Journal articles: 'Spatial and temporal scaling'

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McCulloch, C. E. "Quantitative Ecology: Spatial and Temporal Scaling." Journal of Environmental Quality 24, no. 2 (March 1995): 384. http://dx.doi.org/10.2134/jeq1995.00472425002400020026x.

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Jørgensen, Sven Erik. "Quantitative ecology. Spatial and temporal scaling." Ecological Modelling 79, no. 1-3 (May 1995): 288. http://dx.doi.org/10.1016/0304-3800(95)90066-7.

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Ellis, J., and D. C. Schneider. "Spatial and temporal scaling in benthic ecology." Journal of Experimental Marine Biology and Ecology 366, no. 1-2 (November 2008): 92–98. http://dx.doi.org/10.1016/j.jembe.2008.07.012.

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Schmitz, Oliver, Elga Salvadore, Lien Poelmans, Johannes van der Kwast, and Derek Karssenberg. "A framework to resolve spatio-temporal misalignment in component-based modelling." Journal of Hydroinformatics 16, no. 4 (December 6, 2013): 850–71. http://dx.doi.org/10.2166/hydro.2013.180.

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Process-based spatio-temporal component models simulate real world processes, using encapsulated process representations that operate at individual spatial and temporal discretisations. These component models act as building blocks in the construction of multi-disciplinary, multi-scale integrated models. Coupling these independent component models, however, involves aggregation or disaggregation of the exchanged variables at model runtime, since each of the component models exposes potentially different spatial and temporal discretisations. Although conceptual methodologies for spatial and temporal scaling are available, dedicated tools that assist modellers to implement dynamic spatial and temporal scaling operations are rare. We present the accumulator, a programmable general-purpose model building block executing custom scaling operations at model runtime. We therefore characterise runtime information of input and output variables required for the implementation of scaling operations between component models with different discretisations. The accumulator is a component of an integrated modelling framework and can be completed by the modeller with custom operations for spatial and temporal scaling. To illustrate the applicability of the accumulators an integrated model is developed that couples an existing land use change model and hydrological component models at different spatial and temporal scales. The accumulators as building blocks allow modellers to construct multi-scale integrated models in a flexible manner.

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Eggert, B., P. Berg, J. O. Haerter, D. Jacob, and C. Moseley. "Temporal and spatial scaling impacts on extreme precipitation." Atmospheric Chemistry and Physics Discussions 15, no. 2 (January 23, 2015): 2157–96. http://dx.doi.org/10.5194/acpd-15-2157-2015.

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Abstract. Both in the current climate and in the light of climate change, understanding of the causes and risk of precipitation extremes is essential for protection of human life and adequate design of infrastructure. Precipitation extreme events depend qualitatively on the temporal and spatial scales at which they are measured, in part due to the distinct types of rain formation processes that dominate extremes at different scales. To capture these differences, we first filter large datasets of high-resolution radar measurements over Germany (5 min temporally and 1 km spatially) using synoptic cloud observations, to distinguish convective and stratiform rain events. In a second step, for each precipitation type, the observed data are aggregated over a sequence of time intervals and spatial areas. The resulting matrix allows a detailed investigation of the resolutions at which convective or stratiform events are expected to contribute most to the extremes. We analyze where the statistics of the two types differ and discuss at which resolutions transitions occur between dominance of either of the two precipitation types. We characterize the scales at which the convective or stratiform events will dominate the statistics. For both types, we further develop a mapping between pairs of spatially and temporally aggregated statistics. The resulting curve is relevant when deciding on data resolutions where statistical information in space and time is balanced. Our study may hence also serve as a practical guide for modelers, and for planning the space–time layout of measurement campaigns. We also describe a mapping between different pairs of resolutions, possibly relevant when working with mismatched model and observational resolutions, such as in statistical bias correction.

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Chau, H. F., and K. S. Cheng. "Does spatial scaling imply temporal scaling in sandpile type cellular automata?" Physics Letters A 170, no. 3 (November 1992): 195–200. http://dx.doi.org/10.1016/0375-9601(92)91065-y.

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Lacroix, Benjamin, and Julien Dumont. "Spatial and Temporal Scaling of Microtubules and Mitotic Spindles." Cells 11, no. 2 (January 12, 2022): 248. http://dx.doi.org/10.3390/cells11020248.

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During cell division, the mitotic spindle, a macromolecular structure primarily comprised of microtubules, drives chromosome alignment and partitioning between daughter cells. Mitotic spindles can sense cellular dimensions in order to adapt their length and mass to cell size. This scaling capacity is particularly remarkable during early embryo cleavage when cells divide rapidly in the absence of cell growth, thus leading to a reduction of cell volume at each division. Although mitotic spindle size scaling can occur over an order of magnitude in early embryos, in many species the duration of mitosis is relatively short, constant throughout early development and independent of cell size. Therefore, a key challenge for cells during embryo cleavage is not only to assemble a spindle of proper size, but also to do it in an appropriate time window which is compatible with embryo development. How spatial and temporal scaling of the mitotic spindle is achieved and coordinated with the duration of mitosis remains elusive. In this review, we will focus on the mechanisms that support mitotic spindle spatial and temporal scaling over a wide range of cell sizes and cellular contexts. We will present current models and propose alternative mechanisms allowing cells to spatially and temporally coordinate microtubule and mitotic spindle assembly.

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Gebremichael, Mekonnen, and Witold F. Krajewski. "Effect of Temporal Sampling on Inferred Rainfall Spatial Statistics." Journal of Applied Meteorology 44, no. 10 (October 1, 2005): 1626–33. http://dx.doi.org/10.1175/jam2283.1.

