Flood inundation simulation in a river basin using a physically based distributed hydrologic model

Dutta, Dushmanta; Herath, Srikantha; Musiake, Katumi

doi:10.1002/(sici)1099-1085(20000228)14:3<497::aid-hyp951>3.0.co;2-u

Cited by 77 publications

(63 citation statements)

References 9 publications

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“…The results are then used as input to the hydraulic model, which propagates the flood wave down a watercourse (Dutta et al, 2000;Dal Cin et al, 2005). The water levels produced by the hydraulic model can then be used by GIS to generate flood maps.…”

Section: Uses Of Hydrological Modelsmentioning

confidence: 99%

“…While watersheds can be conceptually compartmentalized in various ways, according to Dutta et al (2000), a physically oriented hydrological model has five major components: interception and evapotranspiration, overland flow, river flow, subsurface flow, and groundwater flow. The simulation of the transport and storage of water through these components is accomplished through the use of basic physical equations describing conservation of various quantities.…”

Section: Approaches To Hydrological Modellingmentioning

confidence: 99%

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Streamflow Modelling: A Primer on Applications, Approaches and Challenges

2012

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This article examines the current practice of streamflow modelling, a field under development for over a century. A sample of the wide range of assessment and planning applications of streamflow models is presented. The diversity in the use of these models is mirrored in the diversity of model complexity, and modelling approaches ranging from empirical to physically based and from lumped to fully distributed are described with examples. Predictions derived from hydrological models are subject to many sources of error; these are discussed along with methods for error minimization or anticipation. Model error is generally quantified using an ensemble of forecasts meant to sample the range of predictive uncertainty. This ensemble can be used to generate reliable probabilistic forecasts of hydrological quantities if all sources of error are accounted for. To date, applications of ensemble methods in streamflow forecasting have typically focused on only one or two error sources. A challenge will be to develop ensemble streamflow forecasts that sample a wider range of predictive uncertainty.

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Section: Uses Of Hydrological Modelsmentioning

confidence: 99%

Section: Approaches To Hydrological Modellingmentioning

confidence: 99%

Streamflow Modelling: A Primer on Applications, Approaches and Challenges

2012

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“…When applying post-flood analyses, the analysed flood peaks could be applied as benchmark and incorporated into model calibration, verification, and validation processes (Han et al, 1998;Dutta et al, 2000;Hervouet, 2000;Hsu et al, 2000a;Hesselink et al, 2003;Romanowicz & Beven, 2003). Typically, peak elevations of flood watermarks are measured by conventional land surveying (Aronica et al, 2002;Werner et al, 2005).…”

Section: Urban Floodingmentioning

confidence: 99%

Point Cloud Data Fusion for Enhancing 2D Urban Flood Modelling

Meesuk¹

2017

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Although all care is taken to ensure the integrity and the quality of this publication and the information herein, no responsibility is assumed by the publishers, the author nor UNESCO-IHE for any damage to the property or persons as a result of operation or use of this publication and/or the information contained herein. A pdf version of this work will be made available as Open Access via http://repository.tudelft.nl/ihe This version is licensed under the Creative Commons Attribution-Non Commercial 4.0 International License, http://creativecommons.org/licenses/by-nc/4.0/Published by: CRC Press/Balkema PO Box 11320, 2301 EH Leiden, the Netherlands e-mail: Pub.NL@taylorandfrancis.com www.crcpress.com -www.taylorandfrancis.com to my dear family SummaryModelling urban flood dynamics requires detailed knowledge of a number of complex processes. Numerical simulation of flood propagation requires proper understanding of all processes involved. Also, special care has to be taken to adequately represent the urban topography, taking into account typical urban features like underpasses and hidden alleyways. Incorrect input data will affect the numerical model results, which may lead to inadequate flood-protection measures or even catastrophic flooding situations. This thesis explores how to include particular urban topographic features in urban flood modelling.Aerial Light Detection and Ranging (LiDAR) systems offer opportunities for achieving good quality topographic data, with less fieldwork on the ground. Even though aerial LiDAR data have long been used in many applications, conventional top-view LiDAR data can not quite capture some hidden urban features. However, recent improvements in Structure from Motion (SfM) techniques provide opportunities to achieve improved quality topographic data by using multiple viewpoints, including side-view data.This dissertation explores insights into the capabilities of assimilating SfM point cloud data by using multi-source views as input for enhancing 2D urban flood modelling. Side-view SfM point cloud data are collected and merged with conventional top-view LiDAR point cloud data to create novel Multi-Source Views (MSV) topographic data. The main objectives of this research are (i) to provide insight into the capabilities of using computer-based environments; (ii) to explore the benefits of using the new MSV data; (iii) to enhance 2D model schematizations; (iv) to compare simulated results using new MSV data and conventional top-view LiDAR data as input for urban flood models; and (v) to help developing flood-protection measures. Findings showed that simulation results using conventional top-view LiDAR-DSM as input show the least flood inundation areas and results contained many dry areas. This is because conventional LiDAR-DSM does not capture hidden urban features like underpasses, high trees, overarching structures, which behave as obstacles in 2D model schematics. When applying extended top-view LiDAR-DBM+ and the newly developed MSV-DEM as input, simulation results showed m...

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“…The nutrient model was built within an existing process-based and distributed hydrological model called IISDHM, (Jha et al 2000;Dutta et al 2000Dutta et al , 2006Asokan and Dutta 2008;Dutta and Nakayama 2009;Kabir et al 2011). There are advancements made in alternative hydrological modelling approach using artificial neural network and optimizing techniques such as genetic algorithm (Taormina and Chau 2015;Saeidifarzad 2014;Wu et al 2009;Chen et al 2015;Chau and Wu 2010).…”

Section: Modelling Approachmentioning

confidence: 99%

A Sub-Catchment Based Approach for Modelling Nutrient Dynamics and Transport at a River Basin Scale

Alam

Dutta

2016

Water Resour Manage

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The prediction of nutrient pollution at realistic details is difficult due to lack of proper description of inherent processes in modelling tools. To overcome that this study has adopted a process based approach to build a semi-distributed model to simulate nutrient pollution in changing environment. The model was built to describe: (1) nutrient generation process in the catchment with consideration of different aspects of external and internal sources, (2) nutrient release from surface to the waterways via runoff and soil erosion, and (3) in-stream transport and chemical reaction process. The key novelty of this research is the linking of the nutrient generation process with transport mechanism for modelling nutrient dynamics at a basin scale. A flow capacity based approach was introduced to determine nutrient export from catchment to the waterways, which was useful to achieve the high resolution outputs from the model. The model performed reasonably well to represent the behaviour of nutrient in high flow events as well as in seasonal flow in two catchments located in distinct hydro-climatic regions. The study has shown that the nutrient model is suitable for predicting actual nutrient pollution in rivers for both high flow and seasonal flow under different hydro-climatic conditions. By simulating organic and inorganic nutrients separately, the model allows to estimate river water quality status in detail.

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Flood inundation simulation in a river basin using a physically based distributed hydrologic model

Cited by 77 publications

References 9 publications

Streamflow Modelling: A Primer on Applications, Approaches and Challenges

Streamflow Modelling: A Primer on Applications, Approaches and Challenges

Point Cloud Data Fusion for Enhancing 2D Urban Flood Modelling

A Sub-Catchment Based Approach for Modelling Nutrient Dynamics and Transport at a River Basin Scale

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