Changes In Net Primary Productivity Research Articles

Precipitation and soil nutrients determine the spatial variability of grassland productivity at large scales in China.

Changes in net primary productivity (NPP) to global change have been studied, yet the relative impacts of global change on grassland productivity at large scales remain poorly understood. Using 182 grassland samples established in 17 alpine meadows (AM) and 21 desert steppes (DS) in China, we show that NPP of AM was significantly higher than that of DS. NPP increased significantly with increasing leaf nitrogen content (LN) and leaf phosphorus content (LP) but decreased significantly with increasing leaf dry matter content (LDMC). Among all abiotic factors, soil nutrient factor was the dominant factor affecting the variation of NPP of AM, while the NPP of DS was mainly influenced by the changing of precipitation. All abiotic factors accounted for 62.4% of the spatial variation in the NPP of AM, which was higher than the ability to explain the spatial variation in the NPP of DS (43.5%). Leaf traits together with soil nutrients and climatic factors determined the changes of the grassland productivity, but the relative contributions varied somewhat among different grassland types. We quantified the effects of biotic and abiotic factors on grassland NPP, and provided theoretical guidance for predicting the impacts of global change on the NPP of grasslands.

Prediction of Net Primary Production Changes in Different Phytogeographical Regions of Iran from 2000 to 2016, Using Time Series Models

Prediction of Net Primary Production Changes in Different Phytogeographical Regions of Iran from 2000 to 2016, Using Time Series Models

Acidification, deoxygenation, and nutrient and biomass declines in a warming Mediterranean Sea

Abstract. The projected warming, nutrient decline, changes in net primary production, deoxygenation and acidification of the global ocean will affect marine ecosystems during the 21st century. Here, the climate change-related impacts on the marine ecosystems of the Mediterranean Sea in the middle and at the end of the 21st century are assessed using high-resolution projections of the physical and biogeochemical state of the basin under Representative Concentration Pathways (RCPs) 4.5 and 8.5. In both scenarios, the analysis shows changes in the dissolved nutrient contents of the euphotic and intermediate layers of the basin, net primary production, phytoplankton respiration and carbon stock (including phytoplankton, zooplankton, bacterial biomass and particulate organic matter). The projections also show uniform surface and subsurface reductions in the oxygen concentration driven by the warming of the water column and by the increase in ecosystem respiration as well as an acidification signal in the upper water column linked to the increase in the dissolved inorganic carbon content of the water column due to CO2 absorption from the atmosphere and the increase in respiration. The projected changes are stronger in the RCP8.5 (worst-case) scenario and, in particular, in the eastern Mediterranean due to the limited influence of the exchanges in the Strait of Gibraltar in that part of the basin. On the other hand, analysis of the projections under the RCP4.5 emission scenario shows a tendency to recover the values observed at the beginning of the 21st century for several biogeochemical variables in the second half of the period. This result supports the idea – possibly based on the existence in a system such as the Mediterranean Sea of a certain buffer capacity and renewal rate – that the implementation of policies for reducing CO2 emission could indeed be effective and could contribute to the foundation of ocean sustainability science and policies.

Assessment of vegetation change on the Mongolian Plateau over three decades using different remote sensing products.

As a major component of temperate steppes in the Eurasian continent, the Mongolian Plateau (MP) plays a pivotal role in the East Asian and global carbon cycles. This paper describes the use of five remote sensing indices derived from satellite data to characterize vegetation cover on MP, namely: gross primary production (GPP), net primary production (NPP), normalized difference vegetation index (NDVI), leaf area index (LAI) and fractional vegetation cover (FVC). It is found that GPP, NPP, and NDVI exhibit increasing trends, whereas LAI and FVC present decreasing trends on the MP since 1982. The different indices highlight discrepancies in the spatial pattern of vegetation growth, with the greatest increase in the southeast of MP. Only 3.4% of the total land area of MP exhibited consistent trends in the indices (0.1% degradation and 3.3% growth, P<0.01), with the synchronous change of both LAI and NPP exhibiting higher consistency than that of raw NDVI and NPP. Understanding of the characteristics and status of vegetation change on the MP has far-reaching implications for its ecological protection management, and climate change mitigation.

