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3,105 results for “Vegetation =”
Plant specialisation may limit climate‐induced vegetation change to within topographic and edaphic niches on a sub‐Antarctic island
<p>Extreme changes in temperature, rainfall and wind regimes have been correlated with plant species range expansion upslope on sub-Antarctic islands. Ongoing climatic changes are expected to continue driving changes in species distributions globally, but niche specialisations may limit the capacity for range shifts. We hypothesised that non-climatic characteristics of ecological niches of vascular plant species could limit climate induced range shifts. We determined the altitudinal ranges of vascular plant species (n=13) on sub-Antarctic Marion Island and measured air temperature, topographic, foliar and soil properties along transects on geologically distinct substrates. Climatic and non-climatic associations were determined using multiple linear regression and boosted regression tree (BRT) analyses. The degree of niche specialisation was determined using outlying mean index analysis (OMI) within the range of species on the island. Several species (7 of 13) exhibited niche-specialisation. Correlation analysis revealed that edaphic properties including soil depth, loss on ignition, the principal component of most soil nutrients (Mg, Cl, K, Ca, Cu, Zn, P, S), Si, Mn and clay dominated the BRT prediction of overall plant cover. Although air temperature was correlated with plant cover in linear models, model simplification dropped temperature in both BRT and linear models. As a consequence, multiple determinants, including temperature, climate, topography and soils control the distribution of vascular plant species on this sub-Antarctic island.</p>
Data from: Fallow deer foraging alone does not preserve the vegetation of traditionally sheep-grazed calcareous grasslands
<p>The goal of this study was to evaluate to what extent wild ungulates (fallow deer) can contribute to the maintenance of semi-natural calcareous grasslands, which are a threatened habitat type (natura 2000 code *6210). In a ten-year exclosure experiment we tested the effects of ungulate foraging using three treatments: (A) control with combined foraging of herded sheep and wild fallow deer, (B) sheep exclosure with only deer foraging and (C) total exclosure with no foraging. Treatments not grazed by sheep (B, C) were characterized by significantly declining species numbers, litter accumulation and shrub encroachment. Despite high population densities, the effect of fallow deer alone (B) was weak: Succession of woody species was only partly inhibited, while annuals, short-growing and rosette-building plant species were strongly suppressed by litter accumulation. Only the combination of sheep and fallow deer foraging preserved vegetation structure and species richness and led to a promotion of target species. Synthesis and applications: We conclude that we need to continue the traditional land-use forms such as sheep grazing in order to maintain calcareous grasslands. However, we should also raise our awareness for wild animals and analyse more in depth their potential contribution to the conservation management of open habitats.</p>
Estimates of soil nutrient limitation on the CO2 fertilization effect for tropical vegetation
<p>Data for CO2 fertilization experiments and CMIP6 model simulations used in publication: "Estimates of soil nutrient limitation on the CO2 fertilization effect for tropical vegetation"</p>
AgsSAT Multiannual (2017-2021) Sentinel-2 Vegetation Indices Composites
<p>AgsSAT Multiannual (2017-2021) Sentinel-2 Vegetation Indices Composites</p> <p>The 2,815 images available for the state of Aguascalientes, Mexico for the years 2017 to 2021 were processed using the Open Data Cube (ODC) platform [Lewis et al. (2017), Gavin et al. (2018), <a href="https://www.opendatacube.org/">https://www.opendatacube.org/</a>]. These images correspond to multiple coverages of the region of interest. The images were then used to generate cloud-free annual composites by applying geometric median (geomedian) algorithm, as defined in [Roberts et al. (2017)]. </p> <p>Geomedian algorithm produces a pixel-level summary for every pixel, in this case this means that each summary corresponds to a 10m x 10m region in the territory and its observations throughout a calendar year. </p> <p>All these summary pixels form a 12-band (coastal aerosol, blue, green, red, vegetation red edge 5, vegetation red edge 6, vegetation