Quantitative Information Fusion for Hydrological Sciences / edited by Xing Cai, T. -C. Jim Yeh.

Springer eBooks

Data Fusion Methods for Integrating Data-driven Hydrological Models -- A New Paradigm for Groundwater Modeling -- Information Fusion using the Kalman Filter based on Karhunen-Loève Decomposition -- Trajectory-Based Methods for Modeling and Characterization -- The Role of Streamline Models for Dynamic Data Assimilation in Petroleum Engineering and Hydrogeology -- Information Fusion in Regularized Inversion of Tomographic Pumping Tests -- Advancing the Use of Satellite Rainfall Datasets for Flood Prediction in Ungauged Basins: The Role of Scale, Hydrologic Process Controls and the Global Precipitation Measurement Mission -- Integrated Methods for Urban Groundwater Management Considering Subsurface Heterogeneity.

In a rapidly evolving world of knowledge and technology, do you ever wonder how hydrology is catching up? This book takes the angle of computational hydrology and envisions one of the future directions, namely, quantitative integration of high-quality hydrologic field data with geologic, hydrologic, chemical, atmospheric, and biological information to characterize and predict natural systems in hydrological sciences. Intelligent computation and information fusion are the key words. The aim is to provide both established scientists and graduate students with a summary of recent developments in this topic. The chapters of this edited volume cover some of the most important ingredients for quantitative hydrological information fusion, including data fusion techniques, interactive computational environments, and supporting mathematical and numerical methods. Real-life applications of hydrological information fusion are also addressed.

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