Publisher's Synopsis
Remote sensing has shown great promise for providing an abundance of data and information that were lacking with the in-situ observations. It has also been a valuable tool in many hydrologic modeling applications due to its capability of providing unrestricted collection of information with wide spatial coverage and temporal repeat. Hydrologic modeling has greatly benefited from observation of land surface water, energy, and carbon conditions which are of critical importance owing to their profound impacts on real world water resources applications such as flood control, weather and climate prediction, agricultural production and water resources management which collectively control the behavior of the climate system. Many studies have demonstrated that initial and boundary conditions of state variables such as soil moisture, soil temperature or vegetation water content at different temporal and spatial scales exercise strong controls on climate, weather and hydrologic processes. Remote sensing, however, holds great promise for soil moisture and snow measurements which have considerable spatial and temporal variability. Merging these measurements with hydrologic model outputs in a systematic and effective way results in an improvement of land surface model prediction. Data Assimilation provides a mechanism to combine these two sources of estimation. Much success has been attained in recent years in using data from passive microwave sensors and assimilating them into the models. This volume provides an overview of the remote sensing measurement techniques for soil moisture and snow data and describes the advances in data assimilation techniques through the ensemble filtering, mainly Ensemble Kalman filter (EnKF) and Particle filter (PF), for improving the model prediction and reducing the uncertainties involved in prediction process. This book reviews multiple remote sensing observations, the application of remote sensing in hydrological modeling, data assimilation and hydrological capacity building in emerging regions. It presents studies entirely on satellite remote sensing data (including topography, land cover, precipitation, and flood inundation extent) to calibrate a hydrologic model, simulate the spatial extent of flooding, and evaluate the probability of detecting inundated areas.
