scholarly journals Seasonal biological carryover dominates northern vegetation growth

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Xu Lian ◽  
Shilong Piao ◽  
Anping Chen ◽  
Kai Wang ◽  
Xiangyi Li ◽  
...  
Keyword(s):  
Northern Hemisphere ◽  
Growing Season ◽  
Ecosystem Dynamics ◽  
Environmental Drivers ◽  
Subsequent Growth ◽  
Ecosystem Models ◽  
Carryover Effect ◽  
Vegetation Growth ◽  
Sequestration Potential ◽  
Future Warming

AbstractThe state of ecosystems is influenced strongly by their past, and describing this carryover effect is important to accurately forecast their future behaviors. However, the strength and persistence of this carryover effect on ecosystem dynamics in comparison to that of simultaneous environmental drivers are still poorly understood. Here, we show that vegetation growth carryover (VGC), defined as the effect of present states of vegetation on subsequent growth, exerts strong positive impacts on seasonal vegetation growth over the Northern Hemisphere. In particular, this VGC of early growing-season vegetation growth is even stronger than past and co-occurring climate on determining peak-to-late season vegetation growth, and is the primary contributor to the recently observed annual greening trend. The effect of seasonal VGC persists into the subsequent year but not further. Current process-based ecosystem models greatly underestimate the VGC effect, and may therefore underestimate the CO2 sequestration potential of northern vegetation under future warming.

scholarly journals A Machine Learning Method for Predicting Vegetation Indices in China

2021 ◽  
Vol 13 (6) ◽  
pp. 1147
Author(s):  
Xiangqian Li ◽  
Wenping Yuan ◽  
Wenjie Dong
Keyword(s):  
Machine Learning ◽  
Growing Season ◽  
Crop Growth ◽  
Spatiotemporal Distribution ◽  
Coefficient Of Determination ◽  
Gradient Boosting ◽  
Severe Drought ◽  
Vegetation Growth ◽  
Extreme Gradient Boosting ◽  
Boosting Method

To forecast the terrestrial carbon cycle and monitor food security, vegetation growth must be accurately predicted; however, current process-based ecosystem and crop-growth models are limited in their effectiveness. This study developed a machine learning model using the extreme gradient boosting method to predict vegetation growth throughout the growing season in China from 2001 to 2018. The model used satellite-derived vegetation data for the first month of each growing season, CO2 concentration, and several meteorological factors as data sources for the explanatory variables. Results showed that the model could reproduce the spatiotemporal distribution of vegetation growth as represented by the satellite-derived normalized difference vegetation index (NDVI). The predictive error for the growing season NDVI was less than 5% for more than 98% of vegetated areas in China; the model represented seasonal variations in NDVI well. The coefficient of determination (R2) between the monthly observed and predicted NDVI was 0.83, and more than 69% of vegetated areas had an R2 > 0.8. The effectiveness of the model was examined for a severe drought year (2009), and results showed that the model could reproduce the spatiotemporal distribution of NDVI even under extreme conditions. This model provides an alternative method for predicting vegetation growth and has great potential for monitoring vegetation dynamics and crop growth.

Legacy effects of spring phenology on vegetation growth under preseason meteorological drought in the Northern Hemisphere

2021 ◽  
Vol 310 ◽  
pp. 108630
Author(s):  
Zhaoqi Zeng ◽  
Wenxiang Wu ◽  
Quansheng Ge ◽  
Zhaolei Li ◽  
Xiaoyue Wang ◽  
...  
Keyword(s):  
Northern Hemisphere ◽  
Meteorological Drought ◽  
Legacy Effects ◽  
Spring Phenology ◽  
Vegetation Growth

Assessing the impact of seasonal precipitation and temperature on vegetation in a grass-dominated rangeland

