@article{CzarnockaVanDerKelenWillemsetal.2017, author = {Czarnocka, Weronika and Van Der Kelen, Katrien and Willems, Patrick and Szechynska-Hebda, Magdalena and Shahnejat-Bushehri, Sara and Balazadeh, Salma and Rusaczonek, Anna and M{\"u}ller-R{\"o}ber, Bernd and Van Breusegem, Frank and Karpinski, Stanislaw}, title = {The dual role of LESION SIMULATING DISEASE 1 as a condition-dependent scaffold protein and transcription regulator}, series = {Plant, cell \& environment : cell physiology, whole-plant physiology, community physiology}, volume = {40}, journal = {Plant, cell \& environment : cell physiology, whole-plant physiology, community physiology}, publisher = {Wiley}, address = {Hoboken}, issn = {0140-7791}, doi = {10.1111/pce.12994}, pages = {2644 -- 2662}, year = {2017}, abstract = {Since its discovery over two decades ago as an important cell death regulator in Arabidopsis thaliana, the role of LESION SIMULATING DISEASE 1 (LSD1) has been studied intensively within both biotic and abiotic stress responses as well as with respect to plant fitness regulation. However, its molecular mode of action remains enigmatic. Here, we demonstrate that nucleo-cytoplasmic LSD1 interacts with a broad range of other proteins that are engaged in various molecular pathways such as ubiquitination, methylation, cell cycle control, gametogenesis, embryo development and cell wall formation. The interaction of LSD1 with these partners is dependent on redox status, as oxidative stress significantly changes the quantity and types of LSD1-formed complexes. Furthermore, we show that LSD1 regulates the number and size of leaf mesophyll cells and affects plant vegetative growth. Importantly, we also reveal that in addition to its function as a scaffold protein, LSD1 acts as a transcriptional regulator. Taken together, our results demonstrate that LSD1 plays a dual role within the cell by acting as a condition-dependent scaffold protein and as a transcription regulator.}, language = {en} } @article{SunyerHundechaLawrenceetal.2015, author = {Sunyer, M. A. and Hundecha, Y. and Lawrence, D. and Madsen, H. and Willems, Patrick and Martinkova, M. and Vormoor, Klaus Josef and B{\"u}rger, Gerd and Hanel, M. and Kriauciuniene, J. and Loukas, A. and Osuch, M. and Yucel, I.}, title = {Inter-comparison of statistical downscaling methods for projection of extreme precipitation in Europe}, series = {Hydrology and earth system sciences : HESS}, volume = {19}, journal = {Hydrology and earth system sciences : HESS}, number = {4}, publisher = {Copernicus}, address = {G{\"o}ttingen}, issn = {1027-5606}, doi = {10.5194/hess-19-1827-2015}, pages = {1827 -- 1847}, year = {2015}, abstract = {Information on extreme precipitation for future climate is needed to assess the changes in the frequency and intensity of flooding. The primary source of information in climate change impact studies is climate model projections. However, due to the coarse resolution and biases of these models, they cannot be directly used in hydrological models. Hence, statistical downscaling is necessary to address climate change impacts at the catchment scale. This study compares eight statistical downscaling methods (SDMs) often used in climate change impact studies. Four methods are based on change factors (CFs), three are bias correction (BC) methods, and one is a perfect prognosis method. The eight methods are used to downscale precipitation output from 15 regional climate models (RCMs) from the ENSEMBLES project for 11 catchments in Europe. The overall results point to an increase in extreme precipitation in most catchments in both winter and summer. For individual catchments, the downscaled time series tend to agree on the direction of the change but differ in the magnitude. Differences between the SDMs vary between the catchments and depend on the season analysed. Similarly, general conclusions cannot be drawn regarding the differences between CFs and BC methods. The performance of the BC methods during the control period also depends on the catchment, but in most cases they represent an improvement compared to RCM outputs. Analysis of the variance in the ensemble of RCMs and SDMs indicates that at least 30\% and up to approximately half of the total variance is derived from the SDMs. This study illustrates the large variability in the expected changes in extreme precipitation and highlights the need for considering an ensemble of both SDMs and climate models. Recommendations are provided for the selection of the most suitable SDMs to include in the analysis.}, language = {en} } @misc{SunyerHundechaLawrenceetal.2015, author = {Sunyer, M. A. and Hundecha, Y. and Lawrence, D. and Madsen, H. and Willems, Patrick and Martinkova, M. and Vormoor, Klaus Josef and B{\"u}rger, Gerd and Hanel, Martin and Kriaučiūnienė, J. and Loukas, A. and Osuch, M. and Y{\"u}cel, I.}, title = {Inter-comparison of statistical downscaling methods for projection of extreme precipitation in Europe}, series = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, journal = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, number = {512}, issn = {1866-8372}, doi = {10.25932/publishup-40892}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-408920}, pages = {21}, year = {2015}, abstract = {Information on extreme precipitation for future climate is needed to assess the changes in the frequency and intensity of flooding. The primary source of information in climate change impact studies is climate model projections. However, due to the coarse resolution and biases of these models, they cannot be directly used in hydrological models. Hence, statistical downscaling is necessary to address climate change impacts at the catchment scale. This study compares eight statistical downscaling methods (SDMs) often