| Session Details | |
| Section | HS - Hydrological Sciences |
| Session Title | Geostatistics for Space-time Analysis of Hydrological Events |
| Main Convener | Dr. Geraldine Wong (Helmholtz Center for Ocean Research Kiel (GEOMAR), Germany) |
| Co-convener(s) | Prof. Gerald Augusto Corzo Perez (Tecnologico de Monterrey, Mexico) Dr. Mikhail Kavenski (Institute of Geomatics and Analysis of Risk, Switzerland) Dr. Kittiwet Kuntiyawichai (Khon Kaen University, Thailand) Dr. Durga Lal Shrestha (Commonwealth Scientific and Industrial Research Organisation, Australia) |
| Session Description | Many environmental and hydrological problems are spatial or temporal, or both in nature. For a more realistic representation of these hydrological events, the spatio-temporal analysis is important and the significance of spatial and spatio-temporal analysis is increasingly recognized over the years. Spatio-temporal analysis allow for identifying and explaining large-scale anomalies which are useful for understanding hydrological characteristics and subsequently predicting these hydrological events. This remains an important challenge in hydrology today. Geostatistics is the statistics of variables that are spatial in nature, and is an emerging field aimed at tackling the spatio-temporal analysis. This area is of increasing importance and is likely to become more so in the future, especially with both short and long-term water management planning and mitigation of extreme hydrological events (e.g. droughts and floods). The aim of this session is to provide a platform and opportunity to demonstrate and discuss innovative applications and methodologies of this emerging area in a hydrological context. The session is targeted at meteorologists, hydrologists and statisticians interested in the framework of spatial and temporal analysis of hydrological events and will allow researchers from a variety of fields to effectively communicate their research. The session topics aims to cover broad scope and is expected to cover the following aspects: 1. New and innovative geostatistical applications in spatial modeling, spatio-temporal modeling, spatial reasoning and data mining. 2. Generalization and optimization of spatial models. 3. Spatial switching and/or ensemble of models. 4. Introducing new spatio-temporal methods for the analysis of hydrological, environment and climate anomalies. 5. Statistical spatial prediction and analysis using Gaussian and non-Gaussian methods. 6. Spatial covariance application revealing spatial links between hydrological variables and extremes. 7. Prediction on regions of unobserved or limited where gridded and point simulated data from physical-based models is available. 8. Generalized extreme value distributions used to model extremes for spatial events analyses. 9. Spatial-temporal methodologies and applications in the area of statistical downscaling. Discussion papers are also welcome. |