red edge 7, near-infrared, narrow nir, water vapor, swir1 and swir2) composite of the state of Aguascalientes. </p> <p>Another product called GeoMad was generated, which calculates the robust dispersion statistic called MAD, as defined in [Roberts, D., Dunn, B., & Mueller, N. (2018)]. In the resulting image composite, each of the three-pixel bands represents the variation over three distances: Spectral Distance (smad), Euclidean Distance (emad) and the Bray-Curtis Distance (bcmad). </p> <p>More bands were generated to represent different environmental conditions during the study years (2017-2021), these conditions can be captured by analyzing various combinations of bands, these combinations are also called spectral indices, which allow detecting vegetation, presence of water, urbanization, etc., Finally, 28 indices divided into 4 categories were calculated: </p> <p>Vegetation Indices </p> <p>(Atmospherically Resistant Vegetation Index, Kaufman 1972) </p> <p>(Enhanced Vegetation Index, Huete 2002): </p> <p>(Modified Soil Adjusted Vegetation Index, Qi Et Al. 1994) </p> <p>(Normalized Difference Chlorophyll Index, Mishra & Mishra, 2012) </p> <p>(Normalised Difference Moisture Index, Gao 1996) </p> <p>(Normalized Difference Vegetation Index, Rouse 1973) </p> <p>(Optimized Soil Adjusted Vegetation Index, Rondeaux. 1996) </p> <p>(Simple Ratio Vegetation Index Jordan, C.F.1 969) </p> <p>(Soil Adjusted Vegetation Index, Huete 1988) </p> <p>(Visible Atmospherically Resistant Index, Gittleson 2002) </p> <p> </p> <p>Built-up Indexes </p> <p>(Band Ration For Built-up Area, Waqar 2012) </p> <p>(Built-up Area Extraction Index, Bouzekri 2015) </p> <p>(Built-up Index, He Et Al. 2010) </p> <p>(Index-based Built-up Index, Xu 2008) </p> <p>(New Built-up Index, Jieli Et Al. 2010) </p> <p>(Normalized Difference Built-up Index, Zha 2003) </p> <p>(Normalized Built-up Area Index, Waqar 2012) </p> <p>(Urban Index, Kawamura 1996) </p> <p> </p> <p>Water Indices </p> <p>(Modified Normalized Difference Water Index, Xu 1996) </p> <p>(Normalized Difference Water Index, Mcfeeters 1996) </p> <p>(Water Index, Fisher 2016) </p> <p> </p> <p>Other Indices </p> <p>(Bare Soil Index, Rikimaru Et Al. 2002) </p> <p>(Bare Soil Index, Wanhui 2004) </p> <p>(Burn Area Index, Martin 1998) </p> <p>(Clay Minerals Ratio, Drury 1987) </p> <p>(Ferrous Minerals Ratio, Segal 1982) </p> <p>(Iron Oxide Ratio, Segal 1982) </p> <p>(Normalized Burn Ratio, Lopez Garcia 1991) </p> <p>(Normalised Difference Snow Index, Hall 1995). </p> <p> </p>
Umbrella review data of colour-associated bioactive pigments found in fruit and vegetables, compared to placebo or low intakes, on human health outcomes relevant to public health
<p>This dataset comprises data extracted from 86 publications included in an umbrella review which compared the effect of colour-associated bioactive pigments found in fruit and vegetables (carotenoids, flavonoids, betalains and chlorophyll) on human health outcomes relevant to public health. Meta-analysed data from 83 systematic literature reviews were available for 17 different bioactive pigments spanning all colours of fruit and vegetables except green, with additional data from two randomised controlled trials and one cohort study for chlorophyll. This dataset represents 2,847 original research studies and data from over 37 million participants. There were 449 meta-analysed health outcomes extracted from the 83 systematic literature reviews. Extracted health outcomes were categorised according to pigment, comparator type, broad health outcome, study design, age group, source of pigment, risk of bias, and confidence in the estimated effect. Data extracted were dose, intervention duration, sample size, number of original studies, effect estimate, 95% confidence intervals, I2 statistics, publication bias and p-value. Rigorous analysis, including estimations of a common effect size, stratification of the evidence, study level sensitivity analyses and reporting on heterogeneity and potential biases, may be carried out using these data, to further evaluate the effect of colour-associated bioactive pigments in fruit and vegetables on human health.</p>
Vegetative cover, light availability, and Buckthorn performance in five forest revegetation treatments
<p>Data describing the cover, light availability, and buckthorn performance of five forest revegetation treatments (passive restoration, herbaceous seeding, sedge planting, shrub planting, and tree planting). </p>
Survey of winter bird communities and vegetation in the Oregon Coast Range
