Effects of disturbance by Siberian marmots (Marmota sibirica) on spatial heterogeneity of vegetation at multiple spatial scales

2009 ◽  
Vol 55 (2) ◽  
pp. 89-95 ◽  
Author(s):  
Yu Yoshihara ◽  
Toshiya Ohkuro ◽  
Buuveibaatar Bayarbaatar ◽  
Kazuhiko Takeuchi
Keyword(s):  
Spatial Heterogeneity ◽  
Spatial Scales ◽  
Multiple Spatial Scales ◽  
Marmota Sibirica

Inhibition of coral settlement at multiple spatial scales by a pervasive algal competitor

2019 ◽  
Vol 612 ◽  
pp. 29-42 ◽  
Author(s):  
NR Evensen ◽  
C Doropoulos ◽  
KM Morrow ◽  
CA Motti ◽  
PJ Mumby
Keyword(s):  
Spatial Scales ◽  
Coral Settlement ◽  
Multiple Spatial Scales

Assessment of clogging in constructed wetlands by saturated hydraulic conductivity measurements

2019 ◽  
Vol 79 (2) ◽  
pp. 314-322 ◽  
Author(s):  
F. Licciardello ◽  
R. Aiello ◽  
V. Alagna ◽  
M. Iovino ◽  
D. Ventura ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Hydraulic Conductivity ◽  
Constructed Wetlands ◽  
Spatial Scales ◽  
Pilot Scale ◽  
Laboratory Scale ◽  
Test Method ◽  
Conductivity Measurements ◽  
Multiple Spatial Scales ◽  
Ks Values

Abstract This study aims at defining a methodology to evaluate Ks reductions of gravel material constituting constructed wetland (CW) bed matrices. Several schemes and equations for the Lefranc's test were compared by using different gravel sizes and at multiple spatial scales. The falling-head test method was implemented by using two steel permeameters: one impervious (IMP) and one pervious (P) on one side. At laboratory scale, mean K values for a small size gravel (8–15 × 10−2 m) measured by the IMP and the P permeameters were equal to 19,466 m/d and 30,662 m/d, respectively. Mean Ks values for a big size gravel (10–25 × 10−2 m) measured by the IMP and the P permeameters were equal to 12,135 m/d and 20,866 m/d, respectively. Comparison of Ks values obtained by the two permeameters at laboratory scale as well as a sensitivity analysis and a calibration, lead to the modification of the standpipe equation, to evaluate also the temporal variation of the horizontal Ks. In particular, both permeameters allow the evaluation of the Ks decreasing after 4 years-operation and 1–1.5 years' operation of the plants at full scale (filled with the small size gravel) and at pilot scale (filled with the big size gravel), respectively.

scholarly journals FORMULATION OF THE HEAT CONDUCTION EQUATION FOR HETEROGENEOUS MEDIA WITH MULTIPLE SPATIAL SCALES USING REITERATED HOMOGENIZATION

2016 ◽  
Vol 15 (1) ◽  
pp. 96
Author(s):  
E. Iglesias-Rodríguez ◽  
M. E. Cruz ◽  
J. Bravo-Castillero ◽  
R. Guinovart-Díaz ◽  
R. Rodríguez-Ramos ◽  
...  
Keyword(s):  
Heat Conduction ◽  
Small Parameter ◽  
Heat Conduction Equation ◽  
Heterogeneous Media ◽  
Spatial Scales ◽  
Conduction Equation ◽  
Small Scale ◽  
Multiple Spatial Scales ◽  
Effective Coefficients ◽  
Reiterated Homogenization

Heterogeneous media with multiple spatial scales are finding increased importance in engineering. An example might be a large scale, otherwise homogeneous medium filled with dispersed small-scale particles that form aggregate structures at an intermediate scale. The objective in this paper is to formulate the strong-form Fourier heat conduction equation for such media using the method of reiterated homogenization. The phases are assumed to have a perfect thermal contact at the interface. The ratio of two successive length scales of the medium is a constant small parameter ε. The method is an up-scaling procedure that writes the temperature field as an asymptotic multiple-scale expansion in powers of the small parameter ε . The technique leads to two pairs of local and homogenized equations, linked by effective coefficients. In this manner the medium behavior at the smallest scales is seen to affect the macroscale behavior, which is the main interest in engineering. To facilitate the physical understanding of the formulation, an analytical solution is obtained for the heat conduction equation in a functionally graded material (FGM). The approach presented here may serve as a basis for future efforts to numerically compute effective properties of heterogeneous media with multiple spatial scales.

