New methods to better use predictions for disaster prevention
Environmental scientist Gabriela Guimarães Nobre investigated the link between climate variability and the occurrence of floods and droughts. She is the first one who demonstrates that flood damage and flood occurrence in Europe are strongly related to these variabilities and introduces new methods to better use the expected effects of a weather forecast for disaster prevention.
10/21/2019 | 3:43 PM
There is a transition from "What will the weather be?" to "What will the weather do?". This question is answered by so-called impact-based forecasts that provide information about the expected effects of a weather forecast. In her dissertation, Guimarães Nobre introduces new methods to translate natural hazards to expected socioeconomic impacts that can be used for disaster prevention, for example by applying a Machine Learning technique called "Fast-and-Frugal Trees". With the help of this technique, she identified regions where deviations in European crop production can be predicted long in advance on the basis of the indices of climate variability.
Guimarães Nobre also investigated predictions that could be made based on the natural phenomenon ENSO (El Niño Southern Oscillation), a phenomenon that includes the reciprocal movement of the atmospheric pressure field between the high pressure area in the Pacific and low pressure in the Indo-Pacific region. These new methods offer opportunities for the application of early action for disaster prevention.
Reduce risks through early warning
The strength of climate variability is of great importance for extremes in weather and climate. In addition to demonstrating that flood damage and flood occurrence in Europe are strongly related to climate fluctuations, Guimarães Nobre also provides an insight into the effect of this on flood losses and how impact-based information can be applied in disaster prevention. These preventive actions can be more cost-effective than actions taken after the occurrence of a disaster.