usgs.gov/. The selected stations (Table 1) cover a wide range of basin sizes, dis- charge values and suspended sediment concentrations, disregarding intermittent or ephemeral streams that require specific descriptions. Various basin typologies and temperate climates are accounted for as the stations find themselves in California (CA), Illinois (IL), Iowa (IA), Missouri (MI), North Carolina (NC), Ohio (OH) and Virginia (VA).
Differential sensitivityanalysis (McCuen, 1973a,b), which was very often the only approach computationally afford- able, is now gradually replaced by assessments carried out in the statistical framework. The Regional Sensitivity Analy- sis (RSA) of Hornberger and Spear (1981) inspired numer- ous applications and developments for the analysis of hy- drological systems including the contribution of Beven and Binley (1992). The combination with recursive estimation techniques (Vrugt et al., 2002; Wagener et al., 2003) or the extension to multiple objectives (Bastidas et al., 1999) can provide an interesting insight into the behaviour of hy- drological models. The use of variance decomposition ap- proaches which are based on unambiguous importance mea- sures (Cukier et al., 1978; Sobol’, 1993; Homma and Saltelli, 1996) is now emerging in the hydrological community (Tang et al., 2007a,b; Yatheendradas et al., 2008; Van Werkhoven et al., 2008a). Using these global sensitivityanalysis tech- niques, it is possible to assess how uncertaintyin the model outputs can be apportioned to different sources of uncertaintyin the model inputs (Saltelli et al., 2000).
tainty than stream flow, anduncertainty even increased in high precipitation condition than dry season. The main uncertainty sources of stream flow mainly came from the catchment process while channel process impacts the sediment simulation greatly. It should be noted that identifiable parameters such as CANMX, ALPHA BNK, SOL K could be obtained optimal parameter range using calibration method. However, equi-
would last for about 2000 years. To some extent these min- imum scenario values remain somewhat arbitrary, and the question is how much they should be lowered in order to cover any possible future glacial scenario. Notwithstanding these remaining uncertainties, so far the 300 m depth may be regarded as a maximum value because the model is con- servative with respect to vegetation and snow cover, ground- water flow and the depth of the unsaturated zone. These con- tributing factors would all push permafrost towards shallower depths. Note that in the OPERA-project the long term safety of a generic repository in the Boom Clay at a generic depth of 500 m will be assessed (Verhoef and Schröder, 2011). Fu- ture research should focus on defining an absolute minimum value for the MAAT used in permafrost calculations. It is also noted that the geothermal gradient, as used here for geother- mal heat fluxcalculation, might not be in equilibrium yet with present-day climatic conditions (ter Voorde et al., 2014). This should be taken into account in future permafrost mod- elling work.
To obtain the mentioned objectives, a methodical approach of an analysis framework, which consists of a Monte-Carlo (MC) based sensitivityanduncertaintyanalysis, is prepared. This is a type of a sensitivityanalysis (Saltelli et al., 2000), which performs multiple simulations with randomly selected model inputs for defined parameters. The current investiga- tions are limited to the spatial resolution of the topography (DEM), the distributed roughness and the hydrograph. The hydrograph is also considered, because measured peak val- ues during extreme flood events with a large return period are significant uncertain (Apel et al., 2004). The computational design is a combination of a MC routine to simulate the pa- rameter inputs, the hydrodynamic model TrimR2D to com- pute the inundation areas and different performance mea- sures to evaluate the quality of the modelled results (Fig. 3). Furthermore the approach is designed in a way that any pa- rameter of the hydrodynamic model could be steered and investigated. Given the duration and required memory of the 2d-modelling the framework is implemented in a high- performance computer cluster with 64 CPUs. Depending on the results of the performance measures, the uncertaintyandsensitivityanalysis will be executed by a stepwise adaptation of the single parameter ranges.
Abstract. WaSiM-ETH (Gurtz et al., 2001), a widely used water balance simulation model, is tested for its suitability to serve for flow analysisin the context of rainfall runoff mod- elling and flood forecasting. In this paper, special focus is on the resolution of the process domain in space as well as in time. We try to couple model runs with different calcula- tion time steps in order to reduce the effort arising from cal- culating the whole flow hydrograph at the hourly time step. We aim at modelling on the daily time step for water bal- ance purposes, switching to the hourly time step whenever high-resolution information is necessary (flood forecasting). WaSiM-ETH is used at different grid resolutions, thus we try to become clear about being able to transfer the model in spatial resolution. We further use two different approaches for the overland flow time calculation within the sub-basins of the test watershed to gain insights about the process dy- namics portrayed by the model. Our findings indicate that the model is very sensitive to time and space resolution and cannot be transferred across scales without recalibration.
