Spatio-temporal Analysis of Extreme Hydrological Events offers an extensive view of the experiences and applications of the latest developments and methodologies for analyzing and understanding extreme environmental and hydrological events. The book addresses the topic using spatio-temporal methods, such as space-time geostatistics, machine learning, statistical theory, hydrological modelling, neural network and evolutionary algorithms. This important resource for both hydrologists and statisticians interested in the framework of spatial and temporal analysis of hydrological events will provide users with an enhanced understanding of the relationship between magnitude, dynamics and the probability of extreme hydrological events.
- Presents spatio-temporal processes, including multivariate dynamic modelling
- Provides varying methodological approaches, giving the readers multiple hydrological modelling information to use in their work
- Includes a variety of case studies making the context of the book relatable to everyday working situations
|
|
|
xi | |
| Preface |
|
xiii | |
|
1 Geostatistics: Mathematical and Statistical Basis |
|
|
1 | (38) |
|
|
|
|
|
1 | (1) |
|
2 Basic Concepts in Random Fields |
|
|
2 | (14) |
|
|
|
16 | (3) |
|
4 Overview of Geostatistical Methodology |
|
|
19 | (1) |
|
5 Interpolation Materials and Methods |
|
|
20 | (7) |
|
6 Spatial Model Validation |
|
|
27 | (1) |
|
7 Comparison of Interpolation Methods |
|
|
28 | (1) |
|
8 Improving Kriging Using Nonlinear Normalizing Transformation |
|
|
29 | (1) |
|
9 Box---Cox Transformation Method |
|
|
29 | (1) |
|
10 Trans-Gaussian Kriging |
|
|
30 | (1) |
|
|
|
31 | (1) |
|
|
|
32 | (7) |
|
|
|
32 | (7) |
|
2 Background of Spatiotemporal Geostatistical Analysis: Application to Aquifer Level Mapping |
|
|
39 | (20) |
|
|
|
1 Spatiotemporal Geostatistics |
|
|
39 | (3) |
|
2 Spatiotemporal Geostatistical Modeling |
|
|
42 | (9) |
|
3 Spatiotemporal Prediction of Groundwater Level Data in Mires Basin, Crete, Greece |
|
|
51 | (1) |
|
|
|
52 | (2) |
|
|
|
54 | (5) |
|
|
|
54 | (5) |
|
3 Large-Scale Exploratory Analysis of the Spatiotemporal Distribution of Climate Projections: Applying the STRIVIng Toolbox |
|
|
59 | (18) |
|
|
|
|
|
|
|
|
|
59 | (1) |
|
2 Framework (Global Climate Model Projections) |
|
|
60 | (1) |
|
|
|
60 | (2) |
|
4 Data, Case Studies, and Experiment Setup |
|
|
62 | (3) |
|
|
|
65 | (9) |
|
|
|
74 | (3) |
|
|
|
75 | (1) |
|
|
|
75 | (2) |
|
4 Spatiotemporal Drought Analysis at Country Scale Through the Application of the STAND Toolbox |
|
|
77 | (18) |
|
|
|
|
|
|
|
|
|
|
|
77 | (1) |
|
2 Spatio-Temporal ANalysis of Drought Toolbox |
|
|
78 | (4) |
|
|
|
82 | (1) |
|
|
|
83 | (1) |
|
|
|
83 | (6) |
|
|
|
89 | (6) |
|
|
|
91 | (1) |
|
|
|
91 | (4) |
|
5 Spatiotemporal Analysis of Extreme Rainfall Events Using an Object-Based Approach |
|
|
95 | (18) |
|
|
|
|
|
|
|
|
|
|
|
|
|
95 | (1) |
|
|
|
96 | (1) |
|
3 Spatiotemporal Object-Based Methods |
|
|
97 | (2) |
|
4 A New Spatiotemporal Object-Based Method for Small-Scale Rainfall Events |
|
|
99 | (4) |
|
5 Applications Over a Subtropical Catchment in Southeastern Brazil |
|
|
103 | (5) |
|
|
|
108 | (5) |
|
|
|
110 | (1) |
|
|
|
110 | (3) |
|
6 Spatial and Temporal Variations' of Habitat Suitability for Fish: A Case Study in Abras de Mantequilla Wetland, Ecuador |
|
|
113 | (30) |
|
|
|
|
|
|
|
|
|
113 | (3) |
|
|
|
116 | (11) |
|
|
|
127 | (7) |
|
|
|
134 | (2) |
|
|
|
136 | (7) |
|
|
|
137 | (1) |
|
|
|
138 | (5) |
|
7 A Comparison of Spatial--Temporal Scale Between Multiscalar Drought Indices in the South Central Region of Vietnam |
|
|
143 | (22) |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
143 | (1) |
|
|
|
143 | (3) |
|
|
|
146 | (7) |
|
|
|
153 | (11) |
|
5 Conclusions and Discussion |
|
|
164 | (1) |
| Appendix 1 Characteristics Description of 30 Rainfall Measurement Stations (1977--2014) |
|
165 | (1) |
| Appendix 2 Characteristics Description of 13 Temperature Measurement Stations (1977--2014) |
|
166 | (1) |
| Acknowledgments |
|
166 | (1) |
| References |
|
167 | (4) |
| Index |
|
171 | |
Dr Gerald A. Corzo Is a Civil engineer by training with a strong background on computational science and specialized in Teleinformatics. His areas of research cover innovative methods for integrating computational intelligent algorithms and hydrological conceptual models for hydrological forecasting (Flood Early Warning System models integration, Delft-FEWS). He developed scripts for areas of computational intelligence, optimization of water resources, online modeling and in fluid dynamics simulation. One of his recent projects focuses on exploring the use of mobile phone antennas in Colombia for measuring precipitation. Dr Corzo has also participated in research projects in different countries like China with the North China University for Water Conservancy and Electric Power in China, Colombia (CINARA), Mexico (Technologico of Monterrey), England (CEH), Norway (University of Oslo) and others. For the last five years he served as chair of the session on geo-statistics at the European Geoscience Union. He lead the LatinAqua network for water research scientist in Latin-America in 2011 Dr. Emmanouil Varouchakis is an Instructor/Researcher at the School of Environmental Engineering, Technical University of Crete, Greece. He holds a PhD in Geo-technology and the Environment-Spatiotemporal Geostatistics from the Technical University of Crete. Since 2013 he teaches the courses Introduction to Geostatistics, Applied Geostatistics and Environmental Risk Analysis at the Schools of Environmental and Mineral Resources Engineering. He has published several research articles in international journals and he has presented his research findings in international conferences. In 2015 he has been awarded the Natural Resources Research grant award by the International Association of Mathematical Geosciences for his research work entitled A Bayesian space-time geostatistical model for groundwater level variability estimation.
Lisainfo e-raamatute kohta