Toward High-resolution Flood Forecasting for Large Urban Areas PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Toward High-resolution Flood Forecasting for Large Urban Areas PDF full book. Access full book title Toward High-resolution Flood Forecasting for Large Urban Areas by Behzad Nazari. Download full books in PDF and EPUB format.
Author: Behzad Nazari
Publisher:
ISBN:
Category : Flood forecasting
Languages : en
Pages : 151
Get Book Here
Book Description
The ability to forecast flow, depth, and velocity in flooding events is one of the most important needs in highly populated urban areas. Urbanization and climate change highlight the necessity to understand and accurately predict water-related hazards in urban areas due to extreme precipitation. Towards that end, this study initially assesses the impact of changes in precipitation magnitude and imperviousness on urban inundation in a flooding prone urban catchment in the Dallas-Fort Worth Metroplex. Consequently, this study focuses on identifying potential alternatives to the conventional inundation models to improve operational viability of real-time flood forecasting in urban areas by downscaling coarse-resolution model output. Taking advantage of high-resolutions physiographic information, the problem is then transformed into developing efficient methods for routing flow in a network of 1D channels to represent sub-grid variability of hydraulic parameters within coarse 2D cells. Accordingly, two existing methods for such a routing problem are discussed, i.e., the diffusion wave routing and nonlinear routing with power-law storage functions. Each of the aforementioned methods is then solved innovatively to improve their efficiency for real-time routing of flow through many small streams quickly over a large area. In this work, two new methods for solving the 1-dimensional linear diffusion wave equation for finite domain is presented. Referred to as the Continuous Time Discrete Space (CTDS) methods, they yield explicit symbolic expressions for time-continuous solutions at discrete points in space. As such, the methods provide a powerful tool for very easily obtaining accurate diffusive wave solutions in lieu of numerical integration when predictions are desired only at specific locations along the channel. The proposed methods are easy to implement and may be used in a variety of routing applications where accurate explicit symbolic solutions are desired for linear advection-diffusion at specific locations. Also, a new direct solution for nonlinear reservoir routing with a general power-law storage function is presented. The resulting implicit solution is expressed in terms of the incomplete Beta function and is valid for inflow hydrographs that may be approximated by a series of pulses of finite duration. A separate solution for zero inflow representing recession is also presented. The new analytical solution extends the previous results reported in the literature which provide direct solutions only for certain exponents in the power-law storage function. In addition to the wide spectrum of applications that require modeling of nonlinear reservoirs or open channels, the direct solution may also be used for physically-based semi-distributed routing of hillslope flow following simplification of the flow paths as a dendritic network of nonlinear reservoirs. The proposed solutions offer new pathways for simple and efficient modeling of flood waves in real-world applications with minimal computational effort that makes them suitable candidates for flood forecasting in large urban areas.
Author: Behzad Nazari
Publisher:
ISBN:
Category : Flood forecasting
Languages : en
Pages : 151
Get Book Here
Book Description
The ability to forecast flow, depth, and velocity in flooding events is one of the most important needs in highly populated urban areas. Urbanization and climate change highlight the necessity to understand and accurately predict water-related hazards in urban areas due to extreme precipitation. Towards that end, this study initially assesses the impact of changes in precipitation magnitude and imperviousness on urban inundation in a flooding prone urban catchment in the Dallas-Fort Worth Metroplex. Consequently, this study focuses on identifying potential alternatives to the conventional inundation models to improve operational viability of real-time flood forecasting in urban areas by downscaling coarse-resolution model output. Taking advantage of high-resolutions physiographic information, the problem is then transformed into developing efficient methods for routing flow in a network of 1D channels to represent sub-grid variability of hydraulic parameters within coarse 2D cells. Accordingly, two existing methods for such a routing problem are discussed, i.e., the diffusion wave routing and nonlinear routing with power-law storage functions. Each of the aforementioned methods is then solved innovatively to improve their efficiency for real-time routing of flow through many small streams quickly over a large area. In this work, two new methods for solving the 1-dimensional linear diffusion wave equation for finite domain is presented. Referred to as the Continuous Time Discrete Space (CTDS) methods, they yield explicit symbolic expressions for time-continuous solutions at discrete points in space. As such, the methods provide a powerful tool for very easily obtaining accurate diffusive wave solutions in lieu of numerical integration when predictions are desired only at specific locations along the channel. The proposed methods are easy to implement and may be used in a variety of routing applications where accurate explicit symbolic solutions are desired for linear advection-diffusion at specific locations. Also, a new direct solution for nonlinear reservoir routing with a general power-law storage function is presented. The resulting implicit solution is expressed in terms of the incomplete Beta function and is valid for inflow hydrographs that may be approximated by a series of pulses of finite duration. A separate solution for zero inflow representing recession is also presented. The new analytical solution extends the previous results reported in the literature which provide direct solutions only for certain exponents in the power-law storage function. In addition to the wide spectrum of applications that require modeling of nonlinear reservoirs or open channels, the direct solution may also be used for physically-based semi-distributed routing of hillslope flow following simplification of the flow paths as a dendritic network of nonlinear reservoirs. The proposed solutions offer new pathways for simple and efficient modeling of flood waves in real-world applications with minimal computational effort that makes them suitable candidates for flood forecasting in large urban areas.
