Spatio-temporal dynamics and climate response of grassland net primary productivity in Shiyang River Basin from 2000 to 2020

The Shiyang River Basin is a typical arid inland river basin in the Hexi river. Its ecological base is sensitive and fragile. Exploring the relationship between the number and distribution of net primary productivity (NPP) and its response to climate is of great significance to grassland management in this area. This study was based on the improved Carnegie-Ames-Stanford approach (CASA) model and the comprehensive and sequential classification system (CSCS) to simulate the grassland NPP in the Shiyang River Basin from 2000 to 2020, supplemented by Sen’s slope, coefficient of variation (CV) and Hurst index. The correlation between accumulated temperature, precipitation, and NPP was analyzed by partial correlation analysis. The results showed that: 1) The average annual NPP value of grassland was 170.24 g·(m²·a)⁻¹. The NPP increased by 28.96 g·m⁻² over 10 years, showing a fluctuating upward trend. The NPP will increase further in the future. 2) The highest annual NPP of grassland was measured in the mountain meadow (ⅡE30) with an annual NPP value of 548.74 g·(m²·a)⁻¹, followed by mountain meadow grassland (ⅡD23) with an annual NPP value of 454.50 g·(m²·a)⁻¹. The lowest annual NPP of grassland was measured in the temperate desert (ⅣA4) with an average annual value of 91.65 g·(m²·a)⁻¹. 3) Overall, the grassland was stable, the grassland with medium fluctuation occupied the prominent position, and only cool temperate-semiarid temperate typical steppe (ⅢC17) had a high fluctuation. 4) Precipitation was the dominant factor in increasing NPP in most grasslands in the basin. The area sensitive to precipitation accounts for 24.93% of the basin area. Only two types of desert grassland (ⅢA3 and ⅣA4) were not visibly sensitive to precipitation; NPP of meadow grassland was the most sensitive to accumulated temperature. There was a certain negative correlation between desert grassland and accumulated temperature.