Applying systematic conservation planning to improve the allocation of restoration actions at multiple spatial scales

2021 ◽  
Author(s):  
Ben L. Gilby ◽  
Andrew D. Olds ◽  
Christopher J. Brown ◽  
Rod M. Connolly ◽  
Christopher J. Henderson ◽  
...  
Keyword(s):  
Conservation Planning ◽  
Spatial Scales ◽  
Systematic Conservation Planning ◽  
Multiple Spatial Scales

scholarly journals Spatial scale-dependent land–atmospheric methane exchanges in the northern high latitudes from 1993 to 2004

2014 ◽  
Vol 11 (7) ◽  
pp. 1693-1704 ◽  
Author(s):  
X. Zhu ◽  
Q. Zhuang ◽  
X. Lu ◽  
L. Song
Keyword(s):  
Spatial Heterogeneity ◽  
Spatial Scale ◽  
Water Table ◽  
Spatial Scales ◽  
Grid Cell ◽  
High Latitudes ◽  
Atmospheric Methane ◽  
Ch4 Emissions ◽  
Biogeochemical Models ◽  
Macro Scale

Abstract. Effects of various spatial scales of water table dynamics on land–atmospheric methane (CH4) exchanges have not yet been assessed for large regions. Here we used a coupled hydrology–biogeochemistry model to quantify daily CH4 exchanges over the pan-Arctic from 1993 to 2004 at two spatial scales of 100 km and 5 km. The effects of sub-grid spatial variability of the water table depth (WTD) on CH4 emissions were examined with a TOPMODEL-based parameterization scheme for the northern high latitudes. We found that both WTD and CH4 emissions are better simulated at a 5 km spatial resolution. By considering the spatial heterogeneity of WTD, net regional CH4 emissions at a 5 km resolution are 38.1–55.4 Tg CH4 yr−1 from 1993 to 2004, which are on average 42% larger than those simulated at a 100 km resolution using a grid-cell-mean WTD scheme. The difference in annual CH4 emissions is attributed to the increased emitting area and enhanced flux density with finer resolution for WTD. Further, the inclusion of sub-grid WTD spatial heterogeneity also influences the inter-annual variability of CH4 emissions. Soil temperature plays an important role in the 100 km estimates, while the 5 km estimates are mainly influenced by WTD. This study suggests that previous macro-scale biogeochemical models using a grid-cell-mean WTD scheme might have underestimated the regional CH4 emissions. The spatial scale-dependent effects of WTD should be considered in future quantification of regional CH4 emissions.

scholarly journals A new uncertainty estimation approach with multiple datasets and implementation for various precipitation products

2020 ◽  
Vol 24 (4) ◽  
pp. 2061-2081 ◽  
Author(s):  
Xudong Zhou ◽  
Jan Polcher ◽  
Tao Yang ◽  
Ching-Sheng Huang
Keyword(s):  
Spatial Heterogeneity ◽  
Spatial Scales ◽  
Three Dimensional ◽  
Variance Partitioning ◽  
Uncertainty Estimation ◽  
The Other ◽  
New Approach ◽  
Multiple Datasets ◽  
Uncertainty Estimates ◽  
Spatiotemporal Scales

Abstract. Ensemble estimates based on multiple datasets are frequently applied once many datasets are available for the same climatic variable. An uncertainty estimate based on the difference between the ensemble datasets is always provided along with the ensemble mean estimate to show to what extent the ensemble members are consistent with each other. However, one fundamental flaw of classic uncertainty estimates is that only the uncertainty in one dimension (either the temporal variability or the spatial heterogeneity) can be considered, whereas the variation along the other dimension is dismissed due to limitations in algorithms for classic uncertainty estimates, resulting in an incomplete assessment of the uncertainties. This study introduces a three-dimensional variance partitioning approach and proposes a new uncertainty estimation (Ue) that includes the data uncertainties in both spatiotemporal scales. The new approach avoids pre-averaging in either of the spatiotemporal dimensions and, as a result, the Ue estimate is around 20 % higher than the classic uncertainty metrics. The deviation of Ue from the classic metrics is apparent for regions with strong spatial heterogeneity and where the variations significantly differ in temporal and spatial scales. This shows that classic metrics underestimate the uncertainty through averaging, which means a loss of information in the variations across spatiotemporal scales. Decomposing the formula for Ue shows that Ue has integrated four different variations across the ensemble dataset members, while only two of the components are represented in the classic uncertainty estimates. This analysis of the decomposition explains the correlation as well as the differences between the newly proposed Ue and the two classic uncertainty metrics. The new approach is implemented and analysed with multiple precipitation products of different types (e.g. gauge-based products, merged products and GCMs) which contain different sources of uncertainties with different magnitudes. Ue of the gauge-based precipitation products is the smallest, while Ue of the other products is generally larger because other uncertainty sources are included and the constraints of the observations are not as strong as in gauge-based products. This new three-dimensional approach is flexible in its structure and particularly suitable for a comprehensive assessment of multiple datasets over large regions within any given period.

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