Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Flood Risk Assessment and Management |
| Untertitel | How to Specify Hydrological Loads, Their Consequences and Uncertainties |
| Herausgeber (Verlag) | Springer Netherlands |
| Seiten | 53-76 |
| Seitenumfang | 24 |
| ISBN (Print) | 9789048199167 |
| Publikationsstatus | Veröffentlicht - 2011 |
| Extern publiziert | Ja |
Abstract
Under the assumption of rational decision making flood forecasts produce economic benefits only if their application reduces the uncertainties of future developments. In general a forecasting system cannot provide the exact future value of the predictand. There are many different sources of uncertainties. In this chapter tools are presented which can be used to characterize them. It is focused on ensemble methods. Ensembles do not only provide descriptions of uncertainties. They can be combined with data assimilation to produce best guess forecasts based on the Bayes' theorem. The forecasting system can be adapted indirectly to the actual state of information by selecting an ensemble member which is in good agreement with observed data. A widely used approach, based on this methodology, is the Ensemble Kalman Filter (EnKF). The EnKF can be used to update state variables of hydrological models. In addition meteorological ensembles forecasts and parameter ensembles are discussed. A case study, demonstrating the applicability of ensemble forecasts, closes this chapter.
ASJC Scopus Sachgebiete
- Umweltwissenschaften (insg.)
- Allgemeine Umweltwissenschaft
- Erdkunde und Planetologie (insg.)
- Allgemeine Erdkunde und Planetologie
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Flood Risk Assessment and Management: How to Specify Hydrological Loads, Their Consequences and Uncertainties. Springer Netherlands, 2011. S. 53-76.
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Beitrag in Buch/Sammelwerk › Forschung › Peer-Review
}
TY - CHAP
T1 - Framing uncertainties in flood forecasting with ensembles
AU - Schumann, Andreas H.
AU - Wang, Yan
AU - Dietrich, Jörg
PY - 2011
Y1 - 2011
N2 - Under the assumption of rational decision making flood forecasts produce economic benefits only if their application reduces the uncertainties of future developments. In general a forecasting system cannot provide the exact future value of the predictand. There are many different sources of uncertainties. In this chapter tools are presented which can be used to characterize them. It is focused on ensemble methods. Ensembles do not only provide descriptions of uncertainties. They can be combined with data assimilation to produce best guess forecasts based on the Bayes' theorem. The forecasting system can be adapted indirectly to the actual state of information by selecting an ensemble member which is in good agreement with observed data. A widely used approach, based on this methodology, is the Ensemble Kalman Filter (EnKF). The EnKF can be used to update state variables of hydrological models. In addition meteorological ensembles forecasts and parameter ensembles are discussed. A case study, demonstrating the applicability of ensemble forecasts, closes this chapter.
AB - Under the assumption of rational decision making flood forecasts produce economic benefits only if their application reduces the uncertainties of future developments. In general a forecasting system cannot provide the exact future value of the predictand. There are many different sources of uncertainties. In this chapter tools are presented which can be used to characterize them. It is focused on ensemble methods. Ensembles do not only provide descriptions of uncertainties. They can be combined with data assimilation to produce best guess forecasts based on the Bayes' theorem. The forecasting system can be adapted indirectly to the actual state of information by selecting an ensemble member which is in good agreement with observed data. A widely used approach, based on this methodology, is the Ensemble Kalman Filter (EnKF). The EnKF can be used to update state variables of hydrological models. In addition meteorological ensembles forecasts and parameter ensembles are discussed. A case study, demonstrating the applicability of ensemble forecasts, closes this chapter.
UR - http://www.scopus.com/inward/record.url?scp=84890001905&partnerID=8YFLogxK
U2 - 10.1007/978-90-481-9917-4_4
DO - 10.1007/978-90-481-9917-4_4
M3 - Contribution to book/anthology
AN - SCOPUS:84890001905
SN - 9789048199167
SP - 53
EP - 76
BT - Flood Risk Assessment and Management
PB - Springer Netherlands
ER -