Mean Annual Net Primary Productivity Research Articles

Climate change driven by LUCC reduced NPP in the Yellow River Basin, China

Anthropogenic activities and the resulting climate change affect the type, structure, and function of ecosystems. Understanding vegetation dynamics related to anthropogenic activities and climate change is critical to address the terrestrial carbon cycle in the context of global warming. The objective of this study is to quantify the effects of human-induced land use and land cover change (LUCC) and LUCC-induced climate change on terrestrial net primary productivity (NPP) in the Yellow River Basin (YRB) during 2000–2020 using Weather Research and Forecasting (WRF) model and Integrated Biosphere Simulator (IBIS) model through different experimental scenarios. Results indicated that LUCC can cause an increase in NPP of 1.2 ± 0.67 gC m−2 yr−1 in YRB. The increased precipitation and decreased temperature due to LUCC showed weak negative effect on annual mean NPP in YRB (−0.2 ± 0.74 gC m−2 yr−1). The coupling of LUCC and LUCC-induced climate change increased annual mean NPP approximately 0.6 ± 0.86 gC m−2 yr−1. The impacts of LUCC and LUCC-induced climate change and their coupling effects on NPP were greatest in spring, increasing NPP by 5.1 ± 0.51, 3.4 ± 0.41, and 6.1 ± 0.79 gC m−2 yr−1, respectively. These findings provide important guidance for the sustainable and adaptive management of terrestrial ecosystems in river basin in the context of global change.

Assessment of climatic influences on net primary productivity along elevation gradients in temperate ecoregions

Elevation gradients significantly influence net primary productivity (NPP), but the relationship between elevation, climate variables, and vegetation productivity remains underexplored, particularly in diverse ecological zones. This study quantifies the impact of elevation and climatic variables on NPP in northern Pakistan, hypothesizing that elevation modulates NPP through its influence on temperature and precipitation patterns. Using remote sensing data (MODIS ERA5) and advanced ecological models like the Eddy Covariance-Light Use Efficiency (EC-LUE) model and the Thornthwaite Memorial Model (TMM), we analyzed Gross Primary Productivity (GPP) dynamics across various vegetation types and elevations from 2001 to 2023. Our findings show a mean annual NPP of 323.46 g C m-2 a-1, with an annual increase of 5.73 g C m-2 a-1. Significant elevation-dependent variations were observed, especially in mid-elevation zones (401 to 1600 meters), where NPP increased at a rate of 0.174 g C m-2 a-1 per meter (R² = 0.808, p < 0.01). In contrast, higher elevations (2800-5200 meters) exhibited a decline in NPP, decreasing by -0.171 g C m-2 a-1 per meter (R² = 0.905, p < 0.001). Temperature and precipitation were key drivers, with precipitation positively correlating with NPP across all vegetation types, particularly in Evergreen Needleleaf and Broadleaf Trees. The EC-LUE model's GPP estimates closely matched MODIS data (R² = 0.82), demonstrating the model's reliability. These findings highlight the critical role of elevation and climatic factors in vegetation productivity and underscore the need for targeted ecological management and conservation strategies. The insights from this research are vital for global climate adaptation policies and sustainable development goals, contributing to ecological resilience and carbon sequestration efforts worldwide.

Vertical stratification of phytoplankton biomass in a deep estuary site: implications for satellite-based net primary productivity

The accuracy of satellite estimates for water column net primary productivity (NPP) are contingent upon the reliability of surface phytoplankton biomass, specifically chlorophyll a (Chl.a) and carbon (Cphyt), as indicators of euphotic biomass and photosynthetic rate. We assessed patterns in water column biomass at a deep estuary site (~40 m) in the Firth of Thames, Hauraki Gulf, New Zealand, using ten years (2005-2015) of in situ sampling (40 seasonal voyages and moored instrumentation). Seasonal biomass stratification coincided with physical and chemical stratification and exhibited a reasonable predictability based on surface Chl.a measures from mooring timeseries. High Chl.a (but not Cphyt) accumulated from late-spring (Nov.) in the lower portion of the water column, under nutrient deficient, clear surface water with deep euphotic zone conditions, peaking in mid-summer (Jan.) and ending by early autumn (Mar.). Satellite (MODIS-Aqua) NPP (2002-2018), was estimated with and without correction for deep biomass in two vertically generalized production models (Chl.a-VGPM and Cphyt-CbPM). Mean annual NPP (220-161 g C m-2 y-1, VGPM and CbPM respectively) increased 5-18% after accounting for euphotic zone deep biomass with a mid-summer maxim (Jan.: 30-33%). Interannual anomalies in biomass and NPP (about -10% to 10%) were an order of magnitude greater than small decreasing trends (&amp;lt;&amp;lt; 1% y-1). We discuss the impacts of observational factors on biomass and NPP estimation. We offer contextual insights into seasonal patterns by considering previous observations of biomass trends and nutrient enrichment in the Firth of Thames region. We propose future directions in accounting for deep biomass variations from shallow coastal areas to deeper continental shelf waters.

