scholarly journals Vegetation memory effects and their association with vegetation resilience in global drylands

2021 ◽  
Author(s):  
Erik Kusch ◽  
Richard Davy ◽  
Alistair Seddon
Keyword(s):  
Spatial Patterns ◽  
Spatial Scales ◽  
Reanalysis Data ◽  
Memory Effects ◽  
Climate Forcing ◽  
Data Sets ◽  
Ecological Memory ◽  
Initial Choice ◽  
Ecosystem State ◽  
Memory Characteristics

Vegetation memory describes the effect of antecedent environmental and ecological conditions on the present ecosystem state and has been proposed as an important proxy for vegetation resilience. In particular, strong vegetation-memory effects have been identified in dryland regions, but the factors underlying the spatial patterns of vegetation memory remain unknown. We aim to map the components and drivers of vegetation memory in dryland regions using state-of-the-art climate reanalysis data and refined approaches to identify vegetation-memory characteristics across dryland regions worldwide. Using a framework which distinguishes between intrinsic and extrinsic ecological memory, we show that: (i) intrinsic memory is a much stronger component than extrinsic memory in the majority of dryland regions; and (ii) climate reanalysis data sets change the detection of extrinsic vegetation memory effects in some global dryland regions. Synthesis: Our study offers a global picture of the vegetation response to two climate forcing variables using satellite data, information which is potentially relevant for mapping components and properties of vegetation responses worldwide. However, the large differences in the spatial patterns in intrinsic vegetation memory in our study compared to previous analyses show the overall sensitivity of this component in particular to the initial choice of extrinsic forcing variables. As a result, we caution against using the oversimplified link between intrinsic vegetation-memory and vegetation recovery rates at large spatial scales.

Recurrent Rossby wave packets and persistent extreme weather

2021 ◽  
Author(s):  
S. Mubashshir Ali ◽  
Olivia Martius ◽  
Matthias Röthlisberger
Keyword(s):  
Southern Hemisphere ◽  
Spatial Patterns ◽  
Rossby Wave ◽  
Wave Packets ◽  
Reanalysis Data ◽  
Synoptic Scale ◽  
Background Flow ◽  
Dry Spell ◽  
Upper Level ◽  
Wet Spells

<p>Upper-level synoptic-scale Rossby wave packets are well-known to affect surface weather. When these Rossby wave packets occur repeatedly in the same phase at a specific location, they can result in persistent hot, cold, dry, and wet conditions. The repeated and in-phase occurrence of Rossby wave packets is termed as recurrent synoptic-scale Rossby wave packets (RRWPs). RRWPs result from multiple transient synoptic-scale wave packets amplifying in the same geographical region over several weeks.</p><p>Our climatological analyses using reanalysis data have shown that RRWPs can significantly modulate the persistence of hot, cold, dry, and wet spells in several regions in the Northern and the Southern Hemisphere.  RRWPs can both shorten or extend hot, cold, and dry spell durations. The spatial patterns of statistically significant links between RRWPs and spell durations are distinct for the type of the spell (hot, cold, dry, or wet) and the season (MJJASO or NDJFMA). In the Northern Hemisphere, the spatial patterns where RRWPs either extend or shorten the spell durations are wave-like. In the Southern Hemisphere, the spatial patterns are either wave-like (hot and cold spells) or latitudinally banded (dry and wet spells).</p><p>Furthermore, we explore the atmospheric drivers behind RRWP events. This includes both the background flow and potential wave-triggers such as the Madden Julian Oscillation or blocking. For 100 events of intense Rossby wave recurrence in the Atlantic, the background flow, the intensity of tropical convection, and the occurrence of blocking are studied using flow composites.</p>

scholarly journals Southern Hemisphere atmospheric blocking diagnostic by ECMWF and NCEP/NCAR data

2012 ◽  
Vol 27 (3) ◽  
pp. 263-271 ◽  
Author(s):  
Monica Cristina Damião Mendes ◽  
Iracema F. A. Cavalcanti ◽  
Dirceu Luis Herdies
Keyword(s):  
Atlantic Ocean ◽  
Southern Hemisphere ◽  
Geopotential Height ◽  
Seasonal Cycle ◽  
South Pacific ◽  
Reanalysis Data ◽  
Data Sets ◽  
Atmospheric Blocking ◽  
Assimilation Scheme

An assessment of blocking episodes over the Southern Hemisphere, selected from the Era-40 and NCEP/NCAR reanalysis are presented in this study. Blocking can be defined by an objective index based on two 500 hPa geopotential height meridional gradients. The seasonal cycle and preferential areas of occurrence are well reproduced by the two data sets. In both reanalysis used in this study, South Pacific and Oceania were the preferred regions for blocking occurrence, followed by the Atlantic Ocean. However the results revealed differences in frequencies of occurrences, which may be related to the choice of assimilation scheme employed to produce the reanalysis data sets. It is important to note that the ERA 40 and NCEP/NCAR reanalysis were produced using consistent models and assimilation schemes throughout the whole reanalyzed period, which are different for each set.

