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In hydrological models, parameters are used to represent the time-invariant characteristics of catchments and to capture different aspects of hydrological response. Hence, model parameters need to be identified based on their role in controlling the hydrological behaviour. For the identification of meaningful parameter values, multiple and complementary performance criteria are used that compare modelled and measured discharge time series. The reliability of the identification of hydrologically meaningful model parameter values depends on how distinctly a model parameter can be assigned to one of the performance criteria.& para;& para;To investigate this, we introduce the new concept of connective strength between model parameters and performance criteria. The connective strength assesses the intensity in the interrelationship between model parameters and performance criteria in a bijective way. In our analysis of connective strength, model simulations are carried out based on a latin hypercube sampling. Ten performance criteria including Nash-Sutcliffe efficiency (NSE), Kling-Gupta efficiency (KGE) and its three components (alpha, beta and r) as well as RSR (the ratio of the root mean square error to the standard deviation) for different segments of the flow duration curve (FDC) are calculated.& para;& para;With a joint analysis of two regression tree (RT) approaches, we derive how a model parameter is connected to different performance criteria. At first, RTs are constructed using each performance criterion as the target variable to detect the most relevant model parameters for each performance criterion. Secondly, RTs are constructed using each parameter as the target variable to detect which performance criteria are impacted by changes in the values of one distinct model parameter. Based on this, appropriate performance criteria are identified for each model parameter.& para;& para;In this study, a high bijective connective strength between model parameters and performance criteria is found for low- and mid-flow conditions. Moreover, the RT analyses emphasise the benefit of an individual analysis of the three components of KGE and of the FDC segments. Furthermore, the RT analyses highlight under which conditions these performance criteria provide insights into precise parameter identification. Our results show that separate performance criteria are required to identify dominant parameters on low- and mid-flow conditions, whilst the number of required performance criteria for high flows increases with increasing process complexity in the catchment. Overall, the analysis of the connective strength between model parameters and performance criteria using RTs contribute to a more realistic handling of parameters and performance criteria in hydrological modelling.
Transition metals in inorganic systems and metalloproteins can occur in different oxidation states, which makes them ideal redox-active catalysts. To gain a mechanistic understanding of the catalytic reactions, knowledge of the oxidation state of the active metals, ideally in operando, is therefore critical. L-edge X-ray absorption spectroscopy (XAS) is a powerful technique that is frequently used to infer the oxidation state via a distinct blue shift of L-edge absorption energies with increasing oxidation state. A unified description accounting for quantum-chemical notions whereupon oxidation does not occur locally on the metal but on the whole molecule and the basic understanding that L-edge XAS probes the electronic structure locally at the metal has been missing to date. Here we quantify how charge and spin densities change at the metal and throughout the molecule for both redox and core-excitation processes. We explain the origin of the L-edge XAS shift between the high-spin complexes Mn-II(acac)(2) and Mn-III(acac)(3) as representative model systems and use ab initio theory to uncouple effects of oxidation-state changes from geometric effects. The shift reflects an increased electron affinity of Mn-III in the core-excited states compared to the ground state due to a contraction of the Mn 3d shell upon core-excitation with accompanied changes in the classical Coulomb interactions. This new picture quantifies how the metal-centered core hole probes changes in formal oxidation state and encloses and substantiates earlier explanations. The approach is broadly applicable to mechanistic studies of redox-catalytic reactions in molecular systems where charge and spin localization/delocalization determine reaction pathways.
The power spectral density (PSD) of any time-dependent stochastic processX (t) is ameaningful feature of its spectral content. In its text-book definition, the PSD is the Fourier transform of the covariance function of X-t over an infinitely large observation timeT, that is, it is defined as an ensemble-averaged property taken in the limitT -> infinity. Alegitimate question is what information on the PSD can be reliably obtained from single-trajectory experiments, if one goes beyond the standard definition and analyzes the PSD of a single trajectory recorded for a finite observation timeT. In quest for this answer, for a d-dimensional Brownian motion (BM) we calculate the probability density function of a single-trajectory PSD for arbitrary frequency f, finite observation time T and arbitrary number k of projections of the trajectory on different axes. We show analytically that the scaling exponent for the frequency-dependence of the PSD specific to an ensemble of BM trajectories can be already obtained from a single trajectory, while the numerical amplitude in the relation between the ensemble-averaged and single-trajectory PSDs is afluctuating property which varies from realization to realization. The distribution of this amplitude is calculated exactly and is discussed in detail. Our results are confirmed by numerical simulations and single-particle tracking experiments, with remarkably good agreement. In addition we consider a truncated Wiener representation of BM, and the case of a discrete-time lattice random walk. We highlight some differences in the behavior of a single-trajectory PSD for BM and for the two latter situations. The framework developed herein will allow for meaningful physical analysis of experimental stochastic trajectories.
