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Journal articles on the topic 'Spatial temporal scales'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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1

Liu, Changliang, Pragya Goel, and Pascal S. Kaeser. "Spatial and temporal scales of dopamine transmission." Nature Reviews Neuroscience 22, no. 6 (2021): 345–58. http://dx.doi.org/10.1038/s41583-021-00455-7.

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2

Fuchs, Markus. "Geomorphology at various spatial and temporal scales." Zeitschrift für Geomorphologie, Supplementary Issues 61, no. 1 (2017): 1–2. http://dx.doi.org/10.1127/zfg_suppl/2017/0438.

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3

Orians, Gordon H., and James F. Wittenberger. "Spatial and Temporal Scales in Habitat Selection." American Naturalist 137 (June 1991): S29—S49. http://dx.doi.org/10.1086/285138.

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4

Onstad, David W. "Temporal and spatial scales in epidemiological concepts." Journal of Theoretical Biology 158, no. 4 (1992): 495–515. http://dx.doi.org/10.1016/s0022-5193(05)80712-x.

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5

van Woesik, Robert. "Coral bleaching: transcending spatial and temporal scales." Trends in Ecology & Evolution 16, no. 3 (2001): 119–21. http://dx.doi.org/10.1016/s0169-5347(00)02083-8.

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6

Thomas, Matthew D., Agatha M. De Boer, Helen L. Johnson, and David P. Stevens. "Spatial and Temporal Scales of Sverdrup Balance*." Journal of Physical Oceanography 44, no. 10 (2014): 2644–60. http://dx.doi.org/10.1175/jpo-d-13-0192.1.

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Abstract Sverdrup balance underlies much of the theory of ocean circulation and provides a potential tool for describing the interior ocean transport from only the wind stress. Using both a model state estimate and an eddy-permitting coupled climate model, this study assesses to what extent and over what spatial and temporal scales Sverdrup balance describes the meridional transport. The authors find that Sverdrup balance holds to first order in the interior subtropical ocean when considered at spatial scales greater than approximately 5°. Outside the subtropics, in western boundary currents a

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7

Zhou, Zehui, Dekai Lu, Bin Yong, Zhehui Shen, Hao Wu, and Lei Yu. "Evaluation of GPM-IMERG Precipitation Product at Multiple Spatial and Sub-Daily Temporal Scales over Mainland China." Remote Sensing 15, no. 5 (2023): 1237. http://dx.doi.org/10.3390/rs15051237.

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The Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (IMERG) provides new-generation satellite precipitation datasets with high spatio-temporal resolution and accuracy, which is widely applied in hydrology and meteorology. However, most examinations of the IMERG were conducted on daily, monthly, and annual scales, and inadequate research focused on the sub-daily scale. Thus, this study set up four sub-daily scales (1 h, 3 h, 12 h, and 24 h at 0.1° spatial resolution) and four spatial scales (0.1°, 0.25°, 0.5°, and 1° at 1 h temporal resolution) to finely evaluate

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8

Dimitriadis, Panayiotis, Theano Iliopoulou, G. Fivos Sargentis, and Demetris Koutsoyiannis. "Spatial Hurst–Kolmogorov Clustering." Encyclopedia 1, no. 4 (2021): 1010–25. http://dx.doi.org/10.3390/encyclopedia1040077.

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The stochastic analysis in the scale domain (instead of the traditional lag or frequency domains) is introduced as a robust means to identify, model and simulate the Hurst–Kolmogorov (HK) dynamics, ranging from small (fractal) to large scales exhibiting the clustering behavior (else known as the Hurst phenomenon or long-range dependence). The HK clustering is an attribute of a multidimensional (1D, 2D, etc.) spatio-temporal stationary stochastic process with an arbitrary marginal distribution function, and a fractal behavior on small spatio-temporal scales of the dependence structure and a pow

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Podesta, J. J. "Spatial scales and temporal scales in the theory of magnetohydrodynamic turbulence." Physics of Plasmas 18, no. 1 (2011): 012906. http://dx.doi.org/10.1063/1.3534824.

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10

Hutchings, Jennifer K., Andrew Roberts, Cathleen A. Geiger, and Jacqueline Richter-Menge. "Spatial and temporal characterization of sea-ice deformation." Annals of Glaciology 52, no. 57 (2011): 360–68. http://dx.doi.org/10.3189/172756411795931769.