scholarly journals CLASSIC: a semi-distributed rainfall-runoff modelling system

2007 ◽  
Vol 11 (1) ◽  
pp. 516-531 ◽  
Author(s):  
S. M. Crooks ◽  
P. S. Naden
Keyword(s):  
Spatial Scales ◽  
Digital Terrain Model ◽  
Flood Frequency ◽  
Data Sets ◽  
Rainfall Runoff ◽  
Terrain Model ◽  
Flow Duration Curves ◽  
Rainfall Runoff Model ◽  
The Uk ◽  
Modelling System

Abstract. This paper describes the development of a semi-distributed conceptual rainfall–runoff model, originally formulated to simulate impacts of climate and land-use change on flood frequency. The model has component modules for soil moisture balance, drainage response and channel routing and is grid-based to allow direct incorporation of GIS- and Digital Terrain Model (DTM)-derived data sets into the initialisation of parameter values. Catchment runoff is derived from the aggregation of components of flow from the drainage module within each grid square and from total routed flow from all grid squares. Calibration is performed sequentially for the three modules using different objective functions for each stage. A key principle of the modelling system is the concept of nested calibration, which ensures that all flows simulated for points within a large catchment are spatially consistent. The modelling system is robust and has been applied successfully at different spatial scales to three large catchments in the UK, including comparison of observed and modelled flood frequency and flow duration curves, simulation of flows for uncalibrated catchments and identification of components of flow within a modelled hydrograph. The role of such a model in integrated catchment studies is outlined.

scholarly journals Land cover change analysis in Mexico using 30m Landsat and 250m MODIS data

2015 ◽  
Vol XL-7/W3 ◽  
pp. 367-374 ◽  
Author(s):  
R. R. Colditz ◽  
R. M. Llamas ◽  
R. A. Ressl
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Spatial Resolution ◽  
Spatial Scales ◽  
Time Interval ◽  
Data Sets ◽  
Change Analysis ◽  
Dimensional Error ◽  
Analysis Technique ◽  
Coarse Spatial Resolution

Change detection is one of the most important and widely requested applications of terrestrial remote sensing. Despite a wealth of techniques and successful studies, there is still a need for research in remote sensing science. This paper addresses two important issues: the temporal and spatial scales of change maps. Temporal scales relate to the time interval between observations for successful change detection. We compare annual change detection maps accumulated over five years against direct change detection over that period. Spatial scales relate to the spatial resolution of remote sensing products. We compare fractions from 30m Landsat change maps to 250m grid cells that match MODIS change products. Results suggest that change detection at annual scales better detect abrupt changes, in particular those that do not persist over a longer period. The analysis across spatial scales strongly recommends the use of an appropriate analysis technique, such as change fractions from fine spatial resolution data for comparison with coarse spatial resolution maps. Plotting those results in bi-dimensional error space and analyzing various criteria, the “lowest cost”, according to a user defined (here hyperbolic) cost function, was found most useful. In general, we found a poor match between Landsat and MODIS-based change maps which, besides obvious differences in the capabilities to detect change, is likely related to change detection errors in both data sets.

scholarly journals Model cascade from meteorological drivers to river flood hazard: flood-cascade v1.0

2021 ◽  
Vol 14 (8) ◽  
pp. 4865-4890
Author(s):  
Peter Uhe ◽  
Daniel Mitchell ◽  
Paul D. Bates ◽  
Nans Addor ◽  
Jeff Neal ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
River Flow ◽  
Climate Model ◽  
Climate Modeling ◽  
Flood Hazard ◽  
Spatial Scales ◽  
Computational Cost ◽  
Data Sets ◽  
Flood Inundation

Abstract. Riverine flood hazard is the consequence of meteorological drivers, primarily precipitation, hydrological processes and the interaction of floodwaters with the floodplain landscape. Modeling this can be particularly challenging because of the multiple steps and differing spatial scales involved in the varying processes. As the climate modeling community increases their focus on the risks associated with climate change, it is important to translate the meteorological drivers into relevant hazard estimates. This is especially important for the climate attribution and climate projection communities. Current climate change assessments of flood risk typically neglect key processes, and instead of explicitly modeling flood inundation, they commonly use precipitation or river flow as proxies for flood hazard. This is due to the complexity and uncertainties of model cascades and the computational cost of flood inundation modeling. Here, we lay out a clear methodology for taking meteorological drivers, e.g., from observations or climate models, through to high-resolution (∼90 m) river flooding (fluvial) hazards. Thus, this framework is designed to be an accessible, computationally efficient tool using freely available data to enable greater uptake of this type of modeling. The meteorological inputs (precipitation and air temperature) are transformed through a series of modeling steps to yield, in turn, surface runoff, river flow, and flood inundation. We explore uncertainties at different modeling steps. The flood inundation estimates can then be related to impacts felt at community and household levels to determine exposure and risks from flood events. The approach uses global data sets and thus can be applied anywhere in the world, but we use the Brahmaputra River in Bangladesh as a case study in order to demonstrate the necessary steps in our hazard framework. This framework is designed to be driven by meteorology from observational data sets or climate model output. In this study, only observations are used to drive the models, so climate changes are not assessed. However, by comparing current and future simulated climates, this framework can also be used to assess impacts of climate change.

