Reply on RC2

Human activities have clearly caused dramatic alterations of the terrestrial nitrogen cycle, and analyses of the extent and effects of such changes are now common in the scientific literature. However, any attempt to evaluate N cycling processes within ecosystems, as well as anthropogenic influences on the N cycle, requires an understanding of the magnitude of inputs via biological nitrogen fixation (BNF). Although there have been many studies addressing the microbiology, physiology, and magnitude of N fixation at local scales, there are very few estimates of BNF over large scales. We utilized >100 preexisting published estimates of BNF to generate biome‐ and global‐level estimates of biological N fixation. We also used net primary productivity (NPP) and evapotranspiration (ET) estimates from the Century terrestrial ecosystem model to examine global relationships between these variables and BNF as well as to compare observed and Century‐modeled BNF. Our data‐based estimates showed a strong positive relationship between ecosystem ET and BNF, and our analyses suggest that while the model's simple relationships for BNF predict broad scale patterns, they do not capture much of the variability or magnitude of published rates. Patterns of BNF were also similar to patterns of ecosystem NPP. Our “best estimate” of potential nitrogen fixation by natural ecosystems is ∼195 Tg N yr−1, with a range of 100–290 Tg N yr−1. Although these estimates do not account for the decrease in natural N fixation due to cultivation, this would not dramatically alter our estimate, as the greatest reductions in area have occurred in systems characterized by relatively low rates of N fixation (e.g., grasslands). Although our estimate of BNF in natural ecosystems is similar to previously published estimates of terrestrial BNF, we believe that this study provides a more documented, constrained estimate of this important flux.

Interannual variation in terrestrial net primary production (NPP) was modeled using the global production efficiency model (GLO‐PEM), a semimechanistic plant photosynthesis and respiration model driven entirely with satellite advanced very high resolution radiometer (AVHRR) observations. The model also estimated a wide range of biophysical variables at 10‐day intervals for the period 1982–1989, including air temperature, vapor pressure deficit, soil moisture, biomass, autotrophic respiration, canopy‐absorbed photosynthetically active radiation, gross primary production, and light use efficiency. The accuracy of the simulated variables has previously been shown to be within 10–30% of field measurements, depending on the specific variable. We analyze here interannual changes in NPP, which showed large spatial variability (0–1500 gC m−2 yr−1) and trends that differed regionally over the 8‐year period. Annually integrated global NPP was found to vary as much as 12% between years and was very sensitive to air temperature. The coefficient of variation in NPP of sparsely vegetated areas (mostly semiarid) on an interannual basis was as much as 80%, whereas densely vegetated areas (broadleaf evergreen and seasonally deciduous forests) varied comparatively little (0–10%). Mean annual NPP of the latter decreased 36 gC m−2 yr−1 over the time series examined. There was extreme seasonal and moderate interannual variation (10–60%) in NPP of middle‐ to high‐latitude regions (temperate and boreal forests) with evidence for a slight trend toward increased values through time (+3 to 12 gC m−2 yr−1). The results indicate significant interannual and regional differences in responses to climate variability, with boreal regions increasing 39 gC m−2 yr−1 compared to a decrease of 116 gC m−2 yr−1 in tropical regions for each 1°C rise in air temperature. We explore a few of the possible reasons for these observations and discuss some of the issues and limitations to the use of the current global AVHRR observational record.

This study reported the spatio-temporal variability of terrestrial net primary production (NPP) in the lower-middle reaches of Yangtze River from 2000 to 2006. The impact of climate change was also described. According to the analysis results, mean NPP of this region during 7 years was 535.8 gCm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−2</sup> yr <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−1</sup> , which was higher than that of the nation during the same period. It was found that NPP showed great inter-annual fluctuation, the controlling factor of which was temperature. NPP demonstrated similar spatial pattern with temperature and precipitation, but inverse to sunshine duration, which suggested climate factors working together exerted obvious effects on spatial characteristics of vegetation production in the study region. These results provided insight into understanding how the climate factors controlled the inter-annual variation and spatial distribution of NPP on region scale and established basis for predicting vegetation features under future climate change.

