Spatio-temporal variation in vegetation net primary productivity and its relationship with climatic factors in the Shule River basin from 2001 to 2010

Pan, Jingxi; Dong, Leilei

doi:10.1080/10807039.2017.1400373

Cited by 35 publications

(15 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Based on SPEI, this study analyzed the temporal and spatial variation characteristics of drought in Gansu from 2000 to 2020, and further analyzed the temporal and spatial variation characteristics of Gansu GPP from 2000 to 2020 and its response to SPEI, which is helpful to better understand and simulate the dynamic characteristics of ecosystems.It revealed the evolution law of drought change and GPP change, and can provide a certain theoretical basis for the research on the driving force of Gansu ecosystem.The study found that the annual SPEI-12 generally showed a increasing trend, which was different from the results of previous studies (Li et al, 2019), which may be due to the different number of selected meteorological stations and different time series.The GPP in this study showed a increasing trend, which was consistent with the findings of the Central Asian Grassland (Yang et al, 2016) and Shulehe River (Pan et al, 2017) .…”

Section: Discussionsupporting

confidence: 91%

Drought Variation Characteristics and Effects on Vegetation Productivity in Gansu, Northwest China

Cheng

Shuyan²

2022

Preprint

View full text Add to dashboard Cite

Changes in vegetation productivity (GPP) have an important impact on the global carbon cycle. Gansu is located in the arid and semi-arid region in northwest China, and its ecological environment is fragile. The study of the impact of drought on GPP is of great significance to the global ecosystem carbon cycle. Based on the SPEI index in Gansu from 2000 to 2020, this paper analyzed the changing characteristics of drought and its impact on GPP. The results revealed that: (1) In the past 21 years, the annual SPEI-12 has been increasing at a rate of 0.3/10a, and it was divided into two distinct dry and wet stages. Before 2018, it was mainly dry, after 2018, it was mainly humid. SPEI-3 decreased at a rate of 0.1/10a in spring, fluctuated significantly in summer, and increased in autumn and winter.(2) The annual SPEI-12 of most meteorological stations was less than 0, the SPEI value increased from northwest to southeast. Except for summer, the SPEI-3 value varied greatly in other seasons, increasing from west to east in spring, decreasing from south to north in autumn, and decreasing from west to east in winter.(3) GPP displayed a significant increasing trend (R2 = 0.57, p < 0.01) in Gansu from 2000 to 2020 .The spatial variation increased from northwest to southeast.(4) Annual SPEI-12 was positively correlated with GPP, SPEI-3 was negatively correlated with GPP in spring and autumn, and positively correlated in summer and winter. The correlation between SPEI-3 and GPP in summer passed the significance test.

show abstract

Section: Discussionsupporting

confidence: 91%

Drought Variation Characteristics and Effects on Vegetation Productivity in Gansu, Northwest China

Cheng

Shuyan²

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…It requires the normalized difference vegetation index (NDVI), total solar radiation, temperature, and precipitation data with few easily obtained parameters, and thus, is considered to be one of the most appropriate models for estimating the vegetation NPP at large scales [9]. In recent years, many scholars have used the CASA model to estimate the vegetation NPP, and further explored its temporal trends, persistence, and mutations from local to global scales [12][13][14][15][16][17][18][19]. In these studies, the CASA model assumed that the vegetation types remained unchanged.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Characteristics of NPP Based on Ensemble Empirical Mode Decomposition from 1982 to 2015—A Case Study of Six Coastal Provinces in Southeast China

