Analysis of the time-lag effects of climate factors on grassland productivity in Inner Mongolia

Understanding the time-lag responses of grassland productivity to climate change is crucial for revealing grassland ecosystem behavior, predicting future grassland productivity, and guiding animal husbandry practices. This study developed a new analytical method to examine lag effects of climate change by integrating three techniques of time-lag effect analysis, time-lag accumulation computation and partial least-square regression. Net primary productivity (NPP) of vegetation was used as an evaluation indicator for grassland productivity. Based on the monthly scale of the continuous growing season (with a delay of 0–3 months), the effects of temperature and precipitation on grassland productivity of Inner Mongolia were assessed. The results showed that the correlation coefficient between the estimated NPP based on the CASA (Carnegie-Ames-Stanford Approach) model and the MOD17A3-NPP data reached 0.949. Compared with the estimation using CASA, the proportion of correlation coefficients for NPP derived from the PLS regression against temperature or precipitation was over 90% at P < 0.01. The lag effect of temperature and precipitation on grassland productivity was approximately 3 months. The contribution rates over 0–3 months gradually decreased from positive to negative. Over 89% of the study areas, the temperature and precipitation of the current month had the most considerable contribution to NPP. In the remaining areas, the strongest lag effect occurred in the second or third month.

Abstract licence: CC BY-NC-ND

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