Biological nitrogen fixation (BNF) is the largest natural source of new nitrogen (N) that supports terrestrial productivity1,2, yet estimates of global terrestrial BNF remain highly uncertain3,4. Here we show that this uncertainty is partly because of sampling bias, asfield BNF measurements in natural terrestrial ecosystems occur where N fixers are 17 times more prevalent than their mean abundances worldwide. To correct this bias, we develop new estimates of global terrestrial BNF by upscaling field BNF measurements using spatially explicit abundances of all major biogeochemical N-fixing niches. We find that natural biomes sustain lower BNF, 65 (52-77) Tg N yr-1, than previous empirical bottom-up estimates3,4, with most BNF occurring in tropical forests and drylands. We also find high agricultural BNF in croplands and cultivated pastures, 56 (54-58) Tg N yr-1. Agricultural BNF has increased terrestrial BNF by 64% and total terrestrial N inputs from all sources by 60% over pre-industrial levels. Our results indicate that BNF may impose stronger constraints on the carbon sink in natural terrestrial biomes and represent a larger source of agricultural N than is generally considered in analyses of the global N cycle5,6, with implications for proposed safe operating limits for N use7,8.

Spatial and temporal variations in net primary production (NPP) are of great importance to ecological studies, natural resource management, and terrestrial carbon sink estimates. However, most of the existing estimates of interannual variation in NPP at regional and global scales were made at coarse resolutions with climate-driven process models. In this study, we quantified global NPP variation at an 8 km and 10-day resolution from 1981 to 2000 based on satellite observations. The high resolution was achieved using the GLObal Production Efficiency Model (GLO-PEM), which was driven with variables derived almost entirely from satellite remote sensing. The results show that there was an increasing trend toward enhanced terrestrial NPP that was superimposed on high seasonal and interannual variations associated with climate variability and that the increase was occurring in both northern and tropical latitudes. NPP generally decreased in El Nino season and increased in La Nina seasons, but the magnitude and spatial pattern of the response varied widely between individual events. Our estimates also indicate that the increases in NPP during the period were caused mainly by increases in atmospheric carbon dioxide and precipitation. The enhancement of NPP by warming was limited to northern high latitudes (above 50°N); in other regions, the interannual variations in NPP were correlated negatively with temperature and positively with precipitation.

Effects of spatial and temporal climatic variability on terrestrial carbon and water fluxes in the Pacific Northwest, USA

Abstract. Systematic evaluation of the carbon cycle physical and biological variables simulated in Earth System Model (ESM) participating in the Coupled Model Intercomparison Project Phase 6 (CMIP 6) is fundamental to the understanding of terrestrial ecosystems, as well as to future projections. Leaf Area Index (LAI), Gross Primary Productivity (GPP), Net Primary Productivity (NPP), Net Ecosystem Productivity (NEP) and Land Surface Temperature (LST) as key indicators of carbon cycle performance in ESM outputs, play a critical role in evaluating ecosystem functions. Assessing these metrics can provide valuable insights into the biases in model-simulated ecosystems and offer guidance for model improvement. In this study, we assessed the interannual trends performance of LAI, GPP, NPP, NEP and LST simulated by 12 CMIP6 ESMs during the historical period by using satellite LAI, NPP, NEP, LST and CSIF data as observations. The findings indicate that: (1) There are significant uncertainties in the overall trends and interannual variability in LAI, NPP, and LST captured by the CMIP6 ESM. Meanwhile, simulated GPP and NEP trends were lower than observations with discrepancies reaching 0.03 yr−1 for GPP and 2.46 gCm-2yr-1 for NEP. (2) Spatially, CMIP6 ESMs exhibited widespread underestimation of trends in LAI, GPP, NPP, and NEP across China. The MME underestimated these variables in 46.29 % (LAI), 43.47 % (GPP), 49.81 % (NPP), and 61.34 % (NEP) of the study area. Meanwhile, the simulated LST trend is underestimated in northern China, while its overestimations in western and southern China. (3) ESMs inadequate responsiveness to anthropogenic and environmental forcing and incomplete mechanistic representation of plant respiration pathways struggled accurate simulation of trends in LAI, GPP, NPP, NEP and LST.

