Article

Comparing Global Models of Terrestrial Net Primary Productivity (NPP): Overview and Key Results

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Abstract

Seventeen global models of terrestrial biogeochemistry were compared with respect to annual and seasonal fluxes of net primary productivity (NPP) for the land biosphere. The comparison, sponsored by IGBP-GAIM/DIS/GCTE, used standardized input variables wherever possible and was carried out through two international workshops and over the Internet. The models differed widely in complexity and original purpose, but could be grouped in three major categories: satellite-based models that use data from the NOAA/AVHRR sensor as their major input stream (CASA, GLO-PEM, SDBM, SIB2 and TURC), models that simulate carbon fluxes using a prescribed vegetation structure (BIOME-BGC, CARAIB 2.1, CENTURY 4.0, FBM 2.2, HRBM 3.0, KGBM, PLAI 0.2, SILVAN 2.2 and TEM 4.0), and models that simulate both vegetation structure and carbon fluxes (BIOME3, DOLY and HYBRID 3.0). The simulations resulted in a range of total NPP values (44.4–66.3 Pg C year–1), after removal of two outliers (which produced extreme results as artefacts due to the comparison). The broad global pattern of NPP and the relationship of annual NPP to the major climatic variables coincided in most areas. Differences could not be attributed to the fundamental modelling strategies, with the exception that nutrient constraints generally produced lower NPP. Regional and global NPP were sensitive to the simulation method for the water balance. Seasonal variation among models was high, both globally and locally, providing several indications for specific deficiencies in some models.

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... In contrast to the estimation of FAGB [56], NPP has been widely modeled on a global scale [32,[140][141][142]. Several regional models have also been developed [33][34][35]38] for its estimation. ...
... Many studies focus on the estimation of vegetation growth by modeling GPP and NPP [35]. Several models exist for NPP estimation based on remote sensing, which differs in approach and complexity, data needs, flexibility, spatial and temporal resolution [140]. ...
... BEPS is a process-based model popular these days to estimate the NPP and GPP [140]. ...
Thesis
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In recognition of the important role of forests in the global carbon cycle, particularly with respects to mitigating carbon dioxide emissions, the ability of accurately and precisely measure the carbon sequestration in forests is increasingly gaining global attention. As being a major part of the carbon cycle, accurate quantification of the forest above ground biomass (FAGB) and net primary productivity (NPP) at local to global scales has become one of a central topic for carbon cycle researchers, foresters, land and resource managers, and politicians. In order to estimate, FAGB and NPP adequately, methodologies, such as forest inventory, remote sensing, and vegetation and carbon modeling have been successfully utilized. However, insufficiency of direct field biomass and NPP observations has severely limited the parameterization, validation, and their estimation. If satellite-derived estimations become precise, efficient and reliable it will help to estimate and monitor forest carbon information in the global forest ecosystem and play a very important role in global climate change mitigation efforts. Despite so many efforts going on, there is still a lack of proper regional and national spatiotemporal FAGB and NPP information in developing countries like Nepal. In addition, there are not many, proper area/nation specific convenient method, for their estimation, that gives a quality estimate with low cost and good replicability, especially for the developing countries. Based on these research problems and research themes, to overcome them our objectives can be broadly classified as i) develop a two-scale FAGB estimation method with the use of multi-resolution optical imageries and Google Earth Very High Resolution (GEVHR) imageries as virtual sample plots, ii.) estimation and spatiotemporal change analysis of forest cover and FAGB in different physiographic regions and forest types of Nepal and iii.) estimation and spatiotemporal trend analysis of NPP in the forest of Nepal with Boreal Ecosystem Productivity Simulator (BEPS) model over years 2000-2015. For the development of the two-scale method of FAGB estimation, the study was conducted in Chitwan district of Nepal using GeoEye-1 (0.46 m), Landsat (30m) and GEVHR Quick Bird (0.65m) imageries. For the local scale (Kayerkhola watershed), tree crowns of the entire area were delineated by object-based image analysis (OBIA) technique on GeoEye imageries. The overall accuracy of 83% was obtained in the delineation of tree canopy cover (TCC) plot-1. A TCC vs. FAGB model was developed based on TCC from GeoEye and FAGB from field sample plots. The coefficient of determination (R2) of 0.76 was obtained in themodeling and 0.83 in the validation of the model. To upscale FAGB to the whole district, open source GEVHR imageries were used as virtual field plots, delineated their TCC and then calculated it’s, FAGB (based on TCC vs. FAGB model). Using Multivariate Adaptive Regression Splines (MARS) machine learning algorithm, model was developed from Landsat 8 bands and vegetation indices. It was then used to extrapolate FAGB in the entire district. This approach considerably reduces field data and commercial very high resolution imageries requirements to achieve two scale forest information and FAGB estimate at high resolution (30m) and accuracy (R2=0.76 & 0.7) with minimal error (RMSE=64 & 38 tons ha-1) at both local and regional scales. The proposed methodology can be one of the promising techniques for the FAGB and carbon estimation in a very cost-efficient way and can be replicated with limited resources and time. It is especially applicable for developing countries with a low budget for carbon estimation and it is very much applicable to the “reducing emissions from deforestation and forest degradation” (REDD+) and “monitoring reporting and verification” (MRV) processes. In the case of estimation and spatiotemporal analysis of forest cover and FAGB, here, we present so far first national scale forest cover type and FAGB study along with TCC of Nepal at 30m resolution for the year of 2000, 2010 and 2015. With the integrated used of Landsat imageries, field sample plots and Google earth imageries the forest cover type and FAGB of Nepal was estimated. A good overall accuracy of 87% with Kappa statistics of 0.89 was obtained for the forest cover type, classification with OBIA. Similarly, the estimation of FAGB with multiple linear regression was significant enough with aggregate R2 of 0.7 and RMSE 98 tons ha-1 at P<0.001for the year 2010. For the FAGB estimation for the year of 2000 and 2015, the FAGB vs. TCC model with aggregate R2 of 0.8 was used over the TCC estimated for them. The overall forest area of Nepal is found to be gradually increasing from 37.9% of the total area in 2000 to 40.2% in 2010 and 42.8% in 2015. Also, the FAGB was increased from 911million tons in 2000 to 1102 million tons in 2010 and 1109 million tons in 2015. The Broad-leaved closed forest (BLCF), was found to play major role, in terms of total FAGB contribution (47%, 47% and 50% of total contribution for years 2000, 2010 and 2015), forest area occupancy (37%, 36% and 38% of total forest for years 2000, 2010 and 2015), and FAGB productivity (214, 242 and 240 tons ha-1 for 2000, 2010 and 2015). In terms of physiographic region, the plain area was found to produce more FAGB (36%, 42% and 39% of the total FAGB for 2000, 2010 and 2015) although the forest area coverage by it was the least (29%, 30% and 27% of total forest for 2000, 2010 and2015) among the threephysiographic regions. The resulting nationwide wall-to-wall FAGB maps will help to improve the accuracy of carbon dynamic prediction in Nepal. It has huge importance to support diverse issues of environmental conservation. The data has big potential use for national and regional level sustainable land use planning strategies and meeting several global commitments. In our study, we also aimed to understand the temporal and spatial variations of NPP in the forest of Nepal. The daily, monthly and annual NPP of the forest was estimated using the BEPS model for the years 2000-2015. The Leaf area index (LAI), meteorological datasets and other parameters as soil data, tree cover, biomass, field capacity, and wilting points were the main input for the BEPS model. We found that the NPP value varied spatially and temporally across the whole forest, which is increasing in general, though there were fluctuations in some years. The average daily NPP over the entire study period ranged from 1.3 to 1.7 gm m-2day-1 with highest in years 2014 and lowest in the year 2000 and overall average NPP trend of 1.65 gm m-2day-1. Within the overall forest, the average NPP productivity is generally highest in the plain followed by the hill and least in the mountain physiographical region. Looking at the intra-annual variability, the average monthly NPP ranged from 4.1 to 7.1 kg m-2month-1 with an average of 6.2 kg m-2month-1. The highest NPP rates were generally in the months of October and then May and the lowest in December and January. Mean seasonal NPP is largest during post-monsoon and lowest during the winter period, thereby indicating the importance of soil moisture and solar radiation for vegetation productivity. The average annual NPP is 1.2 kg m-2year-1 and the total average of 19 kg m-2. NPP was found to be highly influenced by LAI, rainfall, solar radiation and temperature mostly positively correlated in overall. The NPP was found to be highly correlated with the LAI especially in the plain region over the years from 2000-2015. In addition, while looking at the variation of NPP on different forest types, the broad-leaved forest was found to have almost 1.7 times more NPP (1.97 gm m-2day-1) than that of needle leaved forest (NPP 1.18 gm m-2day-1). We also found that the slope percent of <15% is more favorable (NPP 2.15 gm m-2day-1) for higher NPP among different slope percent. The result from this study gives us important information on intra and inter annual spatiotemporal trend and variability of NPP in the forest of Nepal overall and in different physiographic regions. It also gives us information on the relation between NPP and various climatic and vegetation parameters. All these information are very important for the proper forest ecosystem monitoring, management, and planning operations. VII Keywords: Forest above ground biomass, Forest cover change, Net primary productivity, Spatio-temporal dynamics, Change analysis
... Considering the complexity of global carbon cycle and its interconnection with the vegetation, it is important to quantify the response of ecosystems to changes in global biogeochemical cycles (Apps et al., 2005;Cramer et al., 1999). Moreover, for understanding the processes and factors that regulate the terrestrial carbon sink and response of terrestrial ecosystem to future climate warming, it is essential to assess the rate of carbon uptake in terrestrial vegetation (Tum, 2016). ...