2014 ◽  
Vol 36 (2) ◽  
pp. 185 ◽  
Author(s):  
Fang Chen ◽  
Keith T. Weber
Keyword(s):  
Vegetation Index ◽  
Climatic Factors ◽  
Growing Season ◽  
Vegetation Growth ◽  
Factors Affecting ◽  
Growing Seasons ◽  
Normalised Difference Vegetation Index ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
The Impact ◽  
Precipitation And Temperature

Changes in vegetation are affected by many climatic factors and have been successfully monitored through satellite remote sensing over the past 20 years. In this study, the Normalised Difference Vegetation Index (NDVI), derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Terra satellite, was selected as an indicator of change in vegetation. Monthly MODIS composite NDVI at a 1-km resolution was acquired throughout the 2004–09 growing seasons (i.e. April–September). Data describing daily precipitation and temperature, primary factors affecting vegetation growth in the semiarid rangelands of Idaho, were derived from the Surface Observation Gridding System and local weather station datasets. Inter-annual and seasonal fluctuations of precipitation and temperature were analysed and temporal relationships between monthly NDVI, precipitation and temperature were examined. Results indicated NDVI values observed in June and July were strongly correlated with accumulated precipitation (R2 >0.75), while NDVI values observed early in the growing season (May) as well as late in the growing season (August and September) were only moderately related with accumulated precipitation (R2 ≥0.45). The role of ambient temperature was also apparent, especially early in the growing season. Specifically, early growing-season temperatures appeared to significantly affect plant phenology and, consequently, correlations between NDVI and accumulated precipitation. It is concluded that precipitation during the growing season is a better predictor of NDVI than temperature but is interrelated with influences of temperature in parts of the growing season.

scholarly journals Comparison of Phenology Models for Predicting the Onset of Growing Season over the Northern Hemisphere

PLoS ONE ◽  
2014 ◽  
Vol 9 (10) ◽  
pp. e109544 ◽  
Author(s):  
Yang Fu ◽  
Haicheng Zhang ◽  
Wenjie Dong ◽  
Wenping Yuan
Keyword(s):  
Northern Hemisphere ◽  
Growing Season

scholarly journals Environmental drivers of SARS-CoV-2 lineage B.1.1.7 transmission in England, October to December 2020

2021 ◽  
Author(s):  
Thomas P. Smith ◽  
Ilaria Dorigatti ◽  
Swapnil Mishra ◽  
Erik Volz ◽  
Patrick G. T. Walker ◽  
...  
Keyword(s):  
Public Health ◽  
Northern Hemisphere ◽  
Health Interventions ◽  
Environmental Drivers ◽  
Public Health Interventions

AbstractPrevious work has shown that environment affects SARS-CoV-2 transmission, but it is unclear whether emerging strains show similar responses. Here we show that lineage B.1.1.7 spread with greater transmission in colder and more densely populated parts of England. We also find evidence of B.1.1.7’s transmission advantage at warmer temperatures versus other strains, implying that spring conditions may facilitate B.1.1.7’s invasion in Europe and across the Northern hemisphere, undermining the effectiveness of public health interventions.

The use of multi-tracer flask-measurements to understand changes in the land-surface processes.

2021 ◽  
Author(s):  
Theertha Kariyathan ◽  
Wouter Peters ◽  
Julia Marshall ◽  
Ana Bastos ◽  
Markus Reichstein
Keyword(s):  
Northern Hemisphere ◽  
Land Surface ◽  
Seasonal Cycle ◽  
Growing Season ◽  
Plant Productivity ◽  
Surface Processes ◽  
Terrestrial Biosphere ◽  
Land Surface Processes ◽  
Species Analysis

