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Abstract: This paper analyses the effect of spatial resolution and distribution of model input data on the results of regional-scale land use scenarios using three different hydrological catchment models. A 25m resolution data set of a mesoscale catchment and three land use scenarios are used. Data are systematically aggregated to resolutions up to 2km. Land use scenarios are spatially redistributed, both randomly and topography based. Using these data, water fluxes are calculated on a daily time step for a 16 year time period without further calibration. Simulation results are used to identify grid size, distribution and model dependent scenario effects. In the case of data aggregation, all applied models react sensitively to grid size. WASIM and TOPLATS simulate constant water balances for grid sizes from 50m to 300–500m, SWAT is more sensitive to input data aggregation, simulating constant water balances between 50m and 200m grid size. The calculation of scenario effects is less robust to data aggregation. The maximum acceptable grid size reduces to 200–300m for TOPLATS and WASIM. In case of spatial distribution, SWAT and TOPLATS are slightly sensitive to a redistribution of land use (below 1.5% for water balance terms), whereas WASIM shows almost no reaction. Because the aggregation effects were stronger than the redistribution effects, it is concluded that spatial discretisation is more important than spatial distribution. As the aggregation effect was mainly associated with a change in land use fraction, it is concluded that accuracy of data sets is much more important than a high spatial resolution. [Copyright &y& Elsevier]

Abstract: An ensemble of 10 hydrological models was applied to the same set of land use change scenarios. There was general agreement about the direction of changes in the mean annual discharge and 90% discharge percentile predicted by the ensemble members, although a considerable range in the magnitude of predictions for the scenarios and catchments under consideration was obvious. Differences in the magnitude of the increase were attributed to the different mean annual actual evapotranspiration rates for each land use type. The ensemble of model runs was further analyzed with deterministic and probabilistic ensemble methods. The deterministic ensemble method based on a trimmed mean resulted in a single somewhat more reliable scenario prediction. The probabilistic reliability ensemble averaging (REA) method allowed a quantification of the model structure uncertainty in the scenario predictions. It was concluded that the use of a model ensemble has greatly increased our confidence in the reliability of the model predictions. [Copyright &y& Elsevier]

Abstract: This paper introduces the project on ‘Assessing the impact of land use change on hydrology by ensemble modeling (LUCHEM)’ that aims at investigating the envelope of predictions on changes in hydrological fluxes due to land use change. As part of a series of four papers, this paper outlines the motivation and setup of LUCHEM, and presents a model intercomparison for the present-day simulation results. Such an intercomparison provides a valuable basis to investigate the effects of different model structures on model predictions and paves the ground for the analysis of the performance of multi-model ensembles and the reliability of the scenario predictions in companion papers. In this study, we applied a set of 10 lumped, semi-lumped and fully distributed hydrological models that have been previously used in land use change studies to the low mountainous Dill catchment, Germany. Substantial differences in model performance were observed with Nash–Sutcliffe efficiencies ranging from 0.53 to 0.92. Differences in model performance were attributed to (1) model input data, (2) model calibration and (3) the physical basis of the models. The models were applied with two sets of input data: an original and a homogenized data set. This homogenization of precipitation, temperature and leaf area index was performed to reduce the variation between the models. Homogenization improved the comparability of model simulations and resulted in a reduced average bias, although some variation in model data input remained. The effect of the physical differences between models on the long-term water balance was mainly attributed to differences in how models represent evapotranspiration. Semi-lumped and lumped conceptual models slightly outperformed the fully distributed and physically based models. This was attributed to the automatic model calibration typically used for this type of models. Overall, however, we conclude that there was no superior model if several measures of model performance are considered and that all models are suitable to participate in further multi-model ensemble set-ups and land use change scenario investigations. [Copyright &y& Elsevier]

Current model studies on the impact of land use change on water resources often simulate changes in land use without considering changes in the soil properties due to the change in land use. In this study, an artificial study catchment representing the Dill catchment (Germany) was used within the eco-hydrological model SWAT-G to study the sensitivity of SWAT-G simulations towards changes in soil properties during land use change. Since there is little information on these soil–vegetation interactions, we performed a model sensitivity study to investigate the impact of changes in the depth of the top soil layer, bulk density, saturated hydraulic conductivity and available water content on several simulated hydrological fluxes. To assess the significance of the simulated changes due to the changing soil properties, we compared the model sensitivity with the uncertainty in the hydrological fluxes due to the uncertainty in the parameterization of the plant parameters. The results showed that the changes in soil properties due to a land use transition from cropland to pasture only have a minor impact on the simulated mean annual, summer and winter runoff and actual evapotranspiration. Soil–vegetation interactions have a stronger impact on the simulated mean surface runoff, although the absolute contribution of this flux is small in our conceptualization of the Dill catchment. A comparison of the sensitivity and uncertainty of the simulated hydrological fluxes led to the conclusion that changes in soil properties due to land use change are relatively unimportant in our model of the Dill catchment in the light of the low sensitivity of the dominating hydrological fluxes and the large output uncertainty due to the plant parameter uncertainty. [Copyright &y& Elsevier]