<p>We re-surveyed forest bird communities during winter in seven large plots originally surveyed by graduate student, Stanley Anderson, from 1968 through 1970, near Corvallis, Oregon, USA. The forests in plots in the Coast Range Mountain foothills were and are currently dominated by Douglas-fir (Pseudotsuga menziesii). Anderson's thesis included plot locations, summaries of vegetation characteristics and point estimates of bird densities for each plot. Because of the lack of similar data, we re-surveyed his plots after aligning methods with his and added modern components (distance and time interval sampling) to facilitate comparisons of changes in abundances. We preserved more extensive metadata than were preserved from Anderson's surveys, including georeferenced point count survey locations to facilitate more precisely repeatable future re-surveys. We also measured vegetation characteristics to compare structural changes since Anderson's measurements.</p>
Data for NHESS manuscript by Biass et al. (2022): Insights into the vulnerability of vegetation to tephra fallouts from interpretable machine learning and big Earth observation data
<p>This repository contains the data produced in the context of the following paper:</p> <blockquote> <p>Biass S, Jenkins SF, Aberhard WH, Delmelle P, Wilson T (2022): Insights into the vulnerability of vegetation to tephra fallouts from interpretable machine learning and big Earth observation data, Accepted in NHESS</p> </blockquote> <p>Naming convention is: `run_date`_`landcover`_`impact_metrics`_`VI`_`anomaly`_test.pkl, where:</p> <ul> <li>Landcover is either crops, shrubs, herbaceous vegetation (grass), forests (trees) or all together</li> <li>Impact metrics is either minV (impact magnitude) or minT (impact duration)</li> <li>VI is the vegetation index (here, EVI)</li> <li>Anomaly is the impact indicator (here, cumulative difference index)</li> </ul> <p>Refer to the associated paper for more information on the methodology.</p> <p>Files are saved as .pkl and were generated by the <a href="https://explainerdashboard.readthedocs.io/en/latest/">explainerdashboard</a> library. They are the result of <a href="https://xgboost.readthedocs.io/en/stable/">XGBoost</a> runs that were optimised with <a href="https://optuna.org">Optuna</a> and analysed with the <a href="https://shap.readthedocs.io/en/latest/">SHAP</a> library. They contain:</p> <ol> <li>The explanatory variables and observed and computed target variables for all features</li> <li>The SHAP values</li> </ol> <p>To load the files, use <a href="https://explainerdashboard.readthedocs.io/en/latest/cli.html?highlight=load#explainerdashboard.explainers.BaseExplainer.from_file">this method</a>.</p> <p> </p>
Living landscapes: Muddy and vegetated floodplain effects on fluvial pattern in an incised river
<p>The matlab data consists of 14 model runs (lab numbers in filenames), each for two timesteps in separate files, namely one for low river flow (ecological timestep 12, which is in month 6) and one for high river flow (ecological timestep 24, which is in month 12). The hydromorphological model is Delf3D, which is coupled to a riparian vegetation model 24 times per year for 150 years (some for 300 years). Each matlab file contains all variables stored by Delft3D and by the vegetation model. The most relevant variables are three-dimensional matrices containing the yearly maps. The various model runs are permutations on only sand, including mud (at various concentrations and erosion thresholds) and/or riparian vegetation.</p> <table> <caption>Table: Model runs with lab number, number in the publication, and keyworks describing the model</caption> <thead> <tr> <th scope="col">lab run number</th> <th scope="col">run number in Kleinhans et al. 2018</th> <th scope="col">properties</th> </tr> </thead> <tbody> <tr> <td>153</td> <td>4</td> <td>only sand</td> </tr> <tr> <td>149</td> <td>3</td> <td>mud added default concentration default erosion threshold</td> </tr> <tr> <td>131</td> <td>2</td> <td>vegetation added</td> </tr> <tr> <td>132</td> <td>1</td> <td>mud and vegetation</td> </tr> <tr> <td>139</td> <td>5</td> <td>5 mg/L mud</td> </tr> <tr> <td>140</td> <td>6</td> <td>50 mg/L</td> </tr> <tr> <td>141</td> <td>7</td> <td>100 mg/L</td> </tr> <tr> <td>162</td> <td>8</td> <td>500 mg/L</td> </tr> <tr> <td>144</td> <td>9</td> <td>0.1 N/m2 critical shear stress</td> </tr> <tr> <td>145</td> <td>10</td> <td>0.5 N/m2</td> </tr> <tr> <td>161</td> <td>12</td> <td>500 mg/L 0.5 N/m2 vegetation</td> </tr> <tr> <td>160</td> <td>14</td> <td>500 