TY - JOUR A1 - Barthold, Frauke Katrin A1 - Tyralla, Christoph A1 - Schneider, Katrin A1 - Vache, Kellie B. A1 - Frede, Hans-Georg A1 - Breuer, Lutz T1 - How many tracers do we need for end member mixing analysis (EMMA)? - a sensitivity analysis JF - Water resources research N2 - End member mixing analysis (EMMA) is a commonly applied method to identify and quantify the dominant runoff producing sources of water. It employs tracers to determine the dimensionality of the hydrologic system. Many EMMA studies have been conducted using two to six tracers, with some of the main tracers being Ca, Na, Cl(-), water isotopes, and alkalinity. Few studies use larger tracer sets including minor trace elements such as Li, Rb, Sr, and Ba. None of the studies has addressed the question of the tracer set size and composition, despite the fact that these determine which and how many end members (EM) will be identified. We examine how tracer set size and composition affects the conceptual model that results from an EMMA. We developed an automatic procedure that conducts EMMA while iteratively changing tracer set size and composition. We used a set of 14 tracers and 9 EMs. The validity of the resulting conceptual models was investigated under the aspects of dimensionality, EM combinations, and contributions to stream water. From the 16,369 possibilities, 23 delivered plausible results. The resulting conceptual models are highly sensitive to the tracer set size and composition. The moderate reproducibility of EM contributions indicates a still missing EM. It also emphasizes that the major elements are not always the most useful tracers and that larger tracer sets have an enhanced capacity to avoid false conclusions about catchment functioning. The presented approach produces results that may not be apparent from the traditional approach and it is a first step to add the idea of statistical significance to the EMMA approach. Y1 - 2011 U6 - https://doi.org/10.1029/2011WR010604 SN - 0043-1397 VL - 47 IS - 7360 PB - American Geophysical Union CY - Washington ER - TY - JOUR A1 - Barthold, Frauke Katrin A1 - Wiesmeier, Martin A1 - Breuer, L. A1 - Frede, Hans-Georg A1 - Wu, J. A1 - Blank, F. Benjamin T1 - Land use and climate control the spatial distribution of soil types in the grasslands of Inner Mongolia JF - Journal of arid environments N2 - The spatial distribution of soil types is controlled by a set of environmental factors such as climate, organisms, parent material and topography as well as time and space. A change of these factors will lead to a change in the spatial distribution of soil types. In this study, we use a digital soil mapping approach to improve our knowledge about major soil type distributing factors in the steppe regions of Inner Mongolia (China) which currently undergo tremendous environmental change, e.g. climate and land use change. We use Random Forests in an effort to map Reference Soil Groups according to the World Reference Base for Soil Resources (WRB) in the Xilin River catchment. We benefit from the superior prediction capabilities of RF and additional interpretive results in order to identify the major environmental factors that control spatial patterns of soil types. The nine WRB soil groups that were identified and spatially predicted for the study area are Arenosol, Calcisol, Cambisol, Chernozem, Cryosol, Gleysol, Kastanozem, Phaeozem and Regosol. Model and prediction performances of the RF model are high with an Out-of-Bag error of 51.6% for the model and a misclassification error for the predicted map of 28.9%. The main controlling factors of soil type distribution are land use, a set of topographic variables, geology and climate. However, land use and climate are of major importance and topography and geology are of minor importance. The visualizations of the predictions, the variable importance measures as result of RF and the comparisons of these with the spatial distribution of the environmental factors delivered additional, quantitative information of these controlling factors and revealed that intensively grazed areas are subjected to soil degradation. However, most of the area is still governed by natural soil forming processes which are driven by climate, topography and geology. Most importantly though, our study revealed that a shift towards warmer temperatures and lower precipitation regimes will lead to a change of the spatial distribution of RSGs towards steppe soils that store less carbon, i.e. a decrease of spatial extent of Phaeozems and an increase of spatial extent of Chernozems and Kastanozems. KW - Random Forests KW - Soil-environmental relationships KW - Steppe KW - Inner Mongolia KW - Land use change KW - Climate change Y1 - 2013 U6 - https://doi.org/10.1016/j.jaridenv.2012.08.004 SN - 0140-1963 VL - 88 IS - 1 SP - 194 EP - 205 PB - Elsevier CY - London ER - TY - JOUR A1 - Breuer, Lutz A1 - Bormann, Helge A1 - Bronstert, Axel A1 - Croke, Barry F. W. A1 - Frede, Hans-Georg A1 - Gräff, Thomas A1 - Hubrechts, Lode A1 - Kite, Geoffrey A1 - Lanini, Jordan A1 - Leavesley, George A1 - Lettenmaier, Dennis P. A1 - Lindstroem, Goeran A1 - Seibert, Jan A1 - Sivapalan, Mayuran A1 - Viney, Neil R. A1 - Willems, Patrick T1 - Assessing the impact of land use change on hydrology by ensemble modeling (LUCHEM) III : scenario analysis N2 - 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. Y1 - 2009 UR - http://www.sciencedirect.com/science/journal/03091708 U6 - https://doi.org/10.1016/j.advwatres.2008.06.009 SN - 0309-1708 ER - TY - JOUR A1 - Viney, Neil R. A1 - Bormann, Helge A1 - Breuer, Lutz A1 - Bronstert, Axel A1 - Croke, Barry F. W. A1 - Frede, Hans-Georg A1 - Gräff, Thomas A1 - Hubrechts, Lode A1 - Huisman, Johan A. A1 - Jakeman, Anthony J. A1 - Kite, Geoffrey W. A1 - Lanini, Jordan A1 - Leavesley, George A1 - Lettenmaier, Dennis P. A1 - Lindstroem, Goeran A1 - Seibert, Jan A1 - Sivapalan, Murugesu A1 - Willems, Patrick T1 - Assessing the impact of land use change on hydrology by ensemble modelling (LUCHEM) II : ensemble combinations and predictions N2 - 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. Y1 - 2009 UR - http://www.sciencedirect.com/science/journal/03091708 U6 - https://doi.org/10.1016/j.advwatres.2008.05.006 SN - 0309-1708 ER -