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In the Earth sciences, a transition is currently occurring in multiple fields towards an integrated Earth system approach, with applications including numerical weather prediction, hydrological forecasting, climate impact studies, ocean dynamics estimation and monitoring, and carbon cycle monitoring. These approaches rely on coupled modeling techniques using Earth system models that account for an increased level of complexity of the processes and interactions between atmosphere, ocean, sea ice, and terrestrial surfaces. A crucial component of Earth system approaches is the development of coupled data assimilation of satellite observations to ensure consistent initialization at the interface between the different subsystems. Going towards strongly coupled data assimilation involving all Earth system components is a subject of active research. A lot of progress is being made in the ocean–atmosphere domain, but also over land. As atmospheric models now tend to address subkilometric scales, assimilating high spatial resolution satellite data in the land surface models used in atmospheric models is critical. This evolution is also challenging for hydrological modeling. This book gathers papers reporting research on various aspects of coupled data assimilation in Earth system models. It includes contributions presenting recent progress in ocean–atmosphere, land–atmosphere, and soil–vegetation data assimilation.

land data assimilation system --- land data assimilation --- rainfall-runoff simulation --- 4D-Var data assimilation --- total water storage --- accuracy --- ocean–atmosphere assimilation --- precipitation --- Earth system models --- numerical weather prediction --- fluorescence --- GRACE --- MCA analysis --- weakly coupled data assimilation --- GPM IMERG --- atmospheric models --- rainfall correction --- remote sensing --- microwave remote sensing --- SMAP --- land surface modeling --- bending angle --- floods soil moisture --- vegetation --- GPSRO --- WRF --- merged CMORPH --- land surface model --- temperature --- 4D-Var --- data assimilation --- data-driven methods --- GSI --- radio occultation data --- rainfall --- soil moisture --- sea level anomaly --- total cloud cover --- land surface models --- Mediterranean basin --- interpolation --- sea surface height --- drought --- TRMM 3B42 --- analog data assimilation --- ocean models

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This open-access textbook's significant contribution is the unified derivation of data-assimilation techniques from a common fundamental and optimal starting point, namely Bayes' theorem. Unique for this book is the "top-down" derivation of the assimilation methods. It starts from Bayes theorem and gradually introduces the assumptions and approximations needed to arrive at today's popular data-assimilation methods. This strategy is the opposite of most textbooks and reviews on data assimilation that typically take a bottom-up approach to derive a particular assimilation method. E.g., the derivation of the Kalman Filter from control theory and the derivation of the ensemble Kalman Filter as a low-rank approximation of the standard Kalman Filter. The bottom-up approach derives the assimilation methods from different mathematical principles, making it difficult to compare them. Thus, it is unclear which assumptions are made to derive an assimilation method and sometimes even which problem it aspires to solve. The book's top-down approach allows categorizing data-assimilation methods based on the approximations used. This approach enables the user to choose the most suitable method for a particular problem or application. Have you ever wondered about the difference between the ensemble 4DVar and the "ensemble randomized likelihood" (EnRML) methods? Do you know the differences between the ensemble smoother and the ensemble-Kalman smoother? Would you like to understand how a particle flow is related to a particle filter? In this book, we will provide clear answers to several such questions. The book provides the basis for an advanced course in data assimilation. It focuses on the unified derivation of the methods and illustrates their properties on multiple examples. It is suitable for graduate students, post-docs, scientists, and practitioners working in data assimilation.

Earth sciences --- Probability & statistics --- Bayesian inference --- Data Assimilation --- Parameter Estimation --- Ensemble Kalman Filter --- 4DVar --- Representer Method --- Ensemble Methods --- Particle Filter --- Particle Flow

