@article{WalterRueckertVossetal.2009,
  author    = {Walter, Juliane K. and R{\"u}ckert, Christine and Voss, Martin and M{\"u}ller, Sebastian L. and Piontek, Joerg and Gast, Klaus and Blasig, Ingolf E.},
  title     = {The oligomerization of the coiled coil-domain of occluddin is redox sensitive},
  issn      = {0077-8923},
  doi       = {10.1111/j.1749-6632.2009.04058.x},
  year      = {2009},
  abstract  = {The transmembrane tight junction protein occludin is sensitive to oxidative stress. Occludin oligomerizes; however, its function in the tight junction is unknown. The cytosolic C-terminal tail contains a coiled coil-domain and forms dimers contributing to the oligomerization. The regulation of the oligomerization remains unclear. As the domain area contains sulfhydryl residues, we tested the hypothesis that the dimerization of the coiled coil-domain depends on these residues. We showed that the dimerization is modulated by the thiol concentration in the low-millimolar range, which is relevant both for physiological and pathophysiological conditions. Masking the sulfhydryl residues in the fragment by covalent binding of 4-vinyl pyridine prevented the dimerization but did not affect its helical structure and cylindric shape. The data demonstrate, for the first time, that disulfide bridge formation of murine cystein 408 is involved in the dimerization. This process is redox-sensitive but the secondary structure of the domain is not. It is concluded that the dimerization of occludin may play a regulatory role in the tight junction assembly under physiological and pathological conditions.},
  language  = {en}
}
@article{VossZimmermannZimmermann2016,
  author    = {Voss, Sebastian and Zimmermann, Beate and Zimmermann, Alexander},
  title     = {Detecting spatial structures in throughfall data: The effect of extent, sample size, sampling design, and variogram estimation method},
  series = {Journal of hydrology},
  volume    = {540},
  journal   = {Journal of hydrology},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {0022-1694},
  doi       = {10.1016/j.jhydrol.2016.06.042},
  pages     = {527 -- 537},
  year      = {2016},
  abstract  = {In the last decades, an increasing number of studies analyzed spatial patterns in throughfall by means of variograms. The estimation of the variogram from sample data requires an appropriate sampling scheme: most importantly, a large sample and a layout of sampling locations that often has to serve both variogram estimation and geostatistical prediction. While some recommendations on these aspects exist, they focus on Gaussian data and high ratios of the variogram range to the extent of the study area. However, many hydrological data, and throughfall data in particular, do not follow a Gaussian distribution. In this study, we examined the effect of extent, sample size, sampling design, and calculation method on variogram estimation of throughfall data. For our investigation, we first generated non Gaussian random fields based on throughfall data with large outliers. Subsequently, we sampled the fields with three extents (plots with edge lengths of 25 m, 50 m, and 100 m), four common sampling designs (two grid-based layouts, transect and random sampling) and five sample sizes (50, 100, 150, 200, 400). We then estimated the variogram parameters by method-of-moments (non-robust and robust estimators) and residual maximum likelihood. Our key findings are threefold. First, the choice of the extent has a substantial influence on the estimation of the variogram. A comparatively small ratio of the extent to the correlation length is beneficial for variogram estimation. Second, a combination of a minimum sample size of 150, a design that ensures the sampling of small distances and variogram estimation by residual maximum likelihood offers a good compromise between accuracy and efficiency. Third, studies relying on method-of-moments based variogram estimation may have to employ at least 200 sampling points for reliable variogram estimates. These suggested sample sizes exceed the number recommended by studies dealing with Gaussian data by up to 100 \%. Given that most previous through fall studies relied on method-of-moments variogram estimation and sample sizes <<200, currently available data are prone to large uncertainties. (C) 2016 Elsevier B.V. All rights reserved.},
  language  = {en}
}
@article{ZimmermannVossMetzgeretal.2016,
  author    = {Zimmermann, Alexander and Voss, Sebastian and Metzger, Johanna Clara and Hildebrandt, Anke and Zimmermann, Beate},
  title     = {estimating mean throughfall},
  series = {Journal of hydrology},
  volume    = {542},
  journal   = {Journal of hydrology},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {0022-1694},
  doi       = {10.1016/j.jhydrol.2016.09.047},
