HR: 10:20h
AN: H52A-01 INVITED    [Abstracts]
TI: Global uncertainty assessment in hydrological forecasting by means of statistical analysis of forecast errors
AU: * Montanari, A
EM: alberto.montanari@unibo.it
AF: Faculty of Engineering, University of Bologna, Via del Risorgimento 2, Bologna, I-40136, Italy
AU: Grossi, G
EM: giovanna.grossi@ unibs.it
AF: Department DICATA, University of Brescia, Via Branze 38, Brescia, I-25123, Italy
AB: It is well known that uncertainty assessment in hydrological forecasting is a topical issue. Already in 1905 W.E. Cooke, who was issuing daily weather forecasts in Australia, stated: "It seems to me that the condition of confidence or otherwise form a very important part of the prediction, and ought to find expression". Uncertainty assessment in hydrology involves the analysis of multiple sources of error. The contribution of these latter to the formation of the global uncertainty cannot be quantified independently, unless (a) one is willing to introduce subjective assumptions about the nature of the individual error components or (2) independent observations are available for estimating input error, model error, parameter error and state error.
An alternative approach, that is applied in this study and still requires the introduction of some assumptions, is to quantify the global hydrological uncertainty in an integrated way, without attempting to quantify each independent contribution. This methodology can be applied in situations characterized by limited data availability and therefore is gaining increasing attention by end users.
This work aims to propose a statistically based approach for assessing the global uncertainty in hydrological forecasting, by building a statistical model for the forecast error x_t,d, where t is the forecast time and d is the lead time. Accordingly, the probability distribution of x_t,d is inferred through a non linear multiple regression, depending on an arbitrary number of selected conditioning variables. These include the current forecast issued by the hydrological model, the past forecast error and internal state variables of the model. The final goal is to indirectly relate the forecast error to the sources of uncertainty, through a probabilistic link with the conditioning variables.
Any statistical model is based on assumptions whose fulfilment is to be checked in order to assure the validity of the underlying theory. Statistical testing for the proposed approach will be discussed in detail. Particular focus will be given to the hypothesis of stationarity of the forecast error, in view of the key role that stationarity plays in hydrological modeling in general.
Applications are presented in validation mode, that refer to a rainfall-runoff model applied to an Italian river basin.
UR: http://www.costruzioni-idrauliche.ing.unibo.it/people/alberto/
DE: 1816 Estimation and forecasting
DE: 1821 Floods
DE: 1869 Stochastic hydrology
DE: 1872 Time series analysis (3270, 4277, 4475)
DE: 1873 Uncertainty assessment (3275)
SC: Hydrology [H]
MN: 2007 Fall Meeting