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AbstractIn late March 2007 an array of GPS ice drifters was deployed in the Beaufort Sea as part of the Sea Ice Experiment: Dynamic Nature of the Arctic (SEDNA). the drifters were deployed in an array designed to resolve four, nested spatial scales of sea-ice deformation, from 10 to 140 km, with the arrays maintaining appropriate shape for strain-rate calculation until mid-June. In this paper, we test whether sea-ice deformation displays fractal properties in the vicinity of SEDNA. We identify that deformation time series have different spectral properties depending on the spatial scale. At th

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11

Zhang, Wenjun, Jianping Li, and Fei-Fei Jin. "Spatial and temporal features of ENSO meridional scales." Geophysical Research Letters 36, no. 15 (2009): n/a. http://dx.doi.org/10.1029/2009gl038672.

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12

Ladau, Joshua, and Emiley A. Eloe-Fadrosh. "Spatial, Temporal, and Phylogenetic Scales of Microbial Ecology." Trends in Microbiology 27, no. 8 (2019): 662–69. http://dx.doi.org/10.1016/j.tim.2019.03.003.

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13

Hu, W., and B. C. Si. "Estimating spatially distributed soil water content at small watershed scales based on decomposition of temporal anomaly and time stability analysis." Hydrology and Earth System Sciences Discussions 12, no. 7 (2015): 6467–503. http://dx.doi.org/10.5194/hessd-12-6467-2015.

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Abstract. Soil water content (SWC) at watershed scales is crucial to rainfall–runoff response. A model was used to decompose spatiotemporal SWC into time-stable pattern (i.e., temporal mean), space-invariant temporal anomaly, and space-variant temporal anomaly. This model was compared with a previous model that decomposes spatiotemporal SWC into spatial mean and spatial anomaly. The space-variant temporal anomaly or spatial anomaly was further decomposed using the empirical orthogonal function for estimating spatially distributed SWC. These two models are termed temporal anomaly (TA) model and

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14

LEROY, François, Jiří Reif, David Storch, and Petr Keil. "How has bird biodiversity changed over time? A review across spatio-temporal scales." Basic and Applied Ecology 69, no. 2023 (2023): 26–38. https://doi.org/10.1016/j.baae.2023.03.004.

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Empirical quantification of biodiversity changes remains a challenge even in well surveyed groups such as birds. This may be because the change depends on spatio-temporal scales, specifically on spatial grain (i.e. average unit of area of the sampling or the analysis), geographic extent (i.e. size of the area of interest), temporal grain (i.e. average unit of duration of the sampling or the analysis), and temporal extent (i.e. length of the time series). Further, different metrics of biodiversity may exhibit different trends. Here we review the literature assessing the temporal trends of avian

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15

Rosenberg, D. M., F. Berkes, R. A. Bodaly, R. E. Hecky, C. A. Kelly, and J. WM Rudd. "Large-scale impacts of hydroelectric development." Environmental Reviews 5, no. 1 (1997): 27–54. http://dx.doi.org/10.1139/a97-001.

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The substantial size of some hydroelectric projects and the extensive total surface area covered by reservoirs globally require that research determining the impacts of these developments be done at ever-increasing spatial and temporal scales. As a consequence of this research, new views are emerging about the spatial extent and longevity of the environmental and social impacts of such developments. New findings challenge the notion of hydroelectric development as a benign alternative to other forms of power generation. This review examines the intertwined environmental and social effects of m

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16

Ming, Junhao, Dongmei Zhang, and Wei Han. "Multi-Scale Spatial–Temporal Transformer: A Novel Framework for Spatial–Temporal Edge Data Prediction." Applied Sciences 13, no. 17 (2023): 9651. http://dx.doi.org/10.3390/app13179651.

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Spatial–temporal prediction is an important part of a great number of applications, such as urban traffic control, urban traffic management, and urban traffic planning. However, real-world spatial–temporal data often have complex patterns, so it is still challenging to predict them accurately. Most existing spatial–temporal prediction models fail to aggregate the spatial features in a suitable neighborhood during fixed spatial dependencies extraction and lack adequately comprehensive time series analysis for intricate temporal dependencies. This paper proposes a novel model named multi-scale s

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17

Resh, Vincent H., and David M. Rosenberg. "SPATIAL–TEMPORAL VARIABILITY AND THE STUDY OF AQUATIC INSECTS,." Canadian Entomologist 121, no. 11 (1989): 941–63. http://dx.doi.org/10.4039/ent121941-11.

