Improving Hydrologic Prediction for Large Urban Areas Through Stochastic Analysis of Scale-dependent Runoff Response, Advanced Sensing and High-resolution Modeling PDF Download
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Author: Amir Norouzi
Publisher:
ISBN:
Category : Flood control
Languages : en
Pages : 261
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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: Amir Norouzi
Publisher:
ISBN:
Category : Flood control
Languages : en
Pages : 261
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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: Arezoo Rafieei Nasab
Publisher:
ISBN:
Category : Heteroscedasticity
Languages : en
Pages : 198
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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: Bellie Sivakumar
Publisher: World Scientific
ISBN: 9814464759
Category : Science
Languages : en
Pages : 542
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Book Description
This book comprehensively accounts the advances in data-based approaches for hydrologic modeling and forecasting. Eight major and most popular approaches are selected, with a chapter for each — stochastic methods, parameter estimation techniques, scaling and fractal methods, remote sensing, artificial neural networks, evolutionary computing, wavelets, and nonlinear dynamics and chaos methods. These approaches are chosen to address a wide range of hydrologic system characteristics, processes, and the associated problems. Each of these eight approaches includes a comprehensive review of the fundamental concepts, their applications in hydrology, and a discussion on potential future directions.
Author: Fi-John Chang
Publisher: MDPI
ISBN: 3039368044
Category : Technology & Engineering
Languages : en
Pages : 274
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Book Description
The impacts of climate change on water resource management, as well as increasingly severe natural disasters over the last decades, have caught global attention. Reliable and accurate hydrological forecasts are essential for efficient water resource management and the mitigation of natural disasters. While the notorious nonlinear hydrological processes make accurate forecasts a very challenging task, it requires advanced techniques to build accurate forecast models and reliable management systems. One of the newest techniques for modeling complex systems is artificial intelligence (AI). AI can replicate the way humans learn and has great capability to efficiently extract crucial information from large amounts of data to solve complex problems. The fourteen research papers published in this Special Issue contribute significantly to the uncertainty assessment of operational hydrologic forecasting under changing environmental conditions and the promotion of water resources management by using the latest advanced techniques, such as AI techniques. The fourteen contributions across four major research areas: (1) machine learning approaches to hydrologic forecasting; (2) uncertainty analysis and assessment on hydrological modeling under changing environments; (3) AI techniques for optimizing multi-objective reservoir operation; (4) adaption strategies of extreme hydrological events for hazard mitigation. The papers published in this issue will not only advance water sciences but also help policymakers to achieve more sustainable and effective water resource management.
Author: J.B. Marco
Publisher: Springer Science & Business Media
ISBN: 9401116970
Category : Science
Languages : en
Pages : 470
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Book Description
Stochastic hydrology is an essential base of water resources systems analysis, due to the inherent randomness of the input, and consequently of the results. These results have to be incorporated in a decision-making process regarding the planning and management of water systems. It is through this application that stochastic hydrology finds its true meaning, otherwise it becomes merely an academic exercise. A set of well known specialists from both stochastic hydrology and water resources systems present a synthesis of the actual knowledge currently used in real-world planning and management. The book is intended for both practitioners and researchers who are willing to apply advanced approaches for incorporating hydrological randomness and uncertainty into the simulation and optimization of water resources systems. (abstract) Stochastic hydrology is a basic tool for water resources systems analysis, due to inherent randomness of the hydrologic cycle. This book contains actual techniques in use for water resources planning and management, incorporating randomness into the decision making process. Optimization and simulation, the classical systems-analysis technologies, are revisited under up-to-date statistical hydrology findings backed by real world applications.
Author: Renji Remesan
Publisher: Springer
ISBN: 3319092359
Category : Science
Languages : en
Pages : 261
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Book Description
This book explores a new realm in data-based modeling with applications to hydrology. Pursuing a case study approach, it presents a rigorous evaluation of state-of-the-art input selection methods on the basis of detailed and comprehensive experimentation and comparative studies that employ emerging hybrid techniques for modeling and analysis. Advanced computing offers a range of new options for hydrologic modeling with the help of mathematical and data-based approaches like wavelets, neural networks, fuzzy logic, and support vector machines. Recently machine learning/artificial intelligence techniques have come to be used for time series modeling. However, though initial studies have shown this approach to be effective, there are still concerns about their accuracy and ability to make predictions on a selected input space.
Author: Gerald A. Corzo Perez
Publisher: John Wiley & Sons
ISBN: 1119639344
Category : Science
Languages : en
Pages : 483
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Book Description
Advanced Hydroinformatics Advanced Hydroinformatics Machine Learning and Optimization for Water Resources The rapid development of machine learning brings new possibilities for hydroinformatics research and practice with its ability to handle big data sets, identify patterns and anomalies in data, and provide more accurate forecasts. Advanced Hydroinformatics: Machine Learning and Optimization for Water Resources presents both original research and practical examples that demonstrate how machine learning can advance data analytics, accuracy of modeling and forecasting, and knowledge discovery for better water management. Volume Highlights Include: Overview of the application of artificial intelligence and machine learning techniques in hydroinformatics Advances in modeling hydrological systems Different data analysis methods and models for forecasting water resources New areas of knowledge discovery and optimization based on using machine learning techniques Case studies from North America, South America, the Caribbean, Europe, and Asia The American Geophysical Union promotes discovery in Earth and space science for the benefit of humanity. Its publications disseminate scientific knowledge and provide resources for researchers, students, and professionals.
Author: Behzad Nazari
Publisher:
ISBN:
Category : Flood forecasting
Languages : en
Pages : 151
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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: Thorsten Wagener
Publisher: World Scientific
ISBN: 9781860945397
Category : Technology & Engineering
Languages : en
Pages : 320
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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
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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.