... NEP minus the carbon release due to various disturbances is net biome production (NBP) (Yuan et al., 2006). Running et al. (2000) argues that the change in terrestrial biological production is the most hors (Yuan et al., 2006;Running et al., 2000;Cramer et al., 1999) pointed out that NPP is used commonly in carbon cycle models for studying global carbon cycle, carbon source and sink of ecosystem, and spatial and temporal distribution of CO2, and as an index of ecological change (Liu et al. 2015). ...
... This study highlights the practical utility of NPP models in measurement of economically and socially significant products of vegetation growth (crop yield, range forage and forest production). Cramer et al. (1999) also emphasizes the importance of NPP for studying the response of the ecosystems to climate change. ...
Thesis
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Quantifying the rate of carbon uptake in forest ecosystems is essential for understanding how forest ecosystems impact the carbon cycle, to analyse the connection between the global carbon cycle and vegetation and to study factors which regulate spatial and temporal distribution of carbon dioxide (CO2). The MOD17 MODerate Resolution Imaging Spectroradiometer (MODIS) satellite product provides estimations of net primary production (NPP) of vegetation at 500 m spatial resolution. However, the coarse spatial resolution of MODIS NPP has limitations in capturing heterogeneity and fragmentation of forest areas and scaling issues in validation against forest inventory data. The aim of this thesis was to adapt the MOD17 NPP algorithm to generate high spatial resolution (10-30 m) NPP estimates using high spatial resolution input parameters derived from the Harmonized Landsat Sentinel-2 (HLS) project and other Copernicus datasets (climate data). The new high spatial resolution HLS NPP product was compared to MODIS MOD17 NPP data and the connection between NPP and forest inventory data was explored. Moderate consistency was found between MOD17 and HLS NPP product for the area dominated by coniferous forest with a moderate correlation (R2=0.53, p<0.01). For mixed forest areas correlation was weak for the years 2016 and 2018 (R2=0.19 and R2=0.22, p<0.01) and there was no correlation for the year 2017. HLS NPP was lower than MOD17 NPP by 18.67% for the study area in Slovenia and by 23.24% for the southern part of Austria. Correlation analysis between periodic annual timber increment and HLS NPP for inventory plots in Slovenia showed that NPP algorithm cannot represent variation in timber volume increment, which was particularly the case for the plots with low stand density and low timber volume. The 10 m resolution NPP is feasible to obtain for forest areas in Europe and it is useful in estimation of NPP for forest stands and polygons of irregular shape. Variation in growing conditions due to effects of different forest site parameters cannot be fully represented by HLS NPP. Main limitations were the availability of ground data for validation and satellite Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation (FPAR) data for forest. Therefore, for future work, it is significant to develop LAI and FPAR products for forest at high spatial and temporal resolution and to have ground-based estimates of NPP for validation.
... Net primary productivity (NPP) is the total amount of organic dry matter accumulated by green plants in unit time and unit area. It is an indicator of plant growth and reflects the capacity of plants to fix carbon via photosynthesis and is not only the driving force of the carbon cycle but also the main factor for determining the carbon sink and regulating ecological processes (Cramer et al., 1999;Melillo et al., 1993). ...
... The five sample plots showed a similar trend of forest NPP changes, with consistent small forest NPP values in 1957, 1963, 1974, 1985, 1992, 1997, 2001, 2008and consistent large forest NPP values in 1970, 1989, 1994, 1999, 2002). This result indicated that the trees in the five sample plots were growing in the same climatic conditions. ...
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The lack of long-term high-resolution data makes it difficult to determine historical and future trends in net primary productivity (NPP). This study used tree rings as a proxy to investigate the dynamics of NPP in Tianshan forests where coniferous forests are the major species and the other species are deficient. All trees and some tree cores from five sample plots in different geographic locations in the western Tianshan Mountains were selected to reconstruct forest NPP data from 1950 to 2020. Multiple historical events that resulted in large-scale terrestrial carbon fluxes were identified and the existence of 28a and 17a time-scale cycles of historical forest NPP was observed. We discovered that the reconstructed forest NPP in the western Tianshan Mountains did not significantly correlate with satellite-based products (e.g., MODIS NPP, solar-induced chlorophyll fluorescence data). This result was attributed to the lag of forest growth for climate, the accuracy of the satellite-based products and statistical errors due to the short overlap time. We analysed the uncertainties in reconstructing historical forest NPP using tree ring widths and proposed corresponding solutions. We concluded that the reconstructed data remain the ideal proxy for regions lacking long-term empirical data and exhibit a high degree of confidence for expressing trends in forest productivity change over long time series.
... The net primary productivity (NPP) of vegetation is the main factor characterizing the carbon sink of the environment and regulating ecological processes, and it plays a crucial role in the global carbon balance [1]. In recent decades, coal mining and utilization have promoted the rapid development of society and economy but have also caused serious damage to the regional ecological environment [2][3][4]. ...
... In this study, we observed that the relative contributions of soil properties and climate to the spatial variation in NPP both approached 35% (Figure 5a inset). However, current classical vegetation productivity models are driven by climatic factors, such as the Miami [83], BIOME-BGC [84], and CASA models [30], and only a few models focus on soil carbon and nitrogen [1]. This may be because of the poor availability of data on soil properties, which are required for estimating vegetation productivity, where data acquisition is challenging, particularly at large scales. ...
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Spatial differentiation of the net primary productivity (NPP) of vegetation is an important factor in the ecological protection and restoration of mining areas. However, most studies have focused on climatic productivity constraints and rarely considered the effects of soil properties and mining activities. Thus, the impact of the forces driving NPP in mining areas on spatial location remains unclear. Taking the Changhe Basin mining area as an example, we used the Carnegie–Ames–Stanford approach (CASA) model to estimate NPP and quantified the impact of climate, soil properties, and mining activities based on factorial experiments. Our results indicate that the average NPP in the Changhe Basin mining area was 290.13 gC/(m2·yr), and the NPP in the western Changhe Basin, an intensive coal mining area, was significantly lower than that in the east. The correlations between each driver and NPP varied by location, with mean annual temperature and precipitation, soil organic carbon, total nitrogen, and land degradation showing strong correlations. The relative importance of climate, soil properties, and mining activities on the spatial variability of NPP was 38.97%, 31.50%, and 29.53%, respectively. Furthermore, 70.72% of the NPP variability in mining areas was controlled by the coupled effects of climate and soil properties (CS + SC) or climate and mining activities (CM + MC). Meanwhile, The NPP in the western Changhe Basin mining area was mainly controlled by mining activities (M) or climate and mining activities (CM), while that in the east was mainly controlled by soil properties and climate (CS). Overall, our study extends the knowledge regarding the impacts of driving forces on spatial variation of NPP in mining areas and provides a reference point for forming strategies and practices of ecological restoration and land reclamation in different spatial locations in mining areas.
... the Earth have provided critical inputs for global carbon cycle studies, provided observation-based GPP estimates for comparisons with Earth System Models and terrestrial carbon cycle models, and have revolutionized our understanding of the carbon cycle (Anav et al., 2015;Chen et al., 2017;Cramer et al., 1999;Field et al., 1995;Jung et al., 2020;Keenan et al., 2012;O'Sullivan et al., 2020;Prince & Goward, 1995;Ruimy et al., 1996;Running et al., 2004;Xiao et al., 2019;Zhang et al., 2016;Zscheischler et al., 2014). The diurnal to interannual variability of GPP is determined by limiting resources, climate, weather conditions, disturbance, phenology, and extreme events (Beer et al., 2010;Gu et al., 2002;Kannenberg et al., 2020;Randazzo et al., 2020;Roby et al., 2020;Stoy et al., 2005;Zscheischler et al., 2014). ...
... To start estimating GPP at a subdaily temporal resolution from space-based observations, we can look toward various formulations of GPP's response to environmental conditions such as incoming solar radiation. The development of space-based GPP estimates has largely relied on relationships between the fraction of photosynthetically active radiation (PAR) absorbed by plants (fAPAR) and vegetation indices and light-use efficiency (LUE) models that can convert absorbed PAR (APAR) to net primary production (NPP) or GPP (Anderson et al., 2000;Cramer et al., 1999;Field et al., 1995;Joiner et al., 2018;Mahadevan et al., 2008;Running et al., 2004;Xiao et al., 2019;Yuan et al., 2014). Vegetation indices developed from remotely sensed reflectance in visible to near-infrared wavelengths, such as the Normalized Difference Vegetation Index or the Enhanced Vegetation Index, have served as indicators of fAPAR and are often used to estimate APAR in LUE models Mahadevan et al., 2008;Running et al., 2004;Xiao et al., 2019;Yuan et al., 2007). ...
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Gross primary productivity (GPP) is the largest flux in the global carbon cycle and satellite‐based GPP estimates have long been used to study the trends and interannual variability of GPP. With recent updates to geostationary satellites, we can now explore the diurnal variability of GPP at a comparable spatial resolution to polar‐orbiting satellites and at temporal frequencies comparable to eddy covariance (EC) tower sites. We used observations from the Advanced Baseline Imager on the Geostationary Operational Environmental Satellite‐R series (GOES‐R) to test the ability of subdaily satellite data to capture the shifts in the diurnal course of GPP at an oak savanna EC site in California, USA that is subject to seasonal soil moisture declines. We compared three methods to estimate GPP: (a) a light‐use efficiency model, (b) a linear relationship between the product of near‐infrared reflectance of vegetation and photosynthetically active radiation (LIN‐NIRvP) and EC tower GPP, and (c) a light response curve (LRC‐NIRvP) between NIRvP and EC GPP. The LRC‐NIRvP achieved the lowest mean absolute error for winter (2 µmol CO2 m⁻² s⁻¹), spring (2.51 µmol CO2 m⁻² s⁻¹), summer (1.43 µmol CO2 m⁻² s⁻¹), and fall (1.35 µmol CO2 m⁻² s⁻¹). The ecosystem experienced the largest shift in daily peak GPP in relation to the peak of incoming solar radiation toward the morning hours during the dry summers. The LRC‐NIRvP and the light‐use efficiency model were in agreement with these patterns of a shift in peak daily GPP toward the morning hours during summer. Our results can help develop diurnal estimates of GPP from geostationary satellites that are sensitive to fluctuating environmental conditions during the day.