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The book presents recent studies covering the aspects of challenges in predictive modelling and applications. Advanced numerical techniques for accurate and efficient real-time prediction and optimal management in coastal and hydraulic engineering are explored. For example, adaptive unstructured meshes are introduced to capture the important dynamics that operate over a range of length scales. Deep learning techniques enable rapid and accurate modelling simulations and pave the way towards both real-time forecasting and overall optimisation control over time, thus improving profitability and managing risk. The use of data assimilation techniques incorporates information from experiments and observations to reduce uncertainties in predictions and improve predictive accuracy. Targeted observation approaches can be used for identifying when, where, and what types of observations would provide the greatest improvement to specific model forecasts at a future time. Such targeted observations are important as they will allow the most effective use of available monitoring resources. The combination of deep learning and data assimilation enables a rapid and accurate response in emergencies. The technologies discussed here can be also used to determine the sensitivity of outputs to various operational conditions in engineering and management, thus providing reliable information to both the public and policy-makers

Research & information: general --- numerical modelling --- unstructured meshes --- finite volume --- North Sea --- salinity --- deep learning --- martinez boundary salinity generator --- Sacramento–San Joaquin Delta --- residence time --- exposure time --- transport time scale --- hyper-tidal estuary --- singular value decomposition --- data assimilation --- ocean models --- observation strategies --- ocean forecasting systems --- ocean Double Gyre --- 4D-Var --- ROMS --- MEOF --- initial ensemble --- ensemble spread --- LETKF --- n/a --- Sacramento-San Joaquin Delta

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This is a Special Issue (SI) of Hydrology. The title of the SI is “Advances in the Ecohydrology of Arid Lands”. Ecohydrology is an emerging, cross disciplinary subfield of hydrology devoted to the mutual interactions between water and ecosystems. Today, the important question of what these interactions mean for human society and how human society impacts these interactions is also part of this subject. The specific climatic/geographic focus here is on arid lands broadly defined as water-deficient regions where potential evapotranspiration (PET) exceeds precipitation (P). The intent of the SI is to present scientifically accurate information on the current state of leading ecohydrology oriented research on arid lands, representing the best contemporary thinking in the field. The five research articles presented by no means cover the field but provide an introduction to the variety of current research. The intended audience is not only those involved in this field but also those engaged in the more traditional aspects of hydrology, biology, ecology, geography, engineering, water management, agriculture urban planning, and other relevant fields.

reference evapotranspiration --- FAO56-PM --- alternative methods --- calibration/validation --- Senegal River basin --- hydrologic modeling --- SWAT --- climate change --- intermittent flow --- aquatic states --- TREHS tool --- CORDEX model --- IRES --- Tsiknias River --- semi-arid region --- dry tropical forest --- hydrologic processes --- drought --- West Africa --- ecohydrology --- data assimilation --- microwave remote sensing --- vegetation water content --- soil moisture --- locust plague --- high elevation wetlands --- plankton --- flamingos --- hydroclimatic patterns --- limnology --- Andean mountains --- n/a

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The book presents recent studies covering the aspects of challenges in predictive modelling and applications. Advanced numerical techniques for accurate and efficient real-time prediction and optimal management in coastal and hydraulic engineering are explored. For example, adaptive unstructured meshes are introduced to capture the important dynamics that operate over a range of length scales. Deep learning techniques enable rapid and accurate modelling simulations and pave the way towards both real-time forecasting and overall optimisation control over time, thus improving profitability and managing risk. The use of data assimilation techniques incorporates information from experiments and observations to reduce uncertainties in predictions and improve predictive accuracy. Targeted observation approaches can be used for identifying when, where, and what types of observations would provide the greatest improvement to specific model forecasts at a future time. Such targeted observations are important as they will allow the most effective use of available monitoring resources. The combination of deep learning and data assimilation enables a rapid and accurate response in emergencies. The technologies discussed here can be also used to determine the sensitivity of outputs to various operational conditions in engineering and management, thus providing reliable information to both the public and policy-makers

Research & information: general --- numerical modelling --- unstructured meshes --- finite volume --- North Sea --- salinity --- deep learning --- martinez boundary salinity generator --- Sacramento-San Joaquin Delta --- residence time --- exposure time --- transport time scale --- hyper-tidal estuary --- singular value decomposition --- data assimilation --- ocean models --- observation strategies --- ocean forecasting systems --- ocean Double Gyre --- 4D-Var --- ROMS --- MEOF --- initial ensemble --- ensemble spread --- LETKF