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AbstractSpatial and temporal variability are essential considerations in the study of aquatic insects. Traditionally, these two sources of variability are treated separately; however, they should be considered together because they occur concurrently in natural systems. To illustrate this interaction, we constructed two-way variability tables in which spatial (habitat, reach or zone, system, intersystem) and temporal (within a day, within a season, within a year, year to year) scales were ordered on separate axes, and examples of concurrent spatial and temporal variability were entered at the

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18

Pech-Canche, Juan M., Claudia E. Moreno, and Gonzalo Halffter. "Additive partitioning of Phyllostomid bat richness at fine and coarse spatial and temporal scales in Yucatan, Mexico." Écoscience 18, no. 1 (2011): 42–51. https://doi.org/10.5281/zenodo.13436638.

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(Uploaded by Plazi for the Bat Literature Project) Neotropical bats exhibit great diversity in terms of resource use, so their species richness can vary both spatially and temporally over fine and coarse scales. In this paper we analyze the relative contribution of the alpha (F) and beta (G) components of bat species richness at different spatial and temporal scales in deciduous and semideciduous tropical forests in Yucatan, Mexico. We used different levels of the spatial (understory and subcanopy height, site, and vegetation type) and temporal (sampling period within a night, sampling night,

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19

Pech-Canche, Juan M., Claudia E. Moreno, and Gonzalo Halffter. "Additive partitioning of Phyllostomid bat richness at fine and coarse spatial and temporal scales in Yucatan, Mexico." Écoscience 18, no. 1 (2011): 42–51. https://doi.org/10.5281/zenodo.13436638.

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(Uploaded by Plazi for the Bat Literature Project) Neotropical bats exhibit great diversity in terms of resource use, so their species richness can vary both spatially and temporally over fine and coarse scales. In this paper we analyze the relative contribution of the alpha (F) and beta (G) components of bat species richness at different spatial and temporal scales in deciduous and semideciduous tropical forests in Yucatan, Mexico. We used different levels of the spatial (understory and subcanopy height, site, and vegetation type) and temporal (sampling period within a night, sampling night,

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20

Pech-Canche, Juan M., Claudia E. Moreno, and Gonzalo Halffter. "Additive partitioning of Phyllostomid bat richness at fine and coarse spatial and temporal scales in Yucatan, Mexico." Écoscience 18, no. 1 (2011): 42–51. https://doi.org/10.5281/zenodo.13436638.

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(Uploaded by Plazi for the Bat Literature Project) Neotropical bats exhibit great diversity in terms of resource use, so their species richness can vary both spatially and temporally over fine and coarse scales. In this paper we analyze the relative contribution of the alpha (F) and beta (G) components of bat species richness at different spatial and temporal scales in deciduous and semideciduous tropical forests in Yucatan, Mexico. We used different levels of the spatial (understory and subcanopy height, site, and vegetation type) and temporal (sampling period within a night, sampling night,

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21

Pech-Canche, Juan M., Claudia E. Moreno, and Gonzalo Halffter. "Additive partitioning of Phyllostomid bat richness at fine and coarse spatial and temporal scales in Yucatan, Mexico." Écoscience 18, no. 1 (2011): 42–51. https://doi.org/10.5281/zenodo.13436638.

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Abstract:

(Uploaded by Plazi for the Bat Literature Project) Neotropical bats exhibit great diversity in terms of resource use, so their species richness can vary both spatially and temporally over fine and coarse scales. In this paper we analyze the relative contribution of the alpha (F) and beta (G) components of bat species richness at different spatial and temporal scales in deciduous and semideciduous tropical forests in Yucatan, Mexico. We used different levels of the spatial (understory and subcanopy height, site, and vegetation type) and temporal (sampling period within a night, sampling night,

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22

Pech-Canche, Juan M., Claudia E. Moreno, and Gonzalo Halffter. "Additive partitioning of Phyllostomid bat richness at fine and coarse spatial and temporal scales in Yucatan, Mexico." Écoscience 18, no. 1 (2011): 42–51. https://doi.org/10.5281/zenodo.13436638.