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Abstract On the basis of temporally sampled data obtained from satellites, spatial statistics of rainfall can be estimated. In this paper, the authors compare the estimated spatial statistics with their “true” or ensemble values calculated using 5 yr of 15-min radar-based rainfall data at a spatial domain of 512 km × 512 km in the central United States. The authors conducted a Monte Carlo sampling experiment to simulate different sampling scenarios for variable sampling intervals and rainfall averaging periods. The spatial statistics used are the moments of spatial distribution of rainfall, the spatial scaling exponents, and the spatial cross correlations between the sample and ensemble rainfall fields. The results demonstrated that the expected value of the relative error in the mean rain-rate estimate is zero for rainfall averaged over 5 days or longer, better temporal sampling produces average fields that are “less noisy” spatially, an increase in the sampling interval causes the sampled rainfall to be increasingly less correlated with the true rainfall map, and the spatial scaling exponent estimators could give a bias of 40% or less. The results of this study provide a basis for understanding the impact of temporal statistics on inferred spatial statistics.

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Jarzyna, Marta A., Benjamin Zuckerberg, William F. Porter, Andrew O. Finley, and Brian A. Maurer. "Spatial scaling of temporal changes in avian communities." Global Ecology and Biogeography 24, no. 11 (August 10, 2015): 1236–48. http://dx.doi.org/10.1111/geb.12361.

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Eggert, B., P. Berg, J. O. Haerter, D. Jacob, and C. Moseley. "Temporal and spatial scaling impacts on extreme precipitation." Atmospheric Chemistry and Physics 15, no. 10 (May 29, 2015): 5957–71. http://dx.doi.org/10.5194/acp-15-5957-2015.

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Abstract. Convective and stratiform precipitation events have fundamentally different physical causes. Using a radar composite over Germany, this study separates these precipitation types and compares extremes at different spatial and temporal scales, ranging from 1 to 50 km and 5 min to 6 h, respectively. Four main objectives are addressed. First, we investigate extreme precipitation intensities for convective and stratiform precipitation events at different spatial and temporal resolutions to identify type-dependent space and time reduction factors and to analyze regional and seasonal differences over Germany. We find strong differences between the types, with up to 30% higher reduction factors for convective compared to stratiform extremes, exceeding all other observed seasonal and regional differences within one type. Second, we investigate how the differences in reduction factors affect the contribution of each type to extreme events as a whole, again dependent on the scale and the threshold chosen. A clear shift occurs towards more convective extremes at higher resolution or higher percentiles. For horizontal resolutions of current climate model simulations, i.e., ~10 km, the temporal resolution of the data as well as the chosen threshold have profound influence on which type of extreme will be statistically dominant. Third, we compare the ratio of area to duration reduction factor for convective and stratiform events and find that convective events have lower effective advection velocities than stratiform events and are therefore more strongly affected by spatial than by temporal aggregation. Finally, we discuss the entire precipitation distribution regarding data aggregation and identify matching pairs of temporal and spatial resolutions where similar distributions are observed. The information is useful for planning observational networks or storing model data at different temporal and spatial scales.

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Magini, Roberto, Isabella Pallavicini, and Roberto Guercio. "Spatial and Temporal Scaling Properties of Water Demand." Journal of Water Resources Planning and Management 134, no. 3 (May 2008): 276–84. http://dx.doi.org/10.1061/(asce)0733-9496(2008)134:3(276).

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Chen, Rou, Huidan Whitney Yu, Likun Zhu, Raveena M. Patil, and Taehun Lee. "Spatial and temporal scaling of unequal microbubble coalescence." AIChE Journal 63, no. 4 (October 4, 2016): 1441–50. http://dx.doi.org/10.1002/aic.15504.

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Chen, Yanguang, and Yuqing Long. "Spatial Signal Analysis Based on Wave-Spectral Fractal Scaling: A Case of Urban Street Networks." Applied Sciences 11, no. 1 (December 24, 2020): 87. http://dx.doi.org/10.3390/app11010087.

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A number of mathematical methods have been developed to make temporal signal analyses based on time series. However, no effective method for spatial signal analysis, which are as important as temporal signal analyses for geographical systems, has been devised. Nonstationary spatial and temporal processes are associated with nonlinearity, and cannot be effectively analyzed by conventional analytical approaches. Fractal theory provides a powerful tool for exploring spatial complexity and is helpful for spatio-temporal signal analysis. This paper is devoted to developing an approach for analyzing spatial signals of geographical systems by means of wave-spectrum scaling. The traffic networks of 10 Chinese cities are taken as cases for positive studies. Fast Fourier transform (FFT) and ordinary least squares (OLS) regression methods are employed to calculate spectral exponents. The results show that the wave-spectrum density distribution of all these urban traffic networks follows scaling law, and that the spectral scaling exponents can be converted into fractal dimension values. Using the fractal parameters, we can make spatial analyses for the geographical signals. The wave-spectrum scaling methods can be applied to both self-similar fractal signals and self-affine fractal signals in the geographical world. This study has implications for the further development of fractal-based spatiotemporal signal analysis in the future.

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HUTCHINGS, JENNIFER K., ANDREW ROBERTS, CATHLEEN A. GEIGER, and JACQUELINE RICHTER-MENGE. "Corrigendum: Spatial and temporal characterisation of sea-ice deformation." Journal of Glaciology 64, no. 244 (March 27, 2018): 343–46. http://dx.doi.org/10.1017/jog.2018.11.