Grasslands in the Tibetan Plateau are claimed to be sensitive and vulnerable to climate change and anthropogenic activities. Quantifying the impacts of climate change and anthropogenic activities on grassland growth is an essential step for developing sustainable grassland ecosystem management strategies under the background of climate change and increasing anthropogenic activities occurring in the plateau. Net primary productivity (NPP) is one of the key components in the carbon cycle of terrestrial ecosystems, and can serve an important role in the assessment of vegetation growth. In this study, a modified Carnegie–Ames–Stanford Approach (CASA) model, which considers remote sensing information for the estimation of the water stress coefficient and time-lag effects of climatic factors on NPP simulation, was applied to simulate NPP in the Tibetan Plateau from 2001 to 2015. Then, the spatiotemporal variations of NPP and its correlation with climatic factors and anthropogenic activities were analyzed. The results showed that the mean values of NPP were 0.18 kg∙C∙m−2∙a−1 and 0.16 kg∙C∙m−2∙a−1 for the original CASA model and modified CASA model, respectively. The modified CASA model performed well in estimating NPP compared with field-observed data, with root mean square error (RMSE) and mean absolute error (MAE) of 0.13 kg∙C∙m−2∙a−1 and 0.10 kg∙C∙m−2∙a−1, respectively. Relative RMSE and MAE decreased by 45.8% and 44.4%, respectively, compared to the original CASA model. The variation of NPP showed gradients decreasing from southeast to northwest spatially, and displayed an overall decreasing trend for the study area temporally, with a mean value of −0.02 × 10−2 kg∙C∙m−2∙a−1 due to climate change and increasing anthropogenic activities (i.e., land use and land cover change). Generally, 54% and 89% of the total pixels displayed a negative relationship between NPP and mean annual temperature, as well as annual cumulative precipitation, respectively, with average values of –0.0003 (kg∙C∙m−2 a−1)/°C and −0.254 (g∙C∙m−2∙a−1)/mm for mean annual temperature and annual cumulative precipitation, respectively. Additionally, about 68% of the total pixels displayed a positive relationship between annual cumulative solar radiation and NPP, with a mean value of 0.038 (g∙C∙m−2·a−1)/(MJ m−2). Anthropogenic activities had a negative effect on NPP variation, and it was larger than that of climate change, implying that human intervention plays a critical role in mitigating the degenerating ecosystem. In terms of human intervention, ecological destruction has a significantly negative effect on the NPP trend, and the absolute value was larger than that of ecological restoration, which has a significantly positive effect on NPP the trend. Our results indicate that ecological destruction should be paid more attention, and ecological restoration should be conducted to mitigate the overall decreasing trend of NPP in the plateau.

Modeling the interannual variability and trends in gross and net primary productivity of tropical forests from 1982 to 1999

Despite its significance, net primary productivity is rarely used as an indicator of climate change. The purpose of this study was to assess the spatiotemporal dynamics of vegetation net primary productivity and its response to climate variability in the Welmel watershed, southeastern Ethiopia. The investigation utilized data from the Moderate Resolution Imaging Spectroradiometer, and the Climate Hazards Group Infrared Precipitation with Stations accessed through the Google Earth Engine cloud computing platform. To evaluate the trend and response of net primary productivity to climatic factors, the Mann-Kendall and Theil Sen’s tests, the Pearson correlation coefficient, and stability analysis through the coefficient of variation of net primary productivity were utilized. The analysis of the temporal trend of net primary productivity revealed that the entire watershed, forest, wood, and cultivated land areas exhibited decreasing trends with net primary productivity values of -0.681, -4.361, -1.41, and − 3.25 g C m− 2/year, respectively. On the other hand, shrubs and grazing land displayed increasing trends of 1.15 and 2.04 g C m− 2/year, respectively. The wood and shrubland trends were statistically significant (p < 0.05). In the spatial analysis, an area of 64.6% showed a decreasing trend, whereas 33.78% displayed an increasing trend in net primary productivity values. The areas with stable and unstable variations in net primary productivity accounted for 26.47% and 8.02%, respectively. The net primary productivity of the land cover types and entire watershed were positively correlated with rainfall, however, only forests and woodlands were statistically significantly correlated (p < 0.05). The net primary productivity and land surface temperature were negatively correlated, except for forests and cultivated land, which were positively correlated. The results of this study provide essential information for managing ecosystems, supporting agricultural practices, and enhancing community resilience in a changing climate.

