A network model for ponding on sea ice

Author(s):  
Michael Coughlan

<p>I present a physically-based network model for systems of ponds which accounts for both the individual and collective behaviour of ponds, and allows us to investigate the behaviour of both. Each pond initially occupies a distinct catchment basin and evolves according to a mass-conserving differential equation representing the melting dynamics for bare and water-covered ice. Ponds can later connect together to form a network with fluxes of water between catchment areas, constrained by the ice topography and pond water levels.<span> </span></p><p>I use the model to explore how the evolution of pond area and hence melting depends on the governing parameters, and to explore how the connections between ponds develop over the melt season. Comparisons with observations are made to demonstrate the ways in which the model qualitatively replicates properties of pond systems, including fractal dimension of pond areas and two distinct regimes of pond complexity that are observed during their development cycle. The network structure, and tools from percolation theory also allows us to probe how the connectivity of pond systems affect the system at each stage of development.</p>

2020 ◽  
Author(s):  
Michael Coughlan ◽  
Ian Hewitt ◽  
Sam Howison ◽  
Andrew Wells

<p>Arctic sea ice forms a thin but significant layer at the ocean surface, mediating key climate feedbacks. During summer, surface melting produces considerable volumes of water, which collect on the ice surface in ponds. These ponds have long been suggested as a contributing factor to the discrepancy between observed and predicted sea ice extent. When viewed at large scales ponds have a complicated, approximately fractal geometry and vary in area from tens to thousands of square meters. Increases in pond depth and area lead to further increases in heat absorption and overall melting, contributing to the ice-albedo feedback.</p><p>Previous modelling work has focussed either on the physics of individual ponds or on the statistical behaviour of systems of ponds. We present a physically-based network model for systems of ponds which accounts for both the individual and collective behaviour of ponds. Each pond initially occupies a distinct catchment basin and evolves according to a mass-conserving differential equation representing the melting dynamics for bare and water-covered ice. Ponds can later connect together to form a network with fluxes of water between catchment areas, constrained by the ice topography and pond water levels.</p><p>We use the model to explore how the evolution of pond area and hence melting depends on the governing parameters, and to explore how the connections between ponds develop over the melt season. Comparisons with observations are made to demonstrate the ways in which the model qualitatively replicates properties of pond systems, including fractal dimension of pond areas and two distinct regimes of pond complexity that are observed during their development cycle. Different perimeter-area relationships exist for ponds in the two regimes. The model replicates these relationships and exhibits a percolation transition around the transition between these regimes, a facet of pond behaviour suggested by previous studies. Our results reinforce the findings of these studies on percolation thresholds in pond systems and further allow us to constrain pond coverage at this threshold - an important quantity in measuring the scale and effects of the ice-albedo feedback.</p>


