Extreme climate-induced vegetation greenness decline significantly affects the stability of ecosystem function. Extreme climate events have occurred frequently in the recent 20 years and the ...possibility of climate anomalies is forecasted to increase in the future. But currently, the spatial and temporal response of episodic local vegetation decline to climate extremes at a global scale are still unclear. In this study, the detrend NDVI data was utilized as the indicator of vegetation growth, and a spatiotemporally contiguous recognition method was proposed to identify episodic large-scale vegetation decline events globally, subsequently, the spatiotemporal characteristics of these vegetation decline events and their interannual variation trends during 2000–2019 were explored. The results showed that (1) the spatiotemporally contiguous recognition method proposed by this paper was proven to be accurate in identifying the hotspot regions of large-scale vegetation decline. A total of 243 large-scale vegetation decline events were recognized globally during 2000–2019 drived by the method. (2) The global hotspots of large-scale vegetation decline were mainly distributed in the low-elevation areas at middle and low latitudes, especially at 15°S ~ 35°S, 15°N and 35°N, where covered north-western Africa, the Sahel, the Middle East, Central Asia, western India, the border of north-eastern China and Mongolia, western and south-central United States, northern Mexico, southern Africa, Australia, and southern and north-eastern South America. (3) Recent global episodic local vegetation decline has increased significantly since 2000, at the rate of 180,000 km2 of vegetation decline areas increasing per year. Particular, the severity of vegetation decline grew significantly since 2010 at the regions where covered the latitudes of approximately 15°N, 30°N and 65°N. Additionally, the severity of vegetation decline ranging from 20°S to 30°S weakened significantly since 2010. These findings were expected to provide the valuable scientific understanding for global vegetation decline and ecosystem responses to frequent climate extremes.
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•We proposed a spatiotemporal recognition method to extract annual large-scale vegetation decline events.•We identified a total of 243 large-scale vegetation decline events occurred since 2000.•Regions at 15°S ~ 35°S, 15°N and 35°N stand out as large-scale vegetation decline hotspots since 2000.•Global large-scale vegetation decline has increased significantly, especially at 15°N, 30°N and 65°N since 2010.
Global plant transpiration (PT) is a crucial component of the Earth's hydrological cycle and plays a significant role in regulating the exchange of water and energy between the land surface and the ...atmosphere. However, the long-term trend and the underlying driver of global PT remain unclear due to the significant uncertainties in estimating PT on a global scale. This study uses two sub-Mixture Density Networks (MDNc and MDNa) to predict vegetation canopy resistance (rc) and aerodynamic resistance (ra), then the predicted rc and ra are imported into the Penman-Monteith-Leuning (PML) model to simulate PT. The observed PT at 112 SAPFLUXNET sites are used to validate the performance of hybrid MDN-PML model. The verified MDN-PML model is further applied to map the spatial distribution of global PT and reconstruct a long-term (1990–2020) global PT dataset. The results indicate that the long-term average global PT is 397.2 ± 63.1 mm. During the period 1990–2020, the global PT exhibit a significant upward trend (0.79 ± 0.28 mm/year (P < 0.05)), which equates to a 6.0% increase compared with the long-term average global PT. A widespread trend of elevated PT is observed in approximately 70% of the global land surface. The trend attribution analysis results show that the change in leaf area index (LAI) can explain 66.2% of the global PT trend, indicating that elevated LAI due to global greening is the dominant factor contributing to the upward trend in global PT. The elevated LAI can be largely attributed to the CO2 fertilization effect induced by elevated atmospheric CO2 concentration. Additional analysis reveals that the increased global PT is more sensitive to CO2 fertilization effect in high LAI areas than in low LAI areas. Projected climate scenarios indicate that global land surface PT will continue to rise from 2023 to 2100, and the rate of increase in the future will be higher than in historical periods. The rising rates of global PT under the three Representative Concentration Pathway scenarios (RCP) 2.6, RCP4.5, and RCP8.5 climate change scenarios are 0.86 mm/year, 1.16 mm/year, and 1.45 mm/year, respectively, during the period 2023–2100. Our results highlight the impact of global change and vegetation greening on the global PT and hydrological cycle. This study is of great significance for the scientific response to the challenges of climate change for regional water resource management.
•Roughly 70% of global regions show a significant upward trend in PT.•The global land surface PT increase 6.0% during the period 1990–2020.•Global greening is the dominant factor responsible for the rising trend of PT.•The enhanced global PT is more sensitive to CO2 fertilization effect in high LAI areas.•The rising rate of global PT in the future will be higher than that in historical periods.