mg/L 0.5 N/m2</td> </tr> <tr> <td>158</td> <td>13</td> <td>800 mg/L 0.5 N/m2 vegetation</td> </tr> <tr> <td>159</td> <td>11</td> <td>100 mg/L 0.5 N/m2 vegetation</td> </tr> </tbody> </table> <p>The open access paper describing the data and its method of production is:<br> Living landscapes: Muddy and vegetated floodplain effects on fluvial pattern in an incised river</p> <p>Maarten G. Kleinhans, Bente de Vries, Lisanne Braat, Mijke van Oorschot<br> Earth Surf. Process. Landforms 43(14), 2018<br> <a href="https://doi.org/10.1002/esp.4437">https://doi.org/10.1002/esp.4437</a></p> <p>The modelling was conducted by Bente de Vries under supervision of Maarten Kleinhans as part of her MSc thesis research, which was embedded in the ERC Consolidator project of Kleinhans.</p>
Differential effects of vegetation and climate on termite diversity and damage
<p><span>Species diversity shapes ecosystem services. Despite the advantages that this relationship has for pest management, few studies have investigated the links between infrastructure damage (i.e. the percentage amount of infrastructures infested by termites), species richness and the environment. Moreover, it is not clear that which proportion of species richness (total/functional-dominant/common/rare) contributes most to infrastructure damage.</span></p> <p><span>We correlated termite species richness with termite infestation throughout 83 cities in Zhejiang Province, eastern China. Species were classified according to whether or not they fed on wood, and based upon their distributional range, whether they were common or rare. We analyzed the relative importance and the direct/indirect effects of climate, vegetation, anthropogenic activities, and the species richness of four functional categories of termites on the damage levels of eight infrastructure types in populated (i.e. urban and rural building, green space and sea wall) and remote areas (i.e. ancient building, large-old tree, agroforest and reservoir dam). </span></p> <p><span>Common species favoured populated areas, whereas rare species favoured remote areas. Common species, with preferences for deciduous vegetation, caused more damage to the infrastructures of populated areas. Rare species, with preferences for evergreen vegetation, caused more damage in remote areas. Reforestation project which emphasised evergreen trees increased the number of rare species but reduced the number of common species. Elevation and drought risk were positively correlated with rare species richness but neutrally with common species richness.</span></p> <p><span>Structural equation models showed that vegetation predominantly influenced infrastructure damage in populated areas via altering common species richness, whereas climate predominantly and directly influenced infrastructure damage in remote areas. Notably, elevation and drought risk were positively correlated with infrastructure damage especially in remote areas.</span></p> <p><span>Synthesis and applications</span><span>. </span> <span>Termites cause global economic losses of 15~40 billion dollars per year. Our study reveals that managing city forests and green space, for example increasing the proportion of evergreen trees, is a sustainable means of suppressing common termites and thereby reducing infrastructure damage in populated areas. Conservation strategies, supported by regular inspections, will become increasingly important as climate change not only threatens the survival of </span>less harmful <span>rare </span><span>species,</span><span> but also increases infrastructure damage in remote areas.</span></p>
Early nest initiation and vegetation density enhance nest survival in Wild Turkeys
<p>The theory of adaptive habitat selection suggests resource selection by animals should reflect underlying quality, such that individual selection confers an adaptive advantage via increased fitness. Using resource selection functions and nest survival models, we demonstrated that visual obstruction at the nest site was adaptively significant but timing of nest initiation had the greatest effect on nest survival for eastern wild turkeys (Meleagris gallopavo silvestris). Predation risk is a selective pressure, and if individuals can perceive predation risk, they may respond by altering selection of nest site characteristics based on prior experience. We evaluated patterns in nest site selection of 387 wild turkeys and consequences of selection on reproductive success across the southeastern United States from 2014-2019. We