Simulating Spatiotemporal Dynamics of Sichuan Grassland Net Primary Productivity Using the CASA Model and In Situ Observations

Tang, Chuanjiang; Fu, Xiao‐An; Jiang, Dong; Fu, Jingying; Zhang, Xinyue; Su, Zhou

doi:10.1155/2014/956963

Cited by 12 publications

(4 citation statements)

References 29 publications

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“…Next, we tested their weight in the laboratory. According to previous studies, the coefficient of carbon conversion was 0.45 (Tang et al, 2014). Then, we calculated the value of NPP in the quadrates.…”

Section: Sample Collection Of Model Verificationmentioning

confidence: 99%

Estimation of net primary productivity and its driving factors in the Ili River Valley, China

Wei

Chen

et al. 2018

J. Arid Land

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Net primary productivity (NPP), as an important variable and ecological indicator in grassland ecosystems, can reflect environmental change and the carbon budget level. The Ili River Valley is a wetland nestled in the hinterland of the Eurasian continent, which responds sensitively to the global climate change. Understanding carbon budget and their responses to climate change in the ecosystem of Ili River Valley has a significant effect on the adaptability of future climate change and sustainable development. In this study, we calculated the NPP and analyzed its spatio-temporal pattern of the Ili River Valley during the period 2000-2014 using the normalized difference vegetation index (NDVI) and an improved Carnegie-Ames-Stanford (CASA) model. Results indicate that validation showed a good performance of CASA over the study region, with an overall coefficient of determination (R 2) of 0.65 and root mean square error (RMSE) of 20.86 g C/(m 2 •a). Temporally, annual NPP of the Ili River Valley was 599.19 g C/(m 2 •a) and showed a decreasing trend from 2000 to 2014, with an annual decrease rate of-3.51 g C/(m 2 •a). However, the spatial variation was not consistent, in which 55.69% of the areas showed a decreasing tendency, 12.60% of the areas remained relatively stable and 31.71% appeared an increasing tendency. In addition, the decreasing trends in NPP were not continuous throughout the 15-year period, which was likely being caused by a shift in climate conditions. Precipitation was found to be the dominant climatic factor that controlled the inter-annual variability in NPP. Furthermore, the correlations between NPP and climate factors differed along the vertical zonal. In the medium-high altitudes of the Ili River Valley, the NPP was positively correlated to precipitation and negatively correlated to temperature and net radiation. In the low-altitude valley and high-altitude mountain areas, the NPP showed a negative correlation with precipitation and a weakly positive correlation with temperature and net radiation. The results suggested that the vegetation of the Ili River Valley degraded in recent years, and there was a more complex mechanism of local hydrothermal redistribution that controlled the growth of vegetation in this valley ecosystem.

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Section: Sample Collection Of Model Verificationmentioning

confidence: 99%

Estimation of net primary productivity and its driving factors in the Ili River Valley, China

Wei

Chen

et al. 2018

J. Arid Land

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“…In the context of global climate change, studying the carbon sequestration potential of terrestrial ecosystems is not only an important indicator for estimating the Earth's supporting capacity, but also helps to understand the global carbon balance and evaluate the sustainable development of terrestrial ecosystems (Christopher et al, 1998;Carraro and Massetti, 2011;Shen et al, 2016;Chen et al, 2017;Li et al, 2020).Grassland ecosystem is one of the most important and widely distributed ecosystem types in terrestrial ecosystems, and it is also one of the most important carbon stocks and carbon sources/sinks on land. China's grasslands cover almost 41.17% of national territorial area, it accounts for 6%-8% of the total grassland area in the world, and plays a pivotal role in the global carbon cycle and climate regulation, and plays an important role in developing animal husbandry, maintaining biodiversity, conserving soil and water, and maintaining ecosystem balance (Xie et al, 2001;Yang et al, 2012;Li et al, 2014;Tang et al, 2014;Xu et al, 2020). Therefore, estimating the carbon sequestration potential of the net primary productivity of grassland ecosystems correctly in China is of great significance for studying the carbon cycle of terrestrial ecosystems and regulating global climate change (Piao et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

Modeling carbon uptake by vegetation of grassland ecosystems and its associated factors in China based on remote sensing

Sha

2023

Front. Earth Sci.

