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Author: Isnaeni Murdi Hartanto
Publisher: CRC Press
ISBN: 1000468240
Category : Science
Languages : en
Pages : 200
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Book Description
The availability of Earth observation and numerical weather prediction data for hydrological modelling and water management has increased significantly, creating a situation that today, for the same variable, estimates may be available from two or more sources of information. Yet, in hydrological modelling, usually, a particular set of catchment characteristics and input data is selected, possibly ignoring other relevant data sources. In this thesis, therefore, a framework is being proposed to enable effective use of multiple data sources in hydrological modelling. In this framework, each available data source is used to derive catchment parameter values or input time series. Each unique combination of catchment and input data sources thus leads to a different hydrological simulation result: a new ensemble member. Together, the members form an ensemble of hydrological simulations. By following this approach, all available data sources are used effectively and their information is preserved. The framework also accommodates for applying multiple data-model integration methods, e.g. data assimilation. Each alternative integration method leads to yet another unique simulation result. Case study results for a distributed hydrological model of Rijnland, the Netherlands, show that the framework can be applied effectively, improve discharge simulation, and partially account for parameter and data uncertainty.
Author: Isnaeni Murdi Hartanto
Publisher: CRC Press
ISBN: 1000468240
Category : Science
Languages : en
Pages : 200
Get Book Here
Book Description
The availability of Earth observation and numerical weather prediction data for hydrological modelling and water management has increased significantly, creating a situation that today, for the same variable, estimates may be available from two or more sources of information. Yet, in hydrological modelling, usually, a particular set of catchment characteristics and input data is selected, possibly ignoring other relevant data sources. In this thesis, therefore, a framework is being proposed to enable effective use of multiple data sources in hydrological modelling. In this framework, each available data source is used to derive catchment parameter values or input time series. Each unique combination of catchment and input data sources thus leads to a different hydrological simulation result: a new ensemble member. Together, the members form an ensemble of hydrological simulations. By following this approach, all available data sources are used effectively and their information is preserved. The framework also accommodates for applying multiple data-model integration methods, e.g. data assimilation. Each alternative integration method leads to yet another unique simulation result. Case study results for a distributed hydrological model of Rijnland, the Netherlands, show that the framework can be applied effectively, improve discharge simulation, and partially account for parameter and data uncertainty.
Author: Lijing Wang
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Climate change results in both long-term droughts and short-term extreme precipitation, which can significantly affect water quality and quantity. To make smart decisions about water resources under uncertain climates, it is important for scientists to convey accurate predictions of water systems to water resource managers. This requires integrating multiple geophysical, geochemical, and hydrologic datasets to build accurate hydrologic models and provide predictions of water flow and quality. However, the model-data integration process can be hindered by challenges such as complex hydrologic modeling, lack of geologically realistic models, and slow or ineffective model calibration methods. These challenges limit the use of model-data integration methods from theory to practice and make it difficult to translate hydrologic models into effective decisions. In this dissertation, we present new method developments for addressing model-data integration's challenges and provide real-world hydrologic examples of using the process of model-data integration. We start by introducing the model-data integration process and associated challenges in Chapter 1. In Chapter 2, we introduce a new geological interface modeling method to integrate multiple datasets and, most importantly, geological knowledge: a data-knowledge-driven trend surface analysis. We define different density functions for different information sources, and sample trend interfaces using the Metropolis-Hastings algorithm with stationary Gaussian field perturbations. This method works for both explicit and implicit interface modeling, where the key advance of the implicit model is to represent complex interfaces and geometries without heavy parameterization. We demonstrate our method in three different test cases: modeling stochastic interfaces of Greenland subglacial topography, magmatic intrusion, and palaeovalleys for groundwater mapping in South Australia. This new trend surface analysis tool is useful for building geological models and hydrostratigraphic layers for hydrologic site characterization. In Chapter 3, we design the hierarchical Bayesian formulation to invert