... Dynamic Global Vegetation Models (DGVMs), as well as Earth System Models (ESMs), are mathematical models which simulate processes related to vegetation growth and incorporate water, C, and nutrient cycles at different complexity (Brovkin et al., 1999;Cox et al., 1998;Foley et al., 1996). They have been developed independently to better understand and quantify the global terrestrial ecosystem and biogeochemical cycles for about 30 years (Cramer et al., 1999;Steffen et al., 1992). DGVMs combine processes that contribute to vegetation structure dynamics and composition and changes in ecosystem geography with biochemical processes (Sitch et al., 2003). ...
Thesis
Klimawandel und Bodendegradation üben Druck auf die Nahrungsmittelproduktion sowie auf die Fähigkeit des Bodens zur Minderung des Klimawandels beizutragen aus. Bodendegradation hat negative Auswirkungen auf die Bodenqualität. Ziel dieser Arbeit ist die Analyse der Effekte von landwirtschaftlich getriebener Bodendegradation, vor allem durch Pflügen und dem Umgang mit Ernterückständen. Es wird ein Überblick über das Thema Bodendegradation gegeben, gefolgt von Erweiterung des globalen Ökosystemmodells Lund-Potsdam-Jena-managed-Land (LPJmL) um eine detaillierte Prozessabbildung von Pflugpraktiken und Effekten von Ernterückständen. Diese ermöglicht die Analyse der Effekten von landwirtschaftlichen Managements auf die Anpassung und Minderung des Klimawandel. Das Modell kann die Effekte von naturerhaltender landwirtschaftlicher Bewirtschaftung (im Englischen bekannt als Conservation Agriculture) auf Kohlenstoffvorräte im Boden und CO2 Emissionen simulieren. Im letzten Teil wird die historische Dynamik der Entwicklung von Bodenkohlenstoff (engl.: Soil Organic Carbon – SOC) und die Effekte von Annahmen zum zukünftigen Management unter unterschiedlichen Klimaszenarien gezeigt. Die Ergebnisse zeigen, dass durch die historische Umwandlung von natürlicher Vegetation zu landwirtschaftlicher Fläche bis zu 215 Pg SOC im Boden verloren gegangen sind. Bis zum Ende des Jahrhunderts könnten weitere 38 Pg SOC zusätzlich verloren gehen, wird die heutige landwirtschaftliche Fläche nicht nachhaltig bewirtschaften. Die Bewirtschaftung mit dem Pflug zeigt einen geringen Einfluss auf die Kohlenstoffvorräte des Bodens, während die Wahl der Behandlung von Ernterückständen erheblich Einfluss hat. Die Rückführung von Ernterückständen hat positive Einflüsse auf Bodenwassergehalt und Ernteproduktivität, mit regionalen Unterschieden. Insgesamt zeigen 46% der heute Landwirtschaftsfläche das Potenzial zur Steigerung des Bodenkohlenstoff, während mindestens 52% Kohlenstoff im Boden verlieren könnten.
... Currently, dozens of NPP estimation models are available, and the estimation results vary greatly in spatial distribution and value. (26) The input and control parameters are frequently different in different NPP estimation models. Even for consistent input and control parameters, the estimation results vary significantly with different model algorithms, input data sources, and spatial resolutions. ...
... The ecological disturbance on regional and global scales can be analyzed by estimating NPP (Piao et al., 2006). Multiple climatic factors, including pressure, relative humidity, actual vapor pressure, various solar fluxes (i.e., shortwave radiations, longwave radiations, net radiations, ground heat flux, sensible heat flux, and latent heat flux), and temperature were used by various researcher to estimate NPP (Cramer et al., 1999;Eisfelder et al., 2014;Lehuger et al., 2010) however, it cannot be accurately computed on a large scale, but accuracy up to 74% has been achieved by various researchers at local scales (Lauenroth et al., 2006). NPP was primarily estimated through the Miami model by correlating several productivity levels under the influence of average precipitation and temperature without analyzing other climatic factors (Lin et al., 2013). ...
... This challenge is further complicated by expected reductions in crop production caused by climate change and other environmental issues (Ray et al. 2019;Ortiz-Bobea et al. 2021;Soleymani 2022). Therefore, a sustainable solution requires understanding the complexity of agricultural systems and their interaction with other biogeochemical dynamics (Cramer et al. 1999;Sitch et al. 2003;Lindeskog et al. 2013). A class of global gridded crop models (GGCMs) which are able to simulate both crops and biogeochemistry have been developed to explore these multisystem interactions (Prentice et al. 1989;Bondeau et al. 2007; Monfreda et al. 2008;Müller et al. 2017). ...
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Crop yield improvement during the last decades has relied on increasing the ratio of the economic organ to the total aboveground biomass, known as the harvest index (HI). In most crop models, HI is set as a parameter; this empirical approach does not consider that HI not only depends on plant genotype, but is also affected by the environment. An alternative is to simulate allocation mechanistically, as in the LPJ-GUESS crop model, which simulates HI based on daily growing conditions and the crop development stage. Simulated HI is critical for agricultural research due to its economic importance, but it also can validate the robust representation of production processes. However, there is a challenge to constrain parameter values globally for the allocation processes. Therefore, this paper aims to evaluate the sensitivity of yield and HI of wheat and maize simulated with LPJ-GUESS to eight production allocation-related parameters and identify the most suitable parameter values for global simulations. The nitrogen demand reduction after anthesis, the minimum leaf carbon to nitrogen ratio (C:N) and the range of leaf C:N strongly affected carbon assimilation and yield, while the retranslocation of labile stem carbon to grains and the retranslocation rate of nitrogen and carbon from vegetative organs to grains after anthesis mainly influenced HI. A global database of observed HI for both crops was compiled for reference to constrain simulations before calibrating parameters for yield against reference data. Two high- and low-yielding maize cultivars emerged from the calibration, whilst spring and winter cultivars were found appropriate for wheat. The calibrated version of LPJ-GUESS improved the simulation of yield and HI at the global scale for both crops, providing a basis for future studies exploring crop production under different climate and management scenarios.
... With the development of remote sensing and GIS technologies, the estimation of vegetation NPP using remote sensing data has become the most prominent feature of NPP modeling studies and simulation methods [24]. Among them, the Carnegie-Ames-Stanford approach (CASA) model is widely used for NPP estimation at global and regional scales due to its less dependence on ground truth data and the relatively few input parameters of the model, which are easy to collect [25,26]. Chen [27] simulates the month-by-month NPP of terrestrial ecosystems in China for the past 31 years using the CASA model. ...
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Larch (Larix principis-rupprechtii Mayr) is a major coniferous tree species in northern China, and climate change has serious impacts on larch growth. However, the impact of future climate change on net primary productivity (NPP) and the growth suitability of larch is unclear. Based on forest inventory data, spatially continuous environmental factor data (climate, topography, soil), and NPP from the Carnegie-Ames-Stanford approach (CASA) model in the study area, the random forest (RF) model was used to simulate the potential NPP and growth suitability of larch under different shared socioeconomic pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5) for current and future (2041–2060, 2080–2100). The correlation between potential NPP and determinants under different climate scenarios was analyzed at the pixel scale. The results showed that: (1) RF showed excellent performance in predicting the potential NPP of the region (R2 = 0.80, MAE = 15.61 gC·m−2·a−1, RMSE = 29.68 gC·m−2·a−1). (2) Under current climatic conditions, the mean potential NPP of larch was 324.9 gC·m−2·a−1. Low growth suitability of larch occurred in most parts of the study area, and high growth suitability only existed in the Bashang area and the high-elevation mountains. (3) The total area of high and medium growth suitable areas were projected to be 76.0%, 66.7%, 78.2%, and 80.8% by the end of this century under the SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 climate scenarios, respectively. (4) Under the SSP1-2.6 and SSP2-4.5 climate scenarios, the temperature had a significant contribution to the accumulation of the larch’s NPP, whereas precipitation had less effect on the larch’s growth. The results provided a theoretical basis for the adaptive management of larch forests under global climate change.
... Globally, it peaks in Northern Hemisphere summer and has its low point in winter. As shown in Cramer et al. (1999), its representation strongly depends on the land model. ...
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We assess the land surface model JSBACHv4 (Jena Scheme for Biosphere Atmosphere Coupling in Hamburg version 4), which was recently developed at the Max Planck Institute for Meteorology as part of the effort to build the new Icosahedral Nonhydrostatic (ICON) Earth system model (ESM), ICON-ESM. We assess JSBACHv4 in simulations coupled with ICON-A, the atmosphere model of ICON-ESM, hosting JSBACHv4 as land component to provide the surface boundary conditions. The assessment is based on a comparison of simulated albedo, land surface temperature (LST), leaf area index (LAI), terrestrial water storage (TWS), fraction of absorbed photosynthetic active radiation (FAPAR), net primary production (NPP), and water use efficiency (WUE) with corresponding observational data. JSBACHv4 is the successor of JSBACHv3; therefore, another purpose of this study is to document how this step in model development has changed model biases. This is achieved by also assessing, in parallel, the results of coupled land–atmosphere simulations with the preceding model ECHAM6 hosting JSBACHv3. Large albedo biases appear in both models over ice sheets and in central Asia. The temperate to boreal warm bias observed in simulations with JSBACHv3 largely remained in JSBACHv4, despite the very good agreement with observed LST in the global mean. For the assessment of changes in land water storage, a novel procedure is suggested to compare the gravitational data from the Gravity Recovery And Climate Experiment (GRACE) satellites to simulated TWS. It turns out that the agreement of the changes in the seasonal cycle of TWS is sensitive to the representation of precipitation in the atmosphere model. The LAI is generally too high, which is partly caused by too high soil moisture and also by the parameterization of the phenology itself. The pattern of WUE is, for both models, largely as observed. In India, WUE is too high, probably because JSBACH does not incorporate irrigation in our simulations. WUE differences between the two models can be traced back to differences in precipitation patterns in the two coupled land–atmosphere simulations. For both models, most NPP biases can be associated with biases in water stress, LAI, and FAPAR. In particular, the NPP bias of the Eurasian steppes has switched from positive in JSBACHv3 to negative in JSBACHv4. This difference is mainly caused by weaker precipitation and lower FAPAR of ICON-A–JSBACHv4 in July, which is most probably caused by a feedback loop between too little soil moisture, evaporation, and clouds. While the size and patterns of biases in albedo and LST are largely similar between the two model versions, they are less well correlated for precipitation- and vegetation-related variables like FAPAR. Overall, the biases found in the different assessment variables are either already known from the previous implementation in the Max Planck Institute Earth System Model (MPI-ESM) or have changed because of the coupling with the new atmospheric component ICON-A. Accordingly, this study demonstrates the technically successful completion of the re-implementation of JSBACH into ICON-ESM-V1. As discussed, there is a good perspective on mitigating the biases by an improved representation of the processes.