Characteristics of carbon sources and sinks and their relationships with climate factors during the desertification reversal process in Yulin, China

Research on carbon sources/sinks in desert ecosystems is of great importance to understand the carbon cycle and its response to climate change. Net primary productivity (NPP) and net ecosystem productivity (NEP) are the two most important indictors for quantitatively evaluating carbon storage and can be used to indicate the response of terrestrial ecosystems to climate change. In this study, we used remote sensing data, meteorological data and vegetation type data to estimate the NPP and NEP using CASA model and soil respiration model from 2000 to 2020 in the region of Yulin, which is a typical desertification reversal region in the Mu Us Sandy Land. The spatial and temporal features of the NPP and NEP and their relationships with temperature and precipitation were determined. The results showed that both the annual NPP and NEP showed an increasing trend from 2000 to 2020 in the region of Yulin, where the terrestrial ecosystem acted as a carbon source until 2001 but turned into a sink thereafter. The carbon storage showed an increasing trend with a rate of 0.50 Tg C·a−1 from 2000 to 2020. Both the mean annual NPP and the total NEP increased from the west to the east of the region in spatial distribution. The total NEP indicated that the area with a carbon sink accounted for 89.22% of the total area, showing a carbon accumulation of 103.0 Tg C, and the carbon source area accounted for 10.78% of the total area with a carbon emission of 4.40 Tg C. The net carbon sequestration was 99.44 Tg C in the region of Yulin during the period from 2000 to 2020. Temperature had no significant effects on NPP and NEP for most areas of the region, while precipitation had a positive effect on the increasing NPP in 75.3% of areas and NEP in 30.07% of areas of the region. These results indicated that it is of utmost significance to protect terrestrial ecosystems from degradation, and ecological restoration projects are essential in combating desertification, which would be helpful for soil water conservation and could effectively increase carbon storage in desert ecosystems.

The Relation Between Net Primary Productivity And Human Activities For Three Biomes In Bahia State, Brazil

Brazilian biomes are hotspots of global biodiversity, important biomass producers and, consequently, help maintain the world’s carbon balance. Net primary production (NPP) is a variable used to determine carbon uptake by land cover. As environmental factors and human activities vary, net primary production increases or decreases. This study aimed to evaluate NPP in three Brazilian biomes – Atlantic Forest, Cerrado, and Caatinga – in the state of Bahia for the last 17 years, and to understand its relationship with human activities by analyzing burned areas, as well as interrelated environmental factors, such as climate variability and soil heat flux, using remote sensing. Using the MOD17 dataset, we find evidence that the Atlantic Forest biome is the one that absorbs more carbon in comparison to the Caatinga and Cerrado biomes, with a mean annual net primary production in each one of these three biomes equal to 1,227.89 g C m-2, 913.81 g C m-2, and 803.56 g C m-2, respectively. The years of El Niño influenced all biomes, and the results showed a strong relationship between climate and NPP in the studied biomes, especially in Caatinga, which is the most sensitive to climatic variations. Besides these results, we find evidence that, in all these biomes, the NPP dynamics have been affected by the increase in land use for agricultural and livestock activities, mainly because of deforestation and burning.

Net Primary Productivity Variations Associated with Climate Change and Human Activities in Nanjing Metropolitan Area of China.

Rapid economic development has changed land use and population density, which in turn affects the stability and carbon sequestration capacity of regional ecosystems. Net primary productivity (NPP) can reflect the carbon sequestration capacity of ecosystems and is affected by both climate change and human activities. Therefore, quantifying the relative contributions of climate change and human activities on NPP can help us understand the impact of climate change and human activities on the carbon sequestration capacity of ecosystems. At present, researchers have paid more attention to the impact of climate change and land use change on NPP. However, few studies have analyzed the response of the NPP to gross domestic product (GDP) and population density variations on a pixel scale. Therefore, this paper analyzes the impact of climate change and human activities to NPP on a pixel scale in the Nanjing metropolitan area. During the period 2000-2019, the annual mean NPP was 494.89 g C·m-2·year-1, and the NPP in the south of the Nanjing metropolitan area was higher than that in the north. The NPP was higher in the forest, followed by unused land, grassland, and cropland. In the past 20 years, the annual mean NPP showed a significant upward trend, with a growth rate of 3.78 g C·m-2·year-1. The increase in temperature and precipitation has led to an increasing trend of regional NPP, and the impact of precipitation on NPP was more significant than that of temperature. The transformation of land use from low-NPP type to high-NPP type also led to an increase in NPP. Land use change from high-NPP type to low-NPP type was the main cause of regional NPP decline. Residual analysis was used to analyze the impact of human activities on NPP. Over the last 20 years, the NPP affected by human activities (NPPhum) showed a high spatial pattern in the south and a low spatial pattern in the north, and the annual mean NPPhum also showed a fluctuating upward trend, with a growth rate of 2.00 g C·m-2·year-1. The NPPhum was influenced by both GDP and population density, and the impact of population density on NPP was greater than that of GDP.

