Improving Hydrologic Prediction for Large Urban Areas Through Stochastic Analysis of Scale-dependent Runoff Response, Advanced Sensing and High-resolution Modeling - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Improving Hydrologic Prediction for Large Urban Areas Through Stochastic Analysis of Scale-dependent Runoff Response, Advanced Sensing and High-resolution Modeling write by Amir Norouzi. This book was released on 2019. Improving Hydrologic Prediction for Large Urban Areas Through Stochastic Analysis of Scale-dependent Runoff Response, Advanced Sensing and High-resolution Modeling available in PDF, EPUB and Kindle. 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).
Improving Hydrologic Prediction Via Data Assimilation, Data Fusion and High-resolution Modeling
Improving Hydrologic Prediction Via Data Assimilation, Data Fusion and High-resolution Modeling - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Improving Hydrologic Prediction Via Data Assimilation, Data Fusion and High-resolution Modeling write by Arezoo Rafieei Nasab. This book was released on 2017. Improving Hydrologic Prediction Via Data Assimilation, Data Fusion and High-resolution Modeling available in PDF, EPUB and Kindle. 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.
Advances In Data-based Approaches For Hydrologic Modeling And Forecasting
Advances In Data-based Approaches For Hydrologic Modeling And Forecasting - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Advances In Data-based Approaches For Hydrologic Modeling And Forecasting write by Bellie Sivakumar. This book was released on 2010-08-10. Advances In Data-based Approaches For Hydrologic Modeling And Forecasting available in PDF, EPUB and Kindle. 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.
Flood Forecasting Using Machine Learning Methods
Flood Forecasting Using Machine Learning Methods - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Flood Forecasting Using Machine Learning Methods write by Fi-John Chang. This book was released on 2019-02-28. Flood Forecasting Using Machine Learning Methods available in PDF, EPUB and Kindle. 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.
Advances in Hydrologic Forecasts and Water Resources Management
Advances in Hydrologic Forecasts and Water Resources Management - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Advances in Hydrologic Forecasts and Water Resources Management write by Fi-John Chang. This book was released on 2021-01-20. Advances in Hydrologic Forecasts and Water Resources Management available in PDF, EPUB and Kindle. 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.