... Fetzel et al. (2016) explored the patterns and changes in land-use and land-use efficiency in Africa from 1980 to 2005: an analysis based on human occupancy of the net primary production framework. Other scholars have compared global terrestrial net primary productivity (NPP) models with current and future spatial distributions (Cramer et al. 1999;Donmez et al. 2011;Martinez et al. 2019). Many studies have shown that the NPP of vegetation in these specific regions shows obvious spatiotemporal heterogeneity at the local scale Ruimy et al. 1999;Wang et al. 2013). ...
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Rapid urbanization has produced many metropolitan areas in China, resulting in regional human-land-ecological environment contradiction phenomenon. In recent years, Chinese government has launched a series of new national ecological civilization construction and ecological protection projects. Monitoring the changes in land-use/land cove (LULC) and exploring its impact on net primary productivity (NPP) is a key hot issue to support sustainable development. This study took Wuhan metropolitan area as the research area to monitor the distribution and relationship between LULC and vegetation NPP changes is helpful to strengthen carbon balance and improve the quality of human settlements. The results showed that, from 2000 to 2020, land use types in the study area changed dramatically, mainly cultivated land, urban land, and other construction land. The vegetation NPP showed a continuous growth trend in time and space, with growth rate of 114.48%. With the passage of time, LULC spatial distribution had significant effects on NPP and chaotic urban sprawl has seriously affected NPP losses, while the ecological forest and grassland in remote areas can significantly increase NPP yield. This study also revealed the correlation between LULC and NPP, and opened up research approaches for land-use optimization and vegetation NPP based on different scenarios in the future. Our analysis not only contributes urban land-use planners, but also provides important insights into improving the competitiveness of green development among urban agglomeration systems.
... Spaceborne instruments can infer large-scale global carbon fluxes by imaging surface radiation [10,11]. Eddy covariance technique can measure the vertical flux of carbon dioxide directly [12,13]. ...
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A micro-pulse lidar system incorporating differential absorption lidar (DIAL) and coherent Doppler wind lidar (CDWL) is proposed and demonstrated. Due to the high signal-to-noise ratio (SNR) of the superconducting nanowire single-photon detector (SNSPD), the DIAL channel achieves high sensitivity in CO2 measurement. Meanwhile, the CDWL channel is used to obtain the horizontal wind field. In the process of the optimization and calibration of the DIAL receiver, specifically, mode scrambling and temperature control of the connecting fiber between the telescope and the SNSPD enhance the stability and robustness of the system. Horizontal scanning of the CO2 concentration and the wind field is carried out in a 6 km range over a scanning span of 60° with a radial resolution of 150 m and 15 s. The results show that the hybrid lidar system captures the spatial distribution of CO2 concentration and the wind field simultaneously. The horizontal net CO2 flux in a radius of 6 km is estimated by integrating the CO2 concentration and the wind transport vector, indicating different characteristics of horizontal net CO2 fluxes in an industrial area, a university campus, and a park. During most of the experiment, CO2 flux remained positive in the industrial area, but balances fell to nearly zero on the campus and in the park. The horizontal net fluxes averaged over 24 h in the three areas are 3.5 × 105 ppm·m2·s−1, 0.7 × 105 ppm·m2·s−1, and 0.1 × 105 ppm·m2·s−1.
... Plant productivity is lower at temperate latitudes than at tropical latitudes (Cramer et al., 1999). The temperate areas are dominated by evergreen forests and fruit availability is relatively low (Hanya & Chapman, 2013). ...
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Nutrient composition and food availability determine food choices and foraging strategies of animals, while altitude and geographical location affect species distribution and food availability. Tibetan macaques (Macaca thibetana) have sophisticated foraging strategies as the largest species in Macaca. They are important in understanding the ecological evolution of the entire genus. However, the mechanism of food selection in Tibetan macaques at low altitudes remains unclear. In this study, we researched a wild Tibetan macaques group (Tianhu Mountain Group, 29 individuals) living in a low‐altitude area around Mt. Huangshan, Anhui Province, China. We used instantaneous scan sampling to observe these macaques' foraging behavior from September 2020 to August 2021. We recorded the dietary composition and food availability, compared the nutrient content of staple food and non‐food items, and analyzed the role of key nutrients in food selection. We found that Tibetan macaques forage on 111 plants belonging to 93 genera and 55 families. The food types included fruits (52.5%), mature leaves (17.0%), bamboo shoots (14.4%), young leaves (6.3%), flowers (4.5%), others (2.1%), stems (1.9%), and tender shoots (1.3%). Tibetan macaques forage for a maximum of 76 plant species during spring. However, dietary diversity was highest during summer (H′ = 3.052). Monthly fruit consumption was positively correlated with food availability. Staple foods are lower in fiber, tannin, and water than non‐foods. In addition, the time spent foraging for specific foods was negatively correlated with the fiber and tannin content of the food. The results showed that Tibetan macaques' foraging plant species and food types were diverse, and their foraging strategies varied seasonally. Our findings confirmed the effect of nutrients on food choice in Tibetan macaques. We highlighted the important role of fiber and tannin in their food choices and suggested that the foraging behavior of Tibetan macaques is highly flexible and adaptive. Our study found that the foraging behavior of Tibetan macaques is highly flexible and adaptable. They prefer low‐fiber and low‐tannin foods when foraging for food. This dietary preference adapts to higher species richness in low‐altitude habitats, and may allow Tibetan macaques to maintain their large body size. We demonstrated the complexity of primate foraging behavior in terms of nutrition, food availability, and geographical distribution. The diet of primates should be defined comprehensively rather than simply as folivore or frugivore.
... CASA is a process-based model that describes processes of carbon exchange between the terrestrial biosphere and atmosphere (Cramer et al., 1999); it has been widely used to simulate regional or continental NPP over hundreds of published studies (Jay et al., 2016). ...
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The Carnegie–Ames–Stanford Approach (CASA) model is widely used to estimate vegetation net primary productivity (NPP) at regional scales. However, the CASA is still driven by multisource data, e.g. satellite remote sensing (RS) data, and ground observations that are time-consuming to obtain. RS data can conveniently provide real-time regional information and may replace ground observation data to drive the CASA model. We attempted to improve the CASA model in this study using the Moderate Resolution Imaging Spectroradiometer (MODIS) RS products, the GlobeLand30 RS product, and the digital elevation model data derived from radar RS. We applied it to simulate the NPP of alpine grasslands in the Qinghai Lake basin, which is located in the northeastern Qinghai–Tibetan Plateau, China. The accuracy of the RS-data-driven CASA, with a mean absolute percent error (MAPE) of 22.14 % and root mean square error (RMSE) of 26.36 g C m−2 per month, was higher than that of the multisource-data-driven CASA, with a MAPE of 44.80 % and RMSE of 57.43 g C m−2 per month. The NPP simulated by the RS-data-driven CASA in July 2020 shows an average value of 108.01 ± 26.31 g C m−2 per month, which is similar to published results and comparable with the measured NPP. The results of this work indicate that simulating alpine grassland NPP with satellite RS data rather than ground observations is feasible. We may provide a workable reference for rapid simulation of grassland NPP to satisfy the requirements of accounting carbon stocks and other applications.
... NPP refers to the dry organic matter accumulated by plants per unit time and area through photosynthesis, which reflects the growth status of vegetation and the overall health status of the ecosystem (Girardin et al., 2010;Zhou et al., 2021). It plays an important role in promoting material cycling and energy flow in ecosystems and is commonly used to monitor changes in ecosystem function caused by land degradation or improvement (Cramer et al., 1999). Numerous studies have found that the supply of NPP is not only closely related to changes in climate, land use, human activities, and other factors but also influenced by the landscape scale. ...
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Changes in natural and artificial landscapes due to rapid urbanization in recent decades have greatly altered the supply of net primary productivity (NPP) and its regulation mechanisms at the landscape scale, thus affecting the health of the whole ecosystem. Understanding the capacity and potential of NPP supply at the landscape scale based on landscape integrity is critical for regional ecosystem health and management. In this study, the NPP supply capacity of different types of landscape and the influence of urban built-up areas on it were assessed in Hubei Province, China. The optimal capacity criteria of NPP supply in different types of landscape under current conditions were identified, and the promotion potential of NPP was evaluated based on these criteria. The results show that the landscape with natural elements as the main components has a high NPP supply capacity, but it has been greatly influenced by urban development, and the closer the distance to the city is, the lower the NPP supply capacity will be. The plain landscape with construction land and farmland as the main components has weak NPP supply capacity and low sensitivity to urban development. The trend inflection points for the influence of urban development on the NPP supply capacity of different types of landscape can provide more realistic quantitative targets and spatial distribution of NPP improvement potential for decision making. The findings may help in the management of ecosystem health at the landscape scale.
