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- ItemEvapotranspiration simulations in ISIMIP2a—Evaluation of spatio-temporal characteristics with a comprehensive ensemble of independent datasets(Bristol : IOP Publ., 2018) Wartenburger, Richard; Seneviratne, Sonia I; Hirschi, Martin; Chang, Jinfeng; Ciais, Philippe; Deryng, Delphine; Elliott, Joshua; Folberth, Christian; Gosling, Simon N; Gudmundsson, Lukas; Henrot, Alexandra-Jane; Hickler, Thomas; Ito, Akihiko; Khabarov, Nikolay; Kim, Hyungjun; Leng, Guoyong; Liu, Junguo; Liu, Xingcai; Masaki, Yoshimitsu; Morfopoulos, Catherine; Müller, Christoph; Müller Schmied, Hannes; Nishina, Kazuya; Orth, Rene; Pokhrel, Yadu; Pugh, Thomas A M; Satoh, Yusuke; Schaphoff, Sibyll; Schmid, Erwin; Sheffield, Justin; Stacke, Tobias; Steinkamp, Joerg; Tang, Qiuhong; Thiery, Wim; Wada, Yoshihide; Wang, Xuhui; Weedon, Graham P; Yang, Hong; Zhou, TianActual land evapotranspiration (ET) is a key component of the global hydrological cycle and an essential variable determining the evolution of hydrological extreme events under different climate change scenarios. However, recently available ET products show persistent uncertainties that are impeding a precise attribution of human-induced climate change. Here, we aim at comparing a range of independent global monthly land ET estimates with historical model simulations from the global water, agriculture, and biomes sectors participating in the second phase of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2a). Among the independent estimates, we use the EartH2Observe Tier-1 dataset (E2O), two commonly used reanalyses, a pre-compiled ensemble product (LandFlux-EVAL), and an updated collection of recently published datasets that algorithmically derive ET from observations or observations-based estimates (diagnostic datasets). A cluster analysis is applied in order to identify spatio-temporal differences among all datasets and to thus identify factors that dominate overall uncertainties. The clustering is controlled by several factors including the model choice, the meteorological forcing used to drive the assessed models, the data category (models participating in the different sectors of ISIMIP2a, E2O models, diagnostic estimates, reanalysis-based estimates or composite products), the ET scheme, and the number of soil layers in the models. By using these factors to explain spatial and spatio-temporal variabilities in ET, we find that the model choice mostly dominates (24%–40% of variance explained), except for spatio-temporal patterns of total ET, where the forcing explains the largest fraction of the variance (29%). The most dominant clusters of datasets are further compared with individual diagnostic and reanalysis-based estimates to assess their representation of selected heat waves and droughts in the Great Plains, Central Europe and western Russia. Although most of the ET estimates capture these extreme events, the generally large spread among the entire ensemble indicates substantial uncertainties.
- ItemModel-based cluster analysis applied to flow cytometry data of phytoplankton(Berlin : Weierstraß-Institut für Angewandte Analysis und Stochastik, 2002) Mucha, H.-J.; Simon, U.; Brüggemann, R.Starting from well-known model-based clustering models equivalent formulations for some special models based on pairwise distances are presented. Moreover, these models can be generalized in order to taking into account both weights of observations and weights of variables. Well-known cluster analysis techniques like the iterative partitional K-means method or the agglomerative hierarchical Ward method are useful for discovering partitions or hierarchies in the underlying data. Here these methods are generalised in two ways, firstly by using weighted observations and secondly by allowing different volumes of clusters. Then a more general K-means approach based on pair-wise distances is recommended. Simulation studies are carried out in order to compare the new clustering techniques with the well-known ones. Afterwards a successful application in the field of freshwater ecology is presented. As an example, the cluster analysis of a snapshot from monitoring of phytoplankton (algae) is considered in more detail. Indeed, monitoring by microscope is very time- and work-consuming. Flow cytometry provides the opportunity to investigate algae communities in a semiautomatic way. Statistical data analysis and cluster analysis can at least support the investigations. Here a combination of agglomerative hierarchical clustering and iterative clustering is recommended. In order to give some insight into the data under investigation several univariate, bivariate and multivariate visualizations are proposed.
- ItemElectric resistivity and seismic refraction tomography: a challenging joint underwater survey at Äspö Hard Rock Laboratory(Göttingen : Copernicus Publ., 2017) Ronczka, Mathias; Hellman, Kristofer; Günther, Thomas; Wisén, Roger; Dahlin, TorleifTunnelling below water passages is a challenging task in terms of planning, pre-investigation and construction. Fracture zones in the underlying bedrock lead to low rock quality and thus reduced stability. For natural reasons, they tend to be more frequent at water passages. Ground investigations that provide information on the subsurface are necessary prior to the construction phase, but these can be logistically difficult. Geophysics can help close the gaps between local point information by producing subsurface images. An approach that combines seismic refraction tomography and electrical resistivity tomography has been tested at the Äspö Hard Rock Laboratory (HRL). The aim was to detect fracture zones in a well-known but logistically challenging area from a measuring perspective. The presented surveys cover a water passage along part of a tunnel that connects surface facilities with an underground test laboratory. The tunnel is approximately 100 m below and 20 m east of the survey line and gives evidence for one major and several minor fracture zones. The geological and general test site conditions, e.g. with strong power line noise from the nearby nuclear power plant, are challenging for geophysical measurements. Co-located positions for seismic and ERT sensors and source positions are used on the 450 m underwater section of the 700 m profile. Because of a large transition zone that appeared in the ERT result and the missing coverage of the seismic data, fracture zones at the southern and northern parts of the underwater passage cannot be detected by separated inversion. Synthetic studies show that significant three-dimensional (3-D) artefacts occur in the ERT model that even exceed the positioning errors of underwater electrodes. The model coverage is closely connected to the resolution and can be used to display the model uncertainty by introducing thresholds to fade-out regions of medium and low resolution. A structural coupling cooperative inversion approach is able to image the northern fracture zone successfully. In addition, previously unknown sedimentary deposits with a significantly large thickness are detected in the otherwise unusually well-documented geological environment. The results significantly improve the imaging of some geologic features, which would have been undetected or misinterpreted otherwise, and combines the images by means of cluster analysis into a conceptual subsurface model.
- ItemBig data clustering: Data preprocessing, variable selection, and dimension reduction(Berlin : Weierstraß-Institut für Angewandte Analysis und Stochastik, 2017) Mucha, Hans-Joachim[no abstract available]