Temporal and spatial variation of vegetation net primary productivity and its driving factors in Ningxia, China from 2000 to 2019

Ningxia is an important agriculture-pastoral area in China. Research on the temporal and spatial variations of net primary productivity (NPP) in this area and its driving factors would help understand the trends and leading factors of NPP variations, and reveal vegetation restoration status and causes. Based on the NPP data of MODIS, we used Theil-Sen Median trend analysis, correlation analysis, overlay analysis and other methods to analyze the spatiotemporal variations and driving factors of NPP in Ningxia. The results showed that NPP of Ningxia vegetation showed a fluctuating upward trend during 2000-2019, with a linear growth rate of 5.46 g C·m-2·a-1. The NPP presented a spatial characteristics of "two high and two low", with the highest in the southern mountain area, followed by the Yellow River irrigation area, and the lowest in the hilly area of the central arid zone and Helan Mountain. 84.2% of the NPP in the study area was under significant restoration, mainly distributed in the central and southern mountainous and hilly areas. The NPP varied significantly with altitude and was significantly affected by the terrain. NPP center of gravity had generally moved to the south, with the increase and growth rate of NPP of vegetation in the south being greater than that in the north. The annual precipitation in Ningxia had shown an increasing trend, while the average annual temperature had slightly decreased. The NPP was significantly affec-ted by precipitation (R2=0.291), whereas the correlation between mean annual temperature and NPP was weak. 96.9% of the study area was at a state of vegetation restoration, which was promoted by both climate change and human activities.

Spatiotemporal patterns and drivers of net primary production in the terrestrial ecosystem of the Dajiuhu Basin, China, between 1990 and 2018

Net primary production (NPP) plays a vital role in both the evolution of ecosystems and the terrestrial carbon cycle and is influenced by geographical conditions and climate change. Understanding the terrestrial carbon balance requires an in-depth knowledge of the relationships between NPP and geographical and climatic conditions. This study aimed to simulate and map the daily spatiotemporal features of terrestrial NPP in the Dajiuhu Basin (DB), China, using the BEPS-TerrainLab V2.0 model. This area is highly sensitive to climate change and is a water source in the central path of the South-to-North Water Transfer Project. Changes in the distribution of daily and seasonal NPP between 1990 and 2018 were examined using the Mann-Kendall (MK) test, the moving t-test (MTT), and multiple regression analyses. It was found that: 1) The model explained 79% of the variation in eddy covariance (EC)-tower-measured NPP, and could thus be applied to the DB; 2) The mean annual NPP in the DB between 1990 and 2018 was 705 g C/m2/yr, with the terrestrial NPP decreasing before 1999 (−31.8 g C/m2/yr) and increasing after 1999 (0.87 g C/m2/yr); 3) The NPP first increased and then decreased with increasing altitude, with higher NPP values mainly found in the mountains on the periphery of the basin and lower NPP values in the central basin;4) Changes in NPP during autumn and summer contributed the most to the annual NPP trend. Temperature and NPP were positively correlated in summer and autumn, whereas they were negatively correlated in spring and winter. Precipitation and NPP were positively correlated in spring, autumn, and winter; 5) The sensitivities of NPP to temperature and precipitation differed across the different seasons. The sensitivities of the annual NPP to temperature and precipitation decreased and increased, respectively, compared with those before 1999. Although the contribution of precipitation to the NPP trend became more significant after 1999, that of temperature decreased. This study proposes an approach for a detailed study of daily changes in NPP and for examining the link between environmental factors, climatic conditions, and NPP distribution.

Estimation of Terrestrial Net Primary Productivity in the Yellow River Basin of China Using Light Use Efficiency Model

The net primary productivity (NPP) of vegetation is an essential factor of ecosystem functions, including the biological geochemical carbon cycle, which is often impacted by climate change and human activities. It plays a significant role in comprehending the nature of carbon balance in an ecosystem and demonstrates the global and regional carbon cycle dynamics. The present study used an upgraded CASA model to calculate the NPP in the Yellow River Basin (YRB), China. The model’s simulation ability was improved by changing the model parameters. Further, the CASA model was validated by comparing with MODIS-NPP and in situ observed NPP, wherein the accuracy of the CASA model estimation was found satisfactory to estimate NPP changes in the study area. The simulated results of the improved CASA model showed that the mean annual NPP value of vegetation in the YRB was 283.4 gC m–2 a–1 from 2001 to 2020, with a declining trend in spatial distribution from south to north. In contrast, the NPP appeared as an increasing trend in the YRB temporally from 212 gC m–2 a–1 in 2001 to 342 gC m–2 a–1 in 2020, with a mean annual growth rate of 4.6 gC m–2 a–1. The total NPP in the YRB increased by 40,088.3 GgC between 2001 and 2020, from 226.06 TgC to 266.15 TgC. This rise can be attributed to the increase in forests. The average grassland area has reduced by 4651 km2 during the last two decades, significantly impacting the total NPP of grasslands. Although the increase in NPP in wetlands was minimal, accounting for 815.53 GgC, the highest change percentage of 79.78%, could be observed among the six vegetation types due to the anthropogenic influences and climate change. The conditions favorable for vegetation growth and a sustained environment were enhanced by the increased precipitation and temperature and the reinforced ecological protection by the government.