both uncertain global and spatial variables hierarchically. We propose a machine learning-based inversion method that calculates summary statistics using machine learning to invert both linear and non-linear forward models. We also introduce a new local principal component analysis (local PCA) approach that provides a more efficient method for local inversion of large-scale spatial fields. In addition, we provide a likelihood-free inverse method using density estimators, using both traditional kernel density estimation and newly developed neural density estimation. To illustrate the hierarchical Bayesian formulation, one linear volume average inversion, and two non-linear hydrologic modeling cases are presented, including a 3D case study. This Chapter provides possible solutions to many model calibration challenges we face in model-data integration: hierarchical modeling, likelihood definitions, and effective calibration for large spatial fields. In Chapter 4 and Chapter 5, we show two real case studies of model-data integration. Chapter 4 examines the impact of beaver ponds on flow dynamics in a mountainous floodplain in Colorado using hydrologic modeling and model-data integration. The recovery of beavers in North America has been adapted as an ecosystem restoration tool to increase surface and groundwater storage and improve biodiversity on reach scales. We investigate the effects of beavers on hydrologic flows, particularly on the deep baseflow in aquifers, by constructing a 3D hydrologic floodplain model. We calibrate the model to the baseflow piezometer measurement using likelihood-free methods in Chapter 3. Our sensitivity analysis shows that beaver ponds increase the cumulative vertical flow from the fines to the gravel bed but have little effect on the deep underflow in the gravel bed aquifer, suggesting that beaver ponds are disconnected from the main downstream flow. This study aims to improve our understanding of the hydrologic consequences associated with the increasing use of beaver restoration as a climate adaptation strategy. In Chapter 5, we propose a statistical model for constructing 3D redox structures in Danish farmlands to address agricultural nitrogen pollution, which is a global problem that could be exacerbated by hydrologic shifts from climate change. The redox environment in the subsurface is essential for the natural removal of nitrate by denitrification. We combine the towed transient electromagnetic resistivity (tTEM) and redox boreholes to model 3D redox architecture stochastically. However, tTEM survey and redox boreholes are often non-colocated. To address this issue, we perform geostatistical simulations to generate multiple resistivity data colocated with redox boreholes. We then use a statistical learning method, multinomial logistic regression, to predict multiple 3D redox architectures given the uncertain surrounding resistivity structures. We reveal the statistically significant resistivity structures for redox predictions and formulate an inverse problem to better match the redox borehole data using the local PCA method in Chapter 3. These two chapters provide two alternative approaches for providing hydrologic predictions: physics-based modeling or statistical modeling. In Chapter 6, we introduce a fast surrogate flow and transport model to evaluate the climate impact on groundwater contamination. The surrogate modeling approach is applied at the Department of Energy's Savannah River Site F-Area, which contains nuclear wastewater. We present two time-dependent neural network architectures: U-FNO-3D and U-FNO-2D, each with a different approach to incorporating the time dimension. Furthermore, we integrate a custom loss function that takes both data-driven factors and physical boundary constraints into account. This chapter offers a solution to reduce the computational cost of numerical modeling, which is critical in making timely decisions that bridge science and practical applications. This dissertation provides novel methods for geological modeling and model calibration and applies them to real-world problems, highlighting the importance of both method development and practical implementation in addressing hydrologic challenges posed by uncertain climates.
Author: Ramakar Jha
Publisher: Springer Nature
ISBN: 3030813584
Category : Science
Languages : en
Pages : 517
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Book Description
This book carefully considers hydrological models which are essential for predicting floods, droughts, soil moisture estimation, land use change detection, geomorphology and water structures. The book highlights recent advances in the area of hydrological modelling in the Ganga Basin and other internationally important river basins. The impact of climate change on water resources is a global concern. Water resources in many countries are already stressed, and climate change along with burgeoning population, rising standard of living and increasing demand are adding to the stress. Furthermore, river basins are becoming less resilient to climatic vagaries. Fundamental to addressing these issues is hydrological modelling which is covered in this book. Integrated water resources management is vital to ensure water and food security. Integral to the management is groundwater and solute transport, and this book encompasses tools that will be useful to mitigate the adverse consequences of natural disasters.