Ice-rich yedoma-dominated landscapes store con-
siderable amounts of organic carbon (C) and nitrogen (N)
and are vulnerable to degradation under climate warming.
We investigate the C and N pools in two thermokarst-affected
yedoma landscapes – on Sobo-Sise Island and on Bykovsky
Peninsula in the north of eastern Siberia. Soil cores up to 3 m
depth were collected along geomorphic gradients and anal-
ysed for organic C and N contents. A high vertical sampling
density in the profiles allowed the calculation of C and N
stocks for short soil column intervals and enhanced under-
standing of within-core parameter variability. Profile-level C
and N stocks were scaled to the landscape level based on
landform classifications from 5 m resolution, multispectral
RapidEye satellite imagery. Mean landscape C and N storage
in the first metre of soil for Sobo-Sise Island is estimated to
be 20.2 kg C m −2 and 1.8 kg N m −2 and for Bykovsky Penin-
sula 25.9 kg C m −2 and 2.2 kg N m −2 . Radiocarbon dating
demonstrates the Holocene age of thermokarst basin de-
posits but also suggests the presence of thick Holocene-
age cover layers which can reach up to 2 m on top of in-
tact yedoma landforms. Reconstructed sedimentation rates
of 0.10–0.57 mm yr −1 suggest sustained mineral soil accu-
mulation across all investigated landforms. Both yedoma and
thermokarst landforms are characterized by limited accumu-
lation of organic soil layers (peat).
We further estimate that an active layer deepening of
about 100 cm will increase organic C availability in a sea-
sonally thawed state in the two study areas by ∼ 5.8 Tg
(13.2 kg C m −2 ). Our study demonstrates the importance of
increasing the number of C and N storage inventories in ice-
rich yedoma and thermokarst environments in order to ac-
count for high variability of permafrost and thermokarst en-
vironments in pan-permafrost soil C and N pool estimates.
Flash floods are caused by intense rainfall events and represent an insufficiently understood phenomenon in Germany. As a result of higher precipitation intensities, flash floods might occur more frequently in future. In combination with changing land use patterns and urbanisation, damage mitigation, insurance and risk management in flash-flood-prone regions are becoming increasingly important. However, a better understanding of damage caused by flash floods requires ex post collection of relevant but yet sparsely available information for research. At the end of May 2016, very high and concentrated rainfall intensities led to severe flash floods in several southern German municipalities. The small town of Braunsbach stood as a prime example of the devastating potential of such events. Eight to ten days after the flash flood event, damage assessment and data collection were conducted in Braunsbach by investigating all affected buildings and their surroundings. To record and store the data on site, the open-source software bundle KoBoCollect was used as an efficient and easy way to gather information. Since the damage driving factors of flash floods are expected to differ from those of riverine flooding, a post-hoc data analysis was performed, aiming to identify the influence of flood processes and building attributes on damage grades, which reflect the extent of structural damage. Data analyses include the application of random forest, a random general linear model and multinomial logistic regression as well as the construction of a local impact map to reveal influences on the damage grades. Further, a Spearman's Rho correlation matrix was calculated. The results reveal that the damage driving factors of flash floods differ from those of riverine floods to a certain extent. The exposition of a building in flow direction shows an especially strong correlation with the damage grade and has a high predictive power within the constructed damage models. Additionally, the results suggest that building materials as well as various building aspects, such as the existence of a shop window and the surroundings, might have an effect on the resulting damage. To verify and confirm the outcomes as well as to support future mitigation strategies, risk management and planning, more comprehensive and systematic data collection is necessary.
Here we report on a cyclic, physical ice-discharge instability in the Parallel Ice Sheet Model, simulating the flow of a three-dimensional, inherently buttressed ice-sheet-shelf system which periodically surges on a millennial timescale. The thermomechanically coupled model on 1 km horizontal resolution includes an enthalpy-based formulation of the thermodynamics, a nonlinear stress-balance-based sliding law and a very simple subglacial hydrology. The simulated unforced surging is characterized by rapid ice streaming through a bed trough, resulting in abrupt discharge of ice across the grounding line which is eventually calved into the ocean. We visualize the central feedbacks that dominate the subsequent phases of ice buildup, surge and stabilization which emerge from the interaction between ice dynamics, thermodynamics and the subglacial till layer. Results from the variation of surface mass balance and basal roughness suggest that ice sheets of medium thickness may be more susceptible to surging than relatively thin or thick ones for which the surge feedback loop is damped. We also investigate the influence of different basal sliding laws (ranging from purely plastic to nonlinear to linear) on possible surging. The presented mechanisms underlying our simulations of self-maintained, periodic ice growth and destabilization may play a role in large-scale ice-sheet surging, such as the surging of the Laurentide Ice Sheet, which is associated with Heinrich events, and ice-stream shutdown and reactivation, such as observed in the Siple Coast region of West Antarctica.