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Correcting a sign error results in no changes to the key conclusions of Hutchings and others (2011). However, there is an improved agreement with previous work. Mean total sea-ice deformation scales log linearly with distance and the scaling exponent was found to be dependent on time. We find a linear relationship between the temporal scale and spatial scaling exponent, for timescales of an hour to a day. Extrapolating to the timescales of deformation resolved by RADARSAT, we find total deformation and distance scale with an exponent of between −0.16 and −0.19.

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Fischer, Sebastian, and Stephan Schwan. "Comprehending animations: Effects of spatial cueing versus temporal scaling." Learning and Instruction 20, no. 6 (December 2010): 465–75. http://dx.doi.org/10.1016/j.learninstruc.2009.05.005.

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Willis, Gary, and Gunnar Pruessner. "Spatio-temporal correlations in the Manna model in one, three and five dimensions." International Journal of Modern Physics B 32, no. 05 (February 2018): 1830002. http://dx.doi.org/10.1142/s0217979218300025.

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Although the paradigm of criticality is centered around spatial correlations and their anomalous scaling, not many studies of self-organized criticality (SOC) focus on spatial correlations. Often, integrated observables, such as avalanche size and duration, are used, not least as to avoid complications due to the unavoidable lack of translational invariance. The present work is a survey of spatio-temporal correlation functions in the Manna Model of SOC, measured numerically in detail in [Formula: see text] = 1,3 and 5 dimensions and compared to theoretical results, in particular relating them to “integrated” observables such as avalanche size and duration scaling, that measure them indirectly. Contrary to the notion held by some of SOC models organizing into a critical state by re-arranging their spatial structure avalanche by avalanche, which may be expected to result in large, nontrivial, system-spanning spatial correlations in the quiescent state (between avalanches), correlations of inactive particles in the quiescent state have a small amplitude that does not and cannot increase with the system size, although they display (noisy) power law scaling over a range linear in the system size. Self-organization, however, does take place as the (one-point) density of inactive particles organizes into a particular profile that is asymptotically independent of the driving location, also demonstrated analytically in one dimension. Activity and its correlations, on the other hand, display nontrivial long-ranged spatio-temporal scaling with exponents that can be related to established results, in particular avalanche size and duration exponents. The correlation length and amplitude are set by the system size (confirmed analytically for some observables), as expected in systems displaying finite size scaling. In one dimension, we find some surprising inconsistencies of the dynamical exponent. A (spatially extended) mean field theory (MFT) is recovered, with some corrections, in five dimensions.

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Mandapaka, P. V., P. Lewandowski, W. E. Eichinger, and W. F. Krajewski. "Multiscaling analysis of high resolution space-time lidar-rainfall." Nonlinear Processes in Geophysics 16, no. 5 (September 24, 2009): 579–86. http://dx.doi.org/10.5194/npg-16-579-2009.

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Abstract. In this study, we report results from scaling analysis of 2.5 m spatial and 1 s temporal resolution lidar-rainfall data. The high resolution spatial and temporal data from the same observing system allows us to investigate the variability of rainfall at very small scales ranging from few meters to ~1 km in space and few seconds to ~30 min in time. The results suggest multiscaling behaviour in the lidar-rainfall with the scaling regime extending down to the resolution of the data. The results also indicate the existence of a space-time transformation of the form t~Lz at very small scales, where t is the time lag, L is the spatial averaging scale and z is the dynamic scaling exponent.

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Liu, Yupeng, Wei-Qiang Chen, Tao Lin, and Lijie Gao. "How Spatial Analysis Can Help Enhance Material Stocks and Flows Analysis?" Resources 8, no. 1 (March 4, 2019): 46. http://dx.doi.org/10.3390/resources8010046.

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Spatial information can be integrated into almost all fields of industrial ecology. Many researchers have shown that spatial proximity affects a variety of behaviors and interactions, and thus matters for materials stocks and flows analysis. However, normal tools or models in industrial ecology based on temporal dependence cannot be simply applied to the case of spatial dependence. This paper proposes a framework integrating material stocks and flows analysis with spatial analysis. We argue that spatial analysis can help data management and visualization, determine spatio-temporal patterns-processes-drivers, and finally develop dynamic and spatially explicit models, to improve the performance of simulating and assessing stocks and flows of materials. Scaling in spatial, temporal, and organizational dimensions and other current limitations are also discussed. Combined with spatial analysis, industrial ecology can really be more powerful in achieving its origin and destination—sustainability.

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Quattrochi, Dale A., and Narendra S. Goel. "Spatial and temporal scaling of thermal infrared remote sensing data." Remote Sensing Reviews 12, no. 3-4 (January 1995): 255–86. http://dx.doi.org/10.1080/02757259509532287.

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Ewing, Robert P., and Robert Horton. "Diffusion in sparsely connected pore spaces: Temporal and spatial scaling." Water Resources Research 38, no. 12 (December 2002): 21–1. http://dx.doi.org/10.1029/2002wr001412.

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Piatnitski, A., and E. Zhizhina. "Homogenization of Non-Autonomous Operators of Convolution Type in Periodic Media." Markov Processes And Related Fields, no. 2(29) (October 16, 2023): 173–88. http://dx.doi.org/10.61102/1024-2953-mprf.2023.29.2.001.

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The paper deals with periodic homogenization problem for a para- bolic equation whose elliptic part is a convolution type operator with rapidly oscillating coefficients. It is assumed that the coefficients are rapidly oscillating periodic functions both in spatial and temporal variables and that the scal- ing is diffusive, that is, the scaling factor of the temporal variable is equal to the square of the scaling factor of the spatial variable. Under the assumption that the convolution kernel has a nite second moment and that the operator is symmetric in spatial variables we show that the equation under study ad- mits homogenization, and we prove that the limit operator is a second order differential parabolic operator with constant coefficients.

