Large-scale spatial variability of rainfall through hidden semi-Markov models of breakpoint data

Abstract. Past studies on soil moisture spatial variability have been mainly conducted at catchment scales where soil moisture is often sampled over a short time period; as a result, the observed soil moisture often exhibited smaller dynamic ranges, which prevented the complete revelation of soil moisture spatial variability as a function of mean soil moisture. In this study, spatial statistics (mean, spatial variability and skewness) of in situ soil moisture, modeled and satellite-retrieved soil moisture obtained in a warm season (198 days) were examined over three large climate regions in the US. The study found that spatial moments of in situ measurements strongly depend on climates, with distinct mean, spatial variability and skewness observed in each climate zone. In addition, an upward convex shape, which was revealed in several smaller scale studies, was observed for the relationship between spatial variability of in situ soil moisture and its spatial mean when statistics from dry, intermediate, and wet climates were combined. This upward convex shape was vaguely or partially observable in modeled and satellite-retrieved soil moisture estimates due to their smaller dynamic ranges. Despite different environmental controls on large-scale soil moisture spatial variability, the correlation between spatial variability and mean soil moisture remained similar to that observed at small scales, which is attributed to the boundedness of soil moisture. From the smaller support (effective area or volume represented by a measurement or estimate) to larger ones, soil moisture spatial variability decreased in each climate region. The scale dependency of spatial variability all followed the power law, but data with large supports showed stronger scale dependency than those with smaller supports. The scale dependency of soil moisture variability also varied with climates, which may be linked to the scale dependency of precipitation spatial variability. Influences of environmental controls on soil moisture spatial variability at large scales are discussed. The results of this study should be useful for diagnosing large scale soil moisture estimates and for improving the estimation of land surface processes.

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Sampling density and date influence spatial representation of tree ring reconstructions

10.5194/cp-2020-31 ◽

2020 ◽

Author(s):

Justin T. Maxwell ◽

Grant L. Harley ◽

Trevis J. Matheus ◽

Brandon M. Strange ◽

Kayla Van Aken ◽

...

Keyword(s):

Soil Moisture ◽

Spatial Variability ◽

Tree Ring ◽

Tree Rings ◽

Large Scale ◽

The United States ◽

Severe Drought ◽

Ring Network ◽

Tree Ring Data ◽

The Us

Abstract. Our understanding of the natural variability of hydroclimate before the instrumental period (ca. 1900 in the United States; US) is largely dependent on tree-ring-based reconstructions. Large-scale soil moisture reconstructions from a network of tree-ring chronologies have greatly improved our understanding of the spatial and temporal variability in hydroclimate conditions, particularly extremes of both drought and pluvial (wet) events. However, certain regions within these large-scale reconstructions in the US have a sparse network of tree-ring chronologies. Further, several chronologies were collected in the 1980s and 1990s, thus our understanding of the sensitivity of radial growth to soil moisture in the US is based on a period that experienced multiple extremely severe droughts and neglects the impacts of recent, rapid global change. In this study, we expanded the tree-ring network of the Ohio River Valley in the US, a region with sparse coverage. We used a total of 72 chronologies across 15 species to examine how increasing the density of the tree-ring network influences the representation of reconstructing the Palmer Meteorological Drought Index (PMDI). Further, we tested how the sampling date influenced the reconstruction models by creating reconstructions that ended in the year 1980 and compared them to reconstructions ending in 2010 from the same chronologies. We found that increasing the density of the tree-ring network resulted in reconstructed values that better matched the spatial variability of instrumentally recorded droughts and to a lesser extent, pluvials. By sampling tree in 2010 compared to 1980, the sensitivity of tree rings to PMDI decreased in the southern portion of our region where severe drought conditions have been absent over recent decades. We emphasize the need of building a high-density tree-ring network to better represent the spatial variability of past droughts and pluvials. Further, chronologies on the International Tree-Ring Data Bank need updating regularly to better understand how the sensitivity of tree rings to climate may vary through time.