Author: Hamideh Habibi
Publisher:
ISBN:
Category : Emergency management
Languages : en
Pages : 179
Get Book Here
Book Description
Urban flash flooding is a serious problem in large highly populated areas such as the Dallas-Fort Worth metroplex (DFW). Being able to monitor and predict flash flooding at a high spatiotemporal resolution is critical to mitigating its threats and for cost effective emergency management. In this reserach, a high-resolution flash flood forecast system which operates in real time is developed for DFW using a gridded distributed hydrologic model and high-resolution quantitative precipitation estimates from the DFW Demostration Network of the Collaborative Adaptive Sensing of the Atmosphere (CASA) Program high-resolution X band radars and the National Weather Service (NWS) NEXRAD radar. To mitigate hazards and to reduce negative impacts of flooding, urban municipalities operate storm drain networks of varying capacity and complexity. Whereas the conveyance capacities of storm drain systems are generally much smaller than those of the natural channel systems (Rafieeinasab et al. 2015), storm drain networks may significantly alter the severity of flooding and other impacts depending on the location of flooding and the magnitude of rainfall. For accurate flash flood forecasting and effective stormwater planning and management in urban areas, it is necessary to model not only the natural channel systems but also the large and complex networks of storm drains. Most distributed hydrologic models developed for real-time flood forecasting lack the ability to simulate storm drains explicitly. Most urban hydraulic models can simulate storm drains but are not suitable for real-time forecasting for large areas due to computational cost and modeling complexity. In this work, a modular storm drain model that can be easily coupled with existing gridded distributed hydrologic models for real-time flash flood forecasting and stormwater planning and management for large urban areas is described. The integrated model is applied to a 144.6 km2 area consisting of five urban catchments in the Cities of Arlington and Grand Prairie in Texas, US, and the impact of the storm drain network via a combination of simulation experiments, sensitivity analysis and a limited comparison with observed flow is assessed. It is shown how the integrated model may be used to assess the effectiveness of storm drain network over a large area and how areas of potential concern for flooding may be identified under the existing condition and under increased imperviousness. The results show that storm drain modeling increases peak outlet flow for significant events very slightly only for smaller catchments. The simulation experiments with and without storm drain modeling also show that the storm drains reduce surface flow very significantly for a short duration at almost all grid cells in the study area, and that at many locations the flow remains reduced for the entire duration. Sensitivity analysis indicates that significant uncertainties exist in modeling inlet flow and hence partitioning surface runoff into storm drain and natural channel flows. The sources of uncertainties include incomplete information on stormwater infrastructure and uncertainties associated with inlet size, efficiency, clogging and gutter flow modeling. Whereas uncertainty analysis for stormwater infrastructure would be an extremely expensive proposition for both modeling and computing with 1D storm drain-2D surface flow modeling for a large area, the integrated modeling approach developed in this work makes such analysis well within the realm of possibility. The proposed approach hence offers a practical pathway for integrated modeling of storm drains with gridded distributed hydrologic models for large urban areas.
