الصفحة 1
الصفحة 1
Hydrological modelling and the water cycle : Coupling the atmospheric and hydrological models
This collected work reports on the state of the art of hydrological model simulation, as well as the methods for satellite-based rainfall estimation. Mainly addressed to scientists and researchers, the contributions have the structure of a standard paper appearing in most cited hydrological, atmospheric and climate journals. Several already-known hydrological models and a few novel ones are presented, as well as the satellite-based precipitation techniques. As the field of hydrologic modeling is experiencing rapid development and transition to application of distributed models, many challenges including overcoming the requirements of compatible observations of inputs and outputs are addressed.
Groundwater : Resource evaluation, augmentation, contamination, restoration, modeling and management
The demand for water resources is increasing day by day due to ever increasing population, mostly from developing countries. This has resulted in abstracting more water from the subsurface stratum and forcing the water managers to manage the limited groundwater resources in a more scientific way, which in turn needs a more sophisticated way of assessing the underground resource and manage it optimally.
Artificial intelligence techniques in hydrology and water resources management
The sustainable management of water cycles is crucial in the context of climate change and global warming. It involves managing global, regional, and local water cycles, as well as urban, agricultural, and industrial water cycles, to conserve water resources and their relationships with energy, food, microclimates, biodiversity, ecosystem functioning, and anthropogenic activities. Hydrological modeling is indispensable for achieving this goal, as it is essential for water resources management and the mitigation of natural disasters. In recent decades, the application of artificial intelligence (AI) techniques in hydrology and water resources management has led to notable advances. In the face of hydro-geo-meteorological uncertainty, AI approaches have proven to be powerful tools for accurately modeling complex, nonlinear hydrological processes and effectively utilizing various digital and imaging data sources, such as ground gauges, remote sensing tools, and in situ Internet of Things (IoT) devices.
Advances in Digital Terrain Analysis
Terrain analysis has been an active study field for years and attracted research studies from geographers, surveyors, engineers and computer scientists. With the rapid growth of Geographical Information System (GIS) technology, particularly the establishment of high resolution Digital Elevation Models (DEM) at national level, the challenge is now focused on delivering justifiable socio-economical and environmental benefits. The contributions in this book represent the state of the art of terrain analysis methods and techniques in areas of digital representation, morphological and hydrological models, uncertainty and applications of terrain analysis.