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Elias, John G. "Artificial Dendritic Trees." Neural Computation 5, no. 4 (July 1993): 648–64. http://dx.doi.org/10.1162/neco.1993.5.4.648.

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The electronic architecture and dynamic signal processing capabilities of an artificial dendritic tree that can be used to process and classify dynamic signals is described. The electrical circuit architecture is modeled after neurons that have spatially extensive dendritic trees. The artificial dendritic tree is a hybrid VLSI circuit and is sensitive to both temporal and spatial signal characteristics. It does not use the conventional neural network concept of weights, and as such it does not use multipliers, adders, look-up-tables, microprocessors, or other complex computational units to process signals. The weights of conventional neural networks, which take the form of numerical, resistive, voltage, or current values, but do not have any spatial or temporal content, are replaced with connections whose spatial location have both a temporal and scaling significance.

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Bürkner, Hans-Joachim, and Bastian Lange. "New Geographies of Work: Re-Scaling Micro-Worlds." European Spatial Research and Policy 27, no. 1 (June 30, 2020): 53–74. http://dx.doi.org/10.18778/1231-1952.27.1.03.

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The recently emerging new types of collaborative work and unconventional workplaces indicate that shifting social and economic practices have odd spatial implications. The diversity of work, mostly based on hybrid social and economic logics, has brought forth a number of new contextualised spatial constructs in recent years: makerspaces, fab labs, open workshops, and co-working spaces now require detailed analytical reconstruction and conceptualisation. This article is a theoretical discussion of the nature of fluid and contingent spatialisation against the backdrop of binary explanatory categories (e.g. local-global; proximity-distance). Drawing upon modernised concepts of horizontal scaling, we propose a perspective on hybrid work which focuses on contingent multiple, multidirectional and temporal scalings created by a variety of users while developing their own micro-worlds of work.

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Shi, Chenchen, Naliang Guo, Xiaoping Zhu, and Feng Wu. "Assessing Urban Resilience from the Perspective of Scaling Law: Evidence from Chinese Cities." Land 11, no. 10 (October 14, 2022): 1803. http://dx.doi.org/10.3390/land11101803.

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Urban resilience, as an emerging research focus in urban studies, is the capability of an urban system to adapt to the uncertainties and disturbances faced by modern cities. Numerical characterization of an urban system’s resilience can be performed with urban resilience indicators. Moreover, as cities evolve with intensive socio-economic interactions, the performances of urban indicators are heavily dependent on the scale of these interactions; these relationships are conceptualized as urban scaling laws. Therefore, this study explores the scaling patterns of urban resilience, analyzing the scaling relationship between different resilience indicators and urban population size, as well as the spatial–temporal evolutions of the scaling patterns. The empirical case is based on 267 prefectural-level cities in China. The results show resilience indicators demonstrate scaling patterns on both spatial and temporal scales. Moreover, the scale-adjusted metropolitan indicator (SAMI) differs from the commonly used per capita indicator. Therefore, the scale needs to be considered when assessing urban resilience performance. Findings in this study indicate that moderate scale enhances resilience, enriching urban resilience theorization and urban scaling laws application. The empirical results in the case study also provide a reference for future urban resilience planning and management.

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Alvarez-Ramirez, J., J. C. Echeverria, A. Ortiz-Cruz, and E. Hernandez. "Temporal and spatial variations of seismicity scaling behavior in Southern México." Journal of Geodynamics 54 (March 2012): 1–12. http://dx.doi.org/10.1016/j.jog.2011.09.001.

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Rampal, Pierre, Véronique Dansereau, Einar Olason, Sylvain Bouillon, Timothy Williams, Anton Korosov, and Abdoulaye Samaké. "On the multi-fractal scaling properties of sea ice deformation." Cryosphere 13, no. 9 (September 23, 2019): 2457–74. http://dx.doi.org/10.5194/tc-13-2457-2019.

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Abstract. In this paper, we evaluate the neXtSIM sea ice model with respect to the observed scaling invariance properties of sea ice deformation in the spatial and temporal domains. Using an Arctic setup with realistic initial conditions, state-of-the-art atmospheric reanalysis forcing and geostrophic currents retrieved from satellite data, we show that the model is able to reproduce the observed properties of this scaling in both the spatial and temporal domains over a wide range of scales, as well as their multi-fractality. The variability of these properties during the winter season is also captured by the model. We also show that the simulated scaling exhibits a space–time coupling, a suggested property of brittle deformation at geophysical scales. The ability to reproduce the multi-fractality of this scaling is crucial in the context of downscaling model simulation outputs to infer sea ice variables at the sub-grid scale and also has implications for modeling the statistical properties of deformation-related quantities, such as lead fractions and heat and salt fluxes.

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Santini, Paula Tristão, Ronei Aparecido Barbosa, Lorena Gabriela Almeida, Kamila Rezende Dazio De Souza, João Paulo Rodrigues Alves Delfino Barbosa, and José Donizeti Alves. "SPATIAL-TEMPORAL PATTERNS OF COFFEE TREE PHYSIOLOGY." Coffee Science 14, no. 3 (September 25, 2019): 291. http://dx.doi.org/10.25186/cs.v14i3.1574.

