Satellite Remote Sensing in Hydrological Data Assimilation - Rilegato

Khaki, Mehdi

Rilegato

ISBN 10: 3030373746 ISBN 13: 9783030373740

Casa editrice: Springer Nature, 2020

Riassunto

This book presents the fundamentals of data assimilation and reviews the application of satellite remote sensing in hydrological data assimilation. Although hydrological models are valuable tools to monitor and understand global and regional water cycles, they are subject to various sources of errors. Satellite remote sensing data provides a great opportunity to improve the performance of models through data assimilation.

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Informazioni sull?autore

Dr. Mehdi Khaki received his Bachelor of Civil Engineering in Surveying from the University of Tehran (Iran) in 2011. He also holds an M.Sc. in Geodesy from the same institute (2014), and a PhD in Spatial Sciences from Curtin University (Australia). In 2018, he started working as a lecturer at the School of Engineering, at the University of Newcastle (Australia). Mehdi’s research focuses on the application of geodetic and remote sensing techniques and their integration with hydrological models to improve their simulations in various spatial scales. He has developed new satellite data filtering techniques to improve their quality and also new data assimilation methods for integrating multiple satellite-derived measurements, e.g. satellite gravity and soil moisture measurements with hydrologic models. Using these he was able to analyse water storage and its variations, as well as its connection with the anthropogenic and climatic impacts in various parts of the world, such as Australia, Iran, Bangladesh, South America and Africa.

Dalla quarta di copertina

This book presents the fundamentals of data assimilation and reviews the application of satellite remote sensing in hydrological data assimilation. Although hydrological models are valuable tools to monitor and understand global and regional water cycles, they are subject to various sources of errors. Satellite remote sensing data provides a great opportunity to improve the performance of models through data assimilation.

Dettagli dell'edizione

• Editore: Springer Nature
• Data di pubblicazione: 2020
• Lingua: Inglese
• ISBN 10: 3030373746
• ISBN 13: 9783030373740
• Rilegatura: Copertina rigida
• Numero edizione: 1
• Numero di pagine: 308
• Contatto del produttore: non disponibile
• Persona responsabile: CMN Printpool Inh. Mathis Neumann; https://www.also.com/ec/cms5/en_6000/6000/index.jsp; ; Friedrich-Karl-Str. 9; Hamburg; Germania