1956 ◽  
Vol 47 (1) ◽  
pp. 23-42 ◽  
Author(s):  
A. Milne

The Garden Chafer, Phyllopertha horticola (L.), has three larval instars, the third ending in hibernation, which gives way to a prepupal stage. The development cycle occupies 12 months and only one generation is present in the soil at any time. From a study extending over five years, 1948 to 1952, the cycle from egg to adult in the English Lake District may be outlined as follows:—At the earliest, oviposition starts in the latter half of May. In soil 3½ in. deep, eggs are laid at an average depth of 1½ in. (range ¾ to 2½ in.). Other authors report that where the soil is sufficiently deep, the maximum egg depth may extend to four inches in the Lake District and even eight inches elsewhere. The eggs are spaced about a quarter of an inch apart (max. 1 in.), each in a tiny earthen cavity, all more or less directly below the point where the female enters the soil. Incubation of the individual egg averages five weeks.The first instar occupies individually about three weeks on the average; the second instar about four weeks; and the third instar, up to the beginning of hibernation, eight to ten weeks. On the average also, first-instar larvae begin to appear in a population about the first week of July, second instars about the fourth week of July, third instars about the fourth week of August, and the earliest hibernators about the third week of October. Except for a few stragglers occasionally in early December, the entire population is generally hibernating by the end of November. The hibernation is a true diapause.A detailed description of the method of feeding is given. The larva consumes plant roots which it obtains by tunnelling through the soil. Since its natural habitat is pasture land, grass roots are the main food. It probably also eats invertebrate carrion occurring by chance in its path. The first and second larval instars are given over to growth, the third and final is occupied mainly in storing up fat-body. This store has to suffice for maintenance during the remainder of development and also for the entire egg or sperm production.On hatching, the larvae feed at about 1½ inches (¾ to 2½ in.) depth in the soil, i.e., at egg-level. As they grow, however, they ascend until latterly, as third instars, they are feeding about ½ inch (¼ to 1 in.) from the surface. This progressive rise is probably dictated by the increasing need for a more copious food supply. With the possible exception of a very prolonged drought, weather has no effect on the level at which larvae feed.Larvae hibernate at 2 inches (0·8 to 3·8 in.) below the surface of soil 4 inches (2·3 to 6·0 in.) deep, i.e., well above the “ pan ” (gravel bed, or rock). In other localities other authors have recorded hibernation at the same as well as greater depth in deeper soils. The existing data are insufficient to show what governs the choice of depth in the soil.On the average, prepupation begins in a population about the end of March and, individually, lasts between three and four weeks; pupation begins in the third week of April and lasts about four weeks. The pupa lies inside the last larval exuvium in the hibernation cell. Sex can easily be discerned in the pupa. There are always rather more males than females in a field population, considerably more in some years. Pupal sex ratios ranged from 1·13 to 1·89. On the average, male pupation precedes female by one day or a little more.Behaviour after the splitting of the pupal skin is the same in male and female. At first, for about four days (1 to 7), the adult remains motionless in the hibernation cell. Then, alternately burrowing and resting, it ascends to the base of the sward in about two days (¼ to 4). At the sward base it now halts for about two days (½ to 6) before emerging into activity upon the sward surface for the first time; this halt of two days may be prolonged by one or more days if weather is unsuitable when a beetle is ready to become active. In toto, given no weather hindrance, the individual transit from cell to sward surface usually occupies rather more than one week (7·7 days, range 6·0 to 8·5). On the average, the first active beetles are seen in the last week of May but may be as early as the third week of May or as late as the second week of June, according to the weather. The male precedence over females in development is maintained from pupation onwards, hence the first males are always active upon the sward at least one day before the first females.It takes some considerable time for all the individuals in a population to accomplish the change from any one particular stage of development to the next. There are, however, never more than two successive stages in the soil at the same time. The period of overlap of two stages in the population fluctuates widely (8 to. 41 days) from step to step in the development cycle. This is the result of the seasonal rise and fall in the soil temperature, and of diapause. The overlap, which is really a measure of range of developmental age among individuals, contracts to the minimum (8 days) as the population approaches maturity. This facilitates mating.


2021 ◽  
Author(s):  
Ingo Heidbüchel ◽  
Jie Yang ◽  
Jan H. Fleckenstein

<p>In a recent paper we investigated how different catchment and climate properties influence transit time distributions. This was done by employing a physically-based spatially explicit 3D model in a virtual catchment running many different scenarios with different combinations of catchment and climate properties. We found that the velocity distribution of water fluxes through a catchment is more sensitive to certain properties while other factors appear less relevant. Now we expanded the approach by adding vegetation to the model and thus introducing new hydrologic processes (transpiration and evaporation) to the simulated water cycle. On the one hand we wanted to know how these new processes would influence transit times of the water fluxes to the stream, on the other hand we were interested in how exactly differences in the vegetation itself (e.g. rooting depth and leaf area index) would alter the various flux velocities (including transit times of transpiration and evaporation). It was very interesting to observe that streamflow in forested areas appeared to become older on average. We also found that transpiration was generally younger if the vegetation had shallower roots and/or a larger leaf area index. The biggest difference in the age of evaporation was detected for different amounts of subsequent precipitation (evaporation was generally younger in a wetter climate). In conclusion, we found that forests influence the age of the different water fluxes within a catchment. According to our results the overall hydrologic cycle is decelerated when adding vegetation to a model that otherwise only simulates evaporation.</p><p>Still, in order to make meaningful predictions on the age of hydrologic fluxes, it is not constructive to single out specific catchment and climate properties. The multitude of influences from different parameters makes it very challenging to find rules and underlying principles in the integrated catchment response. Therefore it is necessary to look at the individual parameters and their potential interactions and interdependencies in a bottom-up approach.</p>


Author(s):  
Vadim V. Krivorotov ◽  
Alexei V. Kalina ◽  
Sergei E. Erypalov ◽  
Maxim V. Aksenov

Improving the competitiveness of Russian industrial enterprises (including the construction industry) is a priority task at the current stage of development of the country’s economy. The purpose of this study is to develop methodological tools that would allow building strategic plans for the development of a construction company using a dynamic method for assessing its competitiveness. The hypothesis is that the target parameters of the development of a construction company, which take into account the influence of competitive factors, inevitably increase its level. This article provides an analytical review of existing methods for assessing the competitiveness of enterprises, identifies their advantages and disadvantages. The authors have chosen the dynamic approach to assessing the competitiveness of an enterprise; they propose certain aspects of its modernization, taking into account the specifics of construction production; the main indicators and algorithms used in this approach are presented. The competitiveness of the PIK group, Russia’s largest construction company, was evaluated in comparison with the Swedish development company Skanska Group, which is successful on the world market. The most problematic performance indicators of the Russian company that have a negative impact on its competitiveness are identified. Modeling of the dependence of the company’s competitiveness level on these indicators is performed. The results show that the key tool for eliminating these shortcomings can be the introduction of integrated information modeling based on big data for the entire development cycle: building information modeling — BIM (Building Information Modeling), augmented and virtual reality (AR/VR) technologies, and customer relationship management systems (CRM), among some others. The authors show how the key performance indicators of the company change after the introduction of integrated information modeling of the entire development cycle and what the forecast level of the company’s competitiveness can be expected at the end of 2020.


