Many environmental and hydrological problems are spatial or temporal, or both in nature. Spatio-temporal analysis allows identifying and explaining large-scale anomalies which are useful for understanding hydrological characteristics and subsequently predicting hydrological events. Temporal information is sometimes limited; spatial information, on the other hand has increased in recent years due technological advances including the availability of remote sensing data. This development has motivated new research efforts to include data in model representation and analysis.

Geostatistics is the discipline that investigates the statistics of spatially extended variables. Spatio-temporal analysis is at the forefront of geostatistical research these days, and its impact is expected to increase in the future. This trend will be driven by increasing needs for both short and long-term water management planning as well as mitigation of extreme hydrological events (e.g. droughts and floods).

The aim of this session is to provide a platform and an opportunity to demonstrate and discuss innovative applications and methodologies of spatio-temporal Geostatistics in a hydrological context. The session is targeted at both hydrologists and statisticians interested in the spatial and temporal analysis of hydrological events, and it aims to provide a forum for researchers from a variety of fields to effectively communicate their research.

Given the broad scope of this session, the topics of interest include the following non-exclusive list of subjects:

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 displacement phenomena, other).
3. Generalization and optimization of spatial models including 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. Applications of copulas on the identification of spatio-temporal relationships.
9. Prediction on regions of unobserved or limited data where gridded and point simulated data from physical-based models is available.
10. Generalized extreme value distributions used to model extremes for spatial event analysis.
11. Geostatistical characterization of uncertainties.
12. Bayesian Geostatistical Analysis.
13. Geostatistical methods with reduced computational complexity suitable for large-size hydrological problems.