Trend Analysis of Relationship between Primary Productivity, Precipitation and Temperature in Inner Mongolia

Abstract

This study mainly examined the relationships among primary productivity, precipitation and temperature by identifying trends of change embedded in time-series data. The paper also explores spatial variations of the relationship over four types of vegetation and across two precipitation zones in Inner Mongolia, China. Traditional analysis of vegetation response to climate change uses minimum, maximum, average or cumulative measurements; focuses on a whole region instead of fine-scale regional or ecological variations; or adopts generic analysis techniques. We innovatively integrate Empirical Mode Decomposition (EMD) and Redundancy Analysis (RDA) to overcome the weakness of traditional approaches. The EMD filtered trend surfaces reveal clear patterns of Enhanced Vegetation Index (EVI), precipitation, and temperature changes in both time and space. The filtered data decrease noises and cyclic fluctuations in the original data and are more suitable for examining linear relationship than the original data. RDA is further applied to reveal partial effect of precipitation and temperature, and their joint effect on primary productivity. The main findings are as follows: (1) We need to examine relationships between the trends of change of the variables of interest when investigating long-term relationships among them. (2) Long-term trend of change of precipitation or temperature can become a critical factor influencing primary productivity depending on local environments. (3) Synchronization (joint effect) of precipitation and temperature in growing season is critically important to primary productivity in the study area. (4) Partial and joint effects of precipitation and temperature on primary productivity vary over different precipitation zones and different types of vegetation. The method developed in this paper is applicable to ecosystem research in other regions.

Adaptive Decomposition and Multitimescale Analysis of Long Time Series of Climatic Factors and Vegetation Index Based on ICEEMDAN-SVM

“…The study used the ICEEMDA-SVM method to strip out the residual of the long time series to study the spatial and temporal trends of vegetation on the LP from 1982-2015. Previous studies pointed out that the residual component can represent the overall trend of the original time series [35], [36], [78], so it can justify the study in this paper. The study showed that the spatial trend of vegetation mainly increased on LP from 1982-2015.…”

“…Verma and Dutta [34] used EMD to analyze the NDVI time series to obtain different traits of vegetation phenology and confirmed the significance of vegetation change trend by nonparametric seasonal Mann-Kendall test. Chen et al [35] integrated EMD and Redundancy Analysis to show temporal and spatial differences in the Enhanced Vegetation Index, precipitation, and temperature and analyzed the partial effect of precipitation and temperature on primary productivity. Qi et al [36] used EEMD and Residual Trends methods to explore the relationship between climate change, human activities, and vegetation index on multiple timescales in China's Silk Road Economic Belt.…”

Climate seasonality and extremes influence net primary productivity across California’s grasslands, shrublands, and woodlands

“…For example, both positive and negative deviations of mid spring temperatures from average were associated with a greater decline in NPP through time in blue oak systems and drier falls appeared to be associated with a smaller decrease in NPP through time in coastal scrub. The range of factors that drive climate and time may interact to alter NPP, including nonlinear responses of NPP to climate and the potential for NPP to be impacted by the synchronization of precipitation and temperature or possibly long term changes in vegetation health or structure (Knapp and Smith 2001, Huxman et al 2004, Bai et al 2008, Chaplin-Kramer and George 2013, Chen et al 2018, Al-Yaari et al 2020. While we are not able to assign a causal mechanism to these complex interactions, a deeper understanding of how interannual climate conditions differ through time and covary with drought and other climate extremes appears to be crucial to understand how California's vegetation may or may not support the State in meeting its climate goals through time.…”

Study on NDVI Periodic Change Characteristics of Inner Mongolia Grassland Based on Variational Mode Decomposition Algorithm

“…It has the characteristics of adaptability, orthogonality, and completeness, and is widely used in vegetation periodic feature analysis, time series feature analysis, prediction, and forecasting [26]. For example, Han et al studied and analyzed the periodicity of NDVI in the Heihe River basin and its relationship with climate factors using an empirical mode decomposition method; Chen et al used EMD signal trend extraction technology to remove noise and error information, and accurately obtained the growth trend of different types of vegetation; Chen et al applied the EMD method combined with the NDVI data of Huaihe River basin to analyze the periodic change of vegetation in the basin; and Zhang et al studied the evolution of rainfall series characteristics in Hubei Province by coupling EEMD and EMD algorithms, and also achieved the expected research results [27][28][29].…”