<p>Carbon dioxide (CO<sub>2</sub>) is an important greenhouse gas, and it accounts for about 20% of the present-day anthropogenic greenhouse effect. Atmospheric CO<sub>2</sub> is cycled between the terrestrial biosphere and the atmosphere through various land-surface processes and thus links the atmosphere and terrestrial biosphere through positive and negative feedback. Since multiple trace gas elements are linked by common biogeochemical processes, multi-species analysis is useful for reinforcing our understanding and can help in partitioning CO<sub>2</sub> fluxes. For example, in the northern hemisphere, CO<sub>2</sub> has a distinct seasonal cycle mainly regulated by plant photosynthesis and respiration and it has a distinct negative correlation with the seasonal cycle of the δ<sup>13</sup>C isotope of CO<sub>2</sub>, due to a stronger isotopic fractionation associated with terrestrial photosynthesis. Therefore, multi-species flask-data measurements are useful for the long-term analysis of various green-house gases. Here we try to infer the complex interaction between the atmosphere and the terrestrial biosphere by multi-species analysis using atmospheric flask measurement data from different NOAA flask measurement sites across the northern hemisphere.</p><p>This study focuses on the long-term changes in the seasonal cycle of CO<sub>2</sub> over the northern hemisphere and tries to attribute the observed changes to driving land-surface processes through a combined analysis of the δ<sup>13</sup>C seasonal cycle. For this we generate metrics of different parameters of the CO<sub>2</sub> and δ<sup>13</sup>C seasonal cycle like the seasonal cycle amplitude given by the peak-to-peak difference of the cycle (indicative of the amount of CO<sub>2</sub> taken up by terrestrial uptake),  the intensity of plant productivity inferred from the slope of the seasonal cycle during the growing season , length of growing season and the start of the growing season. We analyze the inter-relation between these metrics and how they change across latitude and over time. We hypothesize that the CO<sub>2 </sub>seasonal cycle amplitude is controlled both by the intensity of plant productivity and period of the active growing season and that the timing of the growing season can affect the intensity of plant productivity. We then quantify these relationships, including their variation over time and latitudes and describe the effects of an earlier start of the growing season on the intensity of plant productivity and the CO<sub>2</sub> uptake by plants.</p>

scholarly journals Partitioning net ecosystem exchange of CO<sub>2</sub> on the pedon scale in the Lena River Delta, Siberia

2019 ◽  
Vol 16 (7) ◽  
pp. 1543-1562 ◽  
Author(s):  
Tim Eckhardt ◽  
Christian Knoblauch ◽  
Lars Kutzbach ◽  
David Holl ◽  
Gillian Simpson ◽  
...  
Keyword(s):  
Growing Season ◽  
Arctic Tundra ◽  
River Delta ◽  
Environmental Drivers ◽  
Co2 Fluxes ◽  
Lena River ◽  
Hydrological Conditions ◽  
Tundra Ecosystem ◽  
Water Saturated ◽  
Lena River Delta

Abstract. Arctic tundra ecosystems are currently facing amplified rates of climate warming. Since these ecosystems store significant amounts of soil organic carbon, which can be mineralized to carbon dioxide (CO2) and methane (CH4), rising temperatures may cause increasing greenhouse gas fluxes to the atmosphere. To understand how net the ecosystem exchange (NEE) of CO2 will respond to changing climatic and environmental conditions, it is necessary to understand the individual responses of the processes contributing to NEE. Therefore, this study aimed to partition NEE at the soil–plant–atmosphere interface in an arctic tundra ecosystem and to identify the main environmental drivers of these fluxes. NEE was partitioned into gross primary productivity (GPP) and ecosystem respiration (Reco) and further into autotrophic (RA) and heterotrophic respiration (RH). The study examined CO2 flux data collected during the growing season in 2015 using closed-chamber measurements in a polygonal tundra landscape in the Lena River Delta, northeastern Siberia. To capture the influence of soil hydrology on CO2 fluxes, measurements were conducted at a water-saturated polygon center and a well-drained polygon rim. These chamber-measured fluxes were used to model NEE, GPP, Reco, RH, RA, and net primary production (NPP) at the pedon scale (1–10 m) and to determine cumulative growing season fluxes. Here, the response of in situ measured RA and RH fluxes from permafrost-affected soils of the polygonal tundra to hydrological conditions have been examined. Although changes in the water table depth at the polygon center sites did not affect CO2 fluxes from RH, rising water tables were linked to reduced CO2 fluxes from RA. Furthermore, this work found the polygonal tundra in the Lena River Delta to be a net sink for atmospheric CO2 during the growing season. The NEE at the wet, depressed polygon center was more than twice that at the drier polygon rim. These differences between the two sites were caused by higher GPP fluxes due to a higher vascular plant density and lower Reco fluxes due to oxygen limitation under water-saturated conditions at the polygon center in comparison to the rim. Hence, soil hydrological conditions were one of the key drivers for the different CO2 fluxes across this highly heterogeneous tundra landscape.