Global Sensitivity Analysis of the LPJ Model for Larix olgensis Henry Forests NPP in Jilin Province, China

Parameter sensitivity analysis can determine the influence of the input parameters on the model output. Identification and calibration of critical parameters are the crucial points of the process model optimization. Based on the Extended Fourier Amplitude Sensitivity Test (EFAST) and the Morris method, this paper analyzes and compares the parameter sensitivity of the annual mean net primary productivity (NPP) of Larix olgensis Henry forests in Jilin Province simulated by the Lund–Potsdam–Jena dynamic global vegetation model (LPJ model) in 2009–2014 and 2000–2019, and deeply examines the sensitivity and influence of the two methods to each parameter and their respective influence on the model’s output. Moreover, it optimizes some selected parameters and re-simulates the NPP of Larix olgensis forests in Jilin Province from 2010 to 2019. The conclusions are the following: (1) For the LPJ model, the sensitive and non-influential parameters could be identified, which could guide the optimization order of the model and was valuable for model area applications. (2) The results of the two methods were similar but not identical. The sensitivity parameters were significantly correlated (p &lt; 0.05); parameter krp was the most sensitive parameter, followed by parameters αm, αa and gm. These sensitive parameters were mainly found in the photosynthesis, water balance, and allometric growth modules. (3) The EFAST method had a higher precision than the Morris method, which could calculate quantitatively the contribution rate of each parameter to the variances of the model results; however, the Morris method involved fewer model running times and higher efficiency. (4) The mean relative error (MRE) and mean absolute error (MAE) of the simulated value of LPJ model after parameter optimization decreases. The optimized annual mean value of NPP from 2010 to 2019 was 580 g C m−2 a−1, with a mean annual growth rate of 2.13%, exhibiting a fluctuating growth trend. The MAE of the simulated value of LPJ model after parameter optimization decreases.