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The ecophysiological parameters of coffee canopy were mapped throughout the day. Therefore, evaluations were carried out in ‘Catuaí Vermelho’<em> Coffea arabica</em> L., measuring 1.7 meters. A vertical gradient (from the apex to the base of the plant canopy) and a horizontal gradient (plagiotropic branches) were established to analyze different positions of the canopy. Thus, in the vertical direction, four heights were analyzed in the plant: top, upper, middle and lower regions. In the horizontal gradient, the plagiotropic branches were divided into three parts: basal, median and apical. Collection was performed on the east and west sides of the canopy, at four times of the day: 6 a.m., 9 a.m., noon, and 3 p.m., totaling 24 collection points at each time. Gas exchange, photosynthetically active radiation and leaf temperature were evaluated in each of the 24 points of the coffee canopy. The gas exchange characteristics of an individual coffee leaf diverge considerably from other leaves, which require caution when scaling estimates of leaf photosynthesis at the global canopy level. The analysis of some punctual leaves does not serve to discriminate the overall dynamics of a canopy.

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Powell, Eric N., Roger Mann, Kathryn A. Ashton-Alcox, Yungkul Kim, and David Bushek. "The allometry of oysters: spatial and temporal variation in the length–biomass relationships for Crassostrea virginica." Journal of the Marine Biological Association of the United Kingdom 96, no. 5 (June 5, 2015): 1127–44. http://dx.doi.org/10.1017/s0025315415000703.

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We examine the relationship of biomass B and length L in the eastern oyster Crassostrea virginica by focusing on the scaling exponent b in the allometric equation B = aLb using four datasets: Delaware Bay, Chesapeake Bay, Galveston Bay and a regionally extensive compilation from the NOAA Mussel Watch Program. The average value of the scaling exponent in Delaware Bay and Chesapeake Bay is about 2. For Galveston Bay, the value is distinctly higher, near 2.6. Over all Mussel Watch sites, the value is again near 2. Within Delaware Bay, the salinity gradient exerts an important effect. Shells are longer for their meat weight at lower salinities. The range of scaling exponents revealed by Mussel Watch data is exceedingly large (b < 1 to >3). Scaling exponents below 2.5 are unusual in bivalves. Among bivalves, only other oyster taxa have comparably low scaling exponents averaging near 2. We propose that oyster biomass routinely scales nearer the square of the length rather than the cube and that this is a constraint imposed by the exigency of carbonate production for reef maintenance and accretion in the face of high rates of taphonomic degradation. The adaptation as a reef builder requires the formation of carbonate that rapidly breaks down, thus requiring that carbonate produced be maximized. A biomass-to-length scaling exponent of 2 provides a mechanism to maximize shell production relative to biomass, while at the same time providing maximum surface area for the all-important settling of oyster spat to maintain the population.

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Grim, Joseph A., and James O. Pinto. "Estimating Continuous-Coverage Instantaneous Precipitation Rates Using Remotely Sensed and Ground-Based Measurements." Journal of Applied Meteorology and Climatology 50, no. 10 (October 2011): 2073–91. http://dx.doi.org/10.1175/jamc-d-11-033.1.

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AbstractThis study demonstrates a method of temporally and spatially scaling precipitation rates at low probability of precipitation-rate exceedance levels (e.g., 0.1%) from coarser-resolution global datasets to near-instantaneous localized rain gauge precipitation rates. In particular, the 8-km-, 1-h-resolution Climate Prediction Center Morphing (CMORPH) dataset was scaled to 1-min localized rates using the Automated Surface Observing Station (ASOS) rain gauge data. Maps of these scaled precipitation rates show overall patterns and magnitudes that are nearly identical to the lower-spatial-resolution rain gauge maps yet retain the much higher resolution of the original remotely sensed global dataset, which is particularly important over regions of complex geography and sparse surface observing stations. To scale the CMORPH data, temporal and spatial conversion factor arrays were calculated by comparing precipitation rates at different temporal (ASOS 1-min and 1-h) and spatial (ASOS 1-h and CMORPH 1-h) resolutions. These temporal and spatial conversion factors were found to vary by probability level, season, and climatological region. Meteorological implications of these variations are discussed.

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Kostelic, Katarina. "TEMPORAL AND SPATIAL PERCEPTION IN PURCHASE CHOICE." EURASIAN JOURNAL OF BUSINESS AND MANAGEMENT 9, no. 2 (2021): 100–122. http://dx.doi.org/10.15604/ejbm.2021.09.02.002.

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Time and space are thoroughly interlinked, not only in human perception, but in everyday life. While it may still be unclear where the time and distance perception originates and what function can be assigned to their relationship, the behavioral point of view demonstrates enough evidence of similar patterns. The aim of this research is to make another step in the direction of connecting those constructs and offer a research example with application. The objective of this research is to examine time and distance preferences in purchase situations where additional unit of time or distance saves respondent's money, and to offer a research example with application in consumer choice. Two sets of survey data of responses to hypothetical situations enable response examination. Descriptive statistical analysis is followed with the cluster analysis, multidimensional scaling and unfolding, and nonparametric tests. The results are discussed in terms of possible extension of the intertemporal choice terminology and findings to the spatio-temporal context. The findings indicate dominant susceptibility to discount amount, but also reveal similarity in responses involving the time and space distances at the similar construal level. Practical implications indicate possibility for application of space and time interchangeably in discount management, which might enable higher flexibility in the supply and decrease the customer clustering on the discount days.

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Guo, Wenjing, Xiurong Guo, Yikun Wei, and Yan Zhang. "Temporal–Spatial Evolution of Kinetic and Thermal Energy Dissipation Rates in a Three-Dimensional Turbulent Rayleigh–Taylor Mixing Zone." Entropy 22, no. 6 (June 12, 2020): 652. http://dx.doi.org/10.3390/e22060652.

