Global vegetation variability and its response to elevated CO<sub>2</sub>, global warming, and climate variability – a study using the offline SSiB4/TRIFFID model and satellite data

Abstract. The climate regime shift during the 1980s had a substantial impact on the terrestrial ecosystems and vegetation at different scales. However, the mechanisms driving vegetation changes, before and after the shift, remain unclear. In this study, we used a biophysical-dynamic vegetation model to estimate large-scale trends in terms of carbon fixation, vegetation growth, and expansion during the period 1958–2007, and to attribute these changes to environmental drivers including elevated atmospheric CO2 concentration (hereafter eCO2), global warming, and climate variability (hereafter CV). Simulated Leaf Area Index (LAI) and Gross Primary Product (GPP) were evaluated against observation-based data. Significant spatial correlations are found (correlations > 0.87), along with regionally varying temporal correlations of 0.34–0.80 for LAI and 0.45–0.83 for GPP. More than 40 % of the global land area shows significant trends in LAI and GPP since the 1950s: 11.7 % and 19.3 % of land has consistently positive LAI and GPP trends, respectively; while 17.1 % and 20.1 % of land, saw LAI and GPP trends respectively, reverse during the 1980s. Vegetation fraction cover (FRAC) trends, representing vegetation expansion/shrinking, are found at the edges of semi-arid areas and polar areas. Overall, eCO2 consistently contributes to positive LAI and GPP trends in the tropics. Global warming is shown to mostly affected LAI, with positive effects in high latitudes and negative effects in subtropical semi-arid areas. CV is found to dominate the variability of FRAC, LAI, and GPP in the semi-humid and semi-arid areas. The eCO2 and global warming effects increased after the 1980s, while the CV effect reversed during the 1980s. In addition, plant competition is shown to have played an important role in determining which driver dominated the regional trends. This paper presents a new insight into ecosystem variability and changes in the varying climate since the 1950s.

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Global vegetation variability and its response to elevated CO<sub>2</sub>, global warming, and climate variability – a study using the offline SSiB4/TRIFFID model and satellite data

Earth System Dynamics ◽

10.5194/esd-10-9-2019 ◽

2019 ◽

Vol 10 (1) ◽

pp. 9-29 ◽

Cited By ~ 8

Author(s):

Ye Liu ◽

Yongkang Xue ◽

Glen MacDonald ◽

Peter Cox ◽

Zhengqiu Zhang

Keyword(s):

Global Warming ◽

Climate Variability ◽

Environmental Drivers ◽

Arid Areas ◽

Area Index ◽

Substantial Impact ◽

Positive Effects ◽

Vegetation Fraction ◽

Before And After ◽

Semi Arid

Abstract. The climate regime shift during the 1980s had a substantial impact on the terrestrial ecosystems and vegetation at different scales. However, the mechanisms driving vegetation changes, before and after the shift, remain unclear. In this study, we used a biophysical dynamic vegetation model to estimate large-scale trends in terms of carbon fixation, vegetation growth, and expansion during the period 1958–2007, and to attribute these changes to environmental drivers including elevated atmospheric CO2 concentration (hereafter eCO2), global warming, and climate variability (hereafter CV). Simulated leaf area index (LAI) and gross primary production (GPP) were evaluated against observation-based data. Significant spatial correlations are found (correlations > 0.87), along with regionally varying temporal correlations of 0.34–0.80 for LAI and 0.45–0.83 for GPP. More than 40 % of the global land area shows significant positive (increase) or negative (decrease) trends in LAI and GPP during 1958–2007. Regions over the globe show different characteristics in terms of ecosystem trends before and after the 1980s. While 11.7 % and 19.3 % of land have had consistently positive LAI and GPP trends, respectively, since 1958, 17.1 % and 20.1 % of land saw LAI and GPP trends, respectively, reverse during the 1980s. Vegetation fraction cover (FRAC) trends, representing vegetation expansion and/or shrinking, are found at the edges of semi-arid areas and polar areas. Environmental drivers affect the change in ecosystem trend over different regions. Overall, eCO2 consistently contributes to positive LAI and GPP trends in the tropics. Global warming mostly affects LAI, with positive effects in high latitudes and negative effects in subtropical semi-arid areas. CV is found to dominate the variability of FRAC, LAI, and GPP in the semi-humid and semi-arid areas. The eCO2 and global warming effects increased after the 1980s, while the CV effect reversed during the 1980s. In addition, plant competition is shown to have played an important role in determining which driver dominated the regional trends. This paper presents new insight into ecosystem variability and changes in the varying climate since the 1950s.