Hantavirus assembly and budding are governed by the surface glycoproteins Gn and Gc. In this study, we investigated the glycoproteins of Puumala, the most abundant Hantavirus species in Europe, using fluorescently labeled wild-type constructs and cytoplasmic tail (CT) mutants. We analyzed their intracellular distribution, co-localization and oligomerization, applying comprehensive live, single-cell fluorescence techniques, including confocal microscopy, imaging flow cytometry, anisotropy imaging and Number&Brightness analysis. We demonstrate that Gc is significantly enriched in the Golgi apparatus in absence of other viral components, while Gn is mainly restricted to the endoplasmic reticulum (ER). Importantly, upon co-expression both glycoproteins were found in the Golgi apparatus. Furthermore, we show that an intact CT of Gc is necessary for efficient Golgi localization, while the CT of Gn influences protein stability. Finally, we found that Gn assembles into higher-order homo-oligomers, mainly dimers and tetramers, in the ER while Gc was present as mixture of monomers and dimers within the Golgi apparatus. Our findings suggest that PUUV Gc is the driving factor of the targeting of Gc and Gn to the Golgi region, while Gn possesses a significantly stronger self-association potential.
Germination, a crucial phase in the life cycle of a plant, can be significantly influenced by competition and facilitation. The aim of this study was to test whether differences in cover of surrounding vegetation can lead to population differentiation in germination behaviour of an annual grassland species, and if so, whether such a differentiation can be found in the native as well as in the introduced range. We used maternal progeny of Erodium cicutarium previously propagated under uniform conditions that had been collected in multiple populations in the native and two introduced ranges, in populations representing extremes in terms of mean and variability of the cover of surrounding vegetation. In the first experiment, we tested the effect of germination temperature and mean cover at the source site on germination, and found interlinked effects of these factors. In seeds from one of the introduced ranges (California), we found indication for a 2-fold dormancy, hindering germination at high temperatures even if physical dormancy was broken and water was available. This behaviour was less strong in high cover populations, indicating cross-generational facilitating effects of dense vegetation. In the second experiment, we tested whether spatial variation in cover of surrounding vegetation has an effect on the proportion of dormant seeds. Contrary to our expectations, we found that across source regions, high variance in cover was associated with higher proportions of seeds germinating directly after storage. In all three regions, germination seemed to match the local environment in terms of climate and vegetation cover. We suggest that this is due to a combined effect of introduction of preadapted genotypes and local evolutionary processes.
Plant functional traits reflect individual and community ecological strategies. They allow the detection of directional changes in community dynamics and ecosystemic processes, being an additional tool to assess biodiversity than species richness. Analysis of functional patterns in plant communities provides mechanistic insight into biodiversity alterations due to anthropogenic activity. Although studies have considered of either anthropogenic management or nutrient availability on functional traits in temperate grasslands, studies combining effects of both drivers are scarce. Here, we assessed the impacts of management intensity (fertilization, mowing, grazing), nutrient stoichiometry (C, N, P, K), and vegetation composition on community-weighted means (CWMs) and functional diversity (Rao's Q) from seven plant traits in 150 grasslands in three regions in Germany, using data of 6 years. Land use and nutrient stoichiometry accounted for larger proportions of model variance of CWM and Rao's Q than species richness and productivity. Grazing affected all analyzed trait groups; fertilization and mowing only impacted generative traits. Grazing was clearly associated with nutrient retention strategies, that is, investing in durable structures and production of fewer, less variable seed. Phenological variability was increased. Fertilization and mowing decreased seed number/mass variability, indicating competition-related effects. Impacts of nutrient stoichiometry on trait syndromes varied. Nutrient limitation (large N:P, C:N ratios) promoted species with conservative strategies, that is, investment in durable plant structures rather than fast growth, fewer seed, and delayed flowering onset. In contrast to seed mass, leaf-economics variability was reduced under P shortage. Species diversity was positively associated with the variability of generative traits. Synthesis. Here, land use, nutrient availability, species richness, and plant functional strategies have been shown to interact complexly, driving community composition, and vegetation responses to management intensity. We suggest that deeper understanding of underlying mechanisms shaping community assembly and biodiversity will require analyzing all these parameters.
We show that the residue density of the logarithm of a generalized Laplacian on a closed manifold defines an invariant polynomial-valued differential form. We express it in terms of a finite sum of residues of
classical pseudodifferential symbols. In the case of the square of a Dirac operator, these formulas provide a pedestrian proof of the Atiyah–Singer formula for a pure Dirac operator in four dimensions and for a
twisted Dirac operator on a flat space of any dimension. These correspond to special cases of a more general formula by Scott and Zagier. In our approach, which is of perturbative nature, we use either a Campbell–Hausdorff formula derived by Okikiolu or a noncommutative Taylor-type formula.