  pages     = {781 -- 789},
  year      = {2016},
  abstract  = {The selection of an appropriate spatial extent of a sampling plot is one among several important decisions involved in planning a throughfall sampling scheme. In fact, the choice of the extent may determine whether or not a study can adequately characterize the hydrological fluxes of the studied ecosystem. Previous attempts to optimize throughfall sampling schemes focused on the selection of an appropriate sample size, support, and sampling design, while comparatively little attention has been given to the role of the extent. In this contribution, we investigated the influence of the extent on the representativeness of mean throughfall estimates for three forest ecosystems of varying stand structure. Our study is based on virtual sampling of simulated throughfall fields. We derived these fields from throughfall data sampled in a simply structured forest (young tropical forest) and two heterogeneous forests (old tropical forest, unmanaged mixed European beech forest). We then sampled the simulated throughfall fields with three common extents and various sample sizes for a range of events and for accumulated data. Our findings suggest that the size of the study area should be carefully adapted to the complexity of the system under study and to the required temporal resolution of the throughfall data (i.e. event-based versus accumulated). Generally, event-based sampling in complex structured forests (conditions that favor comparatively long autocorrelations in throughfall) requires the largest extents. For event-based sampling, the choice of an appropriate extent can be as important as using an adequate sample size. (C) 2016 Elsevier B.V. All rights reserved.},
  language  = {en}
}
@article{RudolphVossMoradietal.2013,
  author    = {Rudolph, Nicole and Voss, Sebastian and Moradi, Ahmad B. and Nagl, Stefan and Oswald, Sascha Eric},
  title     = {Spatio-temporal mapping of local soil pH changes induced by roots of lupin and soft-rush},
  series = {Plant and soil},
  volume    = {369},
  journal   = {Plant and soil},
  number    = {1-2},
  publisher = {Springer},
  address   = {Dordrecht},
  issn      = {0032-079X},
  doi       = {10.1007/s11104-013-1775-0},
  pages     = {669 -- 680},
  year      = {2013},
  abstract  = {The rhizosphere is a dynamic system strongly influenced by root activity. Roots modify the pH of their surrounding soil causing the soil pH to vary as a function of distance from root surface, location along root axes, and root maturity. Non-invasive imaging techniques provide the possibility to capture pH patterns around the roots as they develop. We developed a novel fluorescence imaging set up and applied to the root system of two lupin (Lupinus albus L., Lupinus angustifolius L.) and one soft-rush (Juncus effusus L.) species. We grew plants in glass containers filled with soil and equipped with fluorescence sensor foils on the container side walls. We gained highly-resolved data on the spatial distribution of H+ around the roots by taking time-lapse images of the samples over the course of several days. We showed how the soil pH in the vicinity of roots developed over time to different values from that of the original bulk soil. The soil pH in the immediate vicinity of the root surface varied greatly along the root length, with the most acidic point being at 0.56-3.36 mm behind the root tip. Indications were also found for temporal soil pH changes due to root maturity. In conclusion, this study shows that this novel optical fluorescence imaging set up is a powerful tool for studying pH developments around roots in situ.},
  language  = {en}
}
@article{PilzDelgadoVossetal.2019,
  author    = {Pilz, Tobias and Delgado, Jos{\´e} Miguel Martins and Voss, Sebastian and Vormoor, Klaus Josef and Francke, Till and Cunha Costa, Alexandre and Martins, Eduardo and Bronstert, Axel},
  title     = {Seasonal drought prediction for semiarid northeast Brazil},
  series = {Hydrology and Earth System Sciences},
  volume    = {23},
  journal   = {Hydrology and Earth System Sciences},
  publisher = {Copernicus Publications},
  address   = {G{\"o}ttingen},
  issn      = {1027-5606},
  doi       = {10.5194/hess-23-1951-2019},
  pages     = {1951 -- 1971},
  year      = {2019},