... Our results suggest that, on average, variability of soil hydraulic parameters can lead to ∼10% uncertainty in both carbon fluxes and vegetation structure (i.e., LAI). This uncertainty, even though large, is much smaller than the uncertainties reported between different terrestrial biosphere models (typically in the order of 10%-40% for GPP and NPP for different parts of the world; e.g., Keenan & Williams, 2018;Zheng et al., 2020;Cramer et al., 1999). Combined uncertainties related to differences in how they simulate processes from photosynthesis (e.g., Pappas et al., 2013;Rogers, 2014), to phenology (e.g., De Kauwe et al., 2017Richardson et al., 2013), carbon allocation (e.g., Fatichi, Pappas, et al., 2019;Franklin et al., 2012), and vegetation water stress (e.g., Wu et al., 2018;Paschalis et al., 2020) largely exceed the estimated uncertainties introduced by PTFs. ...
Article
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Hydrological, ecohydrological, and terrestrial biosphere models depend on pedotransfer functions for computing soil hydraulic parameters based on easily measurable variables, such as soil textural and physical properties. Several pedotransfer functions have been derived in the last few decades, providing divergent estimates of soil hydraulic parameters. In this study, we quantify how uncertainties embedded in using different pedotransfer functions propagate to ecosystem dynamics, including simulated hydrological fluxes and vegetation response to water availability. Using a state‐of‐the‐art ecohydrological model applied at 79 sites worldwide, we show that uncertainties related to pedotransfer functions can affect both hydrological and vegetation dynamics. Uncertainties in evapotranspiration, plant productivity, and vegetation structure, quantified as leaf area, are in the order of ∼10% at annual time scales. Runoff and groundwater recharge uncertainties are one order of magnitude larger. All uncertainties are largely amplified when small‐scale topography is taken into account in a distributed domain, especially for water‐limited ecosystems with low permeability soils. Overall, pedotransfer function related uncertainties for a given soil type are higher than uncertainties across soil types in both hydrological and ecosystem dynamics. The magnitude of uncertainties is climate‐dependent but not soil type‐dependent. Evapotranspiration, vegetation structure, and plant productivity uncertainties are higher in water‐limited semiarid climates, whereas groundwater recharge uncertainties are higher in climates where potential evapotranspiration is comparable to precipitation.
... Since 1980s, extensive research has been conducted on regional carbon sources and carbon sinks at home and abroad, and the research contents have mainly focused on the following three aspects: firstly, the metrical research on carbon sources and carbon sinks, including the metrical research on gross primary productivity (GPP) [7][8][9], net primary productivity (NPP) [10][11][12], and net ecosystem productivity (NEP) of vegetation [13][14][15]. Secondly, the analysis of the main influencing factors that cause changes in carbon sources and carbon sinks in the region or the responses of various factors to their changes, such as climate change [16,17], human activities [18,19], and the natural environment, etc., [20,21]. ...
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As an important part and the core link of a terrestrial ecosystem, terrestrial vegetation is the main means for human to regulate climate and mitigate the increase in atmospheric CO2 concentration. The Yangtze River Delta (YRD) region is an urban agglomeration with the strongest comprehensive strength among developing countries (China). In the context of global climate change, a rapid, comprehensive, and detailed understanding of the spatio-temporal characteristics and variation tendency of the net ecosystem productivity (NEP) of vegetation and its response to climate during the rapid development of the YRD region is important for protecting ecological land, strengthening land management, and optimizing urban planning. The monthly mean temperature and rainfall data from 63 meteorological stations, the MODIS net primary productivity product, and a land cover product in the YRD region were used to estimate the NEP from 2000 to 2019 based on the soil respiration model, and the correlation between NEP and meteorological factors (such as temperature and rainfall) was analyzed. The results showed that: (1) From 2000 to 2019, the carbon sink area was much larger than the carbon source area in terrestrial vegetation in the Yangtze River Delta, the mean NEP of the vegetation ecosystem in the past 20 years was 253.2 g C·m−2·a−1, and the spatial distribution presented a trend that was higher in the south and lower in the north, higher in the east and lower in the west, and that gradually increased from northwest to southeast; moreover, the NEP of mountain areas was generally higher than that of river courses and urban surroundings. The interannual fluctuation of NEP was small, but presented a slightly increasing trend, and the interannual variation of NEP was significantly correlated with the maximum NEP in this region. (2) The carbon sink capacity of different vegetation cover types was (from strong to weak): forestlands > grasslands > wetlands ≈ croplands. (3) The area with the NEP change rate (θslope) > 0 accounted for 69.0%; however, there was certain spatial difference, the proportions of the areas with θslope < 0 were (from large to small) 14.50% (Zhejiang Province, China), 9.10% (Anhui Province, China), 6.65% (Jiangsu Province, China), and 0.79% (Shanghai, China). In terms of the individual changes of these provinces and municipalities, Shanghai > Zhejiang Province > Jiangsu Province ≈ Anhui Province. (4) There was a correlation between NEP and the annual mean temperature and annual precipitation in some regions.
... NPP results from our simulations are also compared with the NPP from 11 CMIP6 models (the same 11 models evaluated in Arora et al. (2020)), MODIS (Running & Zhao, 2015;Running et al., 2004;Zhao et al., 2005), and the International Satellite Land-Surface Climatology Project, Initiative II (ISLSCP II) collection from the International Geosphere Biosphere Program (IGBP) (Cramer et al., 1999). Projects such as the CMIP6 (Eyring et al., 2016) have allowed for consistent comparison of the response of the C cycle under climate change from existing state-of-the-art ESMs. ...
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Most Earth system models (ESMs) do not explicitly represent the carbon (C) costs of plant nutrient acquisition, which leads to uncertainty in predictions of the current and future constraints to the land C sink. We integrate a plant productivity‐optimizing nitrogen (N) and phosphorus (P) acquisition model (fixation & uptake of nutrients, FUN) into the energy exascale Earth system (E3SM) land model (ELM). Global plant N and P uptake are dynamically simulated by ELM‐FUN based on the C costs of nutrient acquisition from mycorrhizae, direct root uptake, retranslocation from senescing leaves, and biological N fixation. We benchmarked ELM‐FUN with three classes of products: ILAMB, a remotely sensed nutrient limitation product, and CMIP6 models; we found significant improvements in C cycle variables, although the lack of more observed nutrient data prevents a comprehensive level of benchmarking. Overall, we found N and P co‐limitation for 80% of land area, with the remaining 20% being either predominantly N or P limited. Globally, the new model predicts that plants invested 4.1 Pg C yr⁻¹ to acquire 841.8 Tg N yr⁻¹ and 48.1 Tg P yr⁻¹ (1994–2005), leading to significant downregulation of global net primary production (NPP). Global NPP is reduced by 20% with C costs of N and 50% with C costs of NP. Modeled and observed nutrient limitation agreement increases when N and P are considered together (r² from 0.73 to 0.83).
... Far-red photons elicit significant photosynthetic activity under natural light conditions and accounted for a large fraction of photosynthesis, especially in far-red enriched vegetation shade. Modelling of crop and ecosystem productivity currently is based solely on 400-700 nm photons and does not consider the photosynthetic value of far-red photons (Cramer et al., 1999;Zhu et al., 2010;Wang et al., 2020). The photosynthetic activity of far-red photons under full sun and under vegetation shade suggests that crop and ecosystem models may significantly underestimate canopy photosynthesis. ...
Article
The current definition of photosynthetically active radiation includes only photons from 400 up to 700 nm, despite evidence of the synergistic interaction between far‐red photons and shorter‐wavelength photons. The synergy between far‐red and shorter‐wavelength photons has not been studied in sunlight under natural conditions. We used a filter to remove photons above 700 nm to quantify the effects on photosynthesis in diverse species under full sun, medium light intensity, and vegetation shade. Far‐red photons (701 to 750 nm) in sunlight are used efficiently for photosynthesis. This is especially important for leaves in vegetation shade, where far‐red photons can be more than 50% of the total incident photons between 400 and 750 nm. Far‐red photons accounted for 24 to 25% of leaf gross photosynthesis (Pgross) in a C3 and a C4 species when sunlight was filtered through a leaf, and 10 to 14% of leaf Pgross in a tree and an understory species in deep shade. Accounting for the photosynthetic activity of far‐red photons is critical to accurate measurement and modeling of photosynthesis at single leaf, canopy, and ecosystem scales. This in turn is crucial in understanding crop productivity, the global carbon cycle, and climate change impacts on agriculture and ecosystems.
... Xu et al. (2019 concluded that the magnitude of globally averaged reductions in GPP associated with extreme droughts was projected to be nearly tripled by the end of 21st century. However, due to the regional differences, the different forcing data used in the models (Rivington et al., 2006), the discrepancies in model structure (Cramer et al., 1999;Shao et al., 2016), the inconsistency of research regions (Baldocchi, 2008), and so on (Shao et al., 2016), the response mechanism of drought to terrestrial NPP of different regions and vegetation types could be quite different. For example, Kreyling et al. (2008) found that vegetation productivity remained surprisingly stable during the 100-year extreme drought events and extreme drought did not reduce below-ground plant biomass. ...