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(Uploaded by Plazi for the Bat Literature Project) Neotropical bats exhibit great diversity in terms of resource use, so their species richness can vary both spatially and temporally over fine and coarse scales. In this paper we analyze the relative contribution of the alpha (F) and beta (G) components of bat species richness at different spatial and temporal scales in deciduous and semideciduous tropical forests in Yucatan, Mexico. We used different levels of the spatial (understory and subcanopy height, site, and vegetation type) and temporal (sampling period within a night, sampling night,

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23

Dou, Chunhong, and Jinshan Lin. "Adaptive Multiscale Symbolic-Dynamics Entropy for Condition Monitoring of Rotating Machinery." Entropy 21, no. 12 (2019): 1138. http://dx.doi.org/10.3390/e21121138.

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Vibration data from rotating machinery working in different conditions display different properties in spatial and temporal scales. As a result, insights into spatial- and temporal-scale structures of vibration data of rotating machinery are fundamental for describing running conditions of rotating machinery. However, common temporal statistics and typical nonlinear measures have difficulties in describing spatial and temporal scales of data. Recently, statistical linguistic analysis (SLA) has been pioneered in analyzing complex vibration data from rotating machinery. Nonetheless, SLA can exam

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24

Zan, Xin, and Jasmine Siu Lee Lam. "Multibranch Adaptive Fusion Graph Convolutional Network for Traffic Flow Prediction." Journal of Advanced Transportation 2023 (June 13, 2023): 1–13. http://dx.doi.org/10.1155/2023/8256907.

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Urban road networks have complex spatial and temporal correlations, driving a surge of research interest in spatial-temporal traffic flow prediction. However, prior approaches often overlook the temporal-scale differentiation of spatial-temporal features, limiting their ability to extract complex structural information. In this work, we design the multibranch adaptive fusion graph convolutional network (MBAF-GCN) that explicitly exploits the prior spatial-temporal characteristics at different temporal scales, and each branch is responsible for extracting spatial-temporal features at a specific

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25

Nelson, T. Connor, Lee Harrison, and Kristen L. Corbosiero. "Temporal and Spatial Autocorrelations from Expendable Digital Dropsondes (XDDs) in Tropical Cyclones." Journal of Atmospheric and Oceanic Technology 37, no. 3 (2020): 381–99. http://dx.doi.org/10.1175/jtech-d-19-0032.1.

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AbstractThe newly developed Expendable Digital Dropsondes (XDDs) allow for high spatial and temporal resolution observations of the kinematic and thermodynamic structures in tropical cyclones (TCs). It is important to evaluate both the temporal and spatial autocorrelations within the recorded data to address concerns about spatial interpolation, statistical significance of individual data points, and launch-rate spatial requirements for future dropsonde studies in TCs. Data from 437 XDDs launched into Hurricanes Marty (27–28 September), Joaquin (2–5 October), and Patricia (20–23 October) durin

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26

Park, Yonghwan, Taewoong Jang, Jongkuk Kim, et al. "Temporal Variation Dominates in Local Carabid Beetle Communities in Korean Mountains." Insects 12, no. 11 (2021): 1019. http://dx.doi.org/10.3390/insects12111019.

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Spatial and temporal variation in ecological environments may result in spatial and temporal variation in communities. Temporal studies of biodiversity are essential for forecasting future changes in community structure and ecosystem function. Therefore, determining the mechanisms that drive temporal change in communities remains an important and interesting challenge in ecology. We quantified spatial and temporal variations in carabid beetle communities and site-specific environmental factors for 5 years at nine study sites on three mountains in the Baekdudaegan Mountain Range, Korea. Carabid

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27

Reed, Daniel C., Andrew R. Rassweiler, Robert J. Miller, Henry M. Page, and Sally J. Holbrook. "The value of a broad temporal and spatial perspective in understanding dynamics of kelp forest ecosystems." Marine and Freshwater Research 67, no. 1 (2016): 14. http://dx.doi.org/10.1071/mf14158.

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Many ecological processes play out over longer time scales and larger spatial scales than can be studied in a traditional 2–4-year grant cycle. Uncertainties in future funding hinder efforts to implement comprehensive research programs that integrate coupled time series observations of physical variables and ecological responses, manipulative experiments and synthetic analyses over the long term. Such research is essential for advancing our understanding of ecological responses associated with climate change, and the physical and biological processes that control them. This need is perhaps gre

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28

Yang, Xinyu, Yu Sun, Xinyang Chen, Ying Zhang, and Xiaojie Yuan. "Graph Structure Learning for Spatial-Temporal Imputation: Adapting to Node and Feature Scales." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 1 (2025): 959–67. https://doi.org/10.1609/aaai.v39i1.32081.

