Fr. 190.00

Statistical Downscaling for Hydrological and Environmental Application

English · Hardback

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Description

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This book presents statistical downscaling techniques in a practical manner so that readers can easily adopt the techniques for hydrological applications and designs in response to climate change. It also provides numerous examples and background information on reliability of impact assessments of climate change and what the results imply.


List of contents










1. Introduction 2. Statistical Background 3. Data and Format Description 4. Bias Correction 5. Regression Downscalings 6. Weather Generator Downscaling 7. Weather-Type Downscaling 8. Temporal Downscaling 9. Spatial Downscaling


About the author










Taesam Lee, Ph.D., is an associate professor in the Department of Civil Engineering at Gyeongsang National University. He got a Ph. D. degree from Colorado State University with stochastic simulation of streamflow. He specializes in surface-water hydrology, meteorology, and climatic changes in hydrological extremes publishing more than 40 technical papers.
Vijay Singh, Ph.D., D.Sc., D. Eng. (Hon.), Ph.D. (Hon..), P.E., P.H., Hon. D. WRE is a Distinguished Professor, a Regents Professor, and Caroline & William N. Lehrer Distinguished Chair in Water Engineering in the Department of Biological and Agricultural Engineering and Zachry Department of Civil Engineering at Texas A & M University. He specializes in surface-water hydrology, groundwater hydrology, hydraulics, irrigation engineering, and environmental and water resources engineering. Professor Singh has published extensively and has received more than 90 national and international awards. .


Summary

This book presents statistical downscaling techniques in a practical manner so that readers can easily adopt the techniques for hydrological applications and designs in response to climate change. It also provides numerous examples and background information on reliability of impact assessments of climate change and what the results imply.

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