Statistical Downscaling for Hydrological and Environmental Applications

✍ Scribed by Taesam Lee, Vijay P. Singh

• Publisher: CRC Press
• Year: 2018
• Tongue: English
• Leaves: 179
• Edition: 1
• Category: Library

✦ Synopsis

Global climate change is typically understood and modeled using global climate models (GCMs), but the outputs of these models in terms of hydrological variables are only available on coarse or large spatial and time scales, while finer spatial and temporal resolutions are needed to reliably assess the hydro-environmental impacts of climate change. To reliably obtain the required resolutions of hydrological variables, statistical downscaling is typically employed. Statistical Downscaling for Hydrological and Environmental Applications presents statistical downscaling techniques in a practical manner so that both students and practitioners can readily utilize them. Numerous methods are presented, and all are illustrated with practical examples. The book is written so that no prior background in statistics is needed, and it will be useful to graduate students, college faculty, and researchers in hydrology, hydroclimatology, agricultural and environmental sciences, and watershed management. It will also be of interest to environmental policymakers at the local, state, and national levels, as well as readers interested in climate change and its related hydrologic impacts.

Features:

• Examines how to model hydrological events such as extreme rainfall, floods, and droughts at the local, watershed level.
• Explains how to properly correct for significant biases with the observational data normally found in current Global Climate Models (GCMs).
• Presents temporal downscaling from daily to hourly with a nonparametric approach.
• Discusses the myriad effects of climate change on hydrological processes.

✦ Table of Contents

Cover
Half Title
Title Page
Copyright Page
Dedication
Contents
Preface
List of Abbreviations
Authors
Chapter 1 Introduction
1.1 Why Statistical Downscaling?
1.2 Climate Models
1.3 Statistical Downscaling
1.4 Selection of Model Scheme
1.5 Structure of Chapters
1.6 Summary and Conclusion
Chapter 2 Statistical Background
2.1 Probability and Statistics
2.1.1 Probabilistic Theory
2.1.1.1 Probability Density Function and Cumulative Distribution Function
2.1.1.2 Descriptors of Random Variables
2.1.2 Discrete Probability Distributions
2.1.2.1 Bernoulli Distribution
2.1.2.2 Binomial Distribution
2.1.3 Continuous Probability Distributions
2.1.3.1 Normal Distribution and Lognormal Distributions
2.1.3.2 Exponential and Gamma Distributions
2.1.3.3 Generalized Extreme Value and Gumbel Distribution
2.1.4 Parameter Estimation for Probability Distributions
2.1.4.1 Method of Moments
2.1.4.2 Maximum Likelihood Estimation
2.1.5 Histogram and Empirical Distribution
2.2 Multivariate Random Variables
2.2.1 Multivariate Normal Distribution and Its Conditional Distribution
2.2.2 Covariance and Correlation
2.3 Random Simulation
2.3.1 Monte Carlo Simulation and Uniform Random Number
2.3.2 Simulation of Probability Distributions
2.4 Metaheuristic Algorithm
2.4.1 Harmony Search
2.5 Summary and Conclusion
Chapter 3 Data and Format Description
3.1 GCM Data
3.2 Reanalysis Data
3.3 RCM Data
3.4 Summary and Conclusion
Chapter 4 Bias Correction
4.1 Why Bias Correction?
4.2 Occurrence Adjustment for Precipitation Data
4.3 Empirical Adjustment (Delta Method)
4.4 Quantile Mapping
4.4.1 General Quantile Mapping
4.4.2 Nonparametric Quantile Mapping
4.4.3 Quantile Delta Mapping
4.5 Summary and Comparison
Chapter 5 Regression Downscalings
5.1 Linear Regression Based Downscaling
5.1.1 Simple Linear Regression
5.1.1.1 Significance Test
5.1.2 Multiple Linear Regression
5.2 Predictor Selection
5.2.1 Stepwise Regression
5.2.2 Least Absolute Shrinkage and Selection Operator
5.3 Nonlinear Regression Modeling
5.3.1 Artificial Neural Network
5.4 Summary and Conclusion
Chapter 6 Weather Generator Downscaling
6.1 Mathematical Background
6.1.1 Autoregressive Models
6.1.2 Multivariate Autoregressive Model
6.1.3 Markov Chain
6.2 Weather Generator
6.2.1 Model Fitting
6.2.1.1 Precipitation
6.2.1.2 Weather Variables (T[sub(max)], T[sub(min)], SR)
6.2.2 Simulation of Weather Variables
6.2.2.1 Precipitation
6.2.2.2 Weather Variables (T[sub(max)], T[sub(min)], SR)
6.2.3 Implementation of Downscaling
6.3 Nonparametric Weather Generator
6.3.1 Simulation under Current Climate
6.3.2 Simulation under Future Climate Scenarios
6.4 Summary and Conclusion
Chapter 7 Weather-Type Downscaling
7.1 Classification of Weather Types
7.1.1 Empirical Weather Type
7.1.2 Objective Weather Type
7.2 Generation of Daily Rainfall Sequences
7.3 Future Climate with Weather-Type Downscaling
7.4 Summary and Conclusion
Chapter 8 Temporal Downscaling
8.1 Background
8.1.1 K-Nearest Neighbor Resampling
8.2 Daily to Hourly Downscaling
8.3 Summary and Conclusion
Chapter 9 Spatial Downscaling
9.1 Mathematical Background
9.1.1 Bilinear Interpolation
9.1.2 Nearest Neighbor Interpolation
9.2 Bias Correction and Spatial Downscaling
9.3 Bias Correction and Constructed Analogues
9.4 Bias Correction and Stochastic Analogue
9.5 Summary and Comparison
References
Index