monitored 549 nest sites and found that nest initiation date had the strongest effect on daily nest survival rates, wherein adult females at our earliest nest initiation date were ~ 4 times more likely to successfully nest than females at our latest nest initiation dates. Selection of nest sites with greater visual obstruction also increased daily nest survival rates as females were 1.17 (1.100 – 1.234; 95% CI) and 1.37 (1.258 – 1.486; 95% CI) times more likely to select sites for every 10 cm increase in visual obstruction and maximum vegetation height, respectively. Collectively, our results indicate that nest initiation date is likely the critical parameter driving wild turkey nest success, whereas vegetative conditions play a lesser role influencing nest success. Females nesting earlier may be in better body condition and show increased nest attentiveness, which may mediate nest success more than vegetation conditions around nest sites. Our work indicates that increasing the reproductive success of wild turkeys may hinge on females being able to nest as early as possible within the reproductive season.</p>
CONUS VPD and soil moisture thresholds for suboptimal vegetation function
<p class="MsoNormal"><span>The dataset contains minimum and maximum vapor pressure deficit (VPD) and soil moisture parameters that define the range of suboptimal vegetation function (i.e., between uninhibited photosynthesis and complete shutdown). </span>The ranges were estimated using the Dynamic Canopy Biophysical Properties model (DCBP; Lowman & Barros 2018), which consists of a phenology forecasting model based on the growing season index (GSI), which provides a unitless measure of the potential phenologic state of vegetation based on the concurrent meteorological conditions (Jolly et al. 2005).</p>
Figure 1 in Circadian activity patterns and temporal overlap among cracids (Aves: Cracidae) within a vegetation mosaic in the Pantanal of Rio Negro, Brazil
Figure 1. Study area and location of the 90 camera trap stations in the Pantanal of Rio Negro, Aquidauana, Mato Grosso do Sul, Brazil.
Figure 2 in Circadian activity patterns and temporal overlap among cracids (Aves: Cracidae) within a vegetation mosaic in the Pantanal of Rio Negro, Brazil
Figure 2. Circadian activity patterns of three cracid species in the Pantanal of Rio Negro, Aquidauana, Mato Grosso do Sul. The red line represents the mean circular vector (mμ) with CI95% of the distributions.
Fig. 2 in Theodor Kotschy in Iran, 1841-1843. Botanical collections and an early printed vegetation profile
Fig. 2. – Theodor Kotschy's botanical field book, 1840–1842.
Mycorrhizae influence plant vegetative and floral traits and intraspecific trait variation
<p>Premise. Arbuscular mycorrhizal fungi (AMF) can strongly influence host plant vegetative growth, but less is known about AMF effects on other plant traits, the relative impacts of AMF on vegetative growth versus floral traits, or AMF-induced intraspecific variation in traits.</p> <p>Key results. AMF species varied in their effects on host plants, from negative to positive effects. AMF often had inconsistent effects on vegetative biomass versus floral traits, and therefore quantifying one or the other may provide a misleading representation of potential AMF effects. AMF treatments generated key variation in plant traits, especially floral traits, with potential consequences for plant-pollinator interactions. Given increased intraspecific trait variation in Linum lewisii plants across AMF species compared to uninoculated individuals or single AMF treatments, local AMF diversity and their host plant associations may scale up to influence community-wide patterns of trait variation and species interactions.</p> <p>Conclusions. These results have implications for predicting how aboveground communities are affected by belowground communities. Including AMF effects on not just host plant biomass but also functional traits and trait variation will deepen our understanding of community structure and function, including pollination.</p>
Capitell romànic amb motius vegetals
Monestir de sant Joan de les Abadesses Capitell amb motius vegetals de l'antic claustre romànic. Source: Objaverse 1.0 / Sketchfab
Sinopsis bioclimática y vegetacional de Chile / Bioclimatic and vegetational synopsis of Chile
