Geostatistical simulation of space-time stochastic rainfall fields for uncertainty propagation in rainfall-runoff and urban drainage system modelling
Spatial Statistics 2019
By Arturo Torres in Talks
July 12, 2019
Abstract
We demonstrate that rainfall space-time maps will provide much more detailed inputs at the correct location and timing so that simulations become more realistic in particular when we do uncertainty propagation where we need to sample from realistic input space-time distributions and consider the correct scale (or space-time support).
Date
July 12, 2019
Time
11:00 AM
Location
Sitges, Spain
Event
Spatial Statistics 2019
Towards Spatial Data Science
Since 2011, Professor Alfred Stein and colleagues - in collaboration with Elsevier - have been organizing this series of Spatial Statistics conferences every 2 years. The 5th Spatial Statistics conference is held in Sitges, Spain. The conference focus: Towards Spatial Data Science. Covering a wide conference themes:
- Space-time statistics, e.g. geostatistics, point patterns, estimation methods, large dimensions
- New spatial data sources, e.g. social media, Google, citizen science, crowd source maps
- Stochastic geometry, tesselation, point processes, random sets
- Causal statistical modeling
- Trajectory/movement modeling
- Predictive modelling
- Spatial data quality and uncertainty
Space-Time Stochastic Rainfall Fields for Uncertainty Propagation
On 12 July, I gave a talk on application of space-time stocastic rainfall fields and downscaling for uncertainty propagation.