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Different wavelets, levels of decomposition, and model setups are used in this new approach to generate an ensemble of forecasts.

The model setups are presented in table 3. Table 3 Model setups for the variability analysis Model Meteorological input Land cover classification Short Name BETHY/DLR ZAMG CORINE 2000 BETHY(1)   ZAMG reallocated CORINE 2000 BETHY(2)   ECMWF CORINE 2000 BETHY(3)   ECMWF GLC2000 BETHY(4) EPIC ZAMG - EPIC(1)   ZAMG reallocated - EPIC(2)   ECMWF - EPIC 3).

It is notable, that bootstrap support values for potentially correct groupings increased, if mixed RNA/DNA model setups are applied (cf. Figure 4 and Additional file 2 for complete mammalian trees).

The model setups are presented in table 3. The EPIC model requires homogeneity with respect to data input (i.e. soil, topography, weather, crop management) such that the model has been applied for all combinations of climate, soil, and crop management, separately.

All model setups were initialized according to the inventory data for Wetzstein and Tharandt (Table 1).

Performance of different model setups was compared according to recent morphological and molecular expectations of taxonomy.

3. Hydraulic and aquifer properties as well as model discretization for the dissolution model setup are shown in Table 4.

Our results actually imply a reduced impact of stem positions in the combined data set, if mixed RNA/DNA model setup are used.

It needs to be considered, however, that our simple model setup is not appropriate to simulate the long-term feedback mechanisms.

The model setup is capable of producing convergent and divergent channel networks.

Due to the nonbreaking wave conditions, the model setup is simplified to two-dimensions and single-phase laminar flow in the clear-fluid region.