Author: Arezoo Rafieei Nasab
Publisher:
ISBN:
Category : Heteroscedasticity
Languages : en
Pages : 198
Get Book Here
Book Description
With population growth, urbanization and climate change, accurate and skillful monitoring and prediction of water resources and water-related hazards are becoming increasingly important to maintaining and improving the quality of life for human beings and well-being of the ecosystem in which people live. Because most hydrologic systems are driven by atmospheric processes that are chaotic, hydrologic processes operate at many different scales, and the above systems are almost always under-observed, there are numerous sources of error in hydrologic prediction. This study aims to advance the understanding of these uncertainty sources and reduce the uncertainties to the greatest possible extent. Toward that end, we comparatively evaluate two data assimilation (DA) techniques ensemble Kalman filter (EnKF) and maximum likelihood ensemble filter (MLEF) to reduce the uncertainty in initial conditions of soil moisture. Results show MLEF is a strongly favorable technique for assimilating streamflow data for updating soil moisture. In most places, precipitation is by far the most important forcing in hydrologic prediction. Because radars do not measure precipitation directly, radar QPEs are subject to various sources of error. In this study, the three Next Generation Radar (NEXRAD)-based QPE products, the Digital Hybrid Scan Reflectivity (DHR), Multisensor Precipitation Estimator (MPE) and Next Generation Multisensor QPE (Q2), and the radar QPE from the Collaborative Adaptive Sensing of the Atmosphere (CASA) radar are comparatively evaluated for high-resolution hydrologic modeling in the Dallas-Fort Worth Metroplex (DFW) area. Also, since they generally carry complementary information, one may expect to improve accuracy by fusing multiple QPEs. This study develops and comparatively evaluates four different techniques for producing high-resolution QPE by fusing multiple radar-based QPEs. Two experiments were carried out for evaluation; in one, the MPE and Q2 products were fused and, in the other, the MPE and CASA products were fused. Result show that the Simple Estimation (SE) is an effective, robust and computationally inexpensive data fusion algorithm for QPE. The other main goal of this study is to provide accurate spatial information of streamflow and soil moisture via distributed hydrologic modeling. Toward that end, we evaluated the NWS's Hydrology Laboratory Research Distributed Hydrologic Model (HL-RDHM) over the Trinity River Basin for several headwater basins. We also develop a prototype high resolution flash flood prediction system for Cities of Fort Worth, Arlington and Grand Prairie, a highly urbanized area. Ideally, the higher the resolution of distributed modeling and the precipitation input is, the more desirable the model output is as it provides better spatiotemporal specificity. There are, however, practical limits to the resolution of modeling. To test and ascertain the limits of high-resolution polarimetric QPE and distributed hydrologic modeling for advanced flash flood forecasting in large urban area, we performed sensitivity analysis to spatiotemporal resolution. The results indicate little consistent pattern in dependence on spatial resolution while there is a clear pattern for sensitivity to temporal resolution. More research is needed, however, to draw firmer conclusions and to assess dependence on catchment scale.
Author: Amir Norouzi
Publisher:
ISBN:
Category : Flood control
Languages : en
Pages : 261
Get Book Here
Book Description
Due to urbanization and climate change, large urban areas such as the Dallas-Fort Worth Metroplex (DFW) area is vulnerable not only to river flooding but also flash flooding. Due to the nonstationarities involved, projecting how the changes in land cover and climate may modify flood frequency in large urban areas is a challenge. Part I of this work develops a simple spatial stochastic model for rainfall-to-areal runoff in urban areas, evaluates climatological mean and variance of mean areal runoff (MAR) over a range of catchment scales, translates them into runoff frequency as a proxy for flood frequency, and assesses its sensitivity to precipitation, imperviousness and soil, and their changes. The results show that the variability of MAR in urban areas depends significantly on the catchment scale and magnitude of precipitation, and that precipitation, soil, and land cover all exert influences of varying relative importance in shaping the frequency of MAR, and hence flood frequency, for different sizes of urban areas. The findings indicate that, due to large sensitivity of frequency of MAR to multiple hydrometeorological and physiographic factors, estimation of flood frequency for urban catchments is inherently more uncertain, and the approach developed in this work may be useful in developing bounds for flood frequencies in urban areas under nonstationary conditions arising from climate change and urbanization. High-resolution hydrologic and hydraulic models are necessary to provide location- and time-specific warnings in densely populated areas. Due to the errors in precipitation input, and model parameters, structures and states, however, increasing the nominal resolution of the models may not improve