<p>Este set de datos contiene material suplementario a la segunda edición del libro Sinopsis bioclimática y vegetacional de Chile. / This dataset contains supplementary material for the second edition of the book Bioclimatic and vegetational synopsis of Chile (in Spanish).</p> <p>Descripción de los archivos contenidos en este set de datos. / Description of the files contained in this dataset:</p> <p><strong>shape_pisos.zip:</strong> Cobertura cartográfica digital de los pisos de vegetación de Chile. Sistema de coordenadas GCS WGS84. / Digital maps of the Chilean vegetations belts. Coordinate system GCS WGS84.</p> <p><strong>shape_bioclimas.zip:</strong> Cobertura cartográfica digital de los bioclimas de Chile. Sistema de coordenadas GCS WGS84. / Digital maps of the Chilean bioclimates. Coordinate system GCS WGS84.</p> <p><strong>shape_ombrotipos.zip:</strong> Cobertura cartográfica digital de los ombrotipos de Chile. Sistema de coordenadas GCS WGS84. / Digital maps of the Chilean ombrotypes. Coordinate system GCS WGS84.</p> <p><strong>shape_termotipos.zip:</strong> Cobertura cartográfica digital de los termotipos de Chile. Sistema de coordenadas GCS WGS84. / Digital maps of the Chilean thermotypes. Coordinate system GCS WGS84.</p> <p><strong>shape_continentalidad.zip:</strong> Coberturas cartográficas digitales de los tipos de continentalidad de Chile. Sistema de coordenadas GCS WGS84. / Digital maps of the Chilean continentality types. Coordinate system GCS WGS84.</p> <p><strong>referencias.bib:</strong> Lista completa de referencias citadas en el libro (Luebert & Pliscoff, 2017). / Full list of references cited in the book (Luebert & Pliscoff, 2017). </p> <p><strong>otras_unidades_vegetación.csv:</strong> Información geográfica puntual para otras unidades de vegetación. El campo "Fuente" corresponde al código de las referencias contenidas en el archivo "referencias.bib". / Point geographical information for other vegetation units (otras unidades de vegetación). The field "Fuente" corresponds to the reference keys contained in the file "referencias.bib".<br> <br> <strong>extra_intrazonal.csv:</strong> Información geográfica puntual para las unidades de vegetación intra y extrazonal. Los campos "Fuente" y "Fuente_coordenadas"corresponden al código de las referencias contenidas en el archivo "referencias.bib". / Point geographical information for intra- and extrazonal vegetation units (unidades de vegetación intra y extrazonal). The fields "Fuente" and "Fuente_coordenadas" correspond to the reference keys contained in the file "referencias.bib".</p> <p><strong>pisos_bioclimas.csv:</strong> Correspondencias entre pisos de vegetación y bioclimas. / Correspondences between vegetation belts and bioclimates.</p> <p><strong>representatividad.csv:</strong> Información adicional sobre el análisis de representatividad vegetacional de los pisos de vegetación de Chile en las áreas silvestres protegidas. Se incluye información sobre áreas bajo protección oficial, así como áreas protegidas privadas. Se presentan los resultados de las tres fórmulas utilizadas para el cálculo de representatividad (detalles en Luebert & Pliscoff, 2017). BNP: Bienes Nacionales Protegidos; MN: Monumentos Naturales; PN: Parques Nacionales; RN: Reservas Nacionales; RF: Reservas Forestales; SN: Santuarios de la Naturaleza; ICP: Iniciativas de Conservación Privada; PO: Protección oficial (BNP+MN+PN+RN+RF+SN). / Additional information for the analysis of representativeness of Chilean vegetation belts in the protected area system. Included are public and private protected areas. Results of the three formulas for the calculation of representativeness are provided (for details see Luebert & Pliscoff, 2017). BNP: Protected Public Land; MN: Natural Monuments; PN: National Parks; RN: National Reserves; RF: Forest Reserves; SN: Nature Sanctuaries; ICP: Private Conservation Initiatives; PO: Official Protection (BNP+MN+PN+RN+RF+SN).</p> <p><strong>estado_conservacion.csv:</strong> Información adicional para la determinación de las categorías de riesgo UICN de los pisos de vegetación de Chile. LC: Preocupación menor; VU: Vulnerable; EN: En Peligro; CR: En Peligro Crítico. / Additional information for the determination of IUCN risk categories of Chilean vegetation belts. LC: Least concern; VU: Vulnerable; EN: Endangered; CR: Critically Endangered.