  abstract  = {The semiarid northeast of Brazil is one of the most densely populated dryland regions in the world and recurrently affected by severe droughts. Thus, reliable seasonal forecasts of streamflow and reservoir storage are of high value for water managers. Such forecasts can be generated by applying either hydrological models representing underlying processes or statistical relationships exploiting correlations among meteorological and hydrological variables. This work evaluates and compares the performances of seasonal reservoir storage forecasts derived by a process-based hydrological model and a statistical approach. Driven by observations, both models achieve similar simulation accuracies. In a hindcast experiment, however, the accuracy of estimating regional reservoir storages was considerably lower using the process-based hydrological model, whereas the resolution and reliability of drought event predictions were similar by both approaches. Further investigations regarding the deficiencies of the process-based model revealed a significant influence of antecedent wetness conditions and a higher sensitivity of model prediction performance to rainfall forecast quality. Within the scope of this study, the statistical model proved to be the more straightforward approach for predictions of reservoir level and drought events at regionally and monthly aggregated scales. However, for forecasts at finer scales of space and time or for the investigation of underlying processes, the costly initialisation and application of a process-based model can be worthwhile. Furthermore, the application of innovative data products, such as remote sensing data, and operational model correction methods, like data assimilation, may allow for an enhanced exploitation of the advanced capabilities of process-based hydrological models.},
  language  = {en}
}
@misc{PilzDelgadoVossetal.2019,
  author    = {Pilz, Tobias and Delgado, Jos{\´e} Miguel Martins and Voss, Sebastian and Vormoor, Klaus Josef and Francke, Till and Cunha Costa, Alexandre and Martins, Eduardo and Bronstert, Axel},
  title     = {Seasonal drought prediction for semiarid northeast Brazil},
  series = {Postprints der Universit{\"a}t Potsdam Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Postprints der Universit{\"a}t Potsdam Mathematisch-Naturwissenschaftliche Reihe},
  number    = {702},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-42795},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-427950},
  pages     = {21},
  year      = {2019},
  abstract  = {The semiarid northeast of Brazil is one of the most densely populated dryland regions in the world and recurrently affected by severe droughts. Thus, reliable seasonal forecasts of streamflow and reservoir storage are of high value for water managers. Such forecasts can be generated by applying either hydrological models representing underlying processes or statistical relationships exploiting correlations among meteorological and hydrological variables. This work evaluates and compares the performances of seasonal reservoir storage forecasts derived by a process-based hydrological model and a statistical approach. Driven by observations, both models achieve similar simulation accuracies. In a hindcast experiment, however, the accuracy of estimating regional reservoir storages was considerably lower using the process-based hydrological model, whereas the resolution and reliability of drought event predictions were similar by both approaches. Further investigations regarding the deficiencies of the process-based model revealed a significant influence of antecedent wetness conditions and a higher sensitivity of model prediction performance to rainfall forecast quality. Within the scope of this study, the statistical model proved to be the more straightforward approach for predictions of reservoir level and drought events at regionally and monthly aggregated scales. However, for forecasts at finer scales of space and time or for the investigation of underlying processes, the costly initialisation and application of a process-based model can be worthwhile. Furthermore, the application of innovative data products, such as remote sensing data, and operational model correction methods, like data assimilation, may allow for an enhanced exploitation of the advanced capabilities of process-based hydrological models.},
  language  = {en}
}
@misc{DelgadoVossBuergeretal.2018,
  author    = {Delgado, Jos{\´e} Miguel Martins and Voss, Sebastian and B{\"u}rger, Gerd and Vormoor, Klaus Josef and Murawski, Aline and Rodrigues Pereira, Jos{\´e} Marcelo and Martins, Eduardo and Vasconcelos J{\´u}nior, Francisco and Francke, Till},
  title     = {Seasonal drought prediction for semiarid northeastern Brazil},
  series = {Hydrology and Earth System Sciences},
  journal   = {Hydrology and Earth System Sciences},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-418461},
  pages     = {16},
  year      = {2018},