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High biogeographic and latitudinal variability in gastropod drilling predation on molluscs along the eastern Indian coast: Implications on the history of fossil record of drillholes

PLoS ONE ◽

10.1371/journal.pone.0256685 ◽

2021 ◽

Vol 16 (8) ◽

pp. e0256685

Author(s):

Subhronil Mondal ◽

Hindolita Chakraborty ◽

Sandip Saha ◽

Sahana Dey ◽

Deepjay Sarkar

Keyword(s):

Spatial Variability ◽

Large Scale ◽

Abiotic Factors ◽

Eastern Coast ◽

Detailed Knowledge ◽

Biogeographic Pattern ◽

Drilling Predation ◽

History Of ◽

Latitudinal Patterns ◽

Consecutive Time

Studies on the large-scale latitudinal patterns of gastropod drilling predation reveal that predation pressure may decrease or increase with increasing latitude, or even show no trend, questioning the generality of any large-scale latitudinal or biogeographic pattern. Here, we analyze the nature of spatio-environmental and latitudinal variation in gastropod drilling along the Indian eastern coast by using 76 samples collected from 39 locations, covering ~2500 km, incorporating several ecoregions, and ~15° latitudinal extents. We find no environmental or latitudinal gradient. In fact, drilling intensity varies highly within the same latitudinal bin, or oceanic sub-basins, or even the same ecoregions. Moreover, different ecoregions with their distinctive biotic and abiotic environmental variables show similar predation intensities. However, one pattern is prevalent: some small infaunal prey taxa, living in the sandy-muddy substrate—which are preferred by the naticid gastropods—are always attacked more frequently over others, indicating taxon and size selectivity by the predators. The result suggests that the biotic and abiotic factors, known to influence drilling predation, determine only the local predation pattern. In the present case, the nature of substrate and prey composition determines the local predation intensity: soft substrate habitats host dominantly small, infaunal prey. Since the degree of spatial variability in drilling intensity within any time bin can be extremely high, sometimes greater than the variability across consecutive time bins, temporal patterns in drilling predation can never be interpreted without having detailed knowledge of the nature of this spatial variability within a time bin.

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Modulation of A2aR Oligomerisation by Conformational State and PIP2 Interactions Revealed by MD Simulations and Markov Models

10.1101/2020.06.24.168260 ◽

2020 ◽

Cited By ~ 1

Author(s):

Wanling Song ◽

Anna L. Duncan ◽

Mark S.P. Sansom

Keyword(s):

Protein Interactions ◽

Large Scale ◽

Markov Models ◽

Transmembrane Helix ◽

Receptor Activation ◽

Membrane Lipid ◽

Intracellular Loop ◽

Gpcr Activation ◽

State Models

AbstractG protein-coupled receptors (GPCRs) play key roles in cellular signalling. GPCRs are suggested to form dimers and higher order oligomers in response to activation. However, we do not fully understand GPCR activation at larger scales and in an in vivo context. We have characterised oligomeric configurations of the adenosine 2a receptor (A2aR) by combining large-scale molecular dynamics simulations with Markov state models. Receptor activation results in enhanced oligomerisation, more diverse oligomer populations, and a more connected oligomerisation network. The active state conformation of the A2aR shifts protein-protein association interfaces to those involving intracellular loop ICL3 and transmembrane helix TM6. Binding of PIP2 to A2aR stabilises protein-protein interactions via PIP2-mediated association interfaces. These results indicate that A2aR oligomerisation is responsive to the local membrane lipid environment. This in turn suggests a modulatory effect on A2aR whereby a given oligomerisation profile favours the dynamic formation of specific supra-molecular signalling complexes.

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Localized basal area affects soil respiration temperature sensitivity in a coastal deciduous forest

Biogeosciences ◽

10.5194/bg-17-771-2020 ◽

2020 ◽

Vol 17 (3) ◽

pp. 771-780 ◽

Cited By ~ 1

Author(s):

Stephanie C. Pennington ◽

Nate G. McDowell ◽

J. Patrick Megonigal ◽

James C. Stegen ◽

Ben Bond-Lamberty

Keyword(s):

