Quantifying the contributions of human activities and climate change to vegetation net primary productivity dynamics in China from 2001 to 2016

Ge, Wenyan; Deng, Liqiang; Wang, Fei; Han, Jianqiao

doi:10.1016/j.scitotenv.2021.145648

Cited by 303 publications

(166 citation statements)

References 57 publications

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“…The NPP of MOD17A3 was calculated by the BIOME-BGC model with 500 m spatial resolution and 1a temporal resolution [19,20]. NPP dataset was widely used to explore vegetation ecosystem variation [9,19,21]. Hence, we applied the nearest neighbor method to resample the NPP dataset to 1 km, with the same spatial resolution as the forest dataset.…”

Section: Datasetsmentioning

confidence: 99%

“…The NPP of MOD17A3 was calculated by the BIOME-BGC model with 500 m spa resolution and 1a temporal resolution [19,20]. NPP dataset was widely used to exp vegetation ecosystem variation [9,19,21]. Hence, we applied the nearest neighbor met to resample the NPP dataset to 1 km, with the same spatial resolution as the forest data Monthly meteorological data consists of precipitation, temperature, and solar ra tion from 2000 to 2019, which are available in the National Earth System Science D Center (http://www.geodata.cn/.…”

Section: Datasetsmentioning

confidence: 99%

“…Their effects vary across climate zones, geomorphic types, and forest types [3,7]. For example, the temperature is considered as a critical factor for forest restoration in high-latitudes and high latitude regions, precipitation in arid and semiarid areas, and solar radiation in tropical zones, respectively [3,[7][8][9]. With climate change, climate factors such as extreme precipitation, drought, and high temperature can also lead to forest degradation [9,10].…”

Section: Introductionmentioning

confidence: 99%

“…In addition, human activities are another main driving force of forest dynamics. Some human activities (such as urbanization and huge population growth) may be the main driving forces for forest degradation [11], while other activities (such as returning farmland to forestland, mountain closure, and afforestation) promotes forest restoration [9,12,13]. However, the relative effects on NPP from climate change and human activities are still unclear, and it is still ambiguous which factors, natural factors or human factors, dominate forest restoration and degradation.…”

Section: Introductionmentioning

confidence: 99%

“…To date, Great efforts have been made to separately quantify the impacts of climate change and human activities on forest ecosystems. The most commonly used quantitative methods can be classified into three categories: the regression model method, the biophysical model method, and the residual trend method [9,14]. The regression model method is the simplest, but it is hard to distinguish the complex interactions between forest and internal and external influence factors such as climate factors and human activities [14].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Disentangling Climatic Factors and Human Activities in Governing the Old and New Forest Productivity

Chen

Wen

et al. 2021

Remote Sensing

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Forest ecosystem plays a vital role in the global carbon cycle and maintaining climate stability. However, how net primary productivity (NPP) dynamics of different stand ages of forest respond to climatic change and residual (being other climate factors or human activities) still remain unclear. In this study, firstly, forests are divided into two categories based on their stand age: forests appeared before appeared before the research period (Fold), and forests appeared during the research period (Fnew). Secondly, we improved a quantitative method of basic partial derivatives to disentangle the relative contributions of climatic factors, other climate factors, and human activities to the NPP of Fold and Fnew. Then, different scenarios were simulated to identify the dominant drivers for forest restoration and degradation. In this study, we hypothesized the residual of Fold was other climate factors rather than human activities. Our results revealed that from 2000 to 2019, Fold and Fnew of NPP in Yangtze River Basin showed a significant increment trend and precipitation was the major positive contributor among all of the climatic factors. We found that, in Fold, climate change and other climate factors contributed 9.77% and 28.33%, respectively, in explaining NPP. This finding unsupported our initial hypothesis and implied that residuals should be human activities for Fold. Furthermore, we found that human activities dominate either restoration or degradation of Fnew. This result may be due to the attenuated human disturbances and strengthened forest management, such as ecological policies, forest tending, closing the land for reforestation, etc. Therefore, based on disentangling the two types of factors, we concluded that human activities govern the forest change, and imply that the environment-friendly forest managements may favorite to improving the forest NPP against the impacts of climate change. Thus, effective measures and policies are suggested implement in controlling forest degradation in facing climate change.