scholarly journals Nonstationary Hydrologic Behavior in Forested Watersheds Is Mediated by Climate‐Induced Changes in Growing Season Length and Subsequent Vegetation Growth

2018 ◽  
Vol 54 (8) ◽  
pp. 5359-5375 ◽  
Author(s):  
Taehee Hwang ◽  
Katherine L. Martin ◽  
James M. Vose ◽  
David Wear ◽  
Brian Miles ◽  
...  
Keyword(s):  
Growing Season ◽  
Season Length ◽  
Forested Watersheds ◽  
Vegetation Growth ◽  
Growing Season Length ◽  
Induced Changes

scholarly journals Spatio-Temporal Variation of Drought within the Vegetation Growing Season in North Hemisphere (1982–2015)

Water ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 2146 ◽  
Author(s):  
Zhaoqi Zeng ◽  
Yamei Li ◽  
Wenxiang Wu ◽  
Yang Zhou ◽  
Xiaoyue Wang ◽  
...  
Keyword(s):  
Northern Hemisphere ◽  
Gross Primary Production ◽  
Coniferous Forest ◽  
Critical Time ◽  
Growing Season ◽  
Well Being ◽  
Estimation Methods ◽  
Drought Risk ◽  
Drought Characteristics ◽  
The Impact

Drought disasters jeopardize the production of vegetation and are expected to exert impacts on human well-being in the context of global climate change. However, spatiotemporal variations in drought characteristics (including the drought duration, intensity, and frequency), specifically for vegetation areas within a growing season, remain largely unknown. Here, we first constructed a normalized difference vegetation index to estimate the length of the growing season for each pixel (8 km) by four widely used phenology estimation methods; second, we analyzed the temporal and spatial patterns of climate factors and drought characteristics (in terms of the Standardized Precipitation Evapotranspiration Index (SPEI)), within a growing season over vegetation areas of the northern hemisphere before and after the critical time point of 1998, which was marked by the onset of a global warming hiatus. Finally, we extracted the highly drought-vulnerable areas of vegetation by examining the sensitivity of the gross primary production to the SPEI to explore the underlying effects of drought variation on vegetation. The results revealed, first, that significant (p < 0.05) increases in precipitation, temperature, and the SPEI (a wetting trend) occurred from 1982 to 2015. The growing season temperature increased even more statistically significant after 1998 than before. Second, the duration and frequency of droughts changed abruptly and decreased considerably from 1998 to 2015; and this wetting trend was located mainly in high-latitude areas. Third, at the biome level, the wetting areas occurred mainly in the tundra, boreal forest or taiga, and temperate coniferous forest biomes, whereas the highly drought-vulnerable areas were mainly located in the desert and xeric shrubland (43.5%) biomes. Our results highlight the fact that although the drought events within a growing season decreased significantly in the northern hemisphere from 1998 to 2015, the very existence of a mismatch between a reduction in drought areas and an increase in highly drought-vulnerable areas makes the impact of drought on vegetation nonnegligible. This work provides valuable information for designing coping measures to reduce the vegetative drought risk in the Northern Hemisphere.

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