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In this work, the temporal–spatial evolution of kinetic and thermal energy dissipation rates in three-dimensional (3D) turbulent Rayleigh–Taylor (RT) mixing are investigated numerically by the lattice Boltzmann method. The temperature fields, kinetic and thermal energy dissipation rates with temporal–spatial evolution, the probability density functions, the fractal dimension of mixing interface, spatial scaling law of structure function for the kinetic and the thermal energy dissipation rates in 3D space are analysed in detail to provide an improved physical understanding of the temporal–spatial dissipation-rate characteristic in the 3D turbulent Rayleigh–Taylor mixing zone. Our numerical results indicate that the kinetic and thermal energy dissipation rates are concentrated in areas with large gradients of velocity and temperature with temporal evolution, respectively, which is consistent with the theoretical assumption. However, small scale thermal plumes initially at the section of half vertical height increasingly develop large scale plumes with time evolution. The probability density function tail of thermal energy dissipation gradually rises and approaches the stretched exponent function with temporal evolution. The slope of fractal dimension increases at an early time, however, the fractal dimension for the fluid interfaces is 2.4 at times t/τ ≥ 2, which demonstrates the self-similarity of the turbulent RT mixing zone in 3D space. It is further demonstrated that the second, fourth and sixth-order structure functions for velocity and temperature structure functions have a linear scaling within the inertial range.

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Parisi, Jürgen, Joachim Peinke, Brigitte Röhricht, and Klaus Michael Mayer. "Self-Organized Formation of Spatial and Temporal Dissipative Structures in Semiconductors." Zeitschrift für Naturforschung A 42, no. 3 (March 1, 1987): 329–32. http://dx.doi.org/10.1515/zna-1987-0323.

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Nonlinear current transport behavior during avalanche breakdown of germanium comprises the spontaneous symmetry-breaking evocation of both spatially inhomogeneous and temporally unstable dissipative structures in the formerly homogeneous semiconductor. Such kind of nonequilibrium order-disorder transitions are discussed in terms of two simple models based on the new circle-map formalism of universal scaling behavior and the well-known Rashevsky- Turing theory of morphogenesis.

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Cui, Yinan, and Nasr Ghoniem. "Spatio-temporal plastic instabilities at the nano/micro scale." Journal of Micromechanics and Molecular Physics 03, no. 03n04 (September 2018): 1840006. http://dx.doi.org/10.1142/s2424913018400064.

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Recent experimental observations revealed the inherent nature of strong intermittent and heterogeneous plastic deformation at the nano- to micrometer scale. We present here a review of quantitative measures of temporal and spatial material instabilities associated with small-scale plastic flow. Spatial correlation characterization methods are developed and used to obtain information on the width of shear bands resulting from spatial instabilities. The effects of atomic-scale barriers to dislocation motion and the influence of sample size on temporal and spatial plastic instabilities are discussed. A simplified branching model of dislocation source activation is extended to predict dislocation barrier effects on strain burst statistics, and the transition from power law scaling to an exponential-like distribution. The connection between temporal and spatial plastic instabilities is discussed, and the efforts of considering these effects in crystal plasticity theory are also highlighted.

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Henebry, Geoffrey M. "Scale and Rescaling Quantitative Ecology: Spatial and Temporal Scaling David C. Schneider." BioScience 45, no. 8 (September 1995): 567–69. http://dx.doi.org/10.2307/1312705.

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Chen, CC, JE Petersen, and WM Kemp. "Spatial and temporal scaling of periphyton growth on walls of estuarine mesocosms." Marine Ecology Progress Series 155 (1997): 1–15. http://dx.doi.org/10.3354/meps155001.

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Olsson, Jonas, Vijay P. Singh, and Kenji Jinno. "Effect of spatial averaging on temporal statistical and scaling properties of rainfall." Journal of Geophysical Research: Atmospheres 104, no. D16 (August 1, 1999): 19117–26. http://dx.doi.org/10.1029/1999jd900271.

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Feijt, Arnout, and Harm Jonker. "Comparison of scaling parameters from spatial and temporal distributions of cloud properties." Journal of Geophysical Research: Atmospheres 105, no. D23 (December 1, 2000): 29089–97. http://dx.doi.org/10.1029/2000jd900414.

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German, V. I. "Unified scaling theory for distributions of temporal and spatial characteristics in seismology." Tectonophysics 424, no. 3-4 (October 2006): 167–75. http://dx.doi.org/10.1016/j.tecto.2006.03.033.

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De Santis, A., D. R. Barraclough, and R. Tozzi. "Spatial and temporal spectra of the geomagnetic field and their scaling properties." Physics of the Earth and Planetary Interiors 135, no. 2-3 (February 2003): 125–34. http://dx.doi.org/10.1016/s0031-9201(02)00211-x.

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Paulus, Martin P., and Mark A. Geyer. "A temporal and spatial scaling hypothesis for the behavioral effects of psychostimulants." Psychopharmacology 104, no. 1 (May 1991): 6–16. http://dx.doi.org/10.1007/bf02244547.

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Host, G. E., J. G. Isebrands, G. W. Theseira, J. R. Kiniry, and R. L. Graham. "Temporal and spatial scaling from individual trees to plantations: A modeling strategy." Biomass and Bioenergy 11, no. 2-3 (January 1996): 233–43. http://dx.doi.org/10.1016/0961-9534(96)00012-8.

