferguson, G.: Teaching hydrogeology: a review of current practice, Hydrol. Earth Syst. Sci., 16, 2159–2168, https://doi.org/10.5194/hess-16-2159-2012, 2012.
Habib, E., Ma, Y., Williams, D., Sharif, H. O., and Hossain, F.: HydroViz: design and evaluation of a Web-based tool for improving hydrology education, Hydrol. Earth Syst. Sci., 16, 3767–3781, https://doi.org/10.5194/hess-16-3767-2012, 2012.
Hake, R. R.: Interactive-engagement versus traditional methods: A six-thousand-student survey of mechanics test data for introductory physics courses, Am. J. Phys., 66, 64–74, 1998.
Hansen, M. M.: Versatile, immersive, creative and dynamic virtual 3-D healthcare learning environments: a review of the literature, J. Med. Internet Res., 10, e26, https://doi.org/10.2196/jmir.1051, 2008.
Lattuca, L. R., Terenzini, P. T., Volkwein, J. F., and Peterson, G.: The changing face of engineering education, The Bridge, Linking Engineering and Society, 36, 5–13, 2006.
MacDonald, L. H.: Developing a field component in hydrologic education, Water Resour. Bull. Am. Water Resour. Assoc., 29, 357–368, 1993.
McIntosh, B. S. and Taylor, A.: Developing T-Shaped water professionals: Building capacity in collaboration, learning, and leadership to drive innovation, J. Contemp. Water Res. Educ., 150, 6–17, 2013.
Merwade, V. and Ruddell, B. L.: Moving university hydrology education forward with community-based geoinformatics, data and modeling resources, Hydrol. Earth Syst. Sci., 16, 2393–2404, https://doi.org/10.5194/hess-16-2393-2012, 2012.
Miles, J. and Shevlin, M.: Applying Regression and Correlation: a Guide for Students and Re-searchers, Sage, London, UK, 2001.
Nash, J. E., Eagleson, P. S., Philip, J. R., Van der Molen, W. H., and Klemeš, V.: The education of hydrologists (Report of an IAHS/UNESCO Panel on hydrological education), Hydrolog. Sci. J., 35, 597–607, 1990.
Paris, S. G. and Turner, J. C.: Situated Motivation, in: Student motivation, cognition, and learning: Essays in honor of Wilbert J. McKeachie, edited by: Pintrich, P., Brown, D., and Weinstein, C. E., Lawrence Erlbaum Associates, Hillsdale, NJ, 213–237, 1994.
Pathirana, A., Koster, J. H., de Jong, E., and Uhlenbrook, S.: On teaching styles of water educators and the impact of didactic training, Hydrol. Earth Syst. Sci., 16, 3677–3688, https://doi.org/10.5194/hess-16-3677-2012, 2012.
Pinter, N., Baer, S., Chevalier, L., Kowalchuk, R., Lant, C., and Whiles, M.: An "IGERT" Model for Interdisciplinary Doctoral Education in Water-Related Science and Policy, J. Contemp. Water Res. Educ., 150, 53–62, 2013.
Prince, M.: Does active learning work? A review of the research, J. Engin. Educ., 93, 223–231, 2004.
Ruddell, B. L. and Schiesser, R. A.: Developing a Rational Method Hydrograph Model for the Urban Desert Southwest USA, unpublished material, Mesa, AZ, 2012a.
Ruddell, B. L. and Schiesser, R. A.: Calibrating a Rational Method Hydrograph Model for the Urban Desert Southwest USA, unpublished material, Mesa, AZ, 2012b.
Ruddell, B. L. and Wagener, T.: Grand challenges for hydrology education in the 21st century, J. Hydrol. Eng., 20, A4014001, https://doi.org/10.1061/(ASCE)HE.1943-5584.0000956, 2015.
Savery, J. R. and Duffy, T. M.: Problem based learning: An instructional model and its constructivist framework, Educ. Technol., 35, 31–38, 1995.
Schiefele, U.: Interest, learning, and motivation, Educ. Psychol., 26, 299–323, 1991.
Schiesser, R. A.: Geological Disasters Case Studies and Analysis (and Lab 9 manual), 1st Edn., Kendall Hunt, Dubuque, IA, ISBN-978-0-7575-5039-3, 2008.
Shulman, L. S.: If not now, when? The timeliness of scholarship of the education of engineers, J. Engin. Educ., 94, 11–12, 2005.
Smith, K. A., Sheppard, S. D., Johnson, D. W., and Johnson, R. T.: Pedagogies of engagement: Classroom-based practices, J. Engin. Educ., 94, 87–101, 2005.
Smith, L. W. and Van Doren, D. C.: The reality-based learning method: A simple method for keeping teaching activities relevant and effective, J. Marketing Educ., 26, 66–74, 2004.
Thompson, S. E., Ngambeki, I., Troch, P. A., Sivapalan, M., and Evangelou, D.: Incorporating student-centered approaches into catchment hydrology teaching: a review and synthesis, Hydrol. Earth Syst. Sci., 16, 3263–3278, https://doi.org/10.5194/hess-16-3263-2012, 2012.
Uhlenbrook, S. and de Jong, E.: T-shaped competency profile for water professionals of the future, Hydrol. Earth Syst. Sci., 16, 3475–3483, https://doi.org/10.5194/hess-16-3475-2012, 2012.
Wagener, T., Weiler, M., McGlynn, B., Marshall, L., McHale, M., Meixner, T., and McGuire, K.: Taking the pulse of hydrology education, Hydrol. Proc., 21, 1789–1792, 2007.
Wagener, T., Kelleher, C., Weiler, M., McGlynn, B., Gooseff, M., Marshall, L., Meixner, T., McGuire, K., Gregg, S., Sharma, P., and Zappe, S.: It takes a community to raise a hydrologist: the Modular Curriculum for Hydrologic Advancement (MOCHA), Hydrol. Earth Syst. Sci., 16, 3405–3418, https://doi.org/10.5194/hess-16-3405-2012, 2012.
Watters, J. J. and Ginns, I. S.: Developing motivation to teach elementary science: Effect of collaborative and authentic learning practices in preservice education, J. Sci. Teach. Educ., 11, 301–321, 2000.
Wood, D. F.: Problem based learning, Bmj, 326, 328–330, 2003.
Yadav, A., Subedi, D., Lundeberg, M. A., and Bunting, C. F.: Problem-based Learning: Influence on Students' Learning in an Electrical Engineering Course, J. Engin. Educ., 100, 253–280, 2011.
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.
The use of authentic learning activities is especially important for place-based geosciences...
