Hydrologic Modeling

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 design and construction program on simplified relationships variously referred to as the SCS method. I recall announcing that I was going to pursue a doctoral degree and develop a new hydrologic model. One of my agency's chief engineers remarked, "Oh no, not another model!" Since then, I hope that I have not built just another model but have significantly advanced the state of hydrologic modeling for both researchers and practitioners. Using distributed hydrologic techniques described in this book, I also hope one day to forecast the response of the dams I built.

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. Watershed runoff and floods are discussed in Part 6, encompassing hydraulic, experimental, and theoretical aspects. Water modeling constitutes the concluding Part 7. Soil and Water Assessment Tool (SWAT), Xinanjiang, and Soil Conservation Service-Curve Number (SCS-CN) models are discussed. The book is of interest to researchers and practitioners in the field of water resources, hydrology, environmental resources, agricultural engineering, watershed management, earth sciences, as well as those engaged in natural resources planning and management. Graduate students and those wishing to conduct further research in water and environment and their development and management find the book to be of value.

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.

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 estimation from satellites. A number of articles address the issues related to the data merging and use of geo-statistical techniques for addressing data limitations at spatial resolutions to capture the h- erogeneity of physical processes. The participants at the School came from diverse backgrounds and the level of - terest and active involvement in the discussions clearly demonstrated the importance the scienti c community places on challenges related to the coupling of atmospheric and hydrologic models. Along with my colleagues Dr. Erika Coppola and Dr. Kuolin Hsu, co-directors of the School, we greatly appreciate the invited lectures and all the participants. The members of the local organizing committee, Drs Barbara Tomassetti; Marco Verdecchia and Guido Visconti were instrumental in the success of the school and their contributions, both scienti cally and organizationally are much appreciated.

It is the task of the engineer, as of any other professional person, to do everything that is reasonably possible to analyse the difficulties with which his or her client is confronted, and on this basis to design solutions and implement these in practice. The distributed hydrological model is, correspondingly, the means for doing everything that is reasonably possible - of mobilising as much data and testing it with as much knowledge as is economically feasible - for the purpose of analysing problems and of designing and implementing remedial measures in the case of difficulties arising within the hydrological cycle. Thus the aim of distributed hydrologic modelling is to make the fullest use of cartographic data, of geological data, of satellite data, of stream discharge measurements, of borehole data, of observations of crops and other vegetation, of historical records of floods and droughts, and indeed of everything else that has ever been recorded or remembered, and then to apply to this everything that is known about meteorology, plant physiology, soil physics, hydrogeology, sediment transport and everything else that is relevant within this context. Of course, no matter how much data we have and no matter how much we know, it will never be enough to treat some problems and some situations, but still we can aim in this way to do the best that we possibly can.

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.

Digital elevation model issues in water resources modeling - Preparation of DEMs for use in environmental modeling analysis - Source water protection project : a comparison of watershed delineation methods in ARC/INFO and arcView GIS - DEM preprocessing for efficient watershed delineation - Gis tools for HMS modeling support - Hydrologic model of the buffalo bayou using GIS - Development of digital terrain representation for use in river modeling - HEC-GeoRAS : linking GIS to hydraulic analysis using ARC/INFO and HEC-RAS - Floodplain determination using arcView GIS and HEC-RAS - The accuracy and efficiency of GIS-Based floodplain determinations.