scholarly journals Are Distribution Patterns Correlated with Plant Traits?

2021 ◽  
Author(s):  
◽  
Benjamin Magana-Rodriguez
Keyword(s):  
Life History ◽  
Leaf Area ◽  
Spatial Patterns ◽  
Life History Traits ◽  
Multiple Scales ◽  
Spatial Scales ◽  
Regional Scale ◽  
Distribution Patterns ◽  
Spatial Arrangement ◽  
Scaling Relationship

<p>The current crisis in loss of biodiversity requires rapid action. Knowledge of species' distribution patterns across scales is of high importance in determining their current status. However, species display many different distribution patterns on multiple scales. A positive relationship between regional (broad-scale) distribution and local abundance (fine-scale) of species is almost a constant pattern in macroecology. Nevertheless interspecific relationships typically contain much scatter. For example, species that possess high local abundance and narrow ranges, or species that are widespread, but locally rare. One way to describe these spatial features of distribution patterns is by analysing the scaling properties of occupancy (e.g., aggregation) in combination with knowledge of the processes that are generating the specific spatial pattern (e.g., reproduction, dispersal, and colonisation). The main goal of my research was to investigate if distribution patterns correlate with plant life-history traits across multiple scales. First, I compared the performance of five empirical models for their ability to describe the scaling relationship of occupancy in two datasets from Molesworth Station, New Zealand. Secondly, I analysed the association between spatial patterns and life history traits at two spatial scales in an assemblage of 46 grassland species in Molesworth Station. The spatial arrangement was quantified using the parameter k from the Negative Binomial Distribution (NBD). Finally, I investigated the same association between spatial patterns and life-history traits across local, regional and national scales, focusing in one of the most diverse families of plant species in New Zealand, the Veronica sect. Hebe (Plantaginaceae). The spatial arrangement was investigated using the mass fractal dimension. Cross-species correlations and phylogenetically independent contrasts were used to investigate the relationships between plant life-history traits and spatial patterns on both data bases. There was no superior occupancy-area model overall for describing the scaling relationship, however the results showed that a variety of occupancy-area models can be fit to different data sets at diverse spatial scales using nonlinear regression. Additionally, here I showed that it is possible to deduce and extrapolate information on occupancy at fine scales from coarse-scale data. For the 46 plantassemblage in Molesworth Station, Specific leaf area (SLA) exhibits a positive association with aggregation in cross-species analysis, while leaf area showed a negative association, and dispersule mass a positive correlation with degree of aggregation in phylogenetic contrast analysis at a local-scale (20 × 20 m resolution). Plant height was the only life-history trait that was associated with degree of aggregation at a regional-scale (100 × 60 mresolution). For the Veronica sect. Hebe dataset, leaf area showed a positive correlation with aggregation while specific leaf area showed a negative correlation with aggregation at a fine local-scale (2.5-60 m resolution). Inflorescence length, breeding system and leaf area showed a negative correlation with degree of aggregation at a regional-scale (2.5-20 km resolution). Height was positively associated with aggregation at national-scale (20-100 km resolution). Although life-history traits showed low predictive ability in explaining aggregation throughout this thesis, there was a general pattern about which processes and traits were important at different scales. At local scales traits related to dispersal and completion such as SLA , leaf area, dispersule mass and the presence of structures in seeds for dispersal, were important; while at regional scales traits related to reproduction such as breeding system, inflorescence length and traits related to dispersal (seed mass) were significant. At national scales only plant height was important in predicting aggregation. Here, it was illustrated how the parameters of these scaling models capture an important aspect of spatial pattern that can be related to other macroecological relationships and the life-history traits of species. This study shows that when several scales of analysis are considered, we can improve our understanding about the factors that are related to species' distribution patterns.</p>

scholarly journals Fractional snow-covered area: scale-independent peak of winter parameterization