A new conceptual framework for spatial predictive modelling of land degradation in a semiarid area

AbstractAlthough land degradation (LD) is known as a severe environmental problem, spatial predictive modelling of this phenomenon remains a challenge. This research aimed to develop a new conceptual framework to predict LD susceptibility based on net primary production (NPP) and machine learning approaches. The annual NPP over the period 2001–2020 were obtained using MOD17A3 and the trend of NPP changes was considered to investigate the occurrence sites of LD within Qazvin Plain, in Qazvin Province, Iran, under a semiarid climate, with an area of about 9500 km2. An inventory map of LD was generated based on the LD study sites. The locations were randomly split‐sampled as training (70%) and testing (30%) datasets to evaluate the efficiency of the built models. Fifteen geo‐environmental factors were considered as LD predictive variables such as altitude, slope, land use, and temperature. Four advanced machine‐learning techniques were performed to model LD susceptibility. Finally, the predictive efficiency of the models was measured utilizing the area under the (ROC) curve Area Under the ROC Curve(AUC) and true skill as statics (TSS). The results indicated that the randomForest (RF), with the AUC = 0.81 and TSS = 0.5, showed the highest efficiency for predicting LD in the Qazvin Plain followed by boosted regression tree (BRT) with AUC = 0.76 and TSS = 0.47, support vector machine (SVM) with AUC = 0.71 and TSS = 0.39, and classification and regression tree (CART) with AUC = 0.63 and TSS = 0.31. The findings illustrated that altitude was the most influential variable within RF, BRT, and SVM while rainfall showed the most important contribution in modelling based on the CART algorithm. This study proposed a new modelling framework that is easily replicable in different contexts for the assessment of LD modelling and analysis.

Quantitative contribution of climate change and anthropological activities to vegetation carbon storage in the Dongting Lake basin in the last two decades

The Dongting Lake Basin is an important hydrological regulation and flood storage area in the Yangtze River Basin, which plays an important role in maintaining regional ecological security. The watershed vegetation and its carbon sequestration capacity have changed dramatically due to climate change and human activities in the last two decades. In this paper, the monthly and annual vegetation net primary productivity (NPP) of the Dongting Lake basin during 2000 to 2020 was firstly estimated using the improved Carnegie-Ames-Stanford Approach (CASA) model. Then the multi-year NPP change trend and its significance were analyzed based on Theil-Sen median and Mann-Kendall method. Subsequently, the Hurst index was used to simulate the vegetation NPP persistence in the study area. Finally, the driving mechanisms of vegetation NPP changes in the study area were explored using partial correlation coefficients and residual analysis. The results demonstrated that: 1) The annual average NPP in the basin showed a fluctuating and increasing trend from 273.54 to 718.30 g C/m2·yr1 during 2000 to 2020, and except for autumn, all seasons (spring, summer, winter) and annual NPP series showed an upward trend. The spatial distribution of vegetation NPP is characterized by an asymmetrical horseshoe shape in general, with high values in the west, south and east parts, and low values in the Lake area; 2) During the study years, about 84.38 % of the basin area showed a significant and extremely significant increase of NPP; 3) The future trend of vegetation NPP in the basin shows that the area of decrease (22.79 %) is more than the area of increase (11.35 %). The Chang-Zhu-Tan urban agglomeration will generally show a continuous and extremely significant reduction trend, while parts of Dongting Lake will show a continuous and extremely significant increase trend; 4) The correlation between solar radiation and NPP is stronger than the other two meteorological factors (precipitation and temperature). Temperature has a significant inhibitory effect on NPP, solar radiation has a significant promoting effect on NPP, and the effect of precipitation on NPP was relatively complicated. The relative importance of meteorological factors on vegetation NPP was ranked as follows: solar radiation > precipitation > temperature. The impacts of both climate change and human activities on NPP changes showed great spatial variability, and the positive contributions (89.81 % for climate change and 82.87 % for human activities) were both greater than the negative contributions.