An important question in the above analysis, andin the calculation of shadow prices in general, is whether the possible alternative optima in the FBA optimization problem could give rise to degenerate shadow prices, and hence ambiguity in the comparison with experimental data. As described in detail in the Methods, we addressed this issue by recalculating each shadow Figure 2. Shadow prices anticorrelate with experimental measurements of growth limitation. Metabolites exhibiting d log M ð Þ=d log m ð Þw0 were experimentally determined to be growth-limiting. Growth-limitation d log M ð ð Þ=d log m ð Þ Þ and shadow prices in FBA are significantly anticorrelated under all nutrient limitations from . To make the data more comparable across different nutrient limitations, the data is plotted on a log scale. All points to the left of the grey bar have a shadow price of zero. All correlations for this data (calculated using a linear scale, not the log scale depicted in the Figure) are reported in Table S1. Abbreviations: 6PDG, 6-phospho-d-gluconate; ADE, Adenosine; ALA, Alanine; ARG, Arginine; ATP, ATP; CHO, Choline; CTP, CTP; CYD, Cytidine; CYT, Cytosine; DHAP, Dihydroxyacetone-Phosphate; DOG, Deoxyguanosine; DS7P, D- sedoheptulose-7-phosphate; F16P, Fructose-1,6-bisphosphate; GLN, Glutamine; GLU, Glutamate; GUA, Guanosine; HIS, Histidine; INO, Inosine; LEU, Leucine/isoleucine; LYS, Lysine; NAD, NAD+; NAG1P, N-acetyl-glucosamine-1-phosphate; NIC, Nicotinate; ORN, Ornithine; PHP, Phenylpyruvate; PYR, Pyruvate; RIBP, Ribose-phosphate; SER, Serine; SUC, Sucrose; THR, Threonine; TRE, Trehalose; TRP, Tryptophan; UDPG, UDP-D-glucose; UTP, UTP. For clarity, only cytosolic metabolites from the metabolic model are plotted.
The parameters K B and A C were the most sensitive in the model, proving that the JEW is governed by base flow pro- cess. In other studies (Collischon et al., 2007; Mello et al., 2008), A C was considered to have a low sensitivityand, there- fore, it was kept as a constant value. However, in this water- shed our study showed that this parameter was greatly sen- sitive and should be taken into account during the calibra- tion step, especially in watersheds that are governed by base flow. Since A C is computed as function of A m and the latter variable is calculated in a spatially distributed approach throughout the watershed (based on different values of satu- ration soil moisture and wilting point soil moisture), A C might have been estimated better in this study than in the above-mentioned studies. As JEW is composed mostly by deep soils with slope gradients less than 18% (Oxissols) and the mean annual precipitation is greater than 1,400 mm, the aquifer recharge process is highly significant. By analyzing the contribution of each flow component for the studied pe- riod, we found results as follows: base flow (62.51%), sur- face runoff (25.10%), and subsurface flow (12.39%). There- fore, base flow is predominant in comparison to direct sur- face runoff and subsurface flow, thus justifying why the pa- rameter (K B ) associated with the former component was the most sensitive.
We provide a Monte Carlo study to analyze some basic statistical properties of our proposed estimators. We then use the dataset from Ryan (2012) to estimate a dynamic game played between …rms in the US Portland cement industry. In our version of the game, …rms choose whether to enter the market as well as decide on the capacity level of operation (…ve di¤erent levels). We assume …rms compete in a capacity constrained Cournot game, so the period pro…t can be estimated directly from the data as done in Ryan. The remaining part of the payo¤ consists of …xed operating costs and 25 switching cost parameters. Other dynamic parameters we estimate include the discount factor and …xed operating cost. We estimate the model twice. Once using the data from before 1990, and once after 1990, which coincides with the date of the 1990 Clean Air Act Amendments (1990 CAAA). Our switching costs estimates generally appear sensible, having correct signs and relative magnitudes. They show that …rms entering the market with a higher capacity level incur larger costs, and suggest that increasing capacity level is generally costly while a reduction can return some revenue. We also …nd that operating and entry costs are generally higher after the 1990 CAAA, which supports Ryan’s key …nding. We are also able to estimate the discount factor with reasonable precision.
The net LULCC flux is the most uncertain of the directly estimated terms in the global carbon budget, and this un- certainty propagates into estimating the residual flux. Since the net LULCC flux is not directly observable on the global scale, models are an essential tool to estimate it. However, model differences induce a major uncertaintyin net LULCC flux estimates: of the 13 studies on LULCC emissions in Houghton et al. (2012), and five in Le Quéré et al. (2013a), the underlying model estimates differed particularly with re- spect to the assumed rates of deforestation (partly but not entirely dependent on driving data), the carbon densities for vegetation cleared, and the inclusiveness of management ac- tivities. Some of these uncertainties may be reduced in the future due to increasing data availability: for example, esti- mates of biomass can be derived from observations, which recently have become available on a spatially explicit basis for large regions of the world (Baccini et al., 2012); however, they are still subject to considerable uncertaintyand will not be available for the pre-satellite era. In addition, process- based models simulate vegetation biomass as a prognostic variable and as such depend on simplification and parame- terization of various processes. Differences due to input data and processes included in models have been described and
Whereas the Schro¨dinger equation is more appropriate to study wavelike features of quantum mechanics, the hydrody- namical equations are more appropriate to study particlelike features. To see this, assume that the quantum force 2“V in Eq. ~A6! is negligible when compared with the Lorentz force ~and other forces that one might have considered!. In this ‘‘classical limit,’’ Eq. ~A6! reduces to the equations of mo- tion for a flow of noninteracting classical particles. Note that Eq. ~A1! is the corresponding Hamilton-Jacobi equation, where the action is identified with \ x . It is clear that the quantum potential V represents the departure from the clas- sical motion. In regions where the quantum potential is rel- evant, the classical and quantum flows may differ consider- ably from each other. For example, in a region of vanishing field strengths the motion of the classical flow is trivial, since the Lorentz force vanishes there. The motion of the quantum flow, on the other hand, may be quite elaborate due to the presence of the quantum force 2“V in Eq. ~A6!. A nonva- nishing quantum force is the essence of Aharonov-Bohm- like effects.