  abstract  = {A set of seasonal drought forecast models was assessed and verified for the Jaguaribe River in semiarid northeastern Brazil. Meteorological seasonal forecasts were provided by the operational forecasting system used at FUNCEME (Cear{\´a}'s research foundation for meteorology)and by the European Centre for Medium-Range Weather Forecasts (ECMWF). Three downscaling approaches (empirical quantile mapping, extended downscaling and weather pattern classification) were tested and combined with the models in hindcast mode for the period 1981 to 2014. The forecast issue time was January and the forecast period was January to June. Hydrological drought indices were obtained by fitting a multivariate linear regression to observations. In short, it was possible to obtain forecasts for (a) monthly precipitation,(b) meteorological drought indices, and (c) hydrological drought indices. The skill of the forecasting systems was evaluated with regard to root mean square error (RMSE), the Brier skill score (BSS) and the relative operating characteristic skill score (ROCSS). The tested forecasting products showed similar performance in the analyzed metrics. Forecasts of monthly precipitation had little or no skill considering RMSE and mostly no skill with BSS. A similar picture was seen when forecasting meteorological drought indices: low skill regarding RMSE and BSS and significant skill when discriminating hit rate and false alarm rate given by the ROCSS (forecasting drought events of, e.g., SPEI1 showed a ROCSS of around 0.5). Regarding the temporal variation of the forecast skill of the meteorological indices, it was greatest for April, when compared to the remaining months of the rainy season, while the skill of reservoir volume forecasts decreased with lead time. This work showed that a multi-model ensemble can forecast drought events of timescales relevant to water managers in northeastern Brazil with skill. But no or little skill could be found in the forecasts of monthly precipitation or drought indices of lower scales, like SPI1. Both this work and those here revisited showed that major steps forward are needed in forecasting the rainy season in northeastern Brazil.},
  language  = {en}
}
@article{DelgadoVossBuergeretal.2018,
  author    = {Delgado, Jos{\´e} Miguel Martins and Voss, Sebastian and B{\"u}rger, Gerd and Vormoor, Klaus Josef and Murawski, Aline and Rodrigues Pereira, Jos{\´e} Marcelo and Martins, Eduardo and Vasconcelos J{\´u}nior, Francisco and Francke, Till},
  title     = {Seasonal drought prediction for semiarid northeastern Brazil},
  series = {Hydrology and Earth System Sciences},
  volume    = {22},
  journal   = {Hydrology and Earth System Sciences},
  number    = {9},
  publisher = {Copernicus Publ.},
  address   = {G{\"o}ttingen},
  issn      = {1027-5606},
  doi       = {10.5194/hess-22-5041-2018},
  pages     = {5041 -- 5056},
  year      = {2018},
  abstract  = {A set of seasonal drought forecast models was assessed and verified for the Jaguaribe River in semiarid northeastern Brazil. Meteorological seasonal forecasts were provided by the operational forecasting system used at FUNCEME (Cear{\´a}'s research foundation for meteorology)and by the European Centre for Medium-Range Weather Forecasts (ECMWF). Three downscaling approaches (empirical quantile mapping, extended downscaling and weather pattern classification) were tested and combined with the models in hindcast mode for the period 1981 to 2014. The forecast issue time was January and the forecast period was January to June. Hydrological drought indices were obtained by fitting a multivariate linear regression to observations. In short, it was possible to obtain forecasts for (a) monthly precipitation,(b) meteorological drought indices, and (c) hydrological drought indices. The skill of the forecasting systems was evaluated with regard to root mean square error (RMSE), the Brier skill score (BSS) and the relative operating characteristic skill score (ROCSS). The tested forecasting products showed similar performance in the analyzed metrics. Forecasts of monthly precipitation had little or no skill considering RMSE and mostly no skill with BSS. A similar picture was seen when forecasting meteorological drought indices: low skill regarding RMSE and BSS and significant skill when discriminating hit rate and false alarm rate given by the ROCSS (forecasting drought events of, e.g., SPEI1 showed a ROCSS of around 0.5). Regarding the temporal variation of the forecast skill of the meteorological indices, it was greatest for April, when compared to the remaining months of the rainy season, while the skill of reservoir volume forecasts decreased with lead time. This work showed that a multi-model ensemble can forecast drought events of timescales relevant to water managers in northeastern Brazil with skill. But no or little skill could be found in the forecasts of monthly precipitation or drought indices of lower scales, like SPI1. Both this work and those here revisited showed that major steps forward are needed in forecasting the rainy season in northeastern Brazil.},
  language  = {en}
}