Assessment of the impacts of biological nitrogen fixation structural uncertainty in CMIP6 earth system models

Abstract. Biological nitrogen fixation is the main source of new nitrogen into natural terrestrial ecosystems and consequently in the nitrogen cycle in many earth system models. Representation of biological nitrogen fixation varies, and because of the tight coupling between the carbon and nitrogen cycles, previous studies have shown that this affects projected changes in net primary productivity. Here we present the first assessment of the performance of biological nitrogen fixation in models contributing to CMIP6 compared to observed and observation-constrained estimates of biological nitrogen fixation. We find that 9 out of 10 models represent global total biological nitrogen fixation within the uncertainty in recent global estimates. However, 6 out of 10 models overestimate the amount of fixation in the tropics and therefore the extent of the latitudinal gradient in the global distribution. For the SSP3-7.0 scenario of future climate change, models project increases in fixation over the 21st century of up to 80 %. However, while the historical range of biological nitrogen fixation amongst models is large (up to 140 kg N ha−1 yr−1 at the grid cell level and 43–208 Tg N yr−1 globally) this does not have explanatory power for variations within the model ensemble of net primary productivity or the coupled nitrogen–carbon cycle. Models with shared structures can have significant variations in both biological nitrogen fixation and other parts of the nitrogen cycle without differing in their net primary productivity. This points to systematic challenges in the representation of carbon–nitrogen model structures and the severe limitations of models using net primary productivity or evapotranspiration to project the biological nitrogen fixation response to elevated atmospheric carbon dioxide or other environmental changes.

Remote-Sensing-Based Assessment of the Ecological Restoration Degree and Restoration Potential of Ecosystems in the Upper Yellow River over the Past 20 Years

The Upper Yellow River is the most important area for water retention and flow production in the Yellow River basin, and the statuses of the ecosystems in this region are related to the ecological stability of the whole Yellow River basin. In this paper, the fractional vegetation cover (FVC), net primary productivity (NPP) of vegetation and water retention, soil retention, and windbreak and sand fixation services of the Upper Yellow River ecosystems were analysed from 2000 to 2019 with the trend analysis method. Ecological restoration degree evaluation indices were constructed to comprehensively assess the ecological restoration situation and restoration potential of the ecosystems in the Upper Yellow River region over the past 20 years and to quantitatively determine the contribution rates of climate factors and human activities to these ecosystem changes. The results showed that the settlement ecosystem area exhibited the greatest increase, while the grassland ecosystem area decreased significantly over the study period. In the Upper Yellow River region, the ecosystem quality and ecosystem services generally remained stable or improved. Areas with moderately, strongly and extremely improved ecological restoration degrees accounted for 32.9%, 21.0% and 2.8% of the entire Upper Yellow River region, respectively. Areas with strongly improved and extremely improved ecological restoration degrees were mainly distributed in the Loess Plateau gully areas and on the eastern Hetao Plain. The contribution rates of climatic factors and human activities to the NPP changes measured in the Upper Yellow River were 81.6% and 18.4%, respectively, while the contribution rates of these processes to soil erosion modulus changes were 77.6% and 22.4%, respectively. The restoration potential index of the FVC in the Upper Yellow River was 22.7%; that of the forest vegetation coverage was 14.4%; and that of the grassland vegetation coverage was 23.0%. Over the past 20 years, the ecosystems in the Upper Yellow River region have improved and recovered significantly. This study can provide scientific support for the next stage of ecological projects in the Upper Yellow River region.

Asymmetric Impacts of Diurnal Warming on Vegetation Carbon Sequestration of Marshes in the Qinghai Tibet Plateau

AbstractAs the world's Third Pole, the Qinghai‐Tibet Plateau has a large area of marshes, which plays an important role in global carbon cycle. The net primary productivity (NPP) of vegetation is a crucial index for measuring the carbon flux of marsh ecosystems. Understanding the change in marsh NPP and its response to climatic change is important for assessing carbon sequestration of marsh ecosystems. Based on MODIS NPP data and climate data from 2000 to 2020, this study analyzed the spatiotemporal change in marsh NPP and determined its relationship with climate factors on the Qinghai‐Tibet Plateau. The results showed that the average annual NPP of marshes on the Qinghai‐Tibet Plateau increased significantly by 11.70 ± 1.07 g C·m−2/10a during 2000–2020, with an average value of about 184.37 ± 11.12 g C·m−2. Spatially, the annual marsh NPP showed obviously increasing trend in the northeast but decreasing trend in the southwest regions. Daytime maximum and nighttime minimum temperatures had asymmetric effects on marsh NPP, with a larger positive effect of minimum temperature. Warmed minimum temperatures in winter and spring significantly increased the annual marsh NPP on the Qinghai‐Tibet Plateau. The increasing maximum and minimum temperatures in the summer obviously promoted the vegetation growth in marshes. Additionally, the increasing spring precipitation could significantly increase the marsh NPP. Our results highlight that the asymmetric impacts of daytime and nighttime temperatures on marsh vegetation should be adequately considered in predicting marsh carbon sequestration on the Qinghai‐Tibet Plateau, especially in the context of global asymmetric diurnal warming.