Soil Moisture ◽

Soil Respiration ◽

Spatial Variability ◽

Temperature Sensitivity ◽

Large Scale ◽

Deciduous Forest ◽

Basal Area ◽

Soil Surface ◽

Study Sites ◽

Positive Effect

Abstract. Soil respiration (Rs), the flow of CO2 from the soil surface to the atmosphere, is one of the largest carbon fluxes in the terrestrial biosphere. The spatial variability of Rs is both large and poorly understood, limiting our ability to robustly scale it in space. One factor in Rs spatial variability is the autotrophic contribution from plant roots, but it is uncertain how the presence of plants affects the magnitude and temperature sensitivity of Rs. This study used 1 year of Rs measurements to examine the effect of localized basal area on Rs in the growing and dormant seasons, as well as during moisture-limited times, in a temperate, coastal, deciduous forest in eastern Maryland, USA. In a linear mixed-effects model, tree basal area within a 5 m radius (BA5) exerted a significant positive effect on the temperature sensitivity of soil respiration. Soil moisture was the dominant control on Rs during the dry portions of the year, while soil moisture, temperature, and BA5 all exerted significant effects on Rs in wetter periods. Our results suggest that autotrophic respiration is more sensitive to temperature than heterotrophic respiration at these sites, although we did not measure these source fluxes directly, and that soil respiration is highly moisture sensitive, even in a record-rainfall year. The Rs flux magnitudes (0.46–15.0 µmol m−2 s−1) and variability (coefficient of variability 10 %–23 % across plots) observed in this study were comparable to values observed in similar forests. Six Rs observations would be required in order to estimate the mean across all study sites to within 50 %, and 518 would be required in order to estimate it to within 5 %, with 95 % confidence. A better understanding of the spatial interactions between plants and microbes, as well as the strength and speed of above- and belowground coupling, is necessary to link these processes with large-scale soil-to-atmosphere C fluxes.

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Fitting hidden semi-Markov models to breakpoint rainfall data

Journal of Applied Probability ◽

10.1239/jap/1085496598 ◽

2001 ◽

Vol 38 (A) ◽

pp. 142-157 ◽

Cited By ~ 30

Author(s):

John Sansom ◽

Peter Thomson

Keyword(s):

Dwell Time ◽

Large Scale ◽

Markov Models ◽

Transition Probabilities ◽

Rainfall Data ◽

The Em Algorithm ◽

Semi Markov Process ◽

The Times ◽

Log Normal ◽

Disjoint Domains

The paper proposes a hidden semi-Markov model for breakpoint rainfall data that consist of both the times at which rain-rate changes and the steady rates between such changes. The model builds on and extends the seminal work of Ferguson (1980) on variable duration models for speech. For the rainfall data the observations are modelled as mixtures of log-normal distributions within unobserved states where the states evolve in time according to a semi-Markov process. For the latter, parametric forms need to be specified for the state transition probabilities and dwell-time distributions.Recursions for constructing the likelihood are developed and the EM algorithm used to fit the parameters of the model. The choice of dwell-time distribution is discussed with a mixture of distributions over disjoint domains providing a flexible alternative. The methods are also extended to deal with censored data. An application of the model to a large-scale bivariate dataset of breakpoint rainfall measurements at Wellington, New Zealand, is discussed.

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Spatial Variability of Liquid Water Path in Marine Low Cloud: The Importance of Mesoscale Cellular Convection

Journal of Climate ◽

10.1175/jcli3702.1 ◽

2006 ◽

Vol 19 (9) ◽

pp. 1748-1764 ◽

Cited By ~ 233

Author(s):

Robert Wood ◽

Dennis L. Hartmann

Keyword(s):

Spatial Variability ◽

Liquid Water ◽

Large Scale ◽

Marine Boundary Layer ◽

Liquid Water Path ◽

Water Path ◽

Geographical Regions ◽

Low Cloud ◽

Moderate Resolution Imaging Spectroradiometer ◽

Subtropical Oceans

Abstract Liquid water path (LWP) mesoscale spatial variability in marine low cloud over the eastern subtropical oceans is examined using two months of daytime retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the NASA Terra satellite. Approximately 20 000 scenes of size 256 km × 256 km are used in the analysis. It is found that cloud fraction is strongly linked with the LWP variability in the cloudy fraction of the scene. It is shown here that in most cases LWP spatial variance is dominated by horizontal scales of 10–50 km, and increases as the variance-containing scale increases, indicating the importance of organized mesoscale cellular convection (MCC). A neural network technique is used to classify MODIS scenes by the spatial variability type (no MCC, closed MCC, open MCC, cellular but disorganized). It is shown how the different types tend to occupy distinct geographical regions and different physical regimes within the subtropics, although the results suggest considerable overlap of the large-scale meteorological conditions associated with each scene type. It is demonstrated that both the frequency of occurrence, and the variance-containing horizontal scale of the MCC increases as the marine boundary layer (MBL) depth increases. However, for the deepest MBLs, the MCC tends to be replaced by clouds containing cells but lacking organization. In regions where MCC is prevalent, a lack of sensitivity of the MCC type (open or closed) to the large-scale meteorology was found, suggesting a mechanism internal to the MBL may be important in determining MCC type. The results indicate that knowledge of the physics of MCC will be required to completely understand and predict low cloud coverage and variability in the subtropics.

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