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Spatial-temporal data collected across different geographic locations often suffer from missing values, posing challenges to data analysis. Existing methods primarily leverage fixed spatial graphs to impute missing values, which implicitly assume that the spatial relationship is roughly the same for all features across different locations. However, they may overlook the different spatial relationships of diverse features recorded by sensors in different locations. To address this, we introduce the multi-scale Graph Structure Learning framework for spatial-temporal Imputation (GSLI) that dynami

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29

Zhao, Chunli, Jianguo Chen, Peng Du, and Hongyong Yuan. "Characteristics of Climate Change and Extreme Weather from 1951 to 2011 in China." International Journal of Environmental Research and Public Health 15, no. 11 (2018): 2540. http://dx.doi.org/10.3390/ijerph15112540.

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It has been demonstrated that climate change is an established fact. A good comprehension of climate and extreme weather variation characteristics on a temporal and a spatial scale is important for adaptation and response. In this work, the characteristics of temperature, precipitation, and extreme weather distribution and variation is summarized for a period of 60 years and the seasonal fluctuation of temperature and precipitation is also analyzed. The results illustrate the reduction in daily and annual temperature divergence on both temporal and spatial scales. However, the gaps remain rela

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30

Carter, Neil H., Binoj K. Shrestha, Jhamak B. Karki, Narendra Man Babu Pradhan, and Jianguo Liu. "Coexistence between wildlife and humans at fine spatial scales." Proceedings of the National Academy of Sciences 109, no. 38 (2012): 15360–65. http://dx.doi.org/10.1073/pnas.1210490109.

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Many wildlife species face imminent extinction because of human impacts, and therefore, a prevailing belief is that some wildlife species, particularly large carnivores and ungulates, cannot coexist with people at fine spatial scales (i.e., cannot regularly use the exact same point locations). This belief provides rationale for various conservation programs, such as resettling human communities outside protected areas. However, quantitative information on the capacity and mechanisms for wildlife to coexist with humans at fine spatial scales is scarce. Such information is vital, because the wor

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31

Klingaman, Nicholas P., Gill M. Martin, and Aurel Moise. "ASoP (v1.0): a set of methods for analyzing scales of precipitation in general circulation models." Geoscientific Model Development 10, no. 1 (2017): 57–83. http://dx.doi.org/10.5194/gmd-10-57-2017.

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Abstract. General circulation models (GCMs) have been criticized for their failure to represent the observed scales of precipitation, particularly in the tropics where simulated daily rainfall is too light, too frequent and too persistent. Previous assessments have focused on temporally or spatially averaged precipitation, such as daily means or regional averages. These evaluations offer little actionable information for model developers, because the interactions between the resolved dynamics and parameterized physics that produce precipitation occur at the native gridscale and time step. We i

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32

Sánchez-Puig, Fernanda, Rogelio Lozano-Aranda, Dante Pérez-Méndez, et al. "Language Statistics at Different Spatial, Temporal, and Grammatical Scales." Entropy 26, no. 9 (2024): 734. http://dx.doi.org/10.3390/e26090734.

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In recent decades, the field of statistical linguistics has made significant strides, which have been fueled by the availability of data. Leveraging Twitter data, this paper explores the English and Spanish languages, investigating their rank diversity across different scales: temporal intervals (ranging from 3 to 96 h), spatial radii (spanning 3 km to over 3000 km), and grammatical word ngrams (ranging from 1-grams to 5-grams). The analysis focuses on word ngrams, examining a time period of 1 year (2014) and eight different countries. Our findings highlight the relevance of all three scales w

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33

Bunnell, Fred L., and David J. Huggard. "Biodiversity across spatial and temporal scales: problems and opportunities." Forest Ecology and Management 115, no. 2-3 (1999): 113–26. http://dx.doi.org/10.1016/s0378-1127(98)00392-2.

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34

Valachovic, Edward, and Igor Zurbenko. "Multivariate analysis of spatial–temporal scales in melanoma prevalence." Cancer Causes & Control 28, no. 7 (2017): 733–43. http://dx.doi.org/10.1007/s10552-017-0895-x.

