摘要:净初级生产力（NPP）是评估植被生长的重要参数，也是评价区域生态环境质量的重要指标。以"一带一路"枢纽地区西北六省为研究区，基于多年连续的GIMMS NDVI资料和气象数据，利用CASA模型，估算了西北六省34年NPP值，利用MK和EEMD方法，揭示了NPP变化的非线性特征，并探究不同时间尺度植被NPP变化对气候变化的响应。研究结果发现：（1）1982-2015年生长季植被NPP总体呈增加趋势，线性增长率为0.718 gCm^-2 a^-1；大多数研究区植被NPP短时间内将保持现有变化趋势，尤其是青藏高原、塔里木盆地边缘和内蒙古南部一带。（2）1982-2015年植被NPP以3年周期变化和长期增加趋势为主。其中陕西的南部，甘肃、新疆、宁夏和青海的北部以及内蒙古中部和北部以3年周期变化为主导，而陕西的北部，甘肃、新疆、宁夏的南部以及内蒙古东部以长期变化为主。不同植被类型NPP变化差异明显：针叶林、阔叶林以及混交林以3年周期波动为主，而灌木、草地和农田以3年周期波动和长期增长趋势为主。（3）NPP与气温和降水之间的相关性随着时间尺度的增大逐渐显著。在3年时间尺度上，大多数研究区NPP与气温和降水的相关性很小（P > 0.05）。6年时间尺度上，NPP与降水量呈正相关的区域向南略有扩散，其中青海南部高寒草甸NPP与降水的相关性由负相关转为正相关。在长期趋势上，NPP与气温和降水量具有非常显著的相关关系，且呈正相关的区域大于负相关的区域。本研究发现多时间尺度能够更好的分析NPP时空特征以及不同时间尺度NPP对气候变化的响应，有助于揭示全球气候变化背景下植被NPP对气候变化的非线性响应机制，评价气候变化的生态坏境风险，为西北六省区域可持续发展和生态环境保护提供理论依据。



Abstract:Net primary productivity (NPP) is an important parameter for evaluating ecosystem vegetation growth and a key indicator for assessing the quality of the ecological environment. This study examined the six provinces in the hub region of "the Belt and Road"(B&R) in northwestern China as research areas. NPP in the study area during 1982-2015 was estimated using a modified parameter CASA model with GIMMS NDVI data, vegetation type data, and meteorological data. The nonlinear characteristics of NPP changes were revealed with the M-K and EEMD methods, and the response of vegetation NPP changes to climate change at different time scales was explored. The results showed that:(1) The NPP values in the vegetation growing season generally showed an increasing trend with a linear growth rate of 0.718gCm^-2a^-2. In most areas, NPP values should continuously maintain the current trend of change in the future, especially in southern Inner Mongolia, the Qinghai-Tibet Plateau, and the edge of the Tarim Basin. (2) The dynamics of vegetation NPP were mainly divided into 3-and 6-year time scales, and a long-term increasing trend. In northern Shaanxi, northern Xinjiang, northern Ningxia, northern Gansu, northern Qinghai, and central and northern Inner Mongolia, NPP was dominated by 3-year scale changes, whereas it was dominated by long-term changes in northern Shanxi, southern Gansu, southern Ningxia, southern Xinjiang, and eastern Inner Mongolia. For different vegetation types, shrubs, grasslands, and farmland were dominated by 3-year time scale changes and long-term growth trends, whereas coniferous forests, broad-leaved forests, and mixed forests were dominated by changes in the 3-year time scale. (3) The relationship between NPP change and temperature and precipitation became increasingly significant with the increase of the time scales. At the 3-year time scale, the correlation between vegetation change and temperature and precipitation was not significant in most regions (P > 0.05). At the 6-year time scale, there was a positive correlation between NPP and precipitation, which expanded towards the south. The correlation of the alpine meadows in southern Qinghai shifted from negative to positive. Over the long-term time scale, the relationship between vegetation NPP and temperature and precipitation became very significant. In most study areas, areas with positive correlations were much larger than areas with a negative correlation. Our results highlight that multi-time scale analysis can better analyze the spatial and temporal characteristics of NPP and its response to climate change at different time scales, which can help reveal the nonlinear response mechanism of vegetation NPP to climate change under global warming and evaluate the eco-environment induced by climate change. Our data provide scientific support for the sustainable development and ecological environment protection of the six provinces in the northwest.





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