Abstract
Operationally the main purpose of hydrological models is to provide runoff forecasts. The quality of the model
state and the accuracy of the weather forecast together with the model quality define the runoff forecast quality.
Input and model errors accumulate over time and may leave the model in a poor state. Usually model states can be
related to observable conditions in the catchment. Updating of these states, knowing their relation to observable
catchment conditions, influence directly the forecast quality.
Norway is internationally in the forefront in hydropower scheduling both on short and long terms. The inflow
forecasts are fundamental to this scheduling. Their quality directly influence the producers profit as they optimize
hydropower production to market demand and at the same time minimize spill of water and maximize available
hydraulic head.
The quality of the inflow forecasts strongly depends on the quality of the models applied and the quality of the
information they use. In this project the focus has been to improve the quality of the model states which the
forecast is based upon. Runoff and snow storage are two observable quantities that reflect the model state and are
used in this project for updating. Generally the methods used can be divided in three groups: The first re-estimates
the forcing data in the updating period; the second alters the weights in the forecast ensemble; and the third
directly changes the model states.
The uncertainty related to the forcing data through the updating period is due to both uncertainty in the actual
observation and to how well the gauging stations represent the catchment both in respect to temperatures and
precipitation. The project looks at methodologies that automatically re-estimates the forcing data and tests the
result against observed response. Model uncertainty is reflected in a joint distribution of model parameters
estimated using the Dream algorithm.
state and the accuracy of the weather forecast together with the model quality define the runoff forecast quality.
Input and model errors accumulate over time and may leave the model in a poor state. Usually model states can be
related to observable conditions in the catchment. Updating of these states, knowing their relation to observable
catchment conditions, influence directly the forecast quality.
Norway is internationally in the forefront in hydropower scheduling both on short and long terms. The inflow
forecasts are fundamental to this scheduling. Their quality directly influence the producers profit as they optimize
hydropower production to market demand and at the same time minimize spill of water and maximize available
hydraulic head.
The quality of the inflow forecasts strongly depends on the quality of the models applied and the quality of the
information they use. In this project the focus has been to improve the quality of the model states which the
forecast is based upon. Runoff and snow storage are two observable quantities that reflect the model state and are
used in this project for updating. Generally the methods used can be divided in three groups: The first re-estimates
the forcing data in the updating period; the second alters the weights in the forecast ensemble; and the third
directly changes the model states.
The uncertainty related to the forcing data through the updating period is due to both uncertainty in the actual
observation and to how well the gauging stations represent the catchment both in respect to temperatures and
precipitation. The project looks at methodologies that automatically re-estimates the forcing data and tests the
result against observed response. Model uncertainty is reflected in a joint distribution of model parameters
estimated using the Dream algorithm.