et al. 2021

View full text Add to dashboard Cite

Monitoring vegetation net primary productivity (NPP) is very important for evaluating ecosystem health. However, the nonlinear characteristics of the vegetation NPP remain unclear in the six provinces along the Maritime Silk Road in China. In this study, using NDVI and meteorological data from 1982 to 2015, NPP was estimated with the Carnegie-Ames-Stanford Approach (CASA) model based on vegetation type dynamics, and its nonlinear characteristics were explored through the ensemble empirical mode decomposition (EEMD) method. The results showed that: (1) The total NPP in the changed vegetation types caused by ecological engineering and urbanization increased but decreased in those caused by agricultural reclamation and vegetation destruction, (2) the vegetation NPP was dominated by interannual variations, mainly in the middle of the study area, while by long-term trends, mainly in the southwest and northeast, (3) for most of the vegetation types, NPP was dominated by the monotonically increasing trend. Although vegetation NPP in the urban land mainly showed a decreasing trend (monotonic decrease and decrease from increase), there were large areas in which NPP increased from decreasing. Although vegetation NPP in the farmland mainly showed increasing trends, there were large areas that faced the risk of NPP decreasing; (4) dynamical changes of vegetation type by agricultural reclamation and vegetation destruction made the NPP trend monotonically decrease in large areas, leading to ecosystem degradation, while those caused by urbanization and ecological engineering mainly made the NPP increase from decreasing, leading to later recovery from early degradation. Our results highlighted the importance of vegetation type dynamics for accurately estimating vegetation NPP, as well as for assessing their impacts, and the importance of nonlinear analysis for deepening our understanding of vegetation NPP changes.

show abstract

“…However, there are relatively few studies on complete ecosystems at the basin scale. In addition, studies on responses to NEP variations have mostly focused on temperature and precipitation [29,30], without considering the vastly different responses to diurnal warming of different vegetation types [31,32]. The Yellow River Basin (YRB), the birthplace of Chinese civilization, has undulating terrain, varied landform types, and complex ecological environments.…”

Section: Introductionmentioning

confidence: 99%

Spatial Responses of Net Ecosystem Productivity of the Yellow River Basin under Diurnal Asymmetric Warming

Zhang

Jing

et al. 2018

Sustainability

View full text Add to dashboard Cite

The net ecosystem productivity (NEP) of drainage basins plays an important role in maintaining the carbon balance of those ecosystems. In this study, the modified CASA (Carnegie Ames Stanford Approach) model and a soil microbial respiration model were used to estimate net primary productivity (NPP) and NEP of the Yellow River Basin’s (YRB) vegetation in the terrestrial ecosystem (excluding rivers, floodplain lakes and other freshwater ecosystems) from 1982 to 2015. After analyzing the spatiotemporal variations in the NEP using slope analysis, the coefficient of variation, and the Hurst exponent, precipitation was identified as the main factor limiting vegetation growth in the YRB. Hence, precipitation was treated as the control variable and a second-order partial correlation method was used to determine the correlation between diurnal asymmetric warming and the YRB’s NEP. The results indicate that: (i) diurnal asymmetric warming occurred in the YRB from 1982 to 2015, with nighttime warming (Tmin) being 1.50 times that of daytime warming (Tmax). There is a significant correlation between variations in NPP and diurnal warming; (ii) the YRB’s NEP are characterized by upward fluctuations in terms of temporal variations, large differences between the various vegetation types, high values in the western and southeastern regions but low values in the northern region in terms of spatial distribution, overall relative stability in the YRB’s vegetation cover, and changes in the same direction being more dominant than those in the opposite direction (although the former is not sustained); and (iii) positive correlations between the NEP and nighttime and daytime warming are approximately 48.37% and 67.51% for the YRB, respectively, with variations in nighttime temperatures having more extensive impacts on vegetation cover.

show abstract

Spatio-temporal variation in vegetation net primary productivity and its relationship with climatic factors in the Shule River basin from 2001 to 2010

Cited by 35 publications

References 45 publications

Drought Variation Characteristics and Effects on Vegetation Productivity in Gansu, Northwest China

Drought Variation Characteristics and Effects on Vegetation Productivity in Gansu, Northwest China

Nonlinear Characteristics of NPP Based on Ensemble Empirical Mode Decomposition from 1982 to 2015—A Case Study of Six Coastal Provinces in Southeast China

Spatial Responses of Net Ecosystem Productivity of the Yellow River Basin under Diurnal Asymmetric Warming

Contact Info

Product

Resources

About