the accuracy of the model output. Part II of this work tests the current limits of high-resolution hydrologic modeling for real-time forecasting by assessing the sensitivity of stream flow and soil moisture simulations in urban catchments to the spatial resolution of the rainfall input and the a priori model parameters. The hydrologic model used is the National Weather Service (NWS) Hydrology Laboratory's Research Distributed Hydrologic Model (HLRDHM) applied at spatial resolutions of 250 m to 2 km for precipitation and 250 m to 4 km for the a priori model parameters. The precipitation input used are the Collaborative Adaptive Sensing of he Atmosphere (CASA) and the Multisensor Precipitation Estimator (MPE) products available at 500 m and 1 min, and 4 km and 1 hr spatio temporal resolutions, respectively. The stream flow simulation results were evaluated for two urban catchments of 3.4 to 14.4 km2 in Arlington and Grand Prairie, TX. The stream flow observations used in the evaluation were obtained from water level measurements via the rating curves derived from 1-D steady-state non-uniform hydraulic model. The soil moisture simulation result were evaluated for three locations in Arlington where observations are available at depths of 0.05, 0.10, 0.25, 0.50 and 1.00 m. The soil moisture observations were obtained from three Time Domain Transmissometry (TDT) and Time Domain Reflectometry (TDR)sensors newly deployed for this work. The results show that the use of high-resolution QPE improves stream flow simulation significantly, but that, once the resolution of QPE is increased to the scale of the catchment, no clear relationships are found between the simulation accuracy and the resolution of the QPE or hydrologic modeling, presumably because the errors in QPE and models mask the relationships. The soil moisture results suggest that there are disparate infiltration processes at work within a small area in Arlington, and that, while the near-surface simulation of soil moisture is generally skillful, the Sacramento soil moisture accounting model - heat transfer version (SAC-HT) in HLRDHM has difficulty in simulating the vertical dynamics of soil moisture. The findings point to real-time updating of model states to reduce uncertainties in initial soil moisture conditions, and the need for a dense observing network to improve understanding and to assess the impact at the catchment scale. Continuing urbanization will continue to alter the hydrologic response of urban catchments in the DFW area and elsewhere. To assess the impact of recent land cover changes in the study area and to predict what may occur in the future, stream flow and soil moisture were simulated using HLRDHM at 250 m and 5 min resolution with the National Land Cover Data of 2001, 2006 and 2011 for five urban catchments in Arlington and Grand Prairie, TX. The analysis indicates that imperviousness increased by about 15 percent in the DFW area between 2001 and 2011. The findings indicate that, in terms of peak flow, time-to-peak and runoff volume, small events are more sensitive to changes in impervious cover than large events, increase in peak flow is more pronounced for catchments with larger increase in impervious cover, increase in peak flow is also impacted by changes in antecedent soil moisture due to increased impervious cover, runoff volume is not significantly impacted by changes in impervious cover, and changes in time-to-peak relative to the response time of the catchment is impacted by the location of the land cover changes relative to the outlet and the time-to-peak itself. In particular, the Johnson Creek Catchment in Arlington (~40 km2), which has a time-to-peak of only 40 min, shows larger sensitivity in time-to-peak to land cover changes due presumably to the proximity of the area of increased land cover to the catchment outlet. For further evaluation, however, dense observation networks for stream flow and soil moisture, such as the Arlington Urban Hydrology Test bed currently under development, are necessary in addition to the CASA network of X-band polarimetric radars for high-resolution quantitative precipitation information (QPI).
Author: Thorsten Wagener
Publisher: World Scientific
ISBN: 1783260661
Category : Science
Languages : en
Pages : 333
Get Book Here
Book Description
This important monograph is based on the results of a study on the identification of conceptual lumped rainfall-runoff models for gauged and ungauged catchments. The task of model identification remains difficult despite decades of research. A detailed problem analysis and an extensive review form the basis for the development of a Matlab® modelling toolkit consisting of two components: a Rainfall-Runoff Modelling Toolbox (RRMT) and a Monte Carlo Analysis Toolbox (MCAT). These are subsequently applied to study the tasks of model identification and evaluation. A novel dynamic identifiability approach has been developed for the gauged catchment case. The theory underlying the application of rainfall-runoff models for predictions in ungauged catchments is studied, problems are highlighted and promising ways to move forward are investigated. Modelling frameworks for both gauged and ungauged cases are developed. This book presents the first extensive treatment of rainfall-runoff model identification in gauged and ungauged catchments.