This study summarizes the results obtained from measurements of the exchange rates of N2O–N, NO–N, NO2–N and CH4 between Scots pine forest soils of the northeastern German lowlands and the atmosphere in the years 1995–1998. In order to further identify the effects of atmospheric N-deposition on the magnitude of N- and C-trace gas fluxes, five Scots pine forest sites with different loads of atmospheric N-input (15–22 kg N ha−1 per year) were investigated. The measurements show an increase in NO and N2O fluxes at the higher N-affected sites (mean values, NO: 21–39 μg NO–N m−2 h−1, N2O: 16–32 μg N2O–N m−2 h−1) as compared to the sites with moderate atmospheric N-input (mean values, NO: 3–9 μg NO–N m−2 h−1, N2O: 5–10 μg N2O–N m−2 h−1). In addition, it could be demonstrated that atmospheric N-deposition did not only effect N-trace gas fluxes at our study sites, but also the magnitude of atmospheric CH4-uptake by the forest soils, since at the pine forest sites with moderate atmospheric N-deposition CH4-uptake rates were approximately two- to five-fold higher (mean values: −140 to −165 μg CH4 m−2 h−1) than at the sites with high atmospheric N-input (−24 to −77 μg CH4 m−2 h−1). [Copyright &y& Elsevier]

We compiled information on 26 numerical models, which consider the terrestrial phosphorus (P) cycle and compared them regarding process description, model structure and applicability to different ecosystems and scales. We address the differences in their hydrological components and between their soil P routines, the implementation of a preferential flow component in soils, as well as whether the model performance has been tested for P transport. The comparison of the models revealed that none offers the flexibility for a realistic representation of P transport through different ecosystems and on diverging scales. Especially the transport of P through macroporous soils (e.g. forests) is deficient. Five models represent macropores accurately, but all of them lack a validated P routine. We therefore present a model blueprint to be able to incorporate a physically realistic representation of macropore flow and particulate P transport in forested systems. • We review phosphorus models, with a focus on terrestrial process representation. • Transport of colloidal P is important in many soils, but neglected in most models. • Most models lack processes to simulate preferential flow. • A model blueprint is presented to overcome current limitations of P modeling. [ABSTRACT FROM AUTHOR]

Abstract: This paper reports on a project to compare predictions from a range of catchment models applied to a mesoscale river basin in central Germany and to assess various ensemble predictions of catchment streamflow. The models encompass a large range in inherent complexity and input requirements. In approximate order of decreasing complexity, they are DHSVM, MIKE-SHE, TOPLATS, WASIM-ETH, SWAT, PRMS, SLURP, HBV, LASCAM and IHACRES. The models are calibrated twice using different sets of input data. The two predictions from each model are then combined by simple averaging to produce a single-model ensemble. The 10 resulting single-model ensembles are combined in various ways to produce multi-model ensemble predictions. Both the single-model ensembles and the multi-model ensembles are shown to give predictions that are generally superior to those of their respective constituent models, both during a 7-year calibration period and a 9-year validation period. This occurs despite a considerable disparity in performance of the individual models. Even the weakest of models is shown to contribute useful information to the ensembles they are part of. The best model combination methods are a trimmed mean (constructed using the central four or six predictions each day) and a weighted mean ensemble (with weights calculated from calibration performance) that places relatively large weights on the better performing models. Conditional ensembles, in which separate model weights are used in different system states (e.g. summer and winter, high and low flows) generally yield little improvement over the weighted mean ensemble. However a conditional ensemble that discriminates between rising and receding flows shows moderate improvement. An analysis of ensemble predictions shows that the best ensembles are not necessarily those containing the best individual models. Conversely, it appears that some models that predict well individually do not necessarily combine well with other models in multi-model ensembles. The reasons behind these observations may relate to the effects of the weighting schemes, non-stationarity of the climate series and possible cross-correlations between models. [Copyright &y& Elsevier]

This work presents a novel way for assessing and comparing different hydro-biogeochemical model structures and their performances. We used the LandscapeDNDC modelling framework to set up four models of different complexity, considering two soil-biogeochemical and two hydrological modules. The performance of each model combination was assessed using long-term (8 years) data and applying different thresholds, considering multiple criteria and objective functions. Our results show that each model combination had its strength for particular criteria. However, only 0.01% of all model runs passed the complete rejectionist framework. In contrast, our comparatively applied assessments of single thresholds, as frequently used in other studies, lead to a much higher acceptance rate of 40–70%. Therefore, our study indicates that models can be right for the wrong reasons, i.e., matching GHG emissions while at the same time failing to simulate other criteria such as soil moisture or plant biomass dynamics. [ABSTRACT FROM AUTHOR]

Agriculture has been recognized as a major anthropogenic source of surplus loads of nitrogen in the environment. Losses of nitrate via subsurface pathways are severely threatening groundwater and surface waters. This study explored the capability of a coupled hydro-biogeochemical spatially explicit model, simulating nitrogen cycling in agricultural soils and the associated fate of excess nitrate subjected to vertical and lateral displacement towards water bodies. Different vegetated buffer strips (VBS) were tested for their nitrate retention capability and impacts on N 2 O and N 2 emissions. The effectiveness of a VBS to remove nitrate by denitrification strongly depends on soil characteristics and hydrological flow paths. Simulated N 2 emissions from VBS with high soil moisture were up to twenty-fold compared to VBS where groundwater levels were low. Simulated streamwater nitrate concentrations without VBS were 3.7 mg NO 3 − l −1 and showed a decrease to 0.1 mg NO 3 − l −1 for a 20 m VBS. [ABSTRACT FROM AUTHOR]

Abstract: Feedback queues play an important role in real-life service systems, where customers may require repeated services. In this paper, we consider a feedback queue with batch Markovian arrivals and phase type services. We further assume that both the arrival process and service times are influenced by an external finite state Markovian environment. The stationary state distributions of the queue and the sojourn time are calculated and numerical examples are presented. [Copyright &y& Elsevier]