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

spa-stat2019

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.

Posted on:
July 12, 2019
Length:
1 minute read, 119 words
Categories:
Talks
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