Article
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Understanding present and future impacts of drought on the terrestrial carbon budget is of great significance to the evaluation of terrestrial ecosystem disturbance and terrestrial carbon sink. Here, we evaluate the effect of vegetation net primary productivity (NPP) associated with drought through the difference between the mean NPP in the drought and normal years during a specific time period (30 years). Then, the NPP effects in different vegetation types and climatic zones under baseline stage (1981–2010) and future climate scenarios (RCP2.6, RCP4.5, and RCP8.5) is assessed. The results indicate that the negative NPP extremes are captured in most regions, except for the high‐latitude in the Northern Hemisphere. The NPP loss caused by extreme droughts in 2071–2100 is largest under RCPs, followed by the effects of severe and moderate droughts. Regionally, central United States, southern Africa, central Asia, India, Amazon tropical rainforest, and Australia are projected to experience a significant increase in negative NPP extremes and most of these regions are in arid and semi‐arid and tropical rain forest areas. In contrast, tropical Asia suffers little drought effects. For different vegetation, Evergreen Broadleaf Forest, Closed Shrubland, Open Shrubland, Croplands, and Grassland are the most affected by drought. The largest NPP loss occurs in most part of regions under RCP4.5 scenario, not RCP8.5. Climate change is projected to play the largest role in aggravating the risk of drought‐induced NPP reduction. And meanwhile, the adverse effects of drought on vegetation may be resisted through rational fertilizer utilization and land management in future.
... Model estimation is an effective means of obtaining NPP on a regional or global scale. NPP estimation models can be roughly divided into three categories: ecophysiological process models, light-use efficiency models, and climate statistical models [23]. Ecophysiological process models simulate NPP based on the ecophysiological characteristics and growth mechanisms of plants [24]. ...
Article
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The impacts of climate change on ecosystem productivity and water resources over a long term in China are not well quantified. Precipitation-use efficiency (PUE) is a key parameter that describes carbon and water exchange in terrestrial ecosystems. Research on the response of regional PUE to climate change and its driving forces is of great significance to climate-change mitigation and the sustainable development of regional ecology. Based on an improved actual evapotranspiration (ETa) model, the responses of ETa, net primary productivity (NPP), and PUE to climate change in different climatic regions of China were analyzed; the contributions of various environmental factors to PUE changes were quantified; and the conversion characteristics and regulatory mechanisms of the PUE regime in different climatic regions were identified. The results indicate that the improved ETa model, after considering the limiting effect of energy on ETa in humid regions, can simulate the ETa distribution in China well. Over the past 58 years (1960–2017), ETa and NPP have increased in the western regions and decreased in the eastern regions, with the boundary at 103° E. PUE presents a “low-high-low” spatial distribution from northwest to southeast in China. It is noteworthy that there was a zonal distribution for a high value area of PUE, which coincided with the summer monsoon transition zone. The soil moisture (SM) increase in arid regions is the main driving force of the PUE increase, whereas the annual net radiation (Rn) change in humid regions is the main driving force of the PUE change. The transition zone is the conversion zone, where the prevailing factor limiting vegetation growth transitions from water to energy.
... Aboveground N and P contents within the tissue of primary producers may reflect plant nutrition (e.g., if they are showing deficiency or sufficiency level) that in turn determines net primary productivity (NPP) at the tropics (Townsend et al. 2007). In tropical forests, for example, their NPP represents a third of the whole terrestrial NPP (Cramer et al. 1999), and variations in the plant N and P contents and their relationship with nutrient availability are difficult to interpret in affected tropical forests by hurricanes (Dewar 1996). Tropical forests present a wide variety in aboveground stoichiometry, and this variability depends on sampling strategy, plant traits, soil variation, natural disasters, and both horizontal and vertical distribution . ...
Chapter
In this chapter, the soil ecosystem is introduced as a multiphase system that (i) acts as habitat for a wide diversity of soil organisms and (ii) varies at both spatial and temporal scale. The soil formation varies according to a combination of geological factors and biological process (e.g., here included the influence of mankind) that result in an almost infinite variation in soil-forming factors. Basically, five forming factors are defined as the most important factors. These forming factors are parent material, climate, topography, time, and the activity of soil organisms (e.g., plant roots, insects, microorganisms, human influence, etc.). Considering the wide range of soil properties, i.e., physical, chemical, and biochemical variables, in this chapter we focused only on describing the most important and significant properties to soil organisms, such as soil organic carbon, soil pH, soil aggregation, and moisture. Finally, when considering the tremendous variety of soil types, the need for soil ecologist to recognize this variation must be considered to avoid stresses. Especially, if the student is considering both spatial and temporal variation into soil ecosystem. In view of this, it is important that soil ecologist must consider both soil biota and soil ecosystem characterization.
... Aboveground N and P contents within the tissue of primary producers may reflect plant nutrition (e.g., if they are showing deficiency or sufficiency level) that in turn determines net primary productivity (NPP) at the tropics (Townsend et al. 2007). In tropical forests, for example, their NPP represents a third of the whole terrestrial NPP (Cramer et al. 1999), and variations in the plant N and P contents and their relationship with nutrient availability are difficult to interpret in affected tropical forests by hurricanes (Dewar 1996). Tropical forests present a wide variety in aboveground stoichiometry, and this variability depends on sampling strategy, plant traits, soil variation, natural disasters, and both horizontal and vertical distribution . ...
Chapter
This chapter explores how natural and invaded ecosystem provide habitat and energy supply for the entire soil food web, how biological invasion changes habitat of the soil organisms, and two study cases considering invasive plant species (Cryptostegia madagascariensis and Prosopis juliflora) from tropical zones. Natural ecosystem is defined as a community of biotic and abiotic entities that naturally occurs in a specific range, while biological invasion defines the spread and dominance of any organisms in a new range. These two concepts are strongly linked to each other in moist and dry tropical ecosystems, and in some cases, they create a war condition (by antagonism) that affects the entire soil food web. Natural ecosystem can provide a wide range of physical, chemical, and biological processes that promotes the entire soil food web, while invasive organisms just change the habitat for their own benefit.
... Aboveground N and P contents within the tissue of primary producers may reflect plant nutrition (e.g., if they are showing deficiency or sufficiency level) that in turn determines net primary productivity (NPP) at the tropics (Townsend et al. 2007). In tropical forests, for example, their NPP represents a third of the whole terrestrial NPP (Cramer et al. 1999), and variations in the plant N and P contents and their relationship with nutrient availability are difficult to interpret in affected tropical forests by hurricanes (Dewar 1996). Tropical forests present a wide variety in aboveground stoichiometry, and this variability depends on sampling strategy, plant traits, soil variation, natural disasters, and both horizontal and vertical distribution . ...
Chapter
Previously, the soil ecosystem acting as habitat and food resource for soil organisms and the living soil and its wide variety of groups were introduced. This chapter will introduce the functional role of soil organisms on significant ecological processes, such as soil nutrient availability, biological control, soil structure, herbivory, symbiosis, and plant growth. Ecosystem engineers, litter transformers, predators, herbivores, symbionts, microregulators, decomposers, and prokaryotic transformers are essential for the soil ecosystem functioning because they promote services that provide habitat and food resource for the entire soil food web (e.g., including the primary producers). Basically, soil organisms are living individuals which live into soil ecosystem (e.g., by creating nests and galleries), and they have key role in increasing nutrient cycling, plant growth, soil structure, and plant resistance to abiotic and biotic stresses in tropical ecosystems. They can increase plant growth (e.g., by symbiotic relationships helping plants to uptake N, P, and micronutrients), litter deposition (e.g., by increasing the production of phytohormones to start leaf senescence processes), nutrient cycling (e.g., by increasing litter transformation and soil organic matter decomposition), and soil food web (e.g., by promoting a wide range of functional groups, and controlling the dominance of potential pathogen groups).KeywordsBiological control promoted by soil organismsPlant growthSymbionts in tropical ecosystemsSoil nutrient availabilityStructuring soil profiles
... Aboveground N and P contents within the tissue of primary producers may reflect plant nutrition (e.g., if they are showing deficiency or sufficiency level) that in turn determines net primary productivity (NPP) at the tropics (Townsend et al. 2007). In tropical forests, for example, their NPP represents a third of the whole terrestrial NPP (Cramer et al. 1999), and variations in the plant N and P contents and their relationship with nutrient availability are difficult to interpret in affected tropical forests by hurricanes (Dewar 1996). Tropical forests present a wide variety in aboveground stoichiometry, and this variability depends on sampling strategy, plant traits, soil variation, natural disasters, and both horizontal and vertical distribution . ...
Chapter
In the previous chapter, the tropical soil types and their forming factors and inner physicochemical properties were introduced. The next issue to be considered is the living soil that is constituted by a wide diversity of soil organisms. Also, the soil biota classification based on their body size and taxonomic and functional groups is introduced within this chapter. The soil ecologists must consider the structure of the soil food web and the ecosystem services provided by soil organisms into tropical ecosystems. Basically, soil organisms are both above- and belowground individuals which might be living in soil ecosystem. They are difficult to understand and to study because of their vast diversity of taxonomic groups. In tropical ecosystem, soil organisms build a complex soil food web which provides ecosystem services (e.g., soil organic matter formation, nutrient cycling, bioturbation, and control of pests and diseases). Finally, this chapter will introduce the relationships among soil organisms and how do they are influenced by environmental disturbances.KeywordsDiversity of soil organismsMacrofaunaMesofaunaMicrobiotaSoil food web
... Aboveground N and P contents within the tissue of primary producers may reflect plant nutrition (e.g., if they are showing deficiency or sufficiency level) that in turn determines net primary productivity (NPP) at the tropics (Townsend et al. 2007). In tropical forests, for example, their NPP represents a third of the whole terrestrial NPP (Cramer et al. 1999), and variations in the plant N and P contents and their relationship with nutrient availability are difficult to interpret in affected tropical forests by hurricanes (Dewar 1996). Tropical forests present a wide variety in aboveground stoichiometry, and this variability depends on sampling strategy, plant traits, soil variation, natural disasters, and both horizontal and vertical distribution . ...