基于GEE平台的黄河流域森林植被净初级生产力时空变化特征

PDF HTML阅读 XML下载 导出引用 引用提醒 基于GEE平台的黄河流域森林植被净初级生产力时空变化特征 DOI: 10.5846/stxb202104271113 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金(31960330);宁夏自然科学基金(2020AAC03112) Spatio-temporal variation characteristics of forest net primary productivity in the Yellow River Basin based on Google Earth Engine cloud platform Author: Affiliation: Fund Project: National Natural Science Foundation of China (31960330)；Ningxia Natural Science Foundation (2020AAC03112) 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:黄河流域是我国重要的生态屏障,研究黄河流域森林植被净初级生产力(Net Primary Productivity,NPP)的时空变化特征及驱动机制,对解释黄河流域森林碳汇/源变化具有重要意义。基于Google Earth Engine (GEE)云平台,利用MOD17A3H V6 NPP数据、MCD12Q1 V6土地覆盖类型数据、ECMWF/ERA5气象数据和USGS/SRTMGL1_003高程数据,采用岭回归分析、Hurst指数和冗余分析(Redundancy Analysis,RDA)对黄河流域2001-2019年森林NPP的时空变化特征及影响因子进行分析。结果表明:(1)2001-2019年,黄河流域森林平均总面积为3.66万km2,其中阔叶林、针叶林、混交林平均面积分别为:2.64万km2、0.01万km2和1.01万km2,森林NPP年总量呈线性增加趋势,其均值为8.99Tg C,年均增速为0.36Tg C/a,19a增长率为173.60%;不同森林类型的NPP年总量均值分别为:4.79Tg C (阔叶林)、6.04×10-5Tg C (针叶林)和0.64Tg C (混交林),年均增速为:阔叶林(0.16Tg C/a)>混交林(0.04Tg C/a)>针叶林(6.98×10-6Tg C/a)。(2)2001-2019年,黄河流域森林年均NPP呈线性增加趋势,其均值为241.58g C m-2 a-1,年均增速为7.18g C m-2 a-1,19a增长率为108.63%;不同森林类型的年均NPP均值分别为:178.48g C m-2 a-1(阔叶林)、0.60g C m-2 a-1(针叶林)和62.49g C m-2 a-1(混交林),年均增速为:阔叶林(4.75g C m-2 a-1)>混交林(2.39g C m-2 a-1)>针叶林(0.04g C m-2 a-1)。(3)黄河流域森林NPP呈增加趋势的面积占94.50%,其中显著增加的面积占73.29%;呈减少趋势的面积占5.50%,其中显著减少的面积占1.57%。阔叶林NPP显著增加的面积最高(76.78%),其次为混交林(60.84%),针叶林最少(56.76%)。(4)黄河流域森林NPP的Hurst指数(H)介于0.38-1.00之间,平均值为0.87,其中H≥0.5的像元数约占99.34%,黄河流域森林NPP在未来一段时间内仍保持持续增加趋势。(5)归因分析表明环境因子对黄河流域森林NPP时空变化的总解释率为55.80%,显著影响的环境因子为经度(35.50%)、降水(8.00%)、气温(6.50%)和纬度(5.40%)。2001-2019年黄河流域森林NPP呈增加趋势,且呈现较强的可持续性;GEE云平台结合冗余分析可及时、高效获取黄河流域森林NPP的时空变化并对其进行归因分析。 Abstract:The Yellow River Basin is an important ecological barrier in China. To study the spatio-temporal variation characteristics and driving mechanisms of the Net Primary Productivity (NPP) of forest is of great significance to explain the change of forest carbon sink and source in the Yellow River Basin. Based on Google Earth Engine (GEE) cloud platform, MOD17A3H V6 NPP data, MCD12Q1 V6 land cover data, ECMWF/ERA5 weather data, USGS/SRTMGL1_003 elevation data, and ridge regression analysis, Hurst index, redundancy analysis (RDA) were used to analyze the spatio-temporal variation characteristics and influencing factors of the forest NPP in the Yellow River Basin from 2001 to 2019. The results showed that (1) from 2001 to 2019, the average total area of forest in the Yellow River Basin was 36600 km2, of which the average area of broadleaf forest, coniferous forest and mixed forest was 26400 km2, 100 km2 and 10100 km2. The annual total forest NPP showed a linear increasing trend, with an average value of 8.99 Tg C, an average annual growth rate of 0.36 Tg C/a, and a 19-year growth rate of 173.60%. The averagely annual total NPP of different forest types were:4.79 Tg C (broadleaf forest), 6.04×10-5 Tg C (coniferous forest) and 0.64 Tg C (mixed forest), and the averagely annual growth rate were:broadleaf forest (0.16 Tg C/a) > mixed forest (0.04 Tg C/a) > coniferous forest (6.98×10-6 Tg C/a). (2) From 2001 to 2019, the annual average NPP of the forest in the Yellow River Basin increased linearly, with an average value of 241.58 g C m-2 a-1, an average annual growth rate of 7.18 g C m-2 a-1, and a 19-year growth rate of 108.63%. The mean annual average NPP of different forest types were 178.48 g C m-2 a-1 (broadleaf forest), 0.60 g C m-2 a-1 (coniferous forest) and 62.49 g C m-2 a-1(mixed forest), and the averagely annual growth rate were:broadleaf forest (4.75 g C m-2 a-1) > mixed forest (2.39 g C m-2 a-1) > coniferous forest (0.04 g C m-2 a-1). (3) The area of forest NPP showed an increasing trend in the Yellow River Basin accounting for 94.50%, of which 73.29% was significantly increased; the area showed a decreasing trend accounting for 5.50%, of which the area with a significant decrease accounted for 1.57%. The area of the significantly increased of the broadleaf forest NPP was the highest (76.78%), followed by mixed forest (60.84%) and coniferous forest (56.76%). (4) The Hurst index (H) of the forest NPP in the Yellow River Basin was between 0.38-1.00, with an average value of 0.87. Among them, the amount of H ≥ 0.5 accounted for about 99.34%, and the forest NPP in the Yellow River Basin would continue to increase in the future. (5) Attribution analysis showed that the total interpretation rate of the environmental factors on the spatio-temporal variation of the forest NPP in the Yellow River Basin was 55.80%, and the environmental factors that had significant effects were longitude (35.50%), precipitation (8.00%), temperature (6.50%) and latitude (5.40%). From 2001 to 2019, the forest NPP of the Yellow River Basin increased and showed strong sustainability. The GEE cloud platform combined with redundant analysis can timely and efficiently obtain the spatio-temporal variation of the forest NPP of the Yellow River Basin and perform an attribution analysis. 参考文献 相似文献 引证文献

