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The Value of Using Hydrological Datasets for Water Allocation Decisions

Author:

Publisher:

Published: 2019

Total Pages: 166

ISBN-13: 9789463950084

An increasing number of hydrological datasets from earth observations, hydrological models and seasonal forecasts have become available for water managers, consultants and the general public. These datasets are state-of-the-art products which are usually accessible online and may contribute to develop hydrological studies and support water resources management. However, the added value of these datasets has not been completely explored in decision-making processes. Research studies have assessed how well data can help in predicting climate, but there is a lack of knowledge on how well data can help in water allocation decisions. This work provides numerical tools, methods and results to evaluate the value of using hydrological datasets to support water allocation decisions at river basin and irrigation district scale. An integrated approach is used to predict climate, improve decisions and reduce negative impacts. Results show that investing in hydrological data with finer spatial and temporal resolution and longer periods of record improves water allocation decisions and reduces agricultural production loss in large irrigation schemes. Using river discharge data from hydrological models and global precipitation enhances irrigation area planning when little in-situ data is available. Moreover, using seasonal streamflow forecasts improves available water estimates resulting in better water allocation decisions. The framework was tested in Costa Rica, Colombia and Australia, but can be applied in any case study around the world.

The Value of Using Hydrological Datasets for Water Allocation Decisions: Earth Observations, Hydrological Models and Seasonal Forecasts

Author: Alexander José Kaune Schmidt

Publisher: CRC Press

Published: 2019-11-21

Total Pages: 185

ISBN-13: 1000751066

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Advances In Data-based Approaches For Hydrologic Modeling And Forecasting

Author: Bellie Sivakumar

Publisher: World Scientific

Published: 2010-08-10

Total Pages: 542

ISBN-13: 9814464759

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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.

Integrating Multiple Sources of Information for Improving Hydrological Modelling: an Ensemble Approach

Author: Isnaeni Murdi Hartanto

Publisher: CRC Press

Published: 2019-04-24

Total Pages: 200

ISBN-13: 1000468240

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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.

Thriving on Our Changing Planet: A Decadal Strategy for Earth Observation from Space

Author: National Academies of Sciences, Engineering, and Medicine

Publisher: National Academies Press

Published: 2019-06-18

Total Pages: 29

ISBN-13: 0309492432

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We live on a dynamic Earth shaped by both natural processes and the impacts of humans on their environment. It is in our collective interest to observe and understand our planet, and to predict future behavior to the extent possible, in order to effectively manage resources, successfully respond to threats from natural and human-induced environmental change, and capitalize on the opportunities â€" social, economic, security, and more â€" that such knowledge can bring. By continuously monitoring and exploring Earth, developing a deep understanding of its evolving behavior, and characterizing the processes that shape and reshape the environment in which we live, we not only advance knowledge and basic discovery about our planet, but we further develop the foundation upon which benefits to society are built. Thriving on Our Changing Planet: A Decadal Strategy for Earth Observation from Space (National Academies Press, 2018) provides detailed guidance on how relevant federal agencies can ensure that the United States receives the maximum benefit from its investments in Earth observations from space, while operating within realistic cost constraints. This short booklet, designed to be accessible to the general public, provides a summary of the key ideas and recommendations from the full decadal survey report.

Hydrologic Time Series Analysis

Author: Deepesh Machiwal

Publisher: Springer Science & Business Media

Published: 2012-03-05

Total Pages: 316

ISBN-13: 9400718616

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There is a dearth of relevant books dealing with both theory and application of time series analysis techniques, particularly in the field of water resources engineering. Therefore, many hydrologists and hydrogeologists face difficulties in adopting time series analysis as one of the tools for their research. This book fills this gap by providing a proper blend of theoretical and practical aspects of time sereies analysis. It deals with a comprehensive overview of time series characteristics in hydrology/water resources engineering, various tools and techniques for analyzing time series data, theoretical details of 31 available statistical tests along with detailed procedures for applying them to real-world time series data, theory and methodology of stochastic modelling, and current status of time series analysis in hydrological sciences. In adition, it demonstrates the application of most time series tests through a case study as well as presents a comparative performance evaluation of various time series tests, together with four invited case studies from India and abroad. This book will not only serve as a textbook for the students and teachers in water resources engineering but will also serve as the most comprehensive reference to educate researchers/scientists about the theory and practice of time series analysis in hydrological sciences. This book will be very useful to the students, researchers, teachers and professionals involved in water resources, hydrology, ecology, climate change, earth science, and environmental studies.

Advances in Hydrologic Forecasts and Water Resources Management

Author: Fi-John Chang

Publisher: MDPI

Published: 2021-01-20

Total Pages: 274

ISBN-13: 3039368044

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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.

Advanced Hydroinformatics

Author: Gerald A. Corzo Perez

Publisher: John Wiley & Sons

Published: 2023-12-12

Total Pages: 483

ISBN-13: 1119639344

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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.

Hydrological Data Driven Modelling

Author: Renji Remesan

Publisher: Springer

Published: 2014-11-03

Total Pages: 261

ISBN-13: 3319092359

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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.

Improved Hydrologic Forecasting and Hydropower Planning In Data Scarce Regions Using Satellite-Based Remote Sensing

Author: Akash Koppa

Publisher:

Published: 2019

Total Pages: 142

ISBN-13:

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The role of satellite-based remote sensing in improving hydrologic and water resources studies in data-scarce regions is investigated. Specifically, the dissertation focuses on the development of a 1) validation framework for remotely sensed precipitation and evapotranspiration without the use of ground-based observations, 2) methodological framework for calibration of large scale hydrologic models with multiple fluxes, and 3) a seasonal hydropower planning framework for data-scarce regions. In the first part of the dissertation, a root mean square error (RMSE)-based error metric capable of translating individual biasies in precipitation and evapotranspiration onto the Budyko space is developed. It is shown that the framework succeeds in arriving at the same conclusions as a traditional validation method. In the second part, the value of incorporating multiple hydrologic variables such as evapotranspiration, soil moisture and streamflow into model calibration is investigated. It is shown that parameters which are insensitive to individual model responses can influence the trade-off relationship between them. Finally, the potential of using remotely sensed precipitation and evapotranspiration datasets in generating reliable seasonal reservoir inflow forecasts for hydropower planning is investigated. Results highlight the importance of accounting for input and parameter uncertainty in hydropower planning.