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This book analyses climate change influences on rainfall erosivity and soil erosion across Central Asia, provides an overview (past and projections) on the Central Asian countries where projected changes in rainfall erosivity and erosivity density are the greatest, and discusses the potential impacts on the environment across the region. This analysis is accomplished primarily using the RUSLE model with past and future climate projections, spatiotemporal variations of rainfall erosivity and soil erosion based on WorldClim, and Coupled Model Intercomparison Project Phase 5 (CMIP5) climate models (for Central Asia and separately Kazakhstan). The relationship between precipitation characteristics and erosion has been well established, but spatial and temporal projections of future rainfall erosivity in a changing climate in Central Asia have not been published significantly. Therefore, assessing rainfall erosivity and its consequences can assist specialists and researchers in achieving the best practices for soil conservation. The result of this type of research is all-encompassing, and may reflect normal variations in other parts of the world (for example, the arid and semi-arid regions) and is inherently limited to the Central Asian region.
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.
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.
The movement of sediment and associated pollutants over thelandscape and into water bodies is of increasing concern withrespect to pollution control, prevention of muddy floods andenvironmental protection. In addition, the loss of soil on site hasimplications for declining agricultural productivity, loss ofbiodiversity and decreased amenity and landscape value. The fate ofsediment and the conservation of soil are important issues for landmanagers and decision-makers. In developing appropriate policiesand solutions, managers and researchers are making greater use oferosion models to characterise the processes of erosion and theirinteraction with the landscape. A study of erosion requires one to think in terms ofmicroseconds to understand the mechanics of impact of a singleraindrop on a soil surface, while landscapes form over periods ofthousands of years. These processes operate on scales ofmillimetres for single raindrops to mega-metres for continents.Erosion modelling thus covers quite a lot of ground. This bookintroduces the conceptual and mathematical frameworks used toformulate models of soil erosion and uses case studies to show howmodels are applied to a variety of purposes at a range of spatialand temporal scales. The aim is to provide land managers and otherswith the tools required to select a model appropriate to the typeand scale of erosion problem, to show what users can expect interms of accuracy of model predictions and to provide anappreciation of both the advantages and limitations of models.Problems covered include those arising from agriculture, theconstruction industry, pollution and climatic change and range inscale from farms to small and large catchments. The book will alsobe useful to students and research scientists as an up-to-datereview of the state-of-art of erosion modelling and, through aknowledge of how models are used in practice, in highlighting thegaps in knowledge that need to be filled in order to develop evenbetter models.
This book describes recent developments in the modeling of hydro-climatological processes in time and space. The topic brings together a wide range of disciplines, such as climatology, hydrology, geomorphology and ecology, with examples of problems and related modeling approaches. Parsimonious hydro-climatological models hold the potential to simulate the combined effects of rainfall intensity and distribution patterns in the absence of precipitation records for short time intervals (e.g. daily to sub-hourly) and over large areas (e.g. regional to continental). In this book, we show how the principle of parsimony can be followed without sacrificing depth in seeking to understand a variety of landscape and surface processes that include hydrologic phenomena. Geographically speaking, the focus of the book is on Mediterranean environments. In this region, which is characterized by a complex morphology, soil erosion by water is a major cause of landscape degradation and the fragility of ecosystems is abundantly documented. By exploring interactions between erosive storms and land with the help of modeling solutions created at a variety of scales, the book investigates in detail the climatic implications for the Mediterranean landscape in an effort to bridge historical and contemporary research, which makes it unique in its approach. The book provides a valuable resource for environmental scientists, while also providing an important basis for graduate and postgraduate students interested in research on hydrological cycles and environmental changes.
This book gathers recent international research on the association between aggressive rainfall and soil loss and landscape degradation. Different contributions explore these complex relationships and highlight the importance of the spatial patterns of precipitation intensity on land flow under erosive storms, with the support of observational and modelling data. This is a large and multifaceted area of research of growing importance that outlines the challenge of protecting land from natural hazards. The increase in the number of high temporal resolution rainfall records together with the development of new modelling capabilities has opened up new opportunities for the use of large-scale planning and risk prevention methods. These new perspectives should no longer be considered as an independent research topic, but should, above all, support comprehensive land use planning, which is at the core of environmental decision-making and operations. Textbooks such as this one demonstrate the significance of how hydrological science can enable tangible progress in understanding the complexity of water management and its current and future challenges.
Rainfall erosivity concerns the ability of rainfall to cause erosion on the surface of the earth. The difficulty in modeling the distribution, the size, and the terminal velocity of raindrops in relation to the detachment of soil particles led to the use of more tractable rainfall indices. Thus, in the universal soil loss equation (USLE), the coefficient of rainfall erosivity, R, was introduced. This coefficient is based on the product of the rainfall kinetic energy of a storm and its maximum 30-minute intensity. An important problem in the application of USLE and its revisions in various parts of the world concerns the computation of R, which requires pluviograph records with a length of at least 20 years. For this reason, empirical equations have been developed that are based on coarser rainfall data, such as daily, monthly, or yearly, which are available on larger spatial and temporal extents. However, the lack of denser data is dealt more effectively by means of machine learning methods. Computational systems for this purpose were recently developed based on feed-forward neural networks, yielding significantly better results.