Modeling and Practice of Erosion and Sediment Transport under Change


Book Description

Climate and anthropogenic changes impact the conditions of erosion and sediment transport in rivers. Rainfall variability and, in many places, the increase of rainfall intensity have a direct impact on rainfall erosivity. Increasing changes in demography have led to the acceleration of land cover changes in natural areas, as well as in cultivated areas, and, sometimes, in degraded areas and desertified landscapes. These anthropogenized landscapes are more sensitive to erosion. On the other hand, the increase in the number of dams in watersheds traps a great portion of sediment fluxes, which do not reach the sea in the same amount, nor at the same quality, with consequences on coastal geomorphodynamics. This book is dedicated to studies on sediment fluxes from continental areas to coastal areas, as well as observation, modeling, and impact analysis at different scales from watershed slopes to the outputs of large river basins. This book is concentrated on a number of keywords: “erosion” and “sediment transport”, “model” and “practice”, and “change”. The keywords are briefly discussed with respect to the relevant literature. The contributions in this book address observations and models based on laboratory and field data, allowing researchers to make use of such resources in practice under changing conditions.




Modelling Soil Erosion by Water


Book Description

TO THE MODEL EVALUATION 1. MODELLING SOIL EROSION BY WATER l 2 John Boardman and David Favis-Mortlock 1 School of Geography and Environmental Change Unit Mansfield Road University of Oxford Oxford OX1 3TB UK 2 Environmental Change Unit University of Oxford 5 South Parks Road Oxford OX1 3UB UK Introduction This volume is the Proceedings of the NATO Advanced Research Workshop 'Global Change: Modelling Soil Erosion by Water', which was held on II-14th September 1995, at the University of Oxford, UK. The meeting was also one of a series organised by the IGBP 1 GCTE Soil Erosion Network, which is a component of GCTE's Land Degradation Task (3.3.2) (Ingram et aI., 1996; Valentin, this volume). One aim of the GCTE Soil Erosion Network is to evaluate the suitability of existing soil erosion models for predicting the possible impacts of global change upon soil erosion. Due to the wide range of erosion models currently, in use or under development, it was decided to evaluate models in the following sequence Favis-Mortlock et al., 1996): • field-scale water erosion models • catchmenr-scale water erosion models • wind erosion models • models with a landscape-scale and larger focus. As part of this strategy, the first stage of the GCTE validation of field-scale erosion models was carried out at the Oxford NATO-ARW. I A list of Acronyms fonns Appendix A.




Predicting Rainfall Erosion Losses


Book Description

The Universal Soil Loss Equation (USLE) enables planners to predict the average rate of soil erosion for each feasible alternative combination of crop system and management practices in association with a specified soil type, rainfall pattern, and topography. When these predicted losses are compared with given soil loss tolerances, they provide specific guidelines for effecting erosion control within specified limits. The equation groups the numerous interrelated physical and management parameters that influence erosion rate under six major factors whose site-specific values can be expressed numerically. A half century of erosion research in many States has supplied information from which at least approximate values of the USLE factors can be obtained for specified farm fields or other small erosion prone areas throughout the United States. Tables and charts presented in this handbook make this information readily available for field use. Significant limitations in the available data are identified.




Machine Learning Proceedings 1992


Book Description

Machine Learning Proceedings 1992




Predicting Soil Erosion by Water


Book Description

Introduction and history; Rainfall-runoff erosivity factor (R); Soil erodibility factor (K); Slope length and steepness factors (LS); Cover-management factor (C); Support practice factor (P); RUSLE user guide; Coversion to SI metric system; Calculation of EI from recording-raingage records; Estimating random roughness in the field; Parameter values for major agricultural crops and tillage operations.




Artificial Neural Networks in Hydrology


Book Description

R. S. GOVINDARAJU and ARAMACHANDRA RAO School of Civil Engineering Purdue University West Lafayette, IN. , USA Background and Motivation The basic notion of artificial neural networks (ANNs), as we understand them today, was perhaps first formalized by McCulloch and Pitts (1943) in their model of an artificial neuron. Research in this field remained somewhat dormant in the early years, perhaps because of the limited capabilities of this method and because there was no clear indication of its potential uses. However, interest in this area picked up momentum in a dramatic fashion with the works of Hopfield (1982) and Rumelhart et al. (1986). Not only did these studies place artificial neural networks on a firmer mathematical footing, but also opened the dOOf to a host of potential applications for this computational tool. Consequently, neural network computing has progressed rapidly along all fronts: theoretical development of different learning algorithms, computing capabilities, and applications to diverse areas from neurophysiology to the stock market. . Initial studies on artificial neural networks were prompted by adesire to have computers mimic human learning. As a result, the jargon associated with the technical literature on this subject is replete with expressions such as excitation and inhibition of neurons, strength of synaptic connections, learning rates, training, and network experience. ANNs have also been referred to as neurocomputers by people who want to preserve this analogy.







Sediment Transport Technology


Book Description




Watershed Hydrology


Book Description




Flood Forecasting Using Machine Learning Methods


Book Description

Nowadays, the degree and scale of flood hazards has been massively increasing as a result of the changing climate, and large-scale floods jeopardize lives and properties, causing great economic losses, in the inundation-prone areas of the world. Early flood warning systems are promising countermeasures against flood hazards and losses. A collaborative assessment according to multiple disciplines, comprising hydrology, remote sensing, and meteorology, of the magnitude and impacts of flood hazards on inundation areas significantly contributes to model the integrity and precision of flood forecasting. Methodologically oriented countermeasures against flood hazards may involve the forecasting of reservoir inflows, river flows, tropical cyclone tracks, and flooding at different lead times and/or scales. Analyses of impacts, risks, uncertainty, resilience, and scenarios coupled with policy-oriented suggestions will give information for flood hazard mitigation. Emerging advances in computing technologies coupled with big-data mining have boosted data-driven applications, among which Machine Learning technology, with its flexibility and scalability in pattern extraction, has modernized not only scientific thinking but also predictive applications. This book explores recent Machine Learning advances on flood forecast and management in a timely manner and presents interdisciplinary approaches to modelling the complexity of flood hazards-related issues, with contributions to integrative solutions from a local, regional or global perspective.