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Climate Variability, Land-Use, Pastoral and Agropastoral Livelihoods in Arid and Semi-Arid Areas of Kenya

World Journal of Innovative Research ◽

10.31871/wjir.10.5.38 ◽

2021 ◽

Vol 10 (5) ◽

Author(s):

Amwata D. A. ◽

Nyariki D. M.

Keyword(s):

Land Use ◽

Climate Variability ◽

Arid Areas ◽

Semi Arid

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Effects of Super-Absorbent Polymer on Soil Remediation and Crop Growth in Arid and Semi-Arid Areas

Sustainability ◽

10.3390/su12187825 ◽

2020 ◽

Vol 12 (18) ◽

pp. 7825 ◽

Cited By ~ 1

Author(s):

Fang Yang ◽

Rui Cen ◽

Weiying Feng ◽

Jing Liu ◽

Zhongyi Qu ◽

...

Keyword(s):

Surface Temperature ◽

Dry Matter ◽

Leaf Surface ◽

Crop Growth ◽

Dry Matter Accumulation ◽

Arid Areas ◽

Super Absorbent Polymer ◽

Area Index ◽

Absorbent Polymer ◽

Semi Arid

The water-retaining and yield-increasing capacity of super-absorbent polymer (SAP) are essential for soil remediation in arid and semi-arid areas. Therefore, it is of great significance to investigate the influencing factors and mechanisms of SAP effects on soil environments and crop growth for the precise management of agricultural water-saving irrigation. In this study, we adopted SAP as a soil conditioner and monitored changes in soil temperature, photosynthetic rate, leaf transpiration rate, chlorophyll, crop growth indexes (plant height, stem diameter, leaf area index, dry matter accumulation), and yield under different SAP doses during the growth stage of maize, on the basis of which the improvement mechanism of SAP in arid and semi-arid soil was analyzed. The results demonstrated the following: (1) 45 kg/hm2 of SAP application could increase the temperature of the soil layer, effectively reduce the diurnal temperature variation of the soil surface, and promote the stable growth of maize; (2) when different SAP doses were applied, the leaf surface temperature of maize increased by 0.95 °C on average. In particular, when 135 kg/hm2 of SAP was applied, the leaf surface temperature increased by 1.55 °C; (3) SAP could promote the photosynthetic rate of maize. In addition, the plant height, leaf area index, and dry matter accumulation of maize gradually increased with an increasing amount of SAP; (4) the application of SAP not only increased the grain row number, ear row number, and average 100-seed weight, but also increased the crop yield by nearly 6%. The application of SAP demonstrated a comprehensive utility (redistribution of soil water and temperature, synergy between SAPs and plants), which suggests that the most basic goal, to ensure socio-economic and ecological sustainability in dryland systems, was obtained.

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Precipitation-Sensitive Dynamic Threshold: A New and Simple Method to Detect and Monitor Forest and Woody Vegetation Cover in Sub-Humid to Arid Areas

Remote Sensing ◽

10.3390/rs12081231 ◽

2020 ◽

Vol 12 (8) ◽

pp. 1231 ◽

Cited By ~ 2

Author(s):

Ron Drori ◽

Harel Dan ◽

Michael Sprintsin ◽

Efrat Sheffer

Keyword(s):

Remote Sensing ◽

Vegetation Cover ◽

Lower Threshold ◽

Woody Vegetation ◽

Mean Annual Precipitation ◽

Dynamic Threshold ◽

Arid Areas ◽

Simple Method ◽

Area Index ◽

Semi Arid

Remote-sensing tools and satellite data are often used to map and monitor changes in vegetation cover in forests and other perennial woody vegetation. Large-scale vegetation mapping from remote sensing is usually based on the classification of its spectral properties by means of spectral Vegetation Indices (VIs) and a set of rules that define the connection between them and vegetation cover. However, observations show that, across a gradient of precipitation, similar values of VI can be found for different levels of vegetation cover as a result of concurrent changes in the leaf density (Leaf Area Index—LAI) of plant canopies. Here we examine the three-way link between precipitation, vegetation cover, and LAI, with a focus on the dry range of precipitation in semi-arid to dry sub-humid zones, and propose a new and simple approach to delineate woody vegetation in these regions. By showing that the range of values of Normalized Difference Vegetation Index (NDVI) that represent woody vegetation changes along a gradient of precipitation, we propose a data-based dynamic lower threshold of NDVI that can be used to delineate woody vegetation from non-vegetated areas. This lower threshold changes with mean annual precipitation, ranging from less than 0.1 in semi-arid areas, to over 0.25 in mesic Mediterranean area. Validation results show that this precipitation-sensitive dynamic threshold provides a more accurate delineation of forests and other woody vegetation across the precipitation gradient, compared to the traditional constant threshold approach.

