Abstract. Detailed physically based snow models using energy balance approaches are spatially and temporally transferable and hence regarded as particularly suited for scenario applications including changing climate or land use. However, these snow models place high demands on meteorological input data at the model scale. Besides precipitation and temperature, time series of humidity, wind speed, and radiation have to be provided. In many catchments these time series are rarely available or provided by a few meteorological stations only. This study analyzes the effect of improved meteorological input on the results of four snow models with different complexity for the Sieber catchment (44.4 km 2 ) in the Harz Mountains, Germany. The Weather Research and Forecast model (WRF) is applied to derive spatial and temporal fields of meteorological surface variables at hourly temporal resolution for a regular grid of 1.1 km × 1.1 km. All snow models are evaluated at the point and the catchment scale. For catchment-scale simulations, all snow models were integrated into the hydrological modeling system PANTA RHEI. The model results achieved with a simple temperature-index model using observed precipitation and temperature time series as input are compared to those achieved with WRF input. Due to a mismatch between modeled and observed precipitation, the observed melt runoff as provided by a snow lysimeter and the observed streamflow are better reproduced by application of observed meteorological input data. In total, precipitation is simulated statistically reasonably at the seasonal scale but some single precipitation events are not captured by the WRF data set. Regarding the model efficiencies achieved for all simulations using WRF data, energy balance approaches generally perform similarly compared to the temperature-index approach and partially outperform the latter.

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A large complex water quality data set of a polluted river, the Tay Ninh River, was evaluated to identify its water quality problems, to assess spatial variation, to determine the main pollution sources, and to detect relationships between parameters. This river is highly polluted with organic substances, nutrients, and total iron. An important problem of the river is the inhibition of the nitrification. For the evaluation, different statistical techniques including cluster analysis (CA), discriminant analysis (DA), and principal component analysis (PCA) were applied. CA clustered 10 water quality stations into three groups corresponding to extreme, high, and moderate pollution. DA used only seven parameters to differentiate the defined clusters. The PCA resulted in four principal components. The first PC is related to conductivity, NH4-N, PO4-P, and TP and determines nutrient pollution. The second PC represents the organic pollution. The iron pollution is illustrated in the third PC having strong positive loadings for TSS and total Fe. The fourth PC explains the dependence of DO on the nitrate production. The nitrification inhibition was further investigated by PCA. The results showed a clear negative correlation between DO and NH4-N and a positive correlation between DO and NO3-N. The influence of pH on the NH4-N oxidation could not be detected by PCA because of the very low nitrification rate due to the constantly low pH of the river and because of the effect of wastewater discharge with very high NH4-N concentrations. The results are deepening the understanding of the governing water quality processes and hence to manage the river basins sustainably.

Abstract. In order to simulate long-term water balances hydrologic models have to be parameterized for several types of vegetation. Furthermore, a seasonal dependence of vegetation parameters has to be accomplished for a successful application. Many approaches neglect inter-annual variability and shifts due to climate change. In this paper a more comprehensive approach from literature was evaluated and applied to long-term water balance simulations, which incorporates temperature, humidity and maximum bright sunshine hours per day to calculate a growing season index (GSI). A validation of this threshold-related approach is carried out by comparisons with normalized difference vegetation index (NDVI) data and observations from the phenological network in the state of Lower Saxony. The annual courses of GSI and NDVI show a good agreement for numerous sites. A comparison with long-term observations of leaf onset and offset taken from the phenological network also revealed a good model performance. The observed trends indicating a shift toward an earlier leaf onset of 3 days per decade in the lowlands were reproduced very well. The GSI approach was implemented in the hydrologic model Panta Rhei. For the common vegetation parameters like leaf area index, vegetated fraction, albedo and the vegetation height a minimum value and a maximum value were defined for each land surface class. These parameters were scaled with the computed GSI for every time step to obtain a seasonal course for each parameter. Two simulations were carried out each for the current climate and for future climate scenarios. The first run was parameterized with a static annual course of vegetation parameters. The second run incorporates the new GSI approach. For the current climate both models produced comparable results regarding the water balance. Although there are no significant changes in modeled mean annual evapotranspiration and runoff depth in climate change scenarios, mean monthly values of these water balance components are shifted toward a lower runoff in spring and higher values during the winter months.