</p> <p><strong>cambio_climatico.csv:</strong> Información adicional para el análisis de efectos de cambio climático. Para cada piso de vegetación se indica la superficie modelada y el área de reducción, estabilidad y expansión de superficie bajo cada escenario de cambio climático. Una reducción absoluta en la superfice implica reducción>expansión. Una expansión absoluta en la superfice implica reducción<expansión. Los escenarios de cambio climático son los siguientes: 2050_26, trayectoria de concentración representativa RCP 2,6 para el periodo 2040-2070; 2050_85, trayectoria de concentración representativa RCP 8,5 para el periodo 2040-2070. / Additional information for the analysis of climate change. For each vegetation belt the modeled area and the area of reduction, stability and expansion are indicated under each climate change scenario. A net reduction in area implies reduction>expansion. A net expansion in area implies reduction<expansion. The following climate change scenarios were considered: 2050_26, representative concentration pathway RCP 2,6 for the period 2040-2070; 2050_85, representative concentration pathway RCP 8,5 for the period 2040-2070.</p>
LPJmL4 Potential Natural Vegetation (PNV) simulations for use in MAgPIE for ISIMIP3 scenarios
<p>This data set contains output data from simulations with the model LPJmL version 4 for further use in the MAgPIE model. Simulations are based on the ISIMIP3a/b climate input data. No land use is considered here, simulations are for potential natural vegetation. Geospatial information in files <code>grid.clm</code>, data processing is recommended using <a href="https://github.com/PIK-LPJmL/lpjmlkit" target="_blank" rel="noopener">lpjmlkit</a>.</p>
Critical importance of tree and non-tree vegetation for African precipitation
<div>Supporting data and material with the following manuscript:</div> <div>"Critical importance of tree and non-tree vegetation for African precipitation"</div> <div> </div> <div>Authors:</div> <div>Te Wierik, S.A. (1,2,4) <br>Keune, J. (3) <br>Miralles, D. (3),<br>Gupta, J. (2), <br>Artzy-Randrup, Y.A (1), <br>Cammeraat, L.H. (1), </div> <div>van Loon, E.E. (1)</div> <div> </div> <div>1 Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, Amsterdam, the Netherlands </div> <div>2 Governance and Inclusive Development, University of Amsterdam, Amsterdam, the Netherlands </div> <div>3 Hydro-Climate Extremes Lab, Ghent University, Ghent, Belgium</div> <div>4 Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, PO Box 60 12 03, 14412 Potsdam, Germany</div> <div> </div> <div>Last update: 03-07-2024</div> <div> </div> <div>--------------------------------------------------------------------------------------------------------------------------------</div> <div>DATA </div> <div>--------------------------------------------------------------------------------------------------------------------------------</div> <div> </div> <div>1. FLEXPART_output_all Folder containing the precipitation source regions, differentiating between short and tall vegetation, for individual watersheds (NetCDF-file)</div> <div>Output files (<iwatershed>_ALLPBL_biascor-attr_linear_hybrid_monthly_1981-2019_E2P_and_T2P_all.nc) correspond with individual watershed numbers (see below).</div> <div>Files represent output of moisture tracking framework from Keune et al., 2022. </div> <div> </div> <div>2. major_watersheds Folder containing shapefile of the major African watersheds from hydroSHEDS (major_watersheds.shp)</div> <div> </div> <div>3. masks Folder containing:</div> <div>- Mask for all major African watersheds (1 degree) (mask_all_1x1_sum.nc) </div> <div>- Mask representing 1) forest loss hotspot region and 2) all cells with > 30% forest loss (1 degree) (mask_forest_loss.nc)</div> <div>- Mask representing 1) grass,- range- and cropland loss hotspot regions and 2) all cells with >30% grass-, range- and cropland loss (1 degree) (mask_croppasture_loss.nc).</div> <div> </div> <div>4. grid_area.nc Surface area (m2) of individual gridcells from a 1x1 degree global grid</div> <div> </div> <div>5. T_tall_short_mean_annual_1981-2019_1deg.nc GLEAM-Hybrid monthly total T and E data, global, 1 degree.</div> <div> </div> <div>6. vegetation-cover_1deg_monthly_1980-2019.nc MODIS derived vegetation cover fractions of short and tall vegetation and barren soil from MeASURES.</div> <div>Original data (0.05 degree) is resampled to 1 degree. </div> <div> </div> <div> </div> <div>--------------------------------------------------------------------------------------------------------------------------------</div> <div>CODE AND ENVIRONMENT</div> <div>--------------------------------------------------------------------------------------------------------------------------------</div> <div> </div> <div>Short_tall_Tp_Africa.py Python code to analyze the moisture sources over individual watersheds</div> <div> </div> <div>Short_tall.yml Necessary environment to run Short-tall_Tp_Africa.py. Make sure the environment is imported and activated before running the script.