témavezető: Tamás Enikő Anna
helyszín (magyar oldal): DSME helyszín rövidítés: DSME
A kutatási téma leírása:
Formulation of the research topic*
Record floods have been affecting large regions worldwide. In the light of the risk that similar events will occur again, new approaches and reconsidered philosophies are needed in assessment and planning alike.
Forecasting floods and peak discharges is a complex process, and hydrological variables are non-stationary. Thus research related to the development of hydrological forecasting is of extraordinary importance, especially for the most vulnerable small catchments prone to the development of flash floods, for the development of an operative flood alert system.
Flood management includes structural and non-structural measures as well. For structural measures, the diagnostic investigation of flood dikes and structures needs to be developed continually. For non-structural measures, like the decreasing of the roughness of floodplains, the morphological and morpho-dynamic investigation of floodplains is much needed.
It is also important to give equal weight to environmental and economic factors in making decisions. This implies a sound preparation. Models used in flood management substantiation need comprehensive data collection that includes remotely sensed and directly measured data. The quality of a model depends on the empirical ingredients (e.g. elevation data). In addition, the prediction of peak flow and flood hydrographs is a very complex process, as hydrological variables vary both in space and time.
Objectives:
- New knowledge in empirical substantiation of flood modeling and data collection
- New approaches in flood risk management and methods to improve resilience
- To further develop methods of the analysis of datasets
New scientific result to be achieved:
1. further improved methods in flood modeling
2. new possibilities in the improvement of flood resilience
3. new experiences in flood risk mapping and management of less known areas
4. further developed mathematical-statistical methods
5. new empirical data collection methodologies