Hybrid models for Hydrological Forecasting: integration of data-driven and conceptual modelling techniques, Corzo Perez, Gerald Augus
Автор: J. Nemec Название: Hydrological Forecasting ISBN: 9027722595 ISBN-13(EAN): 9789027722591 Издательство: Springer Рейтинг: Цена: 139310.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Since a large percentage of losses in human life and material damage from weather-related disasters are caused by water, either by its excess or scarcity, the concern about water has been increasingly associated with these disasters.
Автор: Okello, Saraiva Название: Improved Hydrological Understanding of a Semi-Arid Subtropical Transboundary Basin Using Multiple Techniques - The Incomati River Basin ISBN: 0367280752 ISBN-13(EAN): 9780367280758 Издательство: Taylor&Francis Рейтинг: Цена: 93910.00 T Наличие на складе: Нет в наличии. Описание: Comprehensive statistical and trend analysis of rainfall and streamflow were conducted to describe the streamflow regime and trends in the semi-arid Incomati basin. This provides the basis for better operational water management in the catchment.
Автор: Jayawardena, A W Название: Environmental and Hydrological Systems Modelling ISBN: 041546532X ISBN-13(EAN): 9780415465328 Издательство: Taylor&Francis Рейтинг: Цена: 78590.00 T Наличие на складе: Есть у поставщика Поставка под заказ.
Автор: Renji Remesan; Jimson Mathew Название: Hydrological Data Driven Modelling ISBN: 3319350285 ISBN-13(EAN): 9783319350288 Издательство: Springer Рейтинг: Цена: 95770.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: 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.
Автор: Remesan, Renji Mathew, Jimson Название: Hydrological data driven modelling ISBN: 3319092340 ISBN-13(EAN): 9783319092348 Издательство: Springer Рейтинг: Цена: 113190.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Hydrological Data Driven Modelling
Автор: J. Nemec Название: Hydrological Forecasting ISBN: 9401085803 ISBN-13(EAN): 9789401085809 Издательство: Springer Рейтинг: Цена: 130590.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Since a large percentage of losses in human life and material damage from weather-related disasters are caused by water, either by its excess or scarcity, the concern about water has been increasingly associated with these disasters.
The Abay / Upper Blue Nile basin contributes the largest share of discharge to the river Nile. However, the basin exhibits large spatio-temporal variability in rainfall and runoff. Moreover, human activities also impact hydrological processes through intensive agriculture, overgrazing and deforestation, which substantially affect the basin hydrology. Thus, understanding hydrological processes and hydro-climatic variables at various spatio-temporal scales is essential for sustainable management of water resources in the region.
This research investigates the hydrology of the basin in depth using a range of methods at various spatio-temporal scales. The methods include long-term trend analysis of hydroclimatic variables, hydrologic responses analysis of land cover change, stable isotope techniques and process based rainfallrunoff modelling. A combination of field investigations with new measurements of precipitation, water levels and stable isotopes as well as existing hydro-climatic data offered gaining new insights about runoff generation processes in headwater catchments. The use of rainfall-runoff modelling in two meso-scale catchments of the Abay basin depict that a single model structure in a lumped way for the entire Abay basin cannot represent all the dominant hydrological processes. The results of the different approaches demonstrated the potential of the methods to better understand the basin hydrology in a data scarce region.
Due to the complexity of hydrological systems a single model may be unable to capture the full range of a catchment response and accurately predict the streamflows. A solution could be the in use of several specialized models organized in the so-called committees. Refining the committee approach is one of the important topics of this study, and it is demonstrated that it allows for increased predictive capability of models.
Another topic addressed is the prediction of hydrologic models' uncertainty. The traditionally used Monte Carlo method is based on the past data and cannot be directly used for estimation of model uncertainty for the future model runs during its operation. In this thesis the so-called MLUE (Machine Learning for Uncertainty Estimation) approach is further explored and extended; in it the machine learning techniques (e.g. neural networks) are used to encapsulate the results of Monte Carlo experiments in a predictive model that is able to estimate uncertainty for the future states of the modelled system.
Furthermore, it is demonstrated that a committee of several predictive uncertainty models allows for an increase in prediction accuracy. Catchments in Nepal, UK and USA are used as case studies.
In flood modelling hydrological models are typically used in combination with hydraulic models forming a cascade, often supported by geospatial processing. For uncertainty analysis of flood inundation modelling of the Nzoia catchment (Kenya) SWAT hydrological and SOBEK hydrodynamic models are integrated, and the parametric uncertainty of the hydrological model is allowed to propagate through the model cascade using Monte Carlo simulations, leading to the generation of the probabilistic flood maps. Due to the high computational complexity of these experiments, the high performance (cluster) computing framework is designed and used.
This study refined a number of hydroinformatics techniques, thus enhancing uncertainty-based hydrological and integrated modelling.
Автор: Hartanto Isnaeni Murdi Название: Integrating Multiple Sources of Information for Improving Hydrological Modelling: An Ensemble Approach ISBN: 0367265435 ISBN-13(EAN): 9780367265434 Издательство: Taylor&Francis Рейтинг: Цена: 50010.00 T Наличие на складе: Невозможна поставка. Описание: A framework is proposed to enable effective use of multiple data sources in hydrological modelling. Together forming an ensemble of hydrological simulations.
Автор: Michael B. Abbott; Jens Christian Refsgaard Название: Distributed Hydrological Modelling ISBN: 9401065993 ISBN-13(EAN): 9789401065993 Издательство: Springer Рейтинг: Цена: 174150.00 T Наличие на складе: Есть у поставщика Поставка под заказ.
Автор: Axel Bronstert; Jesus Carrera; Pavel Kabat; Sabine Название: Coupled Models for the Hydrological Cycle ISBN: 3642061168 ISBN-13(EAN): 9783642061165 Издательство: Springer Рейтинг: Цена: 172350.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book considers an array of state-of-the-art coupling and modelling concepts. First the relevant Earth system cycles are presented, followed by a discussion on scale issues and multiple equilibria. Several applications are presented, where a focus is on cases where the hydrological cycle plays a central role.
Автор: Mehdi Khaki Название: Satellite Remote Sensing in Hydrological Data Assimilation ISBN: 3030373746 ISBN-13(EAN): 9783030373740 Издательство: Springer Рейтинг: Цена: 121110.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book presents the fundamentals of data assimilation and reviews the application of satellite remote sensing in hydrological data assimilation. Satellite remote sensing data provides a great opportunity to improve the performance of models through data assimilation.
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