2021 ◽  
Vol 15 (2) ◽  
pp. 615-632
Author(s):  
Nora Helbig ◽  
Yves Bühler ◽  
Lucie Eberhard ◽  
César Deschamps-Berger ◽  
Simon Gascoin ◽  
...  
Keyword(s):  
Snow Depth ◽  
Hydrological Modeling ◽  
Spatial Scales ◽  
Empirical Relationship ◽  
Ground Surface ◽  
Model Parameters ◽  
Data Sets ◽  
Snow Covered Area ◽  
Covered Area ◽  
Geographical Regions

Abstract. The spatial distribution of snow in the mountains is significantly influenced through interactions of topography with wind, precipitation, shortwave and longwave radiation, and avalanches that may relocate the accumulated snow. One of the most crucial model parameters for various applications such as weather forecasts, climate predictions and hydrological modeling is the fraction of the ground surface that is covered by snow, also called fractional snow-covered area (fSCA). While previous subgrid parameterizations for the spatial snow depth distribution and fSCA work well, performances were scale-dependent. Here, we were able to confirm a previously established empirical relationship of peak of winter parameterization for the standard deviation of snow depth σHS by evaluating it with 11 spatial snow depth data sets from 7 different geographic regions and snow climates with resolutions ranging from 0.1 to 3 m. An enhanced performance (mean percentage errors, MPE, decreased by 25 %) across all spatial scales ≥ 200 m was achieved by recalibrating and introducing a scale-dependency in the dominant scaling variables. Scale-dependent MPEs vary between −7 % and 3 % for σHS and between 0 % and 1 % for fSCA. We performed a scale- and region-dependent evaluation of the parameterizations to assess the potential performances with independent data sets. This evaluation revealed that for the majority of the regions, the MPEs mostly lie between ±10 % for σHS and between −1 % and 1.5 % for fSCA. This suggests that the new parameterizations perform similarly well in most geographical regions.

The use of wavelet transforms for improved interpretation of airborne transient electromagnetic data

Geophysics ◽  
2013 ◽  
Vol 78 (3) ◽  
pp. E117-E123 ◽  
Author(s):  
Vanessa Nenna ◽  
Adam Pidlisecky
Keyword(s):  
Wavelet Transforms ◽  
Oil And Gas ◽  
Spatial Scales ◽  
Data Sets ◽  
Data Set ◽  
Topographic Features ◽  
Transient Electromagnetic ◽  
Power Maps ◽  
Southern Alberta ◽  
Cultural Noise

The continuous wavelet transform (CWT) is used to create maps of dominant spatial scales in airborne transient electromagnetic (ATEM) data sets to identify cultural noise and topographic features. The introduced approach is applied directly to ATEM data, and does not require the measurements be inverted, though it can easily be applied to an inverted model. For this survey, we apply the CWT spatially to B-field and dB/dt ATEM data collected in the Edmonton-Calgary Corridor of southern Alberta. The average wavelet power is binned over four ranges of spatial scale and converted to 2D maps of normalized power within each bin. The analysis of approximately 2 million soundings that make up the survey can be run on the order of minutes on a 2.4 GHz Intel processor. We perform the same CWT analysis on maps of surface and bedrock topography and also compare ATEM results to maps of infrastructure in the region. We find that linear features identified on power maps that differ significantly between B-field and dB/dt data are well correlated with a high density of infrastructure. Features that are well correlated with topography tend to be consistent in power maps for both types of data. For this data set, use of the CWT reveals that topographic features and cultural noise from high-pressure oil and gas pipelines affect a significant portion of the survey region. The identification of cultural noise and surface features in the raw ATEM data through CWT analysis provides a means of focusing and speeding processing prior to inversion, though the magnitude of this affect on ATEM signals is not assessed.

Scaling Issues in Landscape Ecology

2019 ◽  
pp. 14-41
Author(s):  
Kimberly A. With
Keyword(s):  
Landscape Ecology ◽  
Spatial Patterns ◽  
Spatial Scales ◽  
Ecological Data ◽  
Ecological Forecasting ◽  
Spatial And Temporal Scales ◽  
Time Periods ◽  
Patterns And Processes ◽  
Short Time ◽  
Over Time

Spatial patterns are ubiquitous in nature, and ecological systems exhibit patchiness (heterogeneity) across a range of spatial and temporal scales. Landscape ecology is explicitly concerned with understanding how scale affects the measurement of heterogeneity and the scale(s) at which spatial pattern is important for ecological phenomena. Patterns and processes measured at fine spatial scales and over short time periods are unlikely to behave similarly at broader scales and extended time periods. An understanding of pattern-process linkages, a major research focus in landscape ecology, thus requires an understanding of how patterns change with scale, spatially and temporally. The development of methods for extrapolating information across scales is necessary for predicting how landscapes will change over time as well as for ecological forecasting. This chapter explores how scaling issues affect ecological investigations, discusses problems in identifying the correct scale for research, and outlines when and how ecological data can be extrapolated.

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