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35

Chaplin-Kramer, Rebecca, Perry de Valpine, Nicholas J. Mills, and Claire Kremen. "Detecting pest control services across spatial and temporal scales." Agriculture, Ecosystems & Environment 181 (December 2013): 206–12. http://dx.doi.org/10.1016/j.agee.2013.10.007.

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36

Marangos, Jonathan P. "Accessing the quantum spatial and temporal scales with XFELs." Nature Reviews Physics 2, no. 7 (2020): 332–34. http://dx.doi.org/10.1038/s42254-020-0183-7.

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37

Callicott, J. Baird. "Choosing appropriate temporal and spatial scales for ecological restoration." Journal of Biosciences 27, no. 4 (2002): 409–20. http://dx.doi.org/10.1007/bf02704969.

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Com�n, Francisco A., Margarita Men�ndez, and Jorge A. Herrera. "Spatial and temporal scales for monitoring coastal aquatic ecosystems." Aquatic Conservation: Marine and Freshwater Ecosystems 14, S1 (2004): S5—S17. http://dx.doi.org/10.1002/aqc.646.

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Cardona, Andrés H., Márcia M. Peixoto, Tohn Borjigin, and Thomas Gregor. "Bridging spatial and temporal scales of developmental gene regulation." Current Opinion in Genetics & Development 92 (June 2025): 102328. https://doi.org/10.1016/j.gde.2025.102328.

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40

de Melo, Silas Nogueira, Débora V. S. Pereira, Martin A. Andresen, and Lindon Fonseca Matias. "Spatial/Temporal Variations of Crime: A Routine Activity Theory Perspective." International Journal of Offender Therapy and Comparative Criminology 62, no. 7 (2017): 1967–91. http://dx.doi.org/10.1177/0306624x17703654.

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Temporal and spatial patterns of crime in Campinas, Brazil, are analyzed considering the relevance of routine activity theory in a Latin American context. We use geo-referenced criminal event data, 2010-2013, analyzing spatial patterns using census tracts and temporal patterns considering seasons, months, days, and hours. Our analyses include difference in means tests, count-based regression models, and Kulldorff’s scan test. We find that crime in Campinas, Brazil, exhibits both temporal and spatial-temporal patterns. However, the presence of these patterns at the different temporal scales var

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41

Friedel, MH. "How spatial and temporal scale affect the perception of change in rangelands." Rangeland Journal 16, no. 1 (1994): 16. http://dx.doi.org/10.1071/rj9940016.

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Heterogeneity in rangelands occurs at many levels, both spatially and temporally. The different levels are hierarchically ordered, from organism to landscape in the spatial context, and each has its characteristic emergent properties. Models of rangeland succession are dependent on the spatial scales of disturbance and assessment. Detectionof changes in the productive potential of rangelands is also scale dependent. Commonly, the emergent properties of lower levels, e.g, composition within communities, are used incorrectly to indicate the status of higher levels, e.g landscapes. Understanding

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Joo, Jaewon, Yong Tian, Chunmiao Zheng, et al. "An Integrated Modeling Approach to Study the Surface Water-Groundwater Interactions and Influence of Temporal Damping Effects on the Hydrological Cycle in the Miho Catchment in South Korea." Water 10, no. 11 (2018): 1529. http://dx.doi.org/10.3390/w10111529.

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Integrated surface water–groundwater (SW–GW) models could be used to assess the impacts of climate change or variability on the hydrological cycle. However, the damping effects of the hydrological system have rarely been explored via integrated SW–GW modeling. This paper presents an integrated modeling study in a typical humid area, the Miho catchment in Korea, using an integrated model called Groundwater and Surface-water FLOW (GSFLOW). The major findings of this study are as follows: (1) The simulated results from 2005 to 2014 indicate that the temporal variability in the streamflow, stream-

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43

Lijklema, Lambertus. "Dimensions and scales." Water Science and Technology 37, no. 3 (1998): 1–7. http://dx.doi.org/10.2166/wst.1998.0162.

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Phenomena in the environment occur on a wide range of spatial and temporal scales. This puts certain demands on the ways we perform research and model systems. Transverse mixing in rivers and internal loading of lakes with phosphates are examples illustrating certain features. Time lags in both ecosystems and in society in combination tend to postpone the solution of environmental problems. Eutrophication serves as an example.

