The Danube; the dramatic history of the great river and the people touched by its flow.

Includes bibliography.

Crustacean plankton abundance in the Danube River and in its side arms in Hungary

The spatial distribution and seasonal dynamics of the crustacean zooplankton were studied in the Danube River and in its side arms near Budapest, Hungary. Microcrustaceans were sampled biweekly from October 2006 to November 2007 at eleven sites. Thermocyclops crassus, Moina micrura and Bosmina longirostris added up to 57.6% of the total density. Comparisons of the different water bodies stressed the separation of the eupotamal and parapotamal side arms. Densities in the side arms were one respectively two orders of magnitude higher as compared to the main channel, which was relatively poor in plankton. There were remarkable longitudinal and transversal variations in the abundance of the major zooplankton groups (cladocerans, adult copepods, copepodites, nauplii) and dominant species (t-test, P < 0.05). However, no general pattern was observed, the spatial distribution depended on the examined objects. There were statistically significant seasonal differences in zooplankton abundance (Tukey-test, P < 0.05). Water residence time and water discharge were not found to be related to zooplankton abundance, but water temperature was positively correlated with microcrustacean density.

Hydrological cycle in the Danube basin in present-day and XXII century simulations by IPCCAR4 global climate models

We present an intercomparison and verification analysis of 20 GCMs (Global Circulation Models) included in the 4th IPCC assessment report regarding their representation of the hydrological cycle on the Danube river basin for 1961–2000 and for the 2161–2200 SRESA1B scenario runs. The basin-scale properties of the hydrological cycle are computed by spatially integrating the precipitation, evaporation, and runoff fields using the Voronoi-Thiessen tessellation formalism. The span of the model- simulated mean annual water balances is of the same order of magnitude of the observed Danube discharge of the Delta; the true value is within the range simulated by the models. Some land components seem to have deficiencies since there are cases of violation of water conservation when annual means are considered. The overall performance and the degree of agreement of the GCMs are comparable to those of the RCMs (Regional Climate Models) analyzed in a previous work, in spite of the much higher resolution and common nesting of the RCMs. The reanalyses are shown to feature several inconsistencies and cannot be used as a verification benchmark for the hydrological cycle in the Danubian region. In the scenario runs, for basically all models the water balance decreases, whereas its interannual variability increases. Changes in the strength of the hydrological cycle are not consistent among models: it is confirmed that capturing the impact of climate change on the hydrological cycle is not an easy task over land areas. Moreover, in several cases we find that qualitatively different behaviors emerge among the models: the ensemble mean does not represent any sort of average model, and often it falls between the models’ clusters.

Improving the evaluation of hydrological multi-model forecast performance in the Upper Danube Catchment

Medium range flood forecasting activities, driven by various meteorological forecasts ranging from high resolution deterministic forecasts to low spatial resolution ensemble prediction systems, share a major challenge in the appropriateness and design of performance measures. In this paper possible limitations of some traditional hydrological and meteorological prediction quality and verification measures are identified. Some simple modifications are applied in order to circumvent the problem of the autocorrelation dominating river discharge time-series and in order to create a benchmark model enabling the decision makers to evaluate the forecast quality and the model quality. Although the performance period is quite short the advantage of a simple cost-loss function as a measure of forecast quality can be demonstrated.