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

摘要

Uncertainty in climate change prediction arises from three different sources: model uncertainty, scenario uncertainty, and internal variability (Yip et al., 2011). Model uncertainty arises because of an incomplete understanding of the physical processes and the limitations of implementation of the understanding. Scenario uncertainty arises because of incomplete information about future emissions. Internal variability is the natural unforced fluctuation of the climate system, it is aleatoric. A way to evaluate the total of these uncertainties is to calculate the spread of a multimodel ensemble (Boer, 2009), 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 and evaluate the uncertainty of the results.