2010 ◽  
Vol 18 (4) ◽  
pp. 30-40 ◽  
Author(s):  
M. Tegelhoffová

Analysis of the development of a hydrological balance for future decades in the Senianska depression in the Eastern Slovak lowlandThe goal of the article was to analyze the hydrological balance for future decades in a pilot area in the Eastern Slovak lowland. The aim was to set up the physically-based Mike SHE hydrological model for the modeling hydrological balance in the selected wetland ecosystem in the Eastern Slovak Lowland. The pilot area - the Senianska depression is located near the village of Senne, between the Laborec and Uh Rivers. Specifically, it is a traditional landscape of meadows, marshes, cultivated soil, small water control structures and forests. To get a complete model set up for simulating elements of the hydrologic balance in the pilot area, it was necessary to devise a model for a larger area, which includes the pilot area - the Senianska depression. Therefore, both the Mike SHE model was set up for the Laborec River basin (a model domain of 500 × 500 m) and the Čierna voda River basin (a model domain of 100 × 100 m), for the simulation period of 1981-2007, is order to get the boundary conditions (overland flow depth, water levels, discharges and groundwater table) for the model of the pilot area. The Mike SHE model constructed for the pilot area - the Senianska depression (a model domain of 1 × 1 m) -was used to simulate the elements of the hydrological balance for the existing conditions during the simulation period of 1983-2007 and for climate scenarios for the simulation period of 1983-2100. The results of the simulated elements of the hydrological balance for the existing conditions were used for a comparison of the evolution of the hydrologic conditions in the past, for identifying wet and flooded areas and for identifying the spatial distribution of the actual evapotranspiration in the pilot area. The built-up model with setting values was used for modeling the hydrological balance in changed conditions - climate change.


2005 ◽  
Vol 128 (2) ◽  
pp. 259-270 ◽  
Author(s):  
Preethi L. Chandran ◽  
Victor H. Barocas

The microstructure of tissues and tissue equivalents (TEs) plays a critical role in determining the mechanical properties thereof. One of the key challenges in constitutive modeling of TEs is incorporating the kinematics at both the macroscopic and the microscopic scale. Models of fibrous microstructure commonly assume fibrils to move homogeneously, that is affine with the macroscopic deformation. While intuitive for situations of fibril-matrix load transfer, the relevance of the affine assumption is less clear when primary load transfer is from fibril to fibril. The microstructure of TEs is a hydrated network of collagen fibrils, making its microstructural kinematics an open question. Numerical simulation of uniaxial extensile behavior in planar TE networks was performed with fibril kinematics dictated by the network model and by the affine model. The average fibril orientation evolved similarly with strain for both models. The individual fibril kinematics, however, were markedly different. There was no correlation between fibril strain and orientation in the network model, and fibril strains were contained by extensive reorientation. As a result, the macroscopic stress given by the network model was roughly threefold lower than the affine model. Also, the network model showed a toe region, where fibril reorientation precluded the development of significant fibril strain. We conclude that network fibril kinematics are not governed by affine principles, an important consideration in the understanding of tissue and TE mechanics, especially when load bearing is primarily by an interconnected fibril network.


2018 ◽  
Vol 140 (12) ◽  
Author(s):  
Jingwen Zheng ◽  
Juliana Y. Leung ◽  
Ronald P. Sawatzky ◽  
Jose M. Alvarez

Artificial intelligence (AI) tools are used to explore the influence of shale barriers on steam-assisted gravity drainage (SAGD) production. The data are derived from synthetic SAGD reservoir simulations based on petrophysical properties and operational constraints gathered from the Suncor's Firebag project, which is representative of Athabasca oil sands reservoirs. The underlying reservoir simulation model is homogeneous and two-dimensional. Reservoir heterogeneities are modeled by superimposing sets of idealized shale barrier configurations on this homogeneous reservoir model. The individual shale barriers are categorized by their location relative to the SAGD well pair and by their geometry. SAGD production for a training set of shale barrier configurations was simulated. A network model based on AI tools was constructed to match the output of the reservoir simulation for this training set of shale barrier configurations, with a focus on the production rate and the steam-oil ratio (SOR). Then the trained AI proxy model was used to predict SAGD production profiles for arbitrary configurations of shale barriers. The predicted results were consistent with the results of the SAGD simulation model with the same shale barrier configurations. The results of this work demonstrate the capability and flexibility of the AI-based network model, and of the parametrization technique for representing the characteristics of the shale barriers, in capturing the effects of complex heterogeneities on SAGD production. It offers the significant potential of providing an indirect method for inferring the presence and distribution of heterogeneous reservoir features from SAGD field production data.