Study on Ecological Loss in Coal Mining Area Based on Net Primary Productivity of Vegetation

Opencast mining contributes to the severe disturbance of vegetation and serious damage to the ecological environment. Scientific assessment of the ecological losses may provide guidance for the sustainable land use in mining areas. This study uses the Carnegie Ames Stanford Approach (CASA) model to estimate the net primary productivity (NPP) of vegetation in the Pingshuo mining area from 2006 to 2020 and uses statistical analysis and mathematical models to study the impact of mining and land reclamation on NPP. The results show that NPP decreased slowly at a rate of 0.809 gC/(m2·month) per year. In 2006, the urban area of Pinglu and the opencast coal mining area were located in the western part of the study area, while the eastern part was distributed with a large amount of grassland, forested and cultivated land, and the NPP showed a high pattern in the east and low in the west. With the continued eastward movement of Antaibao and Anjialing mining areas, as well as the construction and continuous westward expansion of the East opencast mine, the pits of all three opencast mines moved to a central location in 2020, changing the spatial distribution of NPP to high in the surroundings and low in the middle. The NPP within the three opencast mines decreased significantly, but the change in NPP increased from −7.34 gC/(m2·month) to 7.15 gC/(m2·month) as the reclamation area increased, indicating that mining increased ecological losses while land reclamation mitigated them. The variation of NPP within 600 m outside the mining area is 2.06~7.45 gC/(m2·month), and 0.11~1.00 gC/(m2·month) from 600 m to 1000 m, indicating that NPP is sensitive to the influence of mining. The results may provide guidance for further analysis of the impact of mining on local environments and exploration of more appropriate land reclamation measures.

Impact of climatic changes and anthropogenic activities on ecosystem net primary productivity in India during 2001–2019

Climate change and anthropogenic activities have altered the terrestrial ecosystem dynamics around the globe. Due to the complex ecosystem-atmosphere interactions at different scales, these impacts are difficult to quantify and are poorly understood, especially in developing countries with limited ground-based observations. This study analyzed the impact of climatic changes and anthropogenic activities on ecosystem net primary productivity (NPP) in India using remote sensing-based observations, correlation analysis, and Residual Trend analysis (RESTREND). Using different climate variables such as precipitation, temperature, and solar radiation, along with Land Use and Land Cover (LULC) and NPP maps, we first classified the ecosystems (ES) into two categories: natural ecosystems – influenced only by climate change (ESc), covering about 19.7% of the area, and human-influenced ecosystems – influenced by both climate change and anthropogenic activities (ESc+a), covering about 80.3% of the area. RESTREND analysis was performed on both ESc and ESc+a to analyze the relative contributions of climate change and human activities to changes in NPP. The correlation analysis between NPP and climate variables suggested that precipitation was the dominant control of NPP in about 72% area, whereas temperature and solar radiation controlled NPP in Himalayan and forest-dominated regions, respectively. The human-influenced ecosystems (ESc+a) experienced an increasing trend in NPP, whereas natural ecosystems (ESc) experienced a decreasing trend, particularly in forest-dominated regions. Overall, NPP increased in the country during the study duration. The contributions of climatic changes and anthropogenic activities varied spatially and temporally. In general, climatic factors enhanced the NPP, whereas human activities contributed to a slight decline in NPP. These findings improve our understanding of how ecosystems in India are influenced by climate change and anthropogenic activities in recent decades. The results from this study will aid in identifying ecological hotspots and key drivers for better ecosystem management strategies.