</div> <div> </div> <div>--------------------------------------------------------------------------------------------------------------------------------</div> <div>OUTPUT</div> <div>--------------------------------------------------------------------------------------------------------------------------------</div> <div> </div> <div>1. Figure 1</div> <div>t_volumetric.png 1d. Mean annual TP flow from trees (km3 year-1)</div> <div>t_normalized.png 1f. Mean annual TP flux from trees (mm year-1)</div> <div>h_volumetric.png 1e. Mean annual TP flow from non-tree vegetation (km3 year-1)</div> <div>h_volumetric.png 1g. Mean annual TP flux from non-tree vegetation (mm year-1)</div> <div> </div> <div>2. Supplementary Figure 2</div> <div>extent_t Contributing source regions of P from trees over the African continent using Tmin (mean annual TP > 0.5 mm yr-1).</div> <div>extent_h Contributing source regions of P from non-tree vegetation over the African continent using Tmin (mean annual TP > 0.5 mm yr-1)</div> <div> </div> <div>3. Supporting data</div> <div>0.5pshed_output_allwatersheds CSV file containing output data from all individual watersheds: mean annual contribution of short and tall vegetation (km3/year and mm/year); associated surface area (km2); relative contribution to precipitation from short and tall vegetation (%); relative contribution to dry and wet season precipitation from tall and short vegeation (%) </div> <div>0.5pshed_continental_sum_output.csv CSV file containing output data of aggregated means at continental level: mean annual contribution of short and tall vegetation to precipitation over Africa (mm/year and km3/year); associated surface area (km2).</div> <div>0.5pshed_hotspot_contributions.csv CSV file containing output data of LULC hotspot contributions to precipitation from all individual watersheds: volumetric contribution from hotspot regions (km3); surface area of the hotspot regions (km2); mean annual precipitation (km3/year) and mean annual precipitation from T (km3/year)</div> <div> </div> <div>----------------------------------------------------------------------------------------------------------------------------</div> <div>WATERSHED NUMBER REFERENCES:</div> <div> </div> <div>1 Lake Chad</div> <div>2 Nile</div> <div>3 Rift Valley</div> <div>4 Congo</div> <div>5 Niger</div> <div>6 Volta</div> <div>7 Africa, West Coast</div> <div>8 Africa, North West Coast</div> <div>9 Mediterranean South Coast</div> <div>10 Africa, Red Sea - Gulf of Aden Coast</div> <div>11 Shebelli - Juba</div> <div>12 Africa, East Central Coast</div> <div>13 Madasgacar</div> <div>14 Zambezi</div> <div>15 Africa, Indian Ocean Coast</div> <div>16 Limpopo</div> <div>17 South Africa, South Coast</div> <div>18 South Africa, West Coast</div> <div>19 Orange</div> <div>20 Africa, South Interior</div> <div>21 Namibia, Coast</div> <div>22 Angola, Coast</div> <div>23 Gulf of Guinea</div> <div>24 Senegal</div> <div>25 Africa, North Interior</div> <div> </div> <div>----------------------------------------------------------------------------------------------------------------------------</div> <div>REFERENCES & DATA SOURCES</div> <div> </div> <div>- Keune, J., Schumacher, D.L., and Miralles, D.G. (2022) A unified framework to estimate the origins of atmospheric moisture and heat using Lagrangian models. Geosci. Model Dev., 15, 1875–1898, 2022 https://doi.org/10.5194/gmd-15-1875-2022</div> <div>- te Wierik, S.A., Keune, J., Miralles, D.G., Gupta, J., Artzy-Randrup, Y.A., Gimeno, L., Nieto, R., Cammeraat L.H. (2022) The Contribution of Transpiration to Precipitation Over African Watersheds. Water Resources Research, 58, e2021WR031721. https://doi. org/10.1029/2021WR031721</div> <div> </div> <div>• MEaSUREs vegetation continuous fields (VCF), Yearly, Global, 0.05 degree (https://cmr.earthdata.nasa.gov/search/concepts/C1452975608-LPDAAC_ECS.html; https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MCD12Q1#bands)</div> <div>• HydroSHEDS data is available via https://data.apps.fao.org/catalog/dataset/7707086d-af3c-41cc-8aa5-323d8609b2d1;</div> <div>• ERA-Interim data is available via https://apps.ecmwf.int/datasets/.</div> <div>• GLEAM data is available via https://www.gleam.eu/; </div> <div>• MSWEP data is available via http://www.gloh2o.org/. </div> <div>• OAFlux data is available from https://oaflux.whoi.edu/data-access/. </div> <div> </div>
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