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Section: Datasetsmentioning

confidence: 99%

Section: Datasetsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Disentangling Climatic Factors and Human Activities in Governing the Old and New Forest Productivity

Chen

Wen

et al. 2021

Remote Sensing

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Monitoring long‐term vegetation dynamics over the Yangtze River Basin, China, using multi‐temporal remote sensing data

Fu,

Liu,

Qin

et al. 2024

Ecosphere

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Vegetation plays a crucial role in nature, with intricate interactions between it and the geographical environment. The Yangtze River Basin (YRB) refers to the third largest river basin globally and an essential ecological security barrier in China. Monitoring vegetation dynamics in the basin is of profound significance for addressing climate change, soil erosion, and biodiversity loss in the basin's ecosystems. Here, we investigate the spatiotemporal variations of vegetation at both the basin and land cover scales in the YRB from 2000 to 2020. We elucidate the determinants driving the changes and explore future normalized difference vegetation index (NDVI) trends. The results indicate that NDVI in the YRB increased at a rate of 0.0032 year−1 (p < 0.01) over the past 21 years, and it is anticipated to maintain an upward trend in the future. Regions in the upper and middle reaches of the YRB demonstrated higher NDVI, whereas regions in the headwater area and the lower reaches showed lower NDVI. Significant vegetation improvement was primarily concentrated in the central part of the basin, while noticeable vegetation degradation was observed in the eastern region. Temperature and wind speed were identified as the primary controlling factors affecting vegetation greenness. Global‐scale climate oscillations played a significant role in driving periodic variations in NDVI, with La Niña events tending to increase NDVI, while El Niño events hindered its rise. Land cover types were influenced by long‐term interactions between natural factors and human activities, although short‐term vegetation variations might be more affected by the latter. Our findings provide valuable insights into the mechanisms behind vegetation variability driven by multiple variables, and the strong vegetation carbon sink capacity advances the conservation and development of ecosystems.

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Impacts of climate change on winter wheat net primary production: the regulatory role of crop management

Wu,

Gu,

Sun

et al. 2023

J Sci Food Agric

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BackgroundThe Huang–Huai–Hai Plain (3HP) is the main agricultural area in China. Although climate change (CC) and crop management (CM) are considered factors affecting the winter wheat net primary production (NPP) in this region, their effects remain unclear. In this study, we evaluated the relative contributions of CC and CM to winter wheat aboveground NPP (ANPP) in the 3HP and the relationships between climatic factors therein and ANPP by using the first‐order difference method from 2000 to 2020.ResultsCM had a greater influence on the ANPP of winter wheat than did CC. However, the relative contribution of CM to ANPP gradually decreased in humid and dry sub‐humid regions with the development of winter wheat. Furthermore, in areas characterized by low temperatures and limited precipitation, CC became the dominant factor contributing to ANPP, indicating that varieties resilient to drought and cold should be selected in these regions. Minimum and average temperatures were the dominant factors driving spatiotemporal variations in ANPP during the early stage of winter wheat growth, whereas maximum temperature constrained growth throughout the winter wheat growth cycle. When winter wheat entered the vigorous growth stage, precipitation and solar radiation replaced temperature as the driving factors influencing winter wheat growth.ConclusionOur results provide guidance for optimizing winter wheat crop management in the 3HP.This article is protected by copyright. All rights reserved.

show abstract

Quantifying the contributions of human activities and climate change to vegetation net primary productivity dynamics in China from 2001 to 2016

Cited by 303 publications

References 57 publications

Disentangling Climatic Factors and Human Activities in Governing the Old and New Forest Productivity

Disentangling Climatic Factors and Human Activities in Governing the Old and New Forest Productivity

Monitoring long‐term vegetation dynamics over the Yangtze River Basin, China, using multi‐temporal remote sensing data

Impacts of climate change on winter wheat net primary production: the regulatory role of crop management

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