Elevational Gradient of Climate-Driving Effects on Cropland Ecosystem Net Primary Productivity in Alpine Region of the Southwest China

Investigating elevational gradient of climate driving effects on cropland ecosystem net primary productivity (NPP) plays an important role in food security in alpine region. We simulated cropland NPP by coupling a remote sensing model with an ecosystem process model and explored elevational gradient of climate driving effects on it in an alpine region of the southwest China during 1981–2014. The results showed that cropland NPP increased significantly with a rate of 3.85 gC m−2 year−1 year−1 under significant increasing solar radiation and climate warming and drying, among which the increasing solar radiation was the main driving factor of the increasing NPP. The driving effect of climate warming on cropland NPP shifted from negative at low elevations to positive at high elevations, which was caused by the fragile ecosystem characteristics and frequent drought at low elevations and a higher temperature sensitivity of cropland ecosystem at high elevations. Different effects of climate warming on NPP change at different elevations caused different results when we analyzed the climate-driving effects on cropland NPP at different spatial scales. These results reminded us that we should take the elevational gradient of climate driving effects into account when we manage food security in the alpine region.

Quantitative assessment of the relative contributions of climate change and human activities to NPP changes in the Southwest Karst area of China.

Net primary production (NPP) is an essential component of the terrestrial carbon cycle and an essential factor of ecological processes. In global change research, it was the core content to study the driving forces of NPP change. In this paper, we focused on the Southwest Karst area of China and analyzed the response mechanisms of NPP to topography, land-use types, climatic change, and human activities. Our results showed that (1) changes in elevation and slope lead to significant differences in the spatial distribution of NPP. With the increase of elevation and slope, NPP first increased and then decreased, their critical values were 2000m and 15°, respectively. (2) NPP varied significantly among different land-use types. The average NPP of the forest was the highest, and the average NPP of cultivated land increased fastest. (3) Temperature and precipitation had the most substantial influence on NPP, both of them promoted the increase of NPP, and the effect of temperature was more obvious in the Southwest Karst area. (4) Ecological engineering significantly promoted the change of NPP, while animal husbandry significantly inhibited the change of NPP. (5) There were significant spatial differences in the driving effects and corresponding contributions of climatic change and human activities; both of them promoted the increase of NPP in the Southwest Karst area of China. Under climatic change and human activities, NPP increased by 1.24 gC·m-2·year-1 and 2.29 gC·m-2·year-1, respectively. The contributions rates of climatic change and human activities separately accounted for 35% and 65%. The contribution of human activities on NPP was much higher than that of climatic change in the Southwest Karst area, and the results suggested that we should focus on the role of human activities on NPP change.

The Different Impacts of Climate Variability and Human Activities on NPP in the Guangdong–Hong Kong–Macao Greater Bay Area

As two main drivers of vegetation dynamics, climate variability and human activities greatly influence net primary productivity (NPP) variability by altering the hydrothermal conditions and biogeochemical cycles. Therefore, studying NPP variability and its drivers is crucial to understanding the patterns and mechanisms that sustain regional ecosystem structures and functions under ongoing climate variability and human activities. In this study, three indexes, namely the potential NPP (NPPp), actual NPP (NPPa), and human-induced NPP (NPPh), and their variability from 2000 to 2020 in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) were estimated and analyzed. Six main scenarios were generated based on change trends in the three indexes over the past 21 years, and the different relative impacts of climate variability and human activities on NPPa variability were quantitatively analyzed and identified. The results showed that the NPPp, NPPa, and NPPh had heterogeneous spatial distributions, and the average NPPp and NPPa values over the whole study area increased at rates of 3.63 and 6.94 gC·m−2·yr−1 from 2000 to 2020, respectively, while the NPPh decreased at a rate of −4.43 gC·m−2·yr−1. Climate variability and the combined effects of climate variability and human activities were the major driving factors of the NPPa increases, accounting for more than 72% of the total pixels, while the combined effects of the two factors caused the NPPa values to increase by 32–54% of the area in all cities expect Macao and across all vegetation ecosystems. Human activities often led to decreases in NPPa over more than 16% of the total pixels, and were mainly concentrated in the central cities of the GBA. The results can provide a reference for understanding NPP changes and can offer a theoretical basis for implementing ecosystem restoration, ecological construction, and conservation practices in the GBA.

Spatiotemporal changes in net primary productivity before and after the development of unused land in the hilly areas of Hebei, China.

