A research on low flows may seem controversial for a “wet” country protected by dykes and barriers. However, low flows in dry summers such as in 1976, 1985 and 2003 indicate that it may happen also in the Rhine basin. Improved medium-range and seasonal low fow forecasts are important as there is an increasing interest to account for low flow forecasts in decision support systems, e.g. how to operate river navigation and power plants during low flow periods to maximize the gain. In this thesis, dominant low flow mechanisms in the Rhine basin are identified. These mechanisms are used to select two appropriate conceptual models for 10 day and 90 day ahead low flow forecasts. Moreover, the identified temporal scales of the dominant low flow mechanisms are used to develop two data-driven seasonal models. The effects of major uncertainty sources on low flow forecasts are assessed using Monte Carlo techniques. Parameter uncertainty is found to have the largest effect on 10 day low flow forecasts, whereas ensemble seasonal precipitation forecasts has the largest effect on 90 day low fow forecasts. Climate change impacts on the seasonality of low flows are assessed using the outputs of an ensemble of climate models to run a hydrological model. By 2063-2098, significant changes are expected in the seasonality of low flows in the Rhine basin.
|Award date||20 Feb 2014|
|Place of Publication||Enschede|
|Publication status||Published - 20 Feb 2014|