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Duncan, Jonathan M., Peter M. Groffman, and Lawrence E. Band. "Towards closing the watershed nitrogen budget: Spatial and temporal scaling of denitrification." Journal of Geophysical Research: Biogeosciences 118, no. 3 (July 2013): 1105–19. http://dx.doi.org/10.1002/jgrg.20090.

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Bernis-Fonteneau, Agnès, Rima Alcadi, Marco Frangella, and Devra I. Jarvis. "Scaling Up Pro-Poor Agrobiodiversity Interventions as a Development Option." Sustainability 15, no. 13 (July 4, 2023): 10526. http://dx.doi.org/10.3390/su151310526.

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Pro-poor interventions that use agrobiodiversity for development actions are widely considered relevant only at small scales. Agrobiodiversity interventions are often left out of national-level/large-scale development planning. Scaling-up modalities include adaptation, diffusion, replication, value addition, and temporal scaling up. We undertook a review of 119 interventions that use agrobiodiversity for both the crop and the livestock sector. The interventions ranged from improving the availability of materials and information through management and market-oriented actions to changing norms and enabling policies. The interventions are also organized in accordance with farming-community goals and constraints. The open-access multilingual Diversity Assessment Tool for Agrobiodiversity and Resilience (DATAR) was created as a framework to systemize and structure agrobiodiversity interventions under different scaling-up modalities for the on-the-ground field assessment and scaling-up of agrobiodiversity interventions. The use of the framework enabled the scaling up of small-scale interventions that use agrobiodiversity to have impact on agricultural development at larger spatial and temporal scales.

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Cooper, Bonnie, Barry B. Lee, and Dingcai Cao. "Macaque retinal ganglion cell responses to visual patterns: harmonic composition, noise, and psychophysical detectability." Journal of Neurophysiology 115, no. 6 (June 1, 2016): 2976–88. http://dx.doi.org/10.1152/jn.00411.2015.

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The goal of these experiments was to test how well cell responses to visual patterns can be predicted from the sinewave tuning curve. Magnocellular (MC) and parvocellular (PC) ganglion cell responses to different spatial waveforms (sinewave, squarewave, and ramp waveforms) were measured across a range of spatial frequencies. Sinewave spatial tuning curves were fit with standard Gaussian models. From these fits, waveforms and spatial tuning of a cell's responses to the other waveforms were predicted for different harmonics by scaling in amplitude for the power in the waveform's Fourier expansion series over spatial frequency. Since higher spatial harmonics move at a higher temporal frequency, an additional scaling for each harmonic by the MC (bandpass) or PC (lowpass) temporal response was included, together with response phase. Finally, the model included a rectifying nonlinearity. This provided a largely satisfactory estimation of MC and PC cell responses to complex waveforms. As a consequence of their transient responses, MC responses to complex waveforms were found to have significantly more energy in higher spatial harmonic components than PC responses. Response variance (noise) was also quantified as a function of harmonic component. Noise increased to some degree for the higher harmonics. The data are relevant for psychophysical detection or discrimination of visual patterns, and we discuss the results in this context.

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Thompson, Patrick L., Sonia Kéfi, Yuval R. Zelnik, Laura E. Dee, Shaopeng Wang, Claire de Mazancourt, Michel Loreau, and Andrew Gonzalez. "Scaling up biodiversity–ecosystem functioning relationships: the role of environmental heterogeneity in space and time." Proceedings of the Royal Society B: Biological Sciences 288, no. 1946 (March 10, 2021): 20202779. http://dx.doi.org/10.1098/rspb.2020.2779.

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The biodiversity and ecosystem functioning (BEF) relationship is expected to be scale-dependent. The autocorrelation of environmental heterogeneity is hypothesized to explain this scale dependence because it influences how quickly biodiversity accumulates over space or time. However, this link has yet to be demonstrated in a formal model. Here, we use a Lotka–Volterra competition model to simulate community dynamics when environmental conditions vary across either space or time. Species differ in their optimal environmental conditions, which results in turnover in community composition. We vary biodiversity by modelling communities with different sized regional species pools and ask how the amount of biomass per unit area depends on the number of species present, and the spatial or temporal scale at which it is measured. We find that more biodiversity is required to maintain functioning at larger temporal and spatial scales. The number of species required increases quickly when environmental autocorrelation is low, and slowly when autocorrelation is high. Both spatial and temporal environmental heterogeneity lead to scale dependence in BEF, but autocorrelation has larger impacts when environmental change is temporal. These findings show how the biodiversity required to maintain functioning is expected to increase over space and time.

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Melgarejo, Miguel, and Nelson Obregon. "Information Dynamics in Urban Crime." Entropy 20, no. 11 (November 14, 2018): 874. http://dx.doi.org/10.3390/e20110874.

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Information production in both space and time has been highlighted as one of the elements that shapes the footprint of complexity in natural and socio-technical systems. However, information production in urban crime has barely been studied. This work copes with this problem by using multifractal analysis to characterize the spatial information scaling in urban crime reports and nonlinear processing tools to study the temporal behavior of this scaling. Our results suggest that information scaling in urban crime exhibits dynamics that evolve in low-dimensional chaotic attractors, and this can be observed in several spatio-temporal scales, although some of them are more favorable than others. This evidence has practical implications in terms of defining the characteristic scales to approach urban crime from available data and supporting theoretical perspectives about the complexity of urban crime.

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Bichet, Adeline, Paul J. Kushner, Lawrence Mudryk, Laurent Terray, and John C. Fyfe. "Estimating the Anthropogenic Sea Surface Temperature Response Using Pattern Scaling." Journal of Climate 28, no. 9 (May 1, 2015): 3751–63. http://dx.doi.org/10.1175/jcli-d-14-00604.1.