... Aboveground N and P contents within the tissue of primary producers may reflect plant nutrition (e.g., if they are showing deficiency or sufficiency level) that in turn determines net primary productivity (NPP) at the tropics (Townsend et al. 2007). In tropical forests, for example, their NPP represents a third of the whole terrestrial NPP (Cramer et al. 1999), and variations in the plant N and P contents and their relationship with nutrient availability are difficult to interpret in affected tropical forests by hurricanes (Dewar 1996). Tropical forests present a wide variety in aboveground stoichiometry, and this variability depends on sampling strategy, plant traits, soil variation, natural disasters, and both horizontal and vertical distribution . ...
Chapter
This chapter explores current knowledge used to study the trophic structure and the soil biological communities at the tropics. Trophic structure is defined by the complex of various feeding levels (e.g., producers, decomposers, herbivores, etc.) into an entire community, while the soil biological communities represent the wide range of soil organisms (e.g., roots, insects, nematodes, bacteria, fungi, etc.) that live in soil profile. These two concepts are strongly linked to each other. Trophic groups can influence decomposition, aboveground herbivory, net primary production, and nutrient cycling. It has been recognized that roots are an important driver of soil biological communities and most recently have soil ecologists started describing the effects of root exudates on soil biota diversity and community composition. This chapter examines some of the many ways that root exudates, plant biomass production, and litter may influence trophic structure and soil biological communities, and these changes may in turn feedback to affect the soil ecosystem at the tropics.KeywordsTrophic groupsAutotrophsHeterotrophsRoot exudatesNet primary production
... Aboveground N and P contents within the tissue of primary producers may reflect plant nutrition (e.g., if they are showing deficiency or sufficiency level) that in turn determines net primary productivity (NPP) at the tropics (Townsend et al. 2007). In tropical forests, for example, their NPP represents a third of the whole terrestrial NPP (Cramer et al. 1999), and variations in the plant N and P contents and their relationship with nutrient availability are difficult to interpret in affected tropical forests by hurricanes (Dewar 1996). Tropical forests present a wide variety in aboveground stoichiometry, and this variability depends on sampling strategy, plant traits, soil variation, natural disasters, and both horizontal and vertical distribution . ...
Chapter
This chapter explores how land uses and soil contamination in dry tropical ecosystems must affect soil organisms’ community composition. Land use is defined by the complex of vegetation type, soil management, and aftercare practices commonly used in agroecosystems, while the soil contamination defines the input level of exotic compounds in soil ecosystem that must affect organism’ fitness, reproduction, and behavior. These two concepts are strongly linked to each other in dry tropical ecosystem, and in some cases, they interact in the production with negative effects on the entire soil food web. Conventional farming system can negatively influence soil food web overtime by reducing soil organic matter contents. On the other hand, organic farming systems may improve soil organisms’ community composition by improving both habitat and resources availability. It has been recognized that organic residues are important drivers of soil biological communities. This chapter examines some of the many ways that soil contamination can be studied by using soil organisms as bioindicators.KeywordsAgroecosystemsConventional farming systemsDegraded ecosystemsOrganic farming systemsSoil food web
... Aboveground N and P contents within the tissue of primary producers may reflect plant nutrition (e.g., if they are showing deficiency or sufficiency level) that in turn determines net primary productivity (NPP) at the tropics (Townsend et al. 2007). In tropical forests, for example, their NPP represents a third of the whole terrestrial NPP (Cramer et al. 1999), and variations in the plant N and P contents and their relationship with nutrient availability are difficult to interpret in affected tropical forests by hurricanes (Dewar 1996). Tropical forests present a wide variety in aboveground stoichiometry, and this variability depends on sampling strategy, plant traits, soil variation, natural disasters, and both horizontal and vertical distribution . ...
Chapter
In this chapter, the natural disaster concept is introduced as the main influencer of soil ecosystem structure and functioning. At the tropics, there are several natural disaster types, but in this chapter our aim is to present some related aspects of fire events, landslides, and hurricanes and their influence on soil abiotic and biotic traits. These natural disasters are formed through the human activities, urban growth, nature of soils (geology, morphology, and topography), and climate conditions. In this chapter, we focused only on describing the most important and significative effects of natural disasters on soil ecosystem, such as plant community structure, soil organic matter, and N:P stoichiometry. In view of this, it is important that soil ecologist must consider the characterization of both soil abiotic and biotic traits.KeywordsFireHurricanesLandslidesSoil ecosystemSoil food web
... Aboveground N and P contents within the tissue of primary producers may reflect plant nutrition (e.g., if they are showing deficiency or sufficiency level) that in turn determines net primary productivity (NPP) at the tropics (Townsend et al. 2007). In tropical forests, for example, their NPP represents a third of the whole terrestrial NPP (Cramer et al. 1999), and variations in the plant N and P contents and their relationship with nutrient availability are difficult to interpret in affected tropical forests by hurricanes (Dewar 1996). Tropical forests present a wide variety in aboveground stoichiometry, and this variability depends on sampling strategy, plant traits, soil variation, natural disasters, and both horizontal and vertical distribution . ...
Chapter
A feedback is an event that occurs when the output of an organism metabolism (e.g., litter deposition by the natural process of plant senescence) is used as input back into the soil food web as an energy resource for other organism (e.g., litter transformers – Scarabaeidae and Spirobolida) as part of a chain of cause and effect. This chapter will introduce the main differences between feedback and interaction into soil ecology and soil biology. Primary producers may alter soil ecosystem through litter deposition, rootability, and rhizodeposition. In turns, soil organisms are influenced to act as herbivores (e.g., when feeding the fresh plant tissues), decomposers (e.g., when decomposing the dead plant tissues), and symbionts (e.g., when colonizing the roots through arbuscules and root nodules formation). Here, we will examine the various pathways that primary producers influence soil properties, the plant-arthropod interactions, and soil organisms influence primary producers. Plant-soil feedback over long timescales appears to be important drivers of soil sustainability or soil disturbance levels.KeywordsPrimary producers as soil conditionersPositive plant-soil feedbackNegative plant-soil feedbackPlant-arthropod interactionsSoil sustainability
... We used the normalized difference vegetation index (NDVI) as a measure of primary productivity because it correlates with plant biomass and photosynthetic capacity (Box et al., 1989;Cramer et al., 1999;Reed et al., 1994). NDVI has been used to study the relationship between primary productivity and species richness of birds and butterflies (Seto et al., 2004). ...
Article
Determine whether primary productivity or location (distance to the coast) is more important to migrating bird habitat selection. Yucatan Peninsula, Mexico. August–December and February–May 2009–2016. Migratory birds. Using eBird data, we modelled spatial variation in species richness as a function of primary productivity and distance to the coast and how the relationships vary with time during spring and autumn migration. We compared the standardized regression coefficients of linear models with a Poisson error distribution. We found that three primary productivity indices [normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), heterogeneity] performed similarly in predicting species richness. However, species richness was more strongly associated with distance to the coast. In autumn, richness was higher closer to the coast through most of the season and then shifted inland late in autumn. Richness was also higher in more productive habitats. In spring, richness was higher inland early in the season but then increased closer to the coast. Species richness was relatively similar across the primary productivity gradient, even declining slightly with increasing NDVI. We did not find convincing evidence for a strong relationship between species richness and primary productivity. Instead, migration constrains birds such that they concentrate in coastal environments. In autumn, migrants may favour habitats that are both coastal and productive, such as those that occur along the Caribbean coast. In spring, migrants may also use the less productive Gulf of Mexico coast, possibly due to high arthropod production occurring during an otherwise dry time of year.
... Some works have incorporated remote sensing data (i.e. satellite imagery), which allow for incorporating complex information like net primary productivity (NPP) or normalized difference vegetation index (NDVI), which are not usually computable from local meteorological stations (Cramer et al. 1999). Such data can be used as a proxy for resource availability or habitat use (Willems et al. 2009, Finstad andHein 2012), improving the quality of the model predictions (He et al. 2015). ...
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Species distribution models have grown in complexity by incorporating fine-scale variables, including data on microclimate, physiology and species interactions. Recent studies have acknowledged the importance of the spatial scale by including higher resolution maps and more complex climatic variables. However, models rarely consider the consequences of including data related to time. Indeed, species phenology-and potential shifts in phenology due, for example, to climate change-is potentially one of the most neglected aspects of ecological modelling. We present a literature review of relevant phenological aspects at different temporal scales and across several taxa. Such elements should be considered to define better the environmental niche and project present, future and past distribution models. We considered the available studies on plants, insects, reptiles, birds and mammals to evaluate how they dealt with the phenology of the investigated species, as well as the phenology of other resources and interacting species, to infer present, past and future projections. Here we focus on four main phenological aspects that, if not considered, may easily bias any projection, namely: 1) phenology can be accompanied by a shift in distribution within the year (e.g. migratory species); 2) activity may be restricted to a portion of the year (e.g. most ectotherms from temperate climates); 3) survival and reproduction success may depend on the synchrony with other species phenology (e.g. plants-pollinators interactions); 4) changes in climatic conditions can lead to shifts in phenology (e.g. anticipated or delayed blooms or changes in migration timing). In this review, we show how neglecting such factors may quickly lead to project a biased distribution. Finally, we provide a guide on evaluating whether the case study may be affected by such factors and what actions may improve the models.
... These models are grouped into four categories based on their fundamental theories: enzymes kinetic process-based model, the light-use efficiency (LUE) or production efficiency models, machine learning models based on eddy covariance (EC), and other measurements statistically based on sun-induced chlorophyll fluorescence (SIF) models, respectively [7][8][9][10]. Among these approaches (models), the LUE models are widely used because of their simplicity of using the satellite-based production efficiency models (PEMs) and the capability of offering a relative balance at different spatial-temporal scales (hourly, daily, 8 days, 16 days, monthly and yearly; 250, 500 and 1000 m) [11][12][13]. ...