Uncertaintyanalysis was performed for each pair of response function and site sep- arately. As the key variable to assess uncertainty, we chose the sum of the three carbon pool sizes at the beginning of August as a proxy for mean annual pool size; this choice is motivated by the fact that in LPJ-GUESS, litter is added to the litter pool only at the end of the year. The summed soil carbon pool fluxes were also evaluated
From the analysis, it was found that lands were acquired majorly by inheritance and the resultant effect of this is the fragmentation of land during the acquisition and sharing of either family or community lands. The evident of this was seen on the sizes of the land cultivated by both the male and female farmers of which majority of both sexes cultivate a small farm size of 0.1-2.0ha of land with just a few who cultivate a reasonable land size this can also be seen on the number of bag of both garri and fufu produced yearly. However this Land holding in hectares favors more males than females in the study area and females had better production in cassava than male.
Bispectral analysis incorporates information about the phase related to beginning of considered epoch, from different fre- quencies obtained (Figure 3). Bispectrum measures phase correlation of waves obtained by Fourier analysis among dif- ferent frequencies. In a simplistic model, the higher the de- gree of phase coupling, the smaller the number of “bypass” neurons will be. Bispectral analysis enables to suppress noise Gaussian sources, increasing relationship signal/noise, being able to identify non linear situations important in pro- cess of signal generation. Bispectrum is calculated multiply- ing three complex spectral values (each complex spectral value includes frequency, amplitude and phase information), the spectral value of f 1 and f 2 primary frequencies by spectral value of modulation frequency (f 1 +f 2 ). This product is the most
Combine! framework will take care displaying the matrix and storing the data after it has been changed by the user). The application logic can be programmed with Java or Octave numerical computation language. The Octave code can be placed either within an HTML file or in an external file – all variables defined in the standard form can be accessed and modified within the Octave code. After a DSS is implemented it needs to be tested and debugged. Combine! supports reporting errors in Octave code, what makes them easy to find. Deployment requires copying the DSS files into an appropriate folder in the production server – plug-in architecture will take care of displaying new DSS in the list of available DSSs.
As the angular momentum is increased dispersion coefficient becomes more important and the repulsive part of the potential less important. Turning points will move to the right in this case. One can qualitatively describe the importance of the short range part of the potential by varying the initial value for integrating the VPM equa- tion. The initial values, without changing the number of bound states are, 3.2 au (HeNe) , 4.3 au (HeAr), 3.1 au (HeKr) and 3.6 au (HeXe). The HeHe molecule has already none bound states and this change in the initial condition will not affect the formation of bound state. Therefore, taking the results for HeNe as an example, the short range Figure 1. Radial dependence of phase shift for HeKr at zero angular moment
Ona River is one of the major Rivers in Oyo State; local communities used this River for fishing and agricultural activities. From the results of this study, the evaluation and characterization of sediment quality reflects the impacts of anthropogenic activities on quality of the river. However, the continuous build-up of the metal contaminants can be checked if relevant government agencies ensure strict compliant of industrial standards which stipulate treatment of industrial waste before discharging such contaminated effluents/wastes into River. Therefore, perpetual assessment is highly recommended to minimize the potential health hazards of the people who surely depend on the River water for fishing and agricultural purposes.
Our data revealed two major trends of particle flux evolution with depth: (i) the fe- cal pellet flux decreased and (ii), phytodetrital and fecal aggregate fluxes remained constant or even increased. Establishing a link between these two processes is tempt- ing. It suggests the importance of physical reaggregation in sustaining the carbon flux at depth from fecal pellets that have undergone bacterial degradation or zooplankton
On the other hand, according to Harvey et al. (2004), it can be asserted that within the transdiagnostic model are variations that seek to explain human behavior, in a limited range of behaviors with multiple causal processes; like- wise, some of them relate to cognitive processes that underlie different disorders, other simple but universal processes for most behavioral prob- lems and, ﬁ nally, universal processes present for most mood disorders with or without an integra- tive theory. For this reason, the transdiagnostic model can include different models that would lead to varying opinions from judges, an aspect that would explain the differences found in the initial Angoff scores.
Abstract. In a recent paper on the theory of the Earth’s magnetic field and key features of Sunspot activity (de Paor, 2001), a central role in the calculation of secular variations of the geomagnetic field was played by a newly-introduced pa- rameter called the deflection (abbreviated def ). In this note, the significance of def is elucidated and the method used to calculate it is explained.