HS3.2Geostatistics for space-time analysis of hydrological events
|Convener: Gerald A Corzo P | Co-Convener: Mikhail Kanevski|
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 both 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 (and not only) aspects:
1. New and innovative geostatistical applications in spatial modeling, spatio-temporal modeling, spatial reasoning and data mining.
2. Spatial dynamics of natural events (e.g. Morphological changes, spatial displacements of phenomena, others).
3. Generalization and optimization of spatial models. Monitoring networks optimization.
4. Spatial switching and/or ensemble of models.
5. Spatio-temporal methods for the analysis of hydrological, environmental and climate anomalies.
6. Spatial analysis and predictions using Gaussian and non-Gaussian models.
7. Spatial covariance application revealing links between hydrological variables and extremes.
8. Copulas applications on the identification of spatio-temporal relationships
9. Prediction on regions of unobserved or limited where gridded and point simulated data from physical-based models is available.
10. Generalized extreme value distributions used to model extremes for spatial events analyses.
11. Geostatistical characterization of uncertainties.