A framework for regional and global long-term and middle-term assessment of floods and droughts under an approach of computational intelligence linking Earth observation, global climate and hydrological models

Vitali Diaz Mercado, Gerald Corzo Perez, Dimitri Solomatine

Tuesday 30 june 2015

16:30 - 16:45h at North America (level 0)

Themes: (T) Extreme events, natural variability and climate change, (ST) Hydrological extremes: floods and droughts

Parallel session: 7I. Extreme events - Flood Drought

Uncertainty in climate change prediction arises from three different sources: model uncertainty, scenario uncertainty, and internal variability. A way to evaluate the total of these uncertainties is to calculate the spread of a multimodel ensemble, mostly by employing global models. However, global models have important drawbacks in their simulation of currently measured data as well as in the way this bias are incorporated into the future scenarios. Analysis of this type of uncertainty requires the use of large amount of data, and complex algorithms to determine trends and identify spatial patterns. Some work has been on the development of dynamic thresholds to identify extreme events in space and time. The trend in Europe is to explore adaptation that has the concept of “no regret solutions” and to look into adaptation capability of the different sectors of human development and its environment. For this, two key research areas are to be developed one by assessing the extreme situations using a bottomtop approach evolving the idea of actual changes attributed to unexpected climate (regional) and second on using global information (re-analysis data) and projecting models into the future with expected development of earth scenarios. In this paper we present the principles of a framework to address the long term and middle term prediction of floods and droughts working with ensembles of global hydrological models and using data driven models to investigate the uncertainty. Keywords: Climate change, uncertainty, floods, droughts