used in climate change impact studies. Four methods are based on change factors (CFs), three are bias correction (BC) methods, and one is a perfect prognosis method. The eight methods are used to downscale precipitation output from 15 regional climate models (RCMs) from the ENSEMBLES project for 11 catchments in Europe. The overall results point to an increase in extreme precipitation in most catchments in both winter and summer. For individual catchments, the downscaled time series tend to agree on the direction of the change but differ in the magnitude. Differences between the SDMs vary between the catchments and depend on the season analysed. Similarly, general conclusions cannot be drawn regarding the differences between CFs and BC methods. The performance of the BC methods during the control period also depends on the catchment, but in most cases they represent an improvement compared to RCM outputs. Analysis of the variance in the ensemble of RCMs and SDMs indicates that at least 30\% and up to approximately half of the total variance is derived from the SDMs. This study illustrates the large variability in the expected changes in extreme precipitation and highlights the need for considering an ensemble of both SDMs and climate models. Recommendations are provided for the selection of the most suitable SDMs to include in the analysis.}, language = {en} } @article{HundechaSunyerLawrenceetal.2016, author = {Hundecha, Yeshewatesfa and Sunyer, Maria A. and Lawrence, Deborah and Madsen, Henrik and Willems, Patrick and B{\"u}rger, Gerd and Kriauciuniene, Jurate and Loukas, Athanasios and Martinkova, Marta and Osuch, Marzena and Vasiliades, Lampros and von Christierson, Birgitte and Vormoor, Klaus Josef and Yuecel, Ismail}, title = {Inter-comparison of statistical downscaling methods for projection of extreme flow indices across Europe}, series = {Journal of hydrology}, volume = {541}, journal = {Journal of hydrology}, publisher = {Elsevier}, address = {Amsterdam}, issn = {0022-1694}, doi = {10.1016/j.jhydrol.2016.08.033}, pages = {1273 -- 1286}, year = {2016}, abstract = {The effect of methods of statistical downscaling of daily precipitation on changes in extreme flow indices under a plausible future climate change scenario was investigated in 11 catchments selected from 9 countries in different parts of Europe. The catchments vary from 67 to 6171 km(2) in size and cover different climate zones. 15 regional climate model outputs and 8 different statistical downscaling methods, which are broadly categorized as change factor and bias correction based methods, were used for the comparative analyses. Different hydrological models were implemented in different catchments to simulate daily runoff. A set of flood indices were derived from daily flows and their changes have been evaluated by comparing their values derived from simulations corresponding to the current and future climate. Most of the implemented downscaling methods project an increase in the extreme flow indices in most of the catchments. The catchments where the extremes are expected to increase have a rainfall dominated flood regime. In these catchments, the downscaling methods also project an increase in the extreme precipitation in the seasons when the extreme flows occur. In catchments where the flooding is mainly caused by spring/summer snowmelt, the downscaling methods project a decrease in the extreme flows in three of the four catchments considered. A major portion of the variability in the projected changes in the extreme flow indices is attributable to the variability of the climate model ensemble, although the statistical downscaling methods contribute 35-60\% of the total variance. (C) 2016 Elsevier B.V. All rights reserved.}, language = {en} } @article{VineyBormannBreueretal.2009, author = {Viney, Neil R. and Bormann, Helge and Breuer, Lutz and Bronstert, Axel and Croke, Barry F. W. and Frede, Hans-Georg and Gr{\"a}ff, Thomas and Hubrechts, Lode and Huisman, Johan A. and Jakeman, Anthony J. and Kite, Geoffrey W. and Lanini, Jordan and Leavesley, George and Lettenmaier, Dennis P. and Lindstroem, Goeran and Seibert, Jan and Sivapalan, Murugesu and Willems, Patrick}, title = {Assessing the impact of land use change on hydrology by ensemble modelling (LUCHEM) II : ensemble combinations and predictions}, issn = {0309-1708}, doi = {10.1016/j.advwatres.2008.05.006}, year = {2009}, 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.}, language = {en} } @article{BreuerBormannBronstertetal.2009, author = {Breuer, Lutz and Bormann, Helge and Bronstert, Axel and Croke, Barry F. W. and Frede, Hans-Georg and Gr{\"a}ff, Thomas and Hubrechts, Lode and Kite, Geoffrey and Lanini, Jordan and Leavesley, George and Lettenmaier, Dennis P. and Lindstroem, Goeran and Seibert, Jan and Sivapalan, Mayuran and Viney, Neil R. and Willems, Patrick}, title = {Assessing the impact of land use change on hydrology by ensemble modeling (LUCHEM) III : scenario analysis}, issn = {0309-1708}, doi = {10.1016/j.advwatres.2008.06.009}, year = {2009}, 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.}, language = {en} } @article{BreuerWillemsBormannetal.2009, author = {Breuer, Lutz and Willems, Patrick and Bormann, Helge and Bronstert, Axel and Croke, Barry and Frede, Hans Georg and Gr{\"a}ff, Thomas and Hubrechts, Lode and Kite, Geoffrey and Lanini, Jordan and Leavesley, George and Lettenmaier, Dennis P. and Lindstroem, Goeran and Seibert, Jan and Sivapalan, Mayuran and Viney, Neil R.}, title = {Assessing the impact of land use change on hydrology by ensemble modeling (LUCHEM) : I: model intercomparison with current land use}, issn = {0309-1708}, doi = {10.1016/j.advwatres.2008.10.003}, year = {2009}, 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.}, language = {en} }