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44

Hu, W., and B. C. Si. "Estimating spatially distributed soil water content at small watershed scales based on decomposition of temporal anomaly and time stability analysis." Hydrology and Earth System Sciences 20, no. 1 (2016): 571–87. http://dx.doi.org/10.5194/hess-20-571-2016.

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Abstract. Soil water content (SWC) is crucial to rainfall-runoff response at the watershed scale. A model was used to decompose the spatiotemporal SWC into a time-stable pattern (i.e., temporal mean), a space-invariant temporal anomaly, and a space-variant temporal anomaly. The space-variant temporal anomaly was further decomposed using the empirical orthogonal function (EOF) for estimating spatially distributed SWC. This model was compared to a previous model that decomposes the spatiotemporal SWC into a spatial mean and a spatial anomaly, with the latter being further decomposed using the EO

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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 (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 synopti

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O'Donncha, Fearghal, Yihao Hu, Paulito Palmes, Meredith Burke, Ramon Filgueira, and Jon Grant. "A spatio-temporal LSTM model to forecast across multiple temporal and spatial scales." Ecological Informatics 69 (July 2022): 101687. http://dx.doi.org/10.1016/j.ecoinf.2022.101687.

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Zhou, Lei, and Raghu Murtugudde. "Ocean–Atmosphere Coupling on Different Spatiotemporal Scales: A Mechanism for Intraseasonal Instabilities." Journal of the Atmospheric Sciences 66, no. 6 (2009): 1834–44. http://dx.doi.org/10.1175/2008jas2879.1.

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Abstract The possibility of interactions between oceanic and atmospheric oscillations with different temporal and spatial scales is examined with analytical solutions to idealized linear governing equations. With a reasonable choice for relevant parameters, the mesoscale oceanic features and the large-scale atmospheric oscillations can interact with each other and lead to unstable waves in the intraseasonal band in the specific coupled model presented in this study. This mechanism is different from the resonance mechanism, which requires similar temporal or spatial scales in the two media. Ins

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Ardisson, P. L., E. Bourget, and P. Legendre. "Multivariate Approach to Study Species Assemblages at Large Spatiotemporal Scales: The Community Structure of the Epibenthic Fauna of the Estuary and Gulf of St. Lawrence." Canadian Journal of Fisheries and Aquatic Sciences 47, no. 7 (1990): 1364–77. http://dx.doi.org/10.1139/f90-156.

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We investigated hierarchical organization and spatiotemporal discontinuities in species abundances in the epibenthic community of the Estuary and Gulf of St. Lawrence. Quantitative samples were obtained from 1975 to 1984, on 161 collectors (navigation buoys) moored yearly from May through November. Maximum biomass values of the dominant species, common to all regions studied, were used to assess epibenthic community structure. Numerical methods were used to characterize spatial structure and temporal variability of the dominant assemblage. Spatially constrained clustering and ordination techni

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Yu, Huan, Bo Kong, Shu Qing Zhang, and Xin Pan. "Wetlands Change Simulation Using Cellular Automata at Multi-Scales." Advanced Materials Research 610-613 (December 2012): 3616–23. http://dx.doi.org/10.4028/www.scientific.net/amr.610-613.3616.

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Wetlands are extremely valuable natural resources, the simulation of wetland landscape spatial-temporal evolution can reveal the mechanisms and laws of landscape succession, achieve the sustainable landscape use and provide wetland conservation and management decision support. Thesis takes the inland freshwater wetlands in the Sanjiang Plain for experimental region, carries out experiment of wetland landscape changing process simulation using Cellular Automata, results show that visual effects of simulation and prediction are both good, and the total accuracy of points to points are also above

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Dong, Qifen, Yu Li, Ziwan Zheng, Xun Wang, and Guojun Li. "ST3DNetCrime: Improved ST-3DNet Model for Crime Prediction at Fine Spatial Temporal Scales." ISPRS International Journal of Geo-Information 11, no. 10 (2022): 529. http://dx.doi.org/10.3390/ijgi11100529.

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Crime prediction is crucial for sustainable urban development and protecting citizens’ quality of life. However, there exist some challenges in this regard. First, the spatio-temporal correlations in crime data are relatively complex and are heterogenous in time and space, hence it is difficult to model the spatio-temporal correlation in crime data adequately. Second, crime prediction at fine spatial temporal scales can be applied to micro patrol command; however, crime data are sparse in both time and space, making crime prediction very challenging. To overcome these challenges, based on the

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