2021 ◽  
Author(s):  
Eric C. Rouchka ◽  
Julia L. Chariker ◽  
Kumar Saurabh ◽  
Sabine Waigel ◽  
Wolfgang Zacharias ◽  
...  

AbstractThroughout the course of the ongoing SARS-CoV-2 pandemic there has been a need for approaches that enable rapid monitoring of public health using an unbiased and minimally invasive means. A major way this has been accomplished is through the regular assessment of wastewater samples by qRT-PCR to detect the prevalence of viral nucleic acid with respect to time and location. Further expansion of SARS-CoV-2 wastewater monitoring efforts to include the detection of variants of interest / concern through next-generation sequencing have enhanced the understanding of the SARS-CoV-2 outbreak. In this report we detail the results of a collaborative effort between public health and metropolitan wastewater management authorities and the University of Louisville to monitor the SARS-CoV-2 pandemic through the monitoring of aggregate wastewater samples over a period of 28 weeks. Our data indicates that wastewater monitoring of water quality treatment centers and smaller neighborhood-scale catchment areas is a viable means by which the prevalence and genetic variation of SARS-CoV-2 within a metropolitan community of approximately one million individuals may be monitored. Importantly, these efforts confirm that regional emergence and spread of variants of interest / concern may be detected as readily in aggregate wastewater samples as compared to the individual wastewater sheds.


2021 ◽  
Vol 201 (3) ◽  
pp. 547-560
Author(s):  
D. N. Yuriev ◽  
G. V. Zhukovskaya

Research and commercial trawl catches of humpback shrimp Pandalus hypsinotus from the Tatar Strait (Japan Sea) in 2004–2020 were investigated, with bioanalysis of about 45 thousand specimens. Average timing of group molting, spawning, and eggs laying are determined, terms of gonads and eggs development are estimated. Prespawning and molting of the females occur between January-April, with the peaks in early February and middle February, respectively. All oviparous females have 30–40 days to lay eggs, and molt during 50–55 days; the peak of the eggs laying occurs in late June. The males molt in July-August, afterwards the largest individuals change gender and new intersexes are formed. The males have the second molting in October-December, with the peak in late November. In January, after finish of the males molting, a new annual reproduction cycle starts from the prespawning molting of females. Both vitellogenesis and embryogenesis are observed through the year, though females with developing gonads prevail from August to January (because of a long time span between winter and summer moltings while the egg carrying continued 15 months) but oviparous females — from February to July. The individual reproductive cycle of Pandalus hypsinotus in the Tatar Strait lasts 24 months, with 9 months of vitellogenesis (quick growth of gonads) and 15 months of embryogenesis. During the 2-year reproductive cycle, most of females pass through the following stages: i) gonads development (just after eggs laying) when almost all oviparous females (up to 95 % in May) have green gonads under carapace that corresponds to the stage of development «eggs laid — gonads weakly developed»; ii) summer molting from August when females lose hairs on pleopods and the gonads growth accelerates; iii) respawning in January-March (together with the firstly spawning intersexes, with slight delay of the latter); iv) initial developing of eggs during summer; v) stage of «eyed eggs» from December to March; and vi) eggs laying and molting from late March to late May; then the 2-year reproductive cycle repeats.


2019 ◽  
Author(s):  
Tim Vantilborgh

This chapter introduces the individual Psychological Contract (iPC) network model as an alternative approach to study psychological contracts. This model departs from the basic idea that a psychological contract forms a mental schema containing obligated inducements and contributions, which are exchanged for each other. This mental schema is captured by a dynamic network, in which the nodes represent the inducements and contributions and the ties represent the exchanges. Building on dynamic systems theory, I propose that these networks evolve over time towards attractor states, both at the level of the network structure and at the level of the nodes (i.e., breach and fulfilment attractor states). I highlight how the iPC-network model integrates recent theoretical developments in the psychological contract literature and explain how it may advance scholars understanding of exchange relationships. In particular, I illustrate how iPC-network models allow researchers to study the actual exchanges in the psychological contract over time, while acknowledging its idiosyncratic nature. This would allow for more precise predictions of psychological contract breach and fulfilment consequences and explains how content and process of the psychological contract continuously influence each other.


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