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Abstract This study seeks to derive the sea surface temperature (SST) response to anthropogenic forcing from observations over the last century, using simple methods inspired from pattern scaling. As in pattern scaling, the spatial response is assumed to scale with global-mean and annual-mean surface temperature. The long-term aim of this work is to generate anthropogenically forced SST and sea ice patterns for the recent past and near-term future, and use them to force atmosphere–land climate models for attribution and prediction purposes. The present work compares estimation methodologies and, within a Monte Carlo framework based on large initial condition ensembles of climate model simulations, examines the robustness of the patterns obtained. The different methods explored here yield a similar SST spatial response, mostly reflecting the observed SST linear trend map. The different methods nevertheless provide distinctive temporal evolution of the global-mean and annual-mean SST response, which in turn affects the temporal evolution of the global-mean and annual-mean air surface temperature simulated in corresponding prescribed SST simulations. The estimated SST spatial response consists mostly of a warming of the midlatitude coasts near the western boundary currents, the tropical Indian Ocean, and the Arctic Ocean. This pattern generally agrees with previously published observational and modeling studies. Based on Monte Carlo analysis of the large ensembles, it is found that between 36% and 56% of its spatial variance results from anthropogenic forcing. Overall, the work herein provides constraints on the uncertainty associated with the spatial variability of an anthropogenically forced component of climate change derived from observations, which can potentially be used for climate attribution and prediction.

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Essery, Richard, Peter Bunting, Aled Rowlands, Nick Rutter, Janet Hardy, Rae Melloh, Tim Link, Danny Marks, and John Pomeroy. "Radiative Transfer Modeling of a Coniferous Canopy Characterized by Airborne Remote Sensing." Journal of Hydrometeorology 9, no. 2 (April 1, 2008): 228–41. http://dx.doi.org/10.1175/2007jhm870.1.

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Abstract Solar radiation beneath a forest canopy can have large spatial variations, but this is frequently neglected in radiative transfer models for large-scale applications. To explicitly model spatial variations in subcanopy radiation, maps of canopy structure are required. Aerial photography and airborne laser scanning are used to map tree locations, heights, and crown diameters for a lodgepole pine forest in Colorado as inputs to a spatially explicit radiative transfer model. Statistics of subcanopy radiation simulated by the model are compared with measurements from radiometer arrays, and scaling of spatial statistics with temporal averaging and array size is discussed. Efficient parameterizations for spatial averages and standard deviations of subcanopy radiation are developed using parameters that can be obtained from the model or hemispherical photography.

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Rastin, Sepideh J., David A. Rhoades, and Annemarie Christophersen. "Space–Time Trade-Off of Precursory Seismicity in New Zealand and California Revealed by a Medium-Term Earthquake Forecasting Model." Applied Sciences 11, no. 21 (October 31, 2021): 10215. http://dx.doi.org/10.3390/app112110215.

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The ‘Every Earthquake a Precursor According to Scale’ (EEPAS) medium-term earthquake forecasting model is based on the precursory scale increase (Ψ) phenomenon and associated scaling relations, in which the precursor magnitude MP is predictive of the mainshock magnitude Mm, precursor time TP and precursory area AP. In early studies of Ψ, a relatively low correlation between TP and AP suggested the possibility of a trade-off between time and area as a second-order effect. Here, we investigate the trade-off by means of the EEPAS model. Existing versions of EEPAS in New Zealand and California forecast target earthquakes of magnitudes M > 4.95 from input catalogues with M > 2.95. We systematically vary one parameter each from the EEPAS distributions for time and location, thereby varying the temporal and spatial scales of these distributions by two orders of magnitude. As one of these parameters is varied, the other is refitted to a 20-year period of each catalogue. The resulting curves of the temporal scaling factor against the spatial scaling factor are consistent with an even trade-off between time and area, given the limited temporal and spatial extent of the input catalogue. Hybrid models are formed by mixing several EEPAS models, with parameter sets chosen from points on the trade-off line. These are tested against the original fitted EEPAS models on a subsequent period of the New Zealand catalogue. The resulting information gains suggest that the space–time trade-off can be exploited to improve forecasting.

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Deems, Jeffrey S., Steven R. Fassnacht, and Kelly J. Elder. "Interannual Consistency in Fractal Snow Depth Patterns at Two Colorado Mountain Sites." Journal of Hydrometeorology 9, no. 5 (October 1, 2008): 977–88. http://dx.doi.org/10.1175/2008jhm901.1.

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Abstract Fractal dimensions derived from log–log variograms are useful for characterizing spatial structure and scaling behavior in snow depth distributions. This study examines the temporal consistency of snow depth scaling features at two sites using snow depth distributions derived from lidar datasets collected in 2003 and 2005. The temporal snow accumulation patterns in these two years were substantially different, but both years represent nearly average 1 April accumulation depths for these sites, with consistent statistical distributions. Two distinct fractal regions are observed in each log–log variogram, separated by a scale break, which indicates a length scale at which a substantial change in the driving processes exists. The lag distance of the scale break is 15 m at the Walton Creek site and 40 m at the Alpine site. The datasets show consistent fractal dimensions and scale break distances between the two years, suggesting that the scaling features observed in spatial snow depth distributions are largely determined by physiography and vegetation characteristics and are relatively insensitive to annual variations in snowfall. Directional variograms also show consistent patterns between years, with smaller fractal dimensions aligned with the dominant wind direction at each site.

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Journal articles on the topic 'Spatial and temporal scaling'

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