Net primary productivity (NPP) plays an important role in the carbon cycle of an ecosystem. To explore the impact of unused land development on NPP, this study adopted an improved Carnegie Ames Stanford Approach (CASA) model to analyze the changes in NPP before and after the development of unused land in Tang County, Hebei Province, in 2000, 2007, and 2018. The results showed that, due to the changes in land use types from unused land, forestland, arable land with high NPP values to urban and rural residential land, traffic land with low NPP values, and the changes in precipitation and temperature, the NPP in the study area showed an overall trend of decreasing first and then rising from 2000 to 2018. Before the development of unused land in 2000, the total NPP was 38.45×1010 g C. After the development in 2007 and 2018, the total NPP was 36.44×1010 g C and 41.05×1010 g C, respectively. The NPP of each land type in 2018 was arable land (1046.18 g C m-2) > forestland (464.42 g C m-2) > unused land (356.34 g C m-2) > grassland (343.77 g C m-2) > waters (182.56 g C m-2) > urban and rural settlements (120.86 g C m-2) > traffic land (120.70 g C m-2). The distribution of NPP was generally high in the north and low in the south before and after development. NPP was mainly concentrated in the interval of 300 g C m-2 yr-1–400 g C m-2 yr-1, and the range of NPP change was mostly within 100 g C m-2. The influence of elevation, temperature and precipitation on the spatial distribution of NPP was significant. Elevation and precipitation were positively correlated with NPP, while temperature was negatively correlated with NPP. The increase in NPP mainly originated from the conversion of unused land to forestland and arable land. The loss of NPP was mainly due to the conversion from forestland with high vegetation productivity to a land use type with low vegetation productivity, such as the conversion from forestland to urban and rural residential land. The results can provide references for making reasonable land planning decisions and ecological environment construction.

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.

Estimation and Spatio-Temporal Change Analysis of NPP in Subtropical Forests: A Case Study of Shaoguan, Guangdong, China

Exploring the spatial and temporal dynamic characteristics of regional forest net primary productivity (NPP) in the context of global climate change can not only provide a theoretical basis for terrestrial carbon cycle studies, but also provide data support for medium- and long-term sustainable management planning of regional forests. In this study, we took Shaoguan City, Guangdong Province, China as the study area, and used Landsat images and National Forest Continuous Inventory (NFCI) data in the corresponding years as the main data sources. Random forest (RF), multiple linear regression (MLR), and BP neural network were the three models applied to estimate forest NPP in the study area. Theil–Sen estimation, Mann–Kendall trend analysis and the standard deviation ellipse (SDE) were chosen to analyze the spatial and temporal dynamic characteristics of NPP, whereas structural equation modeling (SEM) was used to analyze the driving factors of NPP changes. The results show that the performance of the RF model is better than the MLR and BP neural network models. The NPP in the study area showed an increasing trend, as the NPP was 5.66 t·hm−2·a−1, 7.68 t·hm−2·a−1, 8.17 t·hm−2·a−1, 8.25 t·hm−2·a−1, and 10.52 t·hm−2·a−1 in 1997, 2002, 2007, 2012, and 2017, respectively. Spatial aggregation of NPP was increased in the period of 1997–2017, and the center shifted from the mid-west to the southwest. In addition, the forest stand factors had the greatest effect on NPP in the study area. The forest stand factors and environmental factors had a positive effect on NPP, and understory factors had a negative effect. Overall, although forest NPP has fluctuated due to the changes of forestry policies and human activities, forest NPP in Shaoguan has been increasing. In the future, the growth potential of NPP in Shaoguan City can be further increased by continuously expanding the area proportion of mixed forests and rationalizing the forest age group structure.

Spatiotemporal Change of Net Primary Productivity and Its Response to Climate Change in Temperate Grasslands of China.

The temperate grasslands in China play a vital part in regulating regional carbon cycle and climate change. Net primary productivity (NPP) is a crucial index that reflects ecological function of plants and the carbon sequestration capacity of grassland ecosystem. Climate change can affect NPP by changing vegetation growth, but the effects of climate change on the NPP of China’s temperate grasslands remain unclear. Based on MODIS data and monthly climate data during 2000–2020, this study explored the spatiotemporal changes in grassland NPP and its response to climate change in temperate grasslands of China. We found that the annual NPP over the entire China’s temperate grasslands increased significantly by 4.0 gC/m2/year from 2000 to 2020. The annual NPP showed increasing trends for all the different grassland vegetation types, with the smallest increase for temperate desert steppe (2.2 gC/m2/year) and the largest increase for temperate meadow (5.4 gC/m2/year). The correlation results showed that increased annual precipitation had a positive relationship with the NPP of temperate grasslands. Increased summer and autumn precipitation could increase grassland NPP, particularly for the temperate meadow. With regard to the effects of temperatures, increased temperature, particularly the summer maximum temperature, could decrease annual NPP. However, increased spring minimum temperature could increase the NPP of temperate desert steppe. In addition, this study found, for the first time, an asymmetric relationship between summer nighttime and daytime warming and the NPP of temperate meadow. Specifically, nighttime warming can increase NPP, while daytime warming can reduce NPP in temperate meadow. Our results highlight the importance of including seasonal climate conditions in assessing the vegetation productivity for different grassland types of temperate grasslands and predicting the influences of future climate change on temperate grassland ecosystems.