Author:
Publisher: Newnes
ISBN: 0444531998
Category : Technology & Engineering
Languages : en
Pages : 2131
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Book Description
Water quality and management are of great significance globally, as the demand for clean, potable water far exceeds the availability. Water science research brings together the natural and applied sciences, engineering, chemistry, law and policy, and economics, and the Treatise on Water Science seeks to unite these areas through contributions from a global team of author-experts. The 4-volume set examines topics in depth, with an emphasis on innovative research and technologies for those working in applied areas. Published in partnership with and endorsed by the International Water Association (IWA), demonstrating the authority of the content Editor-in-Chief Peter Wilderer, a Stockholm Water Prize recipient, has assembled a world-class team of volume editors and contributing authors Topics related to water resource management, water quality and supply, and handling of wastewater are treated in depth
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.
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: Vijay P. Singh
Publisher: Water Resources Publication
ISBN: 9781887201353
Category : Science
Languages : en
Pages : 984
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Book Description
Comprehensive account of some of the most popular models of small watershed hydrology and application ~~ of interest to all hydrologic modelers and model users and a welcome and timely edition to any modeling library
Author: Saeid Eslamian
Publisher: Elsevier
ISBN: 0128219505
Category : Technology & Engineering
Languages : en
Pages : 420
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Book Description
Advanced Machine Learning Techniques includes the theoretical foundations of modern machine learning, as well as advanced methods and frameworks used in modern machine learning. Handbook of HydroInformatics, Volume II: Advanced Machine Learning Techniques presents both the art of designing good learning algorithms, as well as the science of analyzing an algorithm's computational and statistical properties and performance guarantees. The global contributors cover theoretical foundational topics such as computational and statistical convergence rates, minimax estimation, and concentration of measure as well as advanced machine learning methods, such as nonparametric density estimation, nonparametric regression, and Bayesian estimation; additionally, advanced frameworks such as privacy, causality, and stochastic learning algorithms are also included. Lastly, the volume presents Cloud and Cluster Computing, Data Fusion Techniques, Empirical Orthogonal Functions and Teleconnection, Internet of Things, Kernel-Based Modeling, Large Eddy Simulation, Patter Recognition, Uncertainty-Based Resiliency Evaluation, and Volume-Based Inverse Mode. This is an interdisciplinary book, and the audience includes postgraduates and early-career researchers interested in: Computer Science, Mathematical Science, Applied Science, Earth and Geoscience, Geography, Civil Engineering, Engineering, Water Science, Atmospheric Science, Social Science, Environment Science, Natural Resources, Chemical Engineering. - Key insights from 24 contributors in the fields of data management research, climate change and resilience, insufficient data problem, etc. - Offers applied examples and case studies in each chapter, providing the reader with real world scenarios for comparison. - Defines both the designing of good learning algorithms, as well as the science of analyzing an algorithm's computational and statistical properties and performance guarantees.
Author: National Research Council
Publisher: National Academies Press
ISBN: 0309114578
Category : Science
Languages : en
Pages : 210
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Book Description
Water is essential to life for humans and their food crops, and for ecosystems. Effective water management requires tracking the inflow, outflow, quantity and quality of ground-water and surface water, much like balancing a bank account. Currently, networks of ground-based instruments measure these in individual locations, while airborne and satellite sensors measure them over larger areas. Recent technological innovations offer unprecedented possibilities to integrate space, air, and land observations to advance water science and guide management decisions. This book concludes that in order to realize the potential of integrated data, agencies, universities, and the private sector must work together to develop new kinds of sensors, test them in field studies, and help users to apply this information to real problems.
Author: Riccardo Casale
Publisher: Springer Science & Business Media
ISBN: 3642586090
Category : Nature
Languages : en
Pages : 400
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Book Description
A review of such natural disasters as floods and landslides, highlighting the possibility of safe and correct land planning and management by means of a global approach to territory. Since the events deriving from slope and fluvial dynamics are commonly triggered by the same factor, occur at the same time and are closely related, this book analyses floods and slope stability phenomena as different aspects of the same dynamic system: the drainage basin.
