scholarly journals Effects of Temperature and Precipitation on Spatiotemporal Variations of Net Primary Productivity in the Qinling Mountains, China

2020 ◽  
Vol 30 (1) ◽  
pp. 409-422
Author(s):  
Tao Wang ◽  
Meihuan Yang ◽  
Suijun Yan ◽  
Guangpo Geng ◽  
Qihu Li ◽  
...  
2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zhe Yuan ◽  
Yongqiang Wang ◽  
Jijun Xu ◽  
Zhiguang Wu

AbstractThe ecosystem of the Source Region of Yangtze River (SRYR) is highly susceptible to climate change. In this study, the spatial–temporal variation of NPP from 2000 to 2014 was analyzed, using outputs of Carnegie–Ames–Stanford Approach model. Then the correlation characteristics of NPP and climatic factors were evaluated. The results indicate that: (1) The average NPP in the SRYR is 100.0 gC/m2 from 2000 to 2014, and it shows an increasing trend from northwest to southeast. The responses of NPP to altitude varied among the regions with the altitude below 3500 m, between 3500 to 4500 m and above 4500 m, which could be attributed to the altitude associated variations of climatic factors and vegetation types; (2) The total NPP of SRYR increased by 0.18 TgC per year in the context of the warmer and wetter climate during 2000–2014. The NPP was significantly and positively correlated with annual temperature and precipitation at interannual time scales. Temperature in February, March, May and September make greater contribution to NPP than that in other months. And precipitation in July played a more crucial role in influencing NPP than that in other months; (3) Climatic factors caused the NPP to increase in most of the SRYR. Impacts of human activities were concentrated mainly in downstream region and is the primary reason for declines in NPP.


2019 ◽  
Vol 35 ◽  
pp. 45-53 ◽  
Author(s):  
Leandro Schlemmer Brasil ◽  
Divino Vicente Silverio ◽  
Helena Soares Ramos Cabette ◽  
Joana Darc Batista ◽  
Thiago Bernardi Vieira ◽  
...  

Author(s):  
Douglas G. Goodin ◽  
Philip A. Fay

Climate is a fundamental driver of ecosystem structure and function (Prentice et al. 1992). Historically, North American grassland and forest biomes have fluctuated across the landscape in step with century- to millennialscale climate variability (Axelrod 1985; Ritchie 1986). Climate variability of at decadal scale, such as the severe drought of the 1930s in the Central Plains of North America, caused major shifts in grassland plant community composition (Weaver 1954, 1968). However, on a year-to-year basis, climate variability is more likely to affect net primary productivity (NPP; Briggs and Knapp 1995; Knapp et al. 1998; Briggs and Knapp 2001). This is especially true for grasslands, which have recently been shown to display greater variability in net primary production in response to climate variability than forest, desert, or arctic/alpine systems (Knapp and Smith 2001). Although the basic relationships among interannual variability in rainfall, temperature, and grassland NPP have been well studied (Sala et al. 1988; Knapp et al. 1998; Alward et al. 1999), the linkages to major causes of climate variability at quasi-quintennial (~5 years) or interdecadal (~10 year) timescales in the North American continental interior, such as solar activity cycles, the El Niño–Southern Oscillation (ENSO), the North Atlantic Oscillation (NAO), and the North Pacific Index (NP), are less well understood. In this chapter, we will examine how interannual, quasi-quintennial, and interdecadal variation in annual precipitation and mean annual temperature at a tallgrass prairie site (Konza Prairie Biological Station) may be related to indexes of solar activity, ENSO, NAO, and NP, and in turn how these indexes may be related to aboveground net primary productivity (ANPP). Specifically, we present (1) period-spectrum analyses to characterize the predominant timescales of temperature and precipitation variability at Konza Prairie, (2) correlation analyses of quantitative indexes of the major atmospheric processes with Konza temperature and precipitation records, and (3) the implications of variation in major atmospheric processes for seasonal and interannual patterns of ANPP. The Konza Prairie Biological Station (KNZ), which lies in the Flint Hills (39º05' N, 96º35' W), is a 1.6-million-ha region spanning eastern Kansas from the Nebraska border to northeastern Oklahoma (figure 20.1). This region is the largest remaining tract of unbroken tallgrass prairie in North America (Samson and Knopf 1994) and falls in the more mesic eastern portion of the Central Plains grasslands.


2016 ◽  
Vol 20 (2) ◽  
pp. 1-24 ◽  
Author(s):  
Aiwen Lin ◽  
Hongji Zhu ◽  
Lunche Wang ◽  
Wei Gong ◽  
Ling Zou

Abstract Measurements of air temperature and precipitation at 35 stations in Hubei Province, China, during 1962–2011 are used to investigate the regional climate change. There is an increasing trend for observed air temperature (0.23°C decade−1), which is slightly higher than that from multiple model simulations/predictions [phase 5 of CMIP (CMIP5) datasets] (0.16°C decade−1). The observed precipitation increases at the rate of 11.4 mm decade−1, while the CMIP5 results indicate a much lower decreasing trend (0.8 mm decade−1) in this region. To examine the ecological responses to the climate changes in Hubei Province, annual gross primary productivity (GPP) and net primary productivity (NPP) products during 2000–10 and leaf area index (LAI) products during 1981–2011 are also analyzed. It is discovered that GPP, NPP, and LAI increase at the rate of 1.8 TgC yr−1 yr−1, 1.1 TgC yr−1 yr−1, and 0.14 m2 m−2 decade−1, respectively. A linear model is further used to conduct the correlation analyses between climatic parameters (i.e., air temperature and precipitation) and ecological indicators (i.e., GPP, NPP, and LAI). The results indicate that the air temperature has a significant positive correlation with LAI (R2 = 0.311) and GPP (R2 = 0.189); precipitation is positively correlated with NPP (R2 = 0.209). Thus, it is concluded that the air temperature exerts a stronger effect on the ecosystem than precipitation in Hubei Province over the past decades.


2017 ◽  
Author(s):  
Shengwei Zhang ◽  
Rui Zhang ◽  
Tingxi Liu ◽  
Xin Song ◽  
Mark A. Adams

Abstract. Spatiotemporal variations in net primary productivity (NPP) of vegetation offer insights to surface water and carbon dynamics, and are closely related to temperature and precipitation. We employed the Carnegie-Ames-Stanford Approach ecosystem model to estimate NPP of semiarid grassland in northern China between 2001 and 2013. Model estimates were strongly linearly correlated with observed values (R2 = 0.67, RMSE = 35 g C m−2 year−1). We also quantified inter-annual changes in NPP over the 13-year study period. NPP varied between 141 and 313 g C m−2 year−1, with a mean of 240 g C m−2 year−1. NPP increased from west to east each year, and mean precipitation in each county was significantly positively correlated with NPP in annually, summer and autumn. Mean precipitation was also positively correlated with NPP in spring, but the correlation was not significant. Annual and summer temperatures were mostly negatively correlated with NPP, but temperature was positively correlated with spring and autumn NPP. Spatial correlation and partial correlation analyses at the pixel scale confirmed precipitation as a major driver of NPP. Temperature was negatively correlated with NPP in 99 % of the regions at the annual scale, but after removing the effect of precipitation, temperature was positively correlated with the NPP in 77 % of the regions.


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