A wide variety of studies have revealed a substantial increase in nitrogen (N) deposition in China, but the lack of spatially-explicit time-series N deposition data set has long hindered us from ...assessing the impacts of atmospheric N input on ecosystem services. In this study, we combined site-level monitoring, gridded precipitation data and atmospheric transport modeling results to generate annual N bulk deposition data in China with a spatial resolution of 10 km × 10 km and a time span from 1961 to 2008. It shows that national average N deposition rate had large interannual variation, and it increased by 59%, from 12.64 kg N ha−1 yr−1 in the 1960s to 20.07 kg N ha−1 yr−1 in the recent decade, with the most rapid increase centered in the southeastern China that is already N-enriched. Large spatial variation as well as dry deposition input has to be taken into account when estimating the amount of N deposited onto land surface of China. The spatial and temporal information on N deposition derived from this study could be used by ecosystem, hydrological, and climate modeling as well as by policy makers for assessing the impacts of nitrogen enrichment on regional climate, water resources, and biogeochemical cycles.
•Gridded N deposition data in China during 1961–2008 was developed in this study.•China's N deposition is found to increase by 59%, to 20.07 kg N ha−1 yr−1 in the 2000s.•Spatial heterogeneity ought to be considered in estimating China's N deposition.
Tong's B-type water drive method was proposed as early as the 1970s and has been widely applied in the dynamic prediction and effective evaluation of oilfield development. Through extensive ...applications and studies, many researchers found that the statistical constants in the formula of the Tong's B-type water drive method (also referred to as the Tong's B-type formula) are not applicable to multiple types of reservoirs, especially low-permeability ones, due to the limited range of reservoir types when the formula was conceived. Moreover, they put forward suggestions to improve the Tong's B-type formula, most of which focused on the research and calculation of the first constant in the formula. For oilfields in the development stages of high or ultra-high water cuts, it is widely accepted that different types of reservoirs have different limit water cuts. This understanding naturally makes it necessary to further modify the Tong's B-type formula. It is practically significant to establish the water drive formula and cross plot considering that the two constants in the formula vary with reservoir type. By analyzing the derivation process and conditions of the Tong's B-type formula, this study points out two key problems, i.e., the two constants 7.5 and 1.69 in the formula are not applicable to all types of reservoir. Given this, this study establishes a function between key reservoir parameters and the first constant and another function between key reservoir parameters and recovery efficiency. Based on the established two functions and considering that different types of oil reservoir have different limit water cuts, this study develops an improved Tong's B-type formula and prepares the corresponding improved cross plot. The results of this study will improve the applicability and accuracy of Tong's B-type water drive method in predicting the trend of water cut increasing for different types of oil reservoirs.
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•Two constants 7.5 and 1.69 in the Tong's B-type formula are not applicable to all water drive reservoirs.•Establishing two functions applicable to all reservoirs.•Developing a systematically improved formula and cross plot.
It has been found that the concentration of atmospheric methane (CH4) has rapidly increased since 2007 after a decade of nearly constant concentration in the atmosphere. As an important greenhouse ...gas, such an increase could enhance the threat of global warming. To better quantify this increasing trend, a novel statistic method, i.e. the Ensemble Empirical Mode Decomposition (EEMD) method, was used to analyze the CH4 trends from three different measurements: the mid–upper tropospheric CH4 (MUT) from the space-borne measurements by the Atmospheric Infrared Sounder (AIRS), the CH4 in the marine boundary layer (MBL) from NOAA ground-based in-situ measurements, and the column-averaged CH4 in the atmosphere (XCH4) from the ground-based up-looking Fourier Transform Spectrometers at Total Carbon Column Observing Network (TCCON) and the Network for the Detection of Atmospheric Composition Change (NDACC). Comparison of the CH4 trends in the mid–upper troposphere, lower troposphere, and the column average from these three data sets shows that, overall, these trends agree well in capturing the abrupt CH4 increase in 2007 (the first peak) and an even faster increase after 2013 (the second peak) over the globe. The increased rates of CH4 in the MUT, as observed by AIRS, are overall smaller than CH4 in MBL and the column-average CH4. During 2009–2011, there was a dip in the increase rate for CH4 in MBL, and the MUT-CH4 increase rate was almost negligible in the mid-high latitude regions. The increase of the column-average CH4 also reached the minimum during 2009–2011 accordingly, suggesting that the trends of CH4 are not only impacted by the surface emission, however that they also may be impacted by other processes like transport and chemical reaction loss associated with OH. One advantage of the EEMD analysis is to derive the monthly rate and the results show that the frequency of the variability of CH4 increase rates in the mid–high northern latitude regions is larger than those in the tropics and southern hemisphere.
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