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In order to reveal the spatial variation characteristics and influencing factors of grassland net primary productivity (NPP) in China, this paper uses remote sensing data, land use data and meteorological data to simulate and estimate China’s grassland net primary productivity from 2001 to 2019 using the Carnegie-Ames-Stanford Approach (CASA). The trend analysis and complex correlation analysis were used to analyze the relationship with the temporal and spatial changes of grassland NPP from the perspectives of climate factors, topography, longitude and latitude. The results show that: 1) In the past 19 years, the China’s grassland NPP has generally shown a fluctuating upward trend, the spatial distribution of NPP variation shows a characteristic of low in the west and high in the east, with the increased area accounting for 70.39% of the total grassland area, and the low NPP values are mainly distributed in the northwestern part of Tibet and Qinghai and the central part of Inner Mongolia, the average annual NPP is 257.13 g C·m−2·a−1. 2) The change of mean NPP value of grassland in China is more dependent on precipitation (p) than air temperature (T). 3) Grassland NPP showed a decreasing trend with the increase of altitude, and the NPP on the gradient with DEM between 200 m and 500 m was the highest (483.86 g·C·m−2·a−1); The maximum annual mean value (448.42 g C·m−2·a−1) is fallen over the sharp slope of 35°–45°; the NPP of grassland increases with the slope (from shade to sunny), and the NPP of grassland on the semi-sunny slope increases. The annual average NPP is the highest (270.87 g C·m−2·a−1). 4) The mean value of grassland NPP was negatively correlated with the change of latitude, and showed a “wave-like” downward trend from south to north; the mean value of grassland NPP was positively related to the change of longitude. The correlation relationship shows a “stepped” upward trend from west to east.

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“…The relationship betweenclimatic parameters and NPP is estimated utilizing the climatic model. This model has the limitation that actual and estimated values of NPP consist of large variations; thus, the actual vegetation type may not be considered in this model (Tang et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Precise Yield Estimation through Improvised CASA Model by Development of Soil and Atmospheric Constants (Sᵮ) and (Aþ)

Khan¹,

Mahmood²

2022

SJA

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| Net Primary Production (NPP) is an indicator that is widely used to determine the supply of food and wood. NPP incorporates almost all factors that participate in the growth and development of a particular crop. These factors include various heat fluxes (e.g., ground, sensible and latent heat flux), a variety of radiations (e.g., extraterrestrial, shortwave, longwave, and net radiations), and the photosynthesis index. In this research, the net radiations were estimated as 27,428 Wm −2 throughout the wheat growth period (WGP), including (118.3 Wm −2 ) as shortwave and (34.63 Wm −2 ) as longwave radiation. Latent, soil and latent heat fluxes were estimated as 3324 Wm −2 , 16,549 Wm −2 , and 7554 Wm −2 , respectively. Water stress during the complete WGP ranged between 0.5838 and 0.1218 from the start to the end of the cultivation period. The biomass creation was estimated as6.09-1.03 g/m 2 / day, which was higher at the start of WGP than declined at the end. Finally, the estimated yield was very different as compared to the actual yield. The yield estimated through the CASA(Carnegie-Ames-Stanford approach) model was uniform for the complete study site; however, the actual yield was about 74% less than CASA generated yield in various plots. This issue was further investigated and found that the CASA model lacks various aspects. These aspects include soil suitability parameters, including pH, Organic matter, CaCO3, texture, and Electric Conductivity. The largest gap inthe CASA model, which affected the overall productivity of wheat, was the smog/haze factor which was not incorporated. The environmental factors include Black Carbon (BC), Carbon monoxide (CO), Nitrogen dioxide (NO2), Ozone (O3), and Sulphur dioxide (SO2). To cater to these issues, two indicators were developed to incorporate the smog and soil-related issues. These indicators are named Soil Factor (Sᵮ) and Atmospheric Factor (Aþ). If the soil is "Not Suitable," the values of Sᵮ is 0.147, for less suitable soil Sᵮ is 0.21, for moderately suitable soil, Sᵮ is 0.61, and for highly suitable soil, Sᵮ is 0.95. By substituting these values of Sᵮ, the overall results approach to actual yield by 96%. The other factor Aþ is based on incorporating the situation of Atmospheric conditions faced by the wheat crop canopy, whichinclude Black Carbon (BC), Carbon monoxide (CO), Nitrogen dioxide (NO2), Ozone (O3), and Sulphur dioxide (SO2) parameters. If the overall air quality is conducive, the value of Aþ is near 1. The results obtained through the improved CASA model by the addition of Sᵮ and Aþ provided a very accurate yield that was near to actual estimates.This study is important to obtain precise estimates of cereal crops incorporating physioclimatic factor that leads toward precision agriculture.

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Simulating Spatiotemporal Dynamics of Sichuan Grassland Net Primary Productivity Using the CASA Model and In Situ Observations

Cited by 12 publications

References 29 publications

Estimation of net primary productivity and its driving factors in the Ili River Valley, China

Estimation of net primary productivity and its driving factors in the Ili River Valley, China

Modeling carbon uptake by vegetation of grassland ecosystems and its associated factors in China based on remote sensing

Precise Yield Estimation through Improvised CASA Model by Development of Soil and Atmospheric Constants (Sᵮ) and (Aþ)

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