Many efforts have been devoting since last years to reduce uncertainty in hydrological
modeling predictions. The principal sources of uncertainty are provided
by input errors, for inaccurate rainfall prediction, and model errors, given
by the approximation with which the water flow processes in the soil and river
discharges are described.
The aim of the present work is to develop a bayesian model in order to reduce
the uncertainty in the discharge predictions for the Reno river. The ’a priori’
distribution function is given by an autoregressive model, while the likelihood
function is provided by a linear equation which relates observed values of discharge
in the past and hydrological TOPKAPI model predictions obtained by the
rainfall predictions of the limited-area model COSMO-LAMI. The ’a posteriori’
estimations are provided throw a H∞ filter, because the statistical properties of
estimation errors are not known. In this work a stationary and a dual adaptive
filter are implemented and compared. Statistical analysis of estimation errors and
the description of three case studies of flood events occurred during the fall seasons
from 2003 to 2005 are reported. Results have also revealed that errors can be
described as a markovian process only at a first approximation.
For the same period, an ensemble of ’a posteriori’ estimations is obtained
throw the COSMO-LEPS rainfall predictions, but the spread of this ’a posteriori’
ensemble is not enable to encompass observation variability. This fact is related to
the building of the meteorological ensemble, whose spread reaches its maximum
after 5 days.
In the future the use of a new ensemble, COSMO–SREPS, focused on the
first 3 days, could be helpful to enlarge the meteorogical and, consequently, the
hydrological variability.
| Identifer | oai:union.ndltd.org:unibo.it/oai:amsdottorato.cib.unibo.it:987 |
| Date | 27 June 2008 |
| Creators | Nerozzi, Fabrizio <1964> |
| Contributors | Tibaldi, Stefano |
| Publisher | Alma Mater Studiorum - Università di Bologna |
| Source Sets | Università di Bologna |
| Language | Italian |
| Detected Language | English |
| Type | Doctoral Thesis, PeerReviewed |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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