土地利用转变与海拔高度协同作用黄土高原植被固碳变化特征

PDF HTML阅读 XML下载 导出引用 引用提醒 土地利用转变与海拔高度协同作用黄土高原植被固碳变化特征 DOI: 10.5846/stxb202106211643 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 陕西省自然科学基础研究计划项目（2017JZ008） Vegetation carbon sequestration in the Loess Plateau under the synergistic effects of land cover change and elevations Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:全球固碳释碳问题一直是近年来关乎民生的热点话题，区域碳源/碳汇对生态环境的重要性不言而喻。基于CASA模型估算黄土高原1990-2015年植被净初级生产力的年际变化，并分析土地利用变化、海拔高度及两者协同作用对其综合影响，结果表明：（1）黄土高原1990-2015年植被NPP与植被固碳总体呈增加趋势，年均NPP增速2.74 gC m-2 a-1，年均固碳增速1.13 TgC/a，研究区林地年均NPP （619.5 gC m-2 a-1）远超其他用地类型，固碳效果理想；（2）黄土高原年均NPP随高程的增加先降低后升高，年总NPP和固碳量随高程增加变化趋势相反；（3）研究区土地利用转变类型中退耕还林的植被固碳效果最好；而林地变为耕地或草地均不能达到固碳目的，此外，更推荐在研究区海拔低于1500 m变草为耕，海拔高于1500 m退耕还草，海拔高于3000 m变耕、草为林。以期为区域尺度的生态环境建设提供一定的参考和科学依据。 Abstract:Global carbon sequestration and release has been a hotspot issue concerning people's livelihood in recent years. The importance of regional carbon sequestration and release to the ecological environment was self-evident. It is of great theoretical and practical significance to use remote sensing data and appropriate estimation model to quantitatively estimate NPP (Net Primary Productivity) and analyze spatio-temporal pattern of vegetation carbon sequestration and carbon release in the Loess Plateau. Based on the CASA (Carnegie Ames Stanford Approach) model, this paper estimated the annual change of the NPP on the loess plateau from 1990 to 2015, and analyzed the comprehensive effects in the process of Land Use/Land Cover Change (LUCC), elevations and their synergistic effects. It found that:(1) vegetation NPP and carbon sequestration showed an increasing trend in the Loess Plateau from 1990 to 2015. The annual average growth rate of NPP was 2.74 gC m-2 a-1 and the annual average growth rate of carbon sequestration was 1.13 TgC/a. The average annual NPP (619.5 gC m-2 a-1) of forest land in the study area was much higher than that of other land types, which was an ideal means of carbon sequestration. (2) The annual mean NPP of the Loess Plateau increased year by year, but the annual mean annual NPP decreased first and then increased with the increase of elevation, while the annual total NPP and carbon sequestration showed opposite trends with the increase of elevation. (3) In the study area, conversion of cropland to forest contributed the most to vegetation carbon sequestration among all land use changes types, meanwhile, the conversion of forest into cropland or grassland could not achieve carbon sequestration. In addition, it is recommended to convert grassland to cropland when the elevation is lower than 1500 m, return cropland to grassland when the elevation is higher than 1500 m, and convert cropland and grassland to forest when the elevation is higher than 3000 m in the study area. The study focuses on the effect of carbon sequestration under the synergistic effect of land use change and elevations. It is concluded that the development and utilization of land in the study area leads to the increase of carbon sequestration, while the ecological return of land leads to the increase of carbon sequestration; Moreover, it is more suitable to convert cropland to forest at low and high altitude regions than at middle altitude in the study area, in order to provide reference and guidance for the ecological environment in the regional scale. 参考文献 相似文献 引证文献

Assessing the effects of climate variability and vegetation conversion on variations of net primary productivity in the mountainous area of North China

Net primary productivity (NPP) is an important indicator of the terrestrial carbon cycle. Climate variability and land use changes are the two main factors contributing to spatial–temporal variations of NPP, and accurate estimations of these factors are crucial for understanding carbon cycling. In this study, the spatial and temporal patterns of NPP with climate variability and vegetation conversion in the mountainous area of North China were investigated over 2000–2018 by utilizing the Carnegie-Ames-Stanford Approach (CASA) model and remote sensing data, which provides a better understanding of how NPP varied after the implementation of the Grain to Green Program. The results indicate that the annual NPP follows a rising trend at a rate of 7.18 gC/m2·yr and with a mean value of 395.80 gC/m2·yr. Spatially, significant regional heterogeneity was detected in NPP with gradients decreasing from the southeast to the northwest, while steep increases were found in northern Hebei and southern Shanxi. Regarding different vegetation types, the mean annual NPP decreased following the order of broadleaf forest > mixed forest > needleleaf forest > shrubland > grassland > cropland. Furthermore, all vegetation types showed an increasing trend during the study period. Over the conversion from cropland (with low NPP) to forest (with high NPP), the NPP of cropland increased by 9.27 gC/m2·yr, suggesting that in relatively water-scarce regions, forest could fully utilize limited water resources for its growth. Among various meteorological factors, precipitation and DSI had a higher correlation with NPP in the mountainous area of North China. This shows that moisture indexes rather than temperature and solar radiation are the main driving factors of regional NPP. Finally, the combined effects of meteorological factors on NPP were quantified, and the cumulative contribution rate of precipitation, temperature, solar radiation, and DSI to NPP variation is 68%. These results will aid future water resources management and fragile ecosystem optimization to guarantee the sustainable utilization of water resources.