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Phenology of the Alien Invasive Plant Species Prosopis juliflora in Arid and Semi-Arid Areas in Response to Climate Variability and Some Perspectives for Its Control in Ethiopia

Polish Journal of Ecology ◽

10.3161/15052249pje2020.68.1.004 ◽

2020 ◽

Vol 68 (1) ◽

pp. 37

Author(s):

Wakshum Shiferaw ◽

Tamrat Bekele ◽

Sebsebe Demissew ◽

Ermias Aynekulu

Keyword(s):

Climate Variability ◽

Plant Species ◽

Invasive Plant ◽

Prosopis Juliflora ◽

Arid Areas ◽

Invasive Plant Species ◽

Semi Arid

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Global warming and groundwater from semi-arid areas: Essaouira region (Morocco) as an example

SN Applied Sciences ◽

10.1007/s42452-020-3014-7 ◽

2020 ◽

Vol 2 (7) ◽

Author(s):

Bahir Mohammed ◽

Ouhamdouch Salah ◽

Ouazar Driss ◽

Chehbouni Abdelghani

Keyword(s):

Global Warming ◽

Arid Areas ◽

Semi Arid

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Indigenous knowledge related to climate variability and change: insights from droughts in semi-arid areas of former Makueni District, Kenya

Climatic Change ◽

10.1007/s10584-009-9713-0 ◽

2009 ◽

Vol 100 (2) ◽

pp. 295-315 ◽

Cited By ~ 103

Author(s):

Chinwe Ifejika Speranza ◽

Boniface Kiteme ◽

Peter Ambenje ◽

Urs Wiesmann ◽

Samuel Makali

Keyword(s):

Climate Variability ◽

Indigenous Knowledge ◽

Arid Areas ◽

Climate Variability And Change ◽

Semi Arid

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A Modified KNN Method for Mapping the Leaf Area Index in Arid and Semi-Arid Areas of China

Remote Sensing ◽

10.3390/rs12111884 ◽

2020 ◽

Vol 12 (11) ◽

pp. 1884 ◽

Cited By ~ 2

Author(s):

Fugen Jiang ◽

Andrew R. Smith ◽

Mykola Kutia ◽

Guangxing Wang ◽

Hua Liu ◽

...

Keyword(s):

Spectral Reflectance ◽

Prediction Accuracy ◽

Field Data ◽

Vegetation Indices ◽

Arid Areas ◽

Vegetation Canopy ◽

Area Index ◽

Red Edge ◽

Semi Arid ◽

Sentinel 2

As an important vegetation canopy parameter, the leaf area index (LAI) plays a critical role in forest growth modeling and vegetation health assessment. Estimating LAI is helpful for understanding vegetation growth and global ecological processes. Machine learning methods such as k-nearest neighbors (kNN) and random forest (RF) with remote sensing images have been widely used for mapping LAI. However, the accuracy of mapping LAI in arid and semi-arid areas using these methods is limited due to remote and large areas, the high cost of collecting field data, and the great spatial variability of the vegetation canopy. Here, a novel and modified kNN method was presented for mapping LAI in arid and semi-arid areas of China using Sentinel-2 and Landsat 8 images with field data collected in Ganzhou and Kangbao of China. The modified kNN was developed by integrating the traditional kNN estimation and RF classification. The results were compared with those from kNN and RF regression alone using three sets of input predictors: (i) spectral reflectance bands (input 1); (ii) vegetation indices (input 2); and (iii) a combination of spectral reflectance bands and vegetation indices (input 3). Our analysis showed that in Ganzhou, the red-edge bands of the Sentinel-2 image had a high correlation with LAI. Using the red-edge band-derived vegetation indices increased the accuracy of mapping LAI compared with using other spectral variables. Among the three sets of input predictors, input 3 resulted in the highest prediction accuracy. Based on the combination, the values of RMSE obtained by the traditional kNN, RF, and modified kNN were 0.526, 0.523, and 0.372, respectively, and the modified kNN significantly improved the accuracy of LAI prediction by 29.3% and 28.9% compared with the kNN and RF alone, respectively. A similar improvement was achieved for input 1 and input 2. In Kangbao, the improvement of the prediction accuracy obtained by the modified kNN was 31.4% compared with both the kNN and RF. Therefore, this study implied that the modified kNN provided the potential to improve the accuracy of mapping LAI in arid and semi-arid regions using the images.

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