Assessing the Net Primary Productivity Dynamics of the Desert Steppe in Northern China during the Past 20 Years and Its Response to Climate Change

The net primary productivity (NPP) dynamics in arid and semi-arid ecosystems are critical for regional carbon management. Our study applied a light-utilization-efficiency model (CASA: Carnegie–Ames–Stanford Approach) to evaluate the vegetation NPP dynamics of a desert steppe in northern China over the past 20 years, and its response to climate change. Our results show that the annual average NPP of the desert steppe was 132 g C m−2 y−1, of which the grass- and shrub-dominated biome values were 142 and 91 g C m−2 y−1, respectively. The average change rate of NPP was 1.13 g C m−2 y−1, and in the grassland biome 1.31 g C m−2 y−1, a value which was significantly higher than that in shrubland, at 0.84 g C m−2 y−1. The precipitation and temperature at different time scales in the desert steppe showed a slow upward trend, and the degree of aridity tended to weaken. The correlation analysis shows that NPP changes were significantly positively and negatively correlated with precipitation and temperature, respectively. In terms of temperature, 43% of the area was significantly correlated during the growing season, which decreased to 12% on the annual scale. In 31% of the changed areas, the average NPP was 148.1 g C m−2 y−1, which was higher than the remaining significant areas. This suggests that higher NPP levels help to attenuate the negative effects of high temperature during the growing season on plant productivity in the desert steppe. This improves the understanding of the carbon cycle mechanism of arid and semi-arid ecosystems, which is beneficial to improving sustainable grassland development strategies.

Projections of the Net Primary Production of Terrestrial Ecosystem and Spatiotemporal Responses to Climate Change in the Yangtze River Economic Belt

Evaluating the responses of net primary productivity (NPP) to climate change is essential for regional ecosystem management and adaptations to climate change. The Yangtze River Economic Belt (YREB) is a key ecological functional area and hotspot of carbon sequestration in China due to the high degree of forest coverage. We used a process-based ecosystem model to project terrestrial NPP and analyzed the response to climate change over the 21st century in the YREB under two representative concentration pathway (RCP) scenarios using the regional climate model. The results show that the projection of NPP generally increased by 13.5% under RCP4.5 and 16.4% under RCP8.5 in the middle of the century, by 23% under RCP4.5, and by 35% under RCP8.5 in the late term of the century compared with that from a reference period of current climate conditions (1985–2006). The rate of NPP change under the RCP8.5 scenario is higher than that under the RCP4.5 scenario. Similarly, the NPP is also projected to increase both with 1.5 and 2 °C global warming targets in the YREB. The magnitudes of NPP increment are approximately 14.7% with 1.5 °C and 21% with 2 °C warming targets compared with the current climate, which are higher than the average increments of China. Although NPP is projected to increase under the two scenarios, the tendency of NPP increasingly exhibits a slowdown after the 2060 s under the RCP4.5 scenario, and the growth rate of NPP is projected to drop in more than 31% of regional areas with the additional 0.5 °C warming. In contrast, under the RCP8.5 scenario, the trend in NPP keeps rising substantially, even above 2 °C global warming. However, the NPP in some provinces, including Jiangxi and Hunan, is projected to reduce at the end of the 21st century, probably because of temperature rises, precipitation decreases, and water demand increases. Generally, the NPP is projected to increase due to climate change, particularly temperature increase. However, temperature rising does not always show a positive effect on NPP increasing; the growth rate of NPP will slow down under the RCP4.5 scenario in the mid-late 21st century, and NPP will also reduce by the end of this century under the RCP8.5 scenario in some places, probably presenting some risks to terrestrial ecosystems in these areas, in terms of reduced functions and service decline, a weakened capacity of carbon sequestration, and reduced agricultural production.