Assessing the Net Primary Productivity Dynamics of the Desert Steppe in Northern China during the Past 20 Years and Its Response to Climate Change

Abstract

The net primary productivity (NPP) dynamics in arid and semi-arid ecosystems are critical for regional carbon management. Our study applied a light-utilization-efficiency model (CASA: Carnegie–Ames–Stanford Approach) to evaluate the vegetation NPP dynamics of a desert steppe in northern China over the past 20 years, and its response to climate change. Our results show that the annual average NPP of the desert steppe was 132 g C m−2 y−1, of which the grass- and shrub-dominated biome values were 142 and 91 g C m−2 y−1, respectively. The average change rate of NPP was 1.13 g C m−2 y−1, and in the grassland biome 1.31 g C m−2 y−1, a value which was significantly higher than that in shrubland, at 0.84 g C m−2 y−1. The precipitation and temperature at different time scales in the desert steppe showed a slow upward trend, and the degree of aridity tended to weaken. The correlation analysis shows that NPP changes were significantly positively and negatively correlated with precipitation and temperature, respectively. In terms of temperature, 43% of the area was significantly correlated during the growing season, which decreased to 12% on the annual scale. In 31% of the changed areas, the average NPP was 148.1 g C m−2 y−1, which was higher than the remaining significant areas. This suggests that higher NPP levels help to attenuate the negative effects of high temperature during the growing season on plant productivity in the desert steppe. This improves the understanding of the carbon cycle mechanism of arid and semi-arid ecosystems, which is beneficial to improving sustainable grassland development strategies.

Estimation of Terrestrial Net Primary Productivity in the Yellow River Basin of China Using Light Use Efficiency Model

“…The NPP of vegetation is an essential factor and ecological index that can directly reflect changes in an ecosystem and a regional carbon budget [12,13]. The basis of ecosystem energy flow and the material cycle is vegetation productivity, the total amount of dry matter that green plants can fix per unit time and unit area, directly related to an ecosystem carbon sink, is known as NPP [14].…”

Yield estimation of summer maize based on multi‐source remote‐sensing data

“…Among them, CASA is widely used as a typical representative of light‐use efficiency models (Hao et al., 2019). The CASA is a light‐use efficiency model that integrates environmental factors and the characteristics of vegetation, mainly using remote sensing (RS) and geographic information systems (GIS) as technical means, which is driven by remote‐sensing data and meteorological data such as temperature, precipitation, solar radiation, vegetation type, and soil type (B. Yang et al., 2022). Within the Chinese region, compared with statistical models (Thornthwaite–Memorial) and process models, CASA showed the highest agreement with MODIS NPP and observations (Sun et al., 2021).…”