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Hydrologic Modeling
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Hydrological Modeling
This book carefully considers hydrological models which are essential for predicting floods, droughts, soil moisture estimation, land use change detection, geomorphology and water structures. The book highlights recent advances in the area of hydrological modelling in the Ganga Basin and other internationally important river basins. The impact of climate change on water resources is a global concern. Water resources in many countries are already stressed, and climate change along with burgeoning population, rising standard of living and increasing demand are adding to the stress. Furthermore, river basins are becoming less resilient to climatic vagaries. Fundamental to addressing these issues is hydrological modelling which is covered in this book. Integrated water resources management is vital to ensure water and food security. Integral to the management is groundwater and solute transport, and this book encompasses tools that will be useful to mitigate the adverse consequences of natural disasters.
Hydrological Modelling and the Water Cycle
This volume is a collection of a selected number of articles based on presentations at the 2005 L’Aquila (Italy) Summer School on the topic of “Hydrologic Modeling and Water Cycle: Coupling of the Atmosphere and Hydrological Models”. The p- mary focus of this volume is on hydrologic modeling and their data requirements, especially precipitation. As the eld 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 must be addressed. A number of papers address the recent advances in the State-of-the-art distributed precipitation estima...
Hydrological Data Driven Modelling
This book explores a new realm in data-based modeling with applications to hydrology. Pursuing a case study approach, it presents a rigorous evaluation of state-of-the-art input selection methods on the basis of detailed and comprehensive experimentation and comparative studies that employ emerging hybrid techniques for modeling and analysis. Advanced computing offers a range of new options for hydrologic modeling with the help of mathematical and data-based approaches like wavelets, neural networks, fuzzy logic, and support vector machines. Recently machine learning/artificial intelligence techniques have come to be used for time series modeling. However, though initial studies have shown this approach to be effective, there are still concerns about their accuracy and ability to make predictions on a selected input space.
Hydrologic Modeling
This book contains seven parts. The first part deals with some aspects of rainfall analysis, including rainfall probability distribution, local rainfall interception, and analysis for reservoir release. Part 2 is on evapotranspiration and discusses development of neural network models, errors, and sensitivity. Part 3 focuses on various aspects of urban runoff, including hydrologic impacts, storm water management, and drainage systems. Part 4 deals with soil erosion and sediment, covering mineralogical composition, geostatistical analysis, land use impacts, and land use mapping. Part 5 treats remote sensing and geographic information system (GIS) applications to different hydrologic problems....
Advances in Data-based Approaches for Hydrologic Modeling and Forecasting
This book comprehensively accounts the advances in data-based approaches for hydrologic modeling and forecasting. Eight major and most popular approaches are selected, with a chapter for each stochastic methods, parameter estimation techniques, scaling and fractal methods, remote sensing, artificial neural networks, evolutionary computing, wavelets, and nonlinear dynamics and chaos methods. These approaches are chosen to address a wide range of hydrologic system characteristics, processes, and the associated problems. Each of these eight approaches includes a comprehensive review of the fundamental concepts, their applications in hydrology, and a discussion on potential future directions.
Distributed Hydrologic Modeling Using GIS
During ten years serving with the USDA Soil Conservation Service (SCS), now known as the Natural Resources Conservation Service (NRCS), I became amazed at how millions of dollars in contract monies were spent based on simplistic hydrologic models. As project engineer in western Kansas, I was responsible for building flood control dams (authorized under Public Law 566) in the Wet Walnut River watershed. This watershed is within the Arkansas-Red River basin, as is the Illinois River basin referred to extensively in this book. After building nearly 18 of these structures, I became Assistant State Engineer in Michigan and, for a short time, State Engineer for NRCS. Again, we based our entire des...
Hydrologic Modeling
As water is our precious natural resource, hydrology has evolved as a science to helping people understand how the water system works. Hydrologic models are usually simplified, conceptual or physical models which allow people simulate and predict water cycling procedures. In this book, we presented the state-of-the-art hydrological models and cases of their applications. Models type including statistic models like Mann-Kendall method, F-test method, stoichiometry method, merging different hydrologic software models, and building up new integrated models. Applications cases are used to simulate and predict water quality and quantity like runoffs for reservoirs and rivers, urban storm water, estuaries, regional water quantity assessment, sea-level rise on the coast. Some chapters discussed how to improve existing hydrologic models in the way of setting scenarios, how to combine satellite data with models, and downscale to localized the models. Research topics such as aquatic biogeochemistry, fate and transport of bacteria in a coastal area, water management policies, effects from weirs and beaver dams, are also addressed in this book.