The expansion of artificial woodlands in China has contributed significantly to regional land-cover changes and changes in the regional net primary productivity (NPP). This study used Ximeng County in the Yunnan Province as a case study to investigate the overall changes, associated amplitude, and spatio-temporal distribution of NPP from 2000–2015.The Carnegie-Ames-Stanford approach was used in the rapidly expanding artificial woodland area based on MODIS-NDVI data, meteorological data, and Landsat 5 TM data to calculate the NPP. The results show that (1) artificial woodlands experience a 10fold increase and account for 93 % of the land cover transfer, which was mainly from woodland areas. (2) The NPP was 906.2×109 gC·yr-1 in 2000 and 972.0×109 gC·yr-1 in 2015, presenting a total increase of 65.8×109 gC·yr-1 and a mean increase of 52.4 gC·m-2·yr-1 in Ximeng County. (3) The most notable NPP changes take place in the central and the western border regions, with the increasing NPP of artificial woodlands and arable land offsetting the negative effects of the decrease in woodland NPP. (4) The total NPP in the study area kept increasing, primarily due to the growing area of artificial woodlands as well as the stand age of the woods, whereas the mean value change of the NPP is mostly related to the increasing stand age. (5) The artificial woodlands increase the NPP value more than natural woodlands. While protecting and promoting ecologically valuable natural forests at the same time, it seems quite advantageous to establish regional plantations and coordinate their development on a scientific basis with a view to increasing NPP, economic development, but also the ecological stability of this mountain region. Our study reveals the changes in NPP and its distribution in a rapidly expanding area of artificial woodland in southwest China based on remote-sensing data and the CASA model, providing a decision-making basis for rational land-use management, the optimal utilization of land resources, and a county-scale assessment approach.

In recent years, multiple disturbances have significantly altered terrestrial ecosystems in arid and semi-arid regions, particularly on the Mongolian Plateau (MP). Net primary productivity (NPP) of vegetation is an essential component of the surface carbon cycle. As such, it characterizes the state of variation in terrestrial ecosystems and reflects the productive capacity of natural vegetation. This study revealed the complex relationship between the natural environment and NPP in the ecologically fragile and sensitive MP. The modified Carnegie–Ames–Stanford Approach (CASA) model was used to simulate vegetation NPP. Further, the contributions of topography, vegetation, soils, and climate to NPP’s distribution and spatiotemporal variation were explored using the geographic detector model (GDM) and structural equation model (SEM). The study’s findings indicate the following: (1) NPPs for different vegetation types in the MP were in the order of broad-leaved forest &gt; meadow steppe &gt; coniferous forest &gt; cropland &gt; shrub &gt; typical steppe &gt; sandy land &gt; alpine steppe &gt; desert steppe. (2) NPP showed an increasing trend during the growing seasons from 2000 to 2019, with forests providing larger vegetation carbon stocks. It also maintained a more stable level of productivity. (3) Vegetation cover, precipitation, soil moisture, and solar radiation were the key factors affecting NPP’s spatial distribution. NPP’s spatial distribution was primarily explained by the normalized difference vegetation index, solar radiation, precipitation, vegetation type, soil moisture, and soil type (-statistics = 0.86, 0.71, 0.67, 0.67, 0.57, and 0.57, respectively); the contribution of temperature was small (-statistics = 0.26), and topographic factors had the least influence on NPP’s distribution, as their contribution amounted to less than 0.20. (4) A SEM constructed based on the normalized difference vegetation index (NDVI), solar radiation, precipitation, temperature, and soil moisture explained 17% to 65% of the MP’s NPP variations. The total effects of the MP’s NPP variations in absolute values were in the order of NDVI (0.47) &gt; precipitation (0.33) &gt; soil moisture (0.16) &gt; temperature (0.14) &gt; solar radiation (0.02), and the mechanisms responsible for NPP variations differed slightly among the relevant vegetation types. Overall, this study can help understand the mechanisms responsible for the MP’s NPP variations and offer a new perspective for regional vegetation ecosystem management.

The impacts of climate changes and human activities on net primary productivity vary across an ecotone zone in Northwest China

The variation of net primary productivity and underlying mechanisms vary under different drought stress in Central Asia from 1990 to 2020

Assessing the effects of grazing on variations of vegetation NPP in the Xilingol Grassland, China, using a grazing pressure index