Spatiotemporal variation and driving factors of vegetation net primary productivity in a typical karst area in China from 2000 to 2010

Exploring the spatiotemporal variation in vegetation net primary productivity (NPP) and evaluating the impact of different drivers on NPP are vital for vegetation protection and restoration in Guizhou Province, which is a typical karst area in China. In this study, Cox–Stuart test, linear regression and spatial statistics were used to analyze the spatiotemporal variation in vegetation NPP in Guizhou. Random Forest combined with local spatial autocorrelation analysis was applied to quantify the effects of eight drivers from climate, topography, human activities and vegetation growth status on the NPP from different clusters in different years based on the NPP product from the Global Production Efficiency Model (GLO-PEM). The following results were obtained. (1) The average annual NPP in Guizhou during 2000–2010 showed no significant increasing or decreasing trend. (2) The annual mean NPP in the west was the lowest in the period, but the NPP per year had a rising tendency in the west and a declining one in the east from 2000 to 2010. (3) The NPP in Guizhou showed a significant clustering trend across each year in the period, whereas the clustering degree reflected a downward trend over time. The statistically significant clusters of high values (HH) and clusters of low values (LL) of NPP were mainly distributed in the east and west, respectively, in 2000, 2005 and 2010. (4) The accuracy of the driver analysis of the three NPP cluster types combined (HH + LL, HL + LH, and HH + HL + LH + LL) was higher than that of the other cluster types in Guizhou in 2000, 2005 and 2010. Vegetation growth and human activities show greater effect on the NPP of the three combined types than climate and topography. These findings are valuable in refining the results of the driver analysis and improving their accuracy. This study provides a deeper insight into the effect of different drivers on the NPP from different spatial clusters in Guizhou. Our results indicate that implementing appropriate silvicultural measures and formulating effective management policies are essential to improve the NPP and reduce the adverse effects of human activities on NPP in Guizhou.

Decline in net primary productivity caused by severe droughts: evidence from the Pearl River basin in China

AbstractUnderstanding the spatiotemporal characteristics of drought events and their impacts on terrestrial net primary productivity (NPP) is crucial for drought mitigation and environmental protection. This study, by taking the Pearl River basin as the case region, investigated drought duration, severity, intensity, affected area, and centroids during 1960–2015 based on the Standardized Evapotranspiration Deficit Index and three-dimensional clustering algorithm and then revealed how these drought characteristics have affected NPP. Results showed that there were altogether 32 severe drought events lasting at least 3 months in the basin, with half lasting longer than 6 months. The total NPP loss significantly correlated with drought severity and intensity. Most drought events caused a reduction in NPP across more than half of the drought-affected area; specifically, the February–December drought in 2011 has cut NPP by 31.85 Tg C, accounting for 11.7% of the regional annual mean NPP, while the September 2009–September 2010 drought caused a decrease of 20.26 Tg C in NPP. Our research improves the insight into the relationship between NPP and drought, which helps decision-makers manage droughts and provides guidance for drought-related studies across other regions.

Variations of forage yield and forage-livestock balance in grasslands over the Tibetan Pla-teau, China.

An in-depth understanding of variations in grassland productivity and forage-livestock balance is the basis of ecological barrier construction and ecosystem conservation in the Qinghai-Tibetan Plateau. Using an ecohydrological process-based model VIP with remotely sensed vegetation index and leaf area index, we simulated the spatial and temporal variations of grassland productivity in the Tibetan Plateau in 2000-2018. The variations in the status of forage-livestock balance at the county level were analyzed, combining with agriculture and animal husbandry statistics in the same period. The results showed that the mean annual net primary productivity (NPP) of grassland in the Tibetan Plateau was 158.4 g C·m-2·a-1, which had increased significantly in the past 20 years, with a significant increase in 44.7% of the total area. Climate warming, increased precipitation, prolonged growing season, and elevated carbon dioxide concentration were main driving forces for grassland productivity. The mean theoretical livestock carrying capacity estimated based on pasture yield was 1.17 SU·hm-2, with a growth rate of 0.011 SU·hm-2. The situation of overgrazing in the Tibetan Plateau had generally improved since 2000. The proportion of counties with severe overgrazing had dropped to less than 20%. In areas with more severe overgrazing, animal husbandry's maintenance and development mainly relied on supplementation of crop straw. The results could provide a scientific basis for regional agricultural and animal husbandry structural adjustment and environmental protection.

Evaluating the dynamics of grassland net primary productivity in response to climate change in China

Net primary productivity (NPP) has been considered as a direct indicator to access the terrestrial primary production. However, considerable uncertainties remain toward the response mechanism of NPP to climate change across annual and seasonal scales, especially for grassland ecosystems. In this study, the spatiotemporal dynamics of grassland NPP and its prominent climate-controlling factor were evaluated based on the CASA model and ground-based meteorological data across annual and seasonal scales in China from 1982 to 2016. Results showed that the annual mean grassland NPP was 294.58 gC·m−2·a−1 over the 35 years; the largest annual mean NPP was observed in Alpine sub-alpine meadow (235.72 ± 13.21 gC·m−2·a−1), while the smallest annual mean NPP value occurred in Desert grassland (97.27 ± 9.64 gC·m−2·a−1). Moreover, the multiyear mean grassland NPP showed a decreasing trend from the southeast to the northwest spatially. Temporally, grassland NPP exhibited a significant increasing trend (slope = 2.4 gC·m−2·a−1; p < 0.0001) over the whole study period; an abrupt change point across the grassland NPP time series was observed in 1999. Specially, 66.34% of the grassland area showed an increasing trend in NPP, and the areas with a significant increasing trend were concentrated in Qinghai-Tibet Plateau and the northern area of Xinjiang. Further climate factor analysis with NPP indicated that NPP positively correlated with precipitation and negatively correlated with temperature for the whole grassland area. In particular, the temperature was a dominant driver in NPP variations for Alpine sub-alpine meadow, Slope grassland and Alpine sub-alpine grassland, while precipitation controlled the most in NPP variations for Plain grassland and Desert grassland. The accumulation of grassland NPP in spring and autumn benefits from the increase in temperature, and precipitation promoted the accumulated NPP in summer and winter.

