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|Title:||State of the Art of Flood Forecasting - from Deterministic to Probabilistic Approaches|
|Authors:||THIELEN DEL POZO Jutta; PAPPENBERGER Florian; SALAMON Peter; BOGNER Konrad; BUREK PETER ANDREAS; DE ROO Arie|
|Type:||Articles in books|
|Abstract:||Flood forecasting systems form a key part of ¿preparedness¿ strategies for disastrous floods and provide hydrological services, civil protection authorities and the public with information of upcoming events. Provided the warning leadtime is sufficiently long, adequate preparatory actions can be taken to efficiently reduce the impacts of the flooding (Penning-Rowsell et al., 2000, de Roo et al., 2003). Already in 1674, Pierre Perrault established a quantitative relationship between rainfall and flow for the river Seine (Perrault, 1674) effectively allowing realtime forecasting, but only the development of technology, computers and numerical models starting in the 60ies made quantitative flood forecasts possible as we know it today. Increasingly powerful computing systems, data storage capacities and remote sensing technology has led to enhanced observational data collection systems, high resolution spatial data sets over land surfaces and the oceans, and complex mathematical models furthering the understanding of the complex physical hydro-meteorological processes in a river basin. Research has shown that the combination of the particular rainfall climatology in space and time and the manifold and interactive processes at the surface and in the soil result in such highly non-linear hydrologic responses that these become characteristic to this catchment only (Arnaud et al., 2002; Smith et al., 2004, Obled et al., 1994; Segond, 2006; Smith et al., 2004). Therefore, the design of the best flood forecasting system may differ from catchment to catchment. Such a system needs to balance the availability and quality of data on the one hand and the computational representation of the processes in the atmosphere, surface, soil and channels contributing to flooding on the other hand. Furthermore, it needs to respect the particular demands of the enduser, since decision makers have different priorities. For example, urban areas require a significantly different management approach than reservoir operations. Despite the differences in concept and data needs, there is one underlying issue that spans across all systems. There has been an increasing awareness and acceptance that uncertainty is a fundamental issue of flood forecasting and needs to be dealt with at the different spatial and temporal scales as well as at different stages of the flood generating processes (Cloke et al., 2009). The main sources of uncertainties arise either from input data (i.e., physical measurement errors, the difference in spatio-temporal scale between model and measurements, and meteorological forecasts) or from the model itself through the mathematical simplification and parameterisation of the different physical processes contributing to runoff (Thielen et al., 2008).|
|JRC Institute:||Institute for Environment and Sustainability|
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