Author: Fi-John Chang
Publisher: MDPI
ISBN: 3038975486
Category : Technology & Engineering
Languages : en
Pages : 376
Get Book Here
Book Description
Nowadays, the degree and scale of flood hazards has been massively increasing as a result of the changing climate, and large-scale floods jeopardize lives and properties, causing great economic losses, in the inundation-prone areas of the world. Early flood warning systems are promising countermeasures against flood hazards and losses. A collaborative assessment according to multiple disciplines, comprising hydrology, remote sensing, and meteorology, of the magnitude and impacts of flood hazards on inundation areas significantly contributes to model the integrity and precision of flood forecasting. Methodologically oriented countermeasures against flood hazards may involve the forecasting of reservoir inflows, river flows, tropical cyclone tracks, and flooding at different lead times and/or scales. Analyses of impacts, risks, uncertainty, resilience, and scenarios coupled with policy-oriented suggestions will give information for flood hazard mitigation. Emerging advances in computing technologies coupled with big-data mining have boosted data-driven applications, among which Machine Learning technology, with its flexibility and scalability in pattern extraction, has modernized not only scientific thinking but also predictive applications. This book explores recent Machine Learning advances on flood forecast and management in a timely manner and presents interdisciplinary approaches to modelling the complexity of flood hazards-related issues, with contributions to integrative solutions from a local, regional or global perspective.
Author: Mingfu Guan
Publisher: Frontiers Media SA
ISBN: 2889668428
Category : Science
Languages : en
Pages : 148
Get Book Here
Book Description
Author: Guy J-P. Schumann
Publisher: John Wiley & Sons
ISBN: 1119217903
Category : Science
Languages : en
Pages : 270
Get Book Here
Book Description
Global Flood Hazard Subject Category Winner, PROSE Awards 2019, Earth Science Selected from more than 500 entries, demonstrating exceptional scholarship and making a significant contribution to the field of study. Flooding is a costly natural disaster in terms of damage to land, property and infrastructure. This volume describes the latest tools and technologies for modeling, mapping, and predicting large-scale flood risk. It also presents readers with a range of remote sensing data sets successfully used for predicting and mapping floods at different scales. These resources can enable policymakers, public planners, and developers to plan for, and respond to, flooding with greater accuracy and effectiveness. Describes the latest large-scale modeling approaches, including hydrological models, 2-D flood inundation models, and global flood forecasting models Showcases new tools and technologies such as Aqueduct, a new web-based tool used for global assessment and projection of future flood risk under climate change scenarios Features case studies describing best-practice uses of modeling techniques, tools, and technologies Global Flood Hazard is an indispensable resource for researchers, consultants, practitioners, and policy makers dealing with flood risk, flood disaster response, flood management, and flood mitigation.
Author: A.D. Teklesadik
Publisher:
ISBN:
Category :
Languages : en
Pages :
Get Book Here
Book Description
Author: National Academies of Sciences, Engineering, and Medicine
Publisher: National Academies Press
ISBN: 030948961X
Category : Science
Languages : en
Pages : 101
Get Book Here
Book Description
Flooding is the natural hazard with the greatest economic and social impact in the United States, and these impacts are becoming more severe over time. Catastrophic flooding from recent hurricanes, including Superstorm Sandy in New York (2012) and Hurricane Harvey in Houston (2017), caused billions of dollars in property damage, adversely affected millions of people, and damaged the economic well-being of major metropolitan areas. Flooding takes a heavy toll even in years without a named storm or event. Major freshwater flood events from 2004 to 2014 cost an average of $9 billion in direct damage and 71 lives annually. These figures do not include the cumulative costs of frequent, small floods, which can be similar to those of infrequent extreme floods. Framing the Challenge of Urban Flooding in the United States contributes to existing knowledge by examining real-world examples in specific metropolitan areas. This report identifies commonalities and variances among the case study metropolitan areas in terms of causes, adverse impacts, unexpected problems in recovery, or effective mitigation strategies, as well as key themes of urban flooding. It also relates, as appropriate, causes and actions of urban flooding to existing federal resources or policies.