Decadal change and inter-annual variability of net primary productivity on the Tibetan Plateau

Net primary productivity (NPP) is an important indicator of plant dynamics and the net carbon exchange between the terrestrial ecosystem and atmosphere. Both the long-term shifts in climate mean (climate change) and short-term variations around the climate mean (climate variability) have impacts on NPP but studies examining both aspects of climate variations are rare especially in the data-scarce regions such as the Tibetan Plateau (TP). Here, we used a dynamic vegetation model to investigate the impacts of the changes and variabilities in temperature, precipitation, cloud cover and CO2 on NPP on the TP. The simulated NPP was evaluated using field and Moderate-Resolution Imaging Spectroradiometer NPP and was found to be reasonable. At monthly time scale, NPP significantly correlated concurrently and at 1-month lag with temperature, precipitation and cloud cover (coefficient of determination, R2, in 0.52–0.77). Annual NPP variability was high (low) where mean annual NPP was low (high). The effects of annual precipitation, cloud cover and temperature variability on annual NPP variability were spatially heterogeneous, and temperature variability appeared to be the dominant factor (R2 of 0.74). Whereas, NPP changes were very similar to CO2 increases across the TP (spatial correlation of 0.60), indicating that long-term changes in NPP were dominated by CO2 increases. For both variability and long-term changes in NPP, temperature was the major factor of influence (highest spatial correlation of 0.67). These findings could assist in making informed mitigation policies on the impacts of climate change and variability on ecosystem and local nomadic communities.

Spatial-temporal trend of grassland net primary production and their driving factors in the Loess Plateau, China

Net primary productivity (NPP) of grassland is a key link and important part of the ecosystem's carbon cycle. We estimated the changes of NPP in grasslands of the Loess Plateau with unchanged land use types during 2000-2015 and analyzed its responses to the variation of main climate factors (annual precipitation, annual heavy rainfall, annual effective rainfall days, annual average temperature, annual maximum temperature, annual minimum temperature) using piecewise linear regression and Pearson correlation analysis. The driving factors of grassland NPP were further analyzed by pixel-by-pixel with boosted regression tree analysis. The results showed that annual mean grassland NPP in the Loess Plateau showed an increasing trend during the study period, with 51.3% of the total grassland area showing a significant increasing trend. The average increase rate of annual mean NPP declined from 15.23 g C·m-2·a-1 in 2000-2004 to 3.58 g C·m-2·a-1 in 2004-2015. There was a significant positive correlation between grassland NPP and precipitation, but negative correlation with temperature factors. Annual precipitation was the dominant climatic factor affecting NPP of the whole study area with the highest relative importance. Annual maximum temperature was the dominant driving force of grassland NPP of central Loess Plateau, while annual minimum temperature mainly affected the growth of grassland in high-altitude area of the western Loess Plateau.

Effects of Land Cover Changes on Net Primary Productivity in the Terrestrial Ecosystems of China from 2001 to 2012

The 2001–2012 MODIS MCD12Q1 land cover data and MOD17A3 NPP data were used to calculate changes in land cover in China and annual changes in net primary productivity (NPP) during a 12-year period and to quantitatively analyze the effects of land cover change on the NPP of China’s terrestrial ecosystems. The results revealed that during the study period, no changes in land cover type occurred in 7447.31 thousand km2 of China, while the area of vegetation cover increased by 160.97 thousand km2 in the rest of the country. Forest cover increased to 20.91%, which was mainly due to the conversion of large areas of savanna (345.19 thousand km2) and cropland (178.96 thousand km2) to forest. During the 12-year study period, the annual mean NPP of China was 2.70 PgC and increased by 0.25 PgC, from 2.50 to 2.75 PgC. Of this change, 0.21 PgC occurred in areas where there was no land cover change, while 0.04 PgC occurred in areas where there was land cover change. The contributions of forest and cropland to NPP exhibited increasing trends, while the contributions of shrubland and grassland to NPP decreased. Among these land cover types, the contributions of forest and cropland to the national NPP were the greatest, accounting for 40.97% and 27.95%, respectively, of the annual total NPP. There was no significant correlation between changes in forest area and changes in total annual NPP (R2 &lt; 0.1), while the correlation coefficient for changes in cropland area and total annual NPP was 0.48. Additionally, the area of cropland converted to other land cover types was negatively correlated with the changes in NPP, and the loss of cropland caused a reduction in the national NPP.