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The impacts of climate on spatiotemporal variations of eco-physiological and bio-physical factors have been widely explored in previous research, especially in dry areas. However, the understanding of gross primary productivity (GPP) variations and its interactions with climate in humid and semi-humid areas remains unclear. Based on hyperspectral satellite remotely sensed vegetation phenology processes and related indices and the re-analysed climate datasets, we investigated the seasonal and inter-annual variability of GPP by using different light-use efficiency (LUE) models including the Carnegie–Ames–Stanford Approaches (CASA) model, vegetation photosynthesis models (VPMChl and VPMCanopy) and Moderate Resolution Imaging Spectroradiometer (MODIS) GPP products (MOD17A2H) during 2001–2020 over the Great Lakes region of Sub-Saharan Africa (GLR-SSA). The models’ validation against the in situ GPP-based upscaled observations (GPP-EC) indicated that these three models can explain 82%, 79% and 80% of GPP variations with root mean square error (RMSE) values of 5.7, 8.82 and 10.12 g C·m−2 ·yr−1 , respectively. The spatiotemporal variations of GPP showed that the GLR-SSA experienced: (i) high GPP values during December-May; (ii) high annual GPP increase during 2002–2003, 2011–2013 and 2015–2016 and annual decreasing with a marked alternation in other years; (iii) evergreen broadleaf forests having the highest GPP values while grasslands and croplands showing lower GPP values. The spatial correlation between GPP and climate factors indicated 60% relative correlation between precipitation and GPP and 65% correction between surface air temperature and GPP. The results also showed high GPP values under wet conditions (in rainy seasons and humid areas) that significantly fell by the rise of dry conditions (in long dry season and arid areas). Therefore, these results showed that climate factors have potential impact on GPP variability in this region. However, these findings may provide a better understanding of climate implications on GPP variability in the GLR-SSA and other tropical climate zones.
... The vegetation ecosystem is found to be more vulnerable and sensitive to climate change than the other ecosystems. As a variable that reflects the efficiency of vegetation fixation and conversion of light energy, net primary productivity (NPP) is widely used in the monitoring of vegetation dynamics [11,12]. It is related to life activities such as vegetation growth, development, and reproduction, and it also provides an indispensable material basis for the life activities of other biological members in the entire ecosystem. ...
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Global warming has exerted widespread impacts on the terrestrial ecosystem in the past three decades. Vegetation is an important part of the terrestrial ecosystem, and its net primary productivity (NPP) is an important variable in the exchange of materials and energy in the terrestrial ecosystem. However, the effect of climate variation on the spatial pattern of zonal distribution of NPP has remained unclear over the past two decades. Therefore, we analyzed the spatiotemporal patterns and trends of MODIS NPP and environmental factors (temperature, radiation, and soil moisture) derived from three sets of reanalysis data. The moving window method and digital elevation model (DEM) were used to explore their changes along elevation gradients. Finally, we explored the effect of environmental factors on the changes in NPP and its elevation distribution patterns. Results showed that nearly 60% of the global area exhibited an increase in NPP with increasing elevation. Soil moisture has the largest uncertainty either in the spatial pattern or inter-annual variation, while temperature has the smallest uncertainty among the three environmental factors. The uncertainty of environmental factors is also reflected in its impact on the elevation distribution of NPP, and temperature is still the main dominating environmental factor. Our research results imply that the carbon sequestration capability of vegetation is becoming increasingly prominent in high-elevation regions. However, the quantitative evaluation of its carbon sink (source) functions needs further research under global warming.
... 3 of 16 (Henderson-Sellers et al., 1993), VEMAP (VEMAP, 1995), CCMLP , NPP MIP (Cramer et al., 1999), DGVM MIP (Cramer et al., 2001), C4MIP (Friedlingstein et al., 2006), AMMA (Redelsperger et al., 2006), GSWP (Dirmeyer et al., 2006), WETCHIMP (Melton et al., 2013), LBA-DMIP (de Gonçalves et al., 2013), NACP Interim Site and Regional Syntheses (Huntzinger et al., 2012;Schwalm et al., 2010), TRENDY (Sitch et al., 2013), MsTMIP (Huntzinger et al., 2013), ISI-MIP (Warszawski et al., 2014), FACE MDS (Medlyn et al., 2015), CMIP (Arora et al., 2020), and PalEON (Rollinson et al., 2021) (see Table 1 for acronyms and abbreviations). MIPs typically specify common forcing data, but some models need the data transformed (e.g., temporally) or require additional individualized data sets. ...
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Model Intercomparison Projects (MIPs) are fundamental to our understanding of how the land surface responds to changes in climate. However, MIPs are challenging to conduct, requiring the organization of multiple, decentralized modeling teams throughout the world running common protocols. We explored centralizing these models on a single supercomputing system. We ran nine offline terrestrial biosphere models through the Terrestrial Biosphere Model Farm: CABLE, CENTURY, HyLand, ISAM, JULES, LPJ‐GUESS, ORCHIDEE, SiB‐3, and SiB‐CASA. All models were wrapped in a software framework driven with common forcing data, spin‐up, and run protocols specified by the Multi‐scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP) for years 1901–2100. We ran more than a dozen model experiments. We identify three major benefits and three major challenges. The benefits include: (a) processing multiple models through a MIP is relatively straightforward, (b) MIP protocols are run consistently across models, which may reduce some model output variability, and (c) unique multimodel experiments can provide novel output for analysis. The challenges are: (a) technological demand is large, particularly for data and output storage and transfer; (b) model versions lag those from the core model development teams; and (c) there is still a need for intellectual input from the core model development teams for insight into model results. A merger with the open‐source, cloud‐based Predictive Ecosystem Analyzer (PEcAn) ecoinformatics system may be a path forward to overcoming these challenges.
... Changes in the FT state dynamics can also signal climate change (Schuur et 40 al., 2015) and invoke permafrost carbon feedback (Zhao et al., 2018a). Besides, ecosystem responses to seasonal FT-state changes are rapid via significant changes in evapotranspiration, soil respiration, plant photosynthetic activity, liquid water availability, vegetation net primary production, and net ecosystem CO2 exchange (NEE) with the atmosphere (Kimball et al., 1997;Li et al., 2014;Cramer et al., 1999;Matzner and Borken, 2008;Wang et al., 2016). Thus, the knowledge of the FT state is required for modeling work in the above subjects, which invoke different parametrizations for frozen and unfrozen soil (Xie 45 et al., 2018;Swenson et al., 2012;Yu et al., 2020a;Yu et al., 2018;Mwangi et al., 2020). ...
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Knowing the Freeze-Thaw (FT) state of the land surface is essential for many aspects of weather forecasting, climate, hydrology, and agriculture. Near-surface air temperature and land surface temperature are usually used in meteorology to infer the FT-state. However, the uncertainty is large because both temperatures can hardly be distinguished from remote sensing. Microwave L-band emission contains rather direct information about the FT-state because of its impact on the soil dielectric constant, which determines microwave emissivity and the optical depth profile. However, current L band-based FT algorithms need reference values to distinguish between frozen and thawed soil, which are often not known sufficiently well. We present a new FT-state detection algorithm based on the daily variation of the H-polarized brightness temperature of the SMAP L3c FT global product for the northern hemisphere, which is available from 2015 to 2021. The exploitation of the daily variation signal allows for a more reliable state detection, particularly during the transitions periods, when the near-surface soil layer may freeze and thaw on sub-daily time scales. The new algorithm requires no reference values; its results agree with the SMAP FT state product by up to 98 % in summer and up to 75 % in winter. Compared to the FT state inferred indirectly from the 2-m air temperature of the ERA5-land reanalysis, the new FT algorithm has a similar performance as the SMAP FT product. The most significant differences occur over the midlatitudes, including the Tibetan plateau and its downstream area. Here, daytime surface heating may lead to daily FT transitions, which are not considered by the SMAP FT state product but are correctly identified by the new algorithm. The new FT algorithm suggests a 15 days earlier start of the frozen-soil period than the ERA5-land’s 2-m air temperature estimate. This study is expected to extend L-band microwave remote sensing data for improved FT detection.
Article
Urban expansion patterns whether rather “dispersed” or “compact”, have profound impacts on vegetation net primary productivity (NPP), which substantially alters the ecosystem functioning and its resources. However, there remains limited understanding of which pattern is more conducive towards NPP. Different studies have different and even contradictory views. Hence, to better understand the relationship of NPP and the underlying urban spatial patterns further research is needed. In this study, we compare the impacts of different urban expansion patterns on NPP at varying scales for the time period of 2000–2020. We exemplify this for differing city types in China (Chengdu and Hangzhou) and the USA (Chicago and Raleigh). The results showed cities with dispersed spatial patterns caused higher NPP loss rates (17.93%) than cities with compact spatial patterns (10.40%). The majority of NPP loss (more than 72%) caused by urbanization occurred predominantly in suburban and urban fringe areas. In both, suburban and urban fringe areas, the American cities with low population density and dispersed expansion patterns showed more NPP loss per new urban resident (880.713–8076.308 Mg C 10⁻⁴ persons) as well as more NPP loss per square kilometer of built-up land (51.480–881.737 Mg C km⁻²). The dispersed spatial pattern with high green space ratios significantly alleviated the NPP loss at the local scale but caused more overall NPP loss for the entire city. These findings shed new light on the scale dependence of urbanization-induced impacts on vegetation, and thus, help to better understand the effects of urban expansion patterns on our environment from local to planetary scales.
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Accurately estimating regional‐scale crop yields is substantial in determining current agricultural production performance and effective agricultural land management. The Yuncheng Basin is an important grain‐producing area in the Shanxi Province. This paper used Sentinel 2A with a spatial resolution of 10 m and MODIS with a temporal resolution of 1 d in 2020. The spatial and temporal nonlocal filter‐based fusion model (STNLFFM) was used to obtain fused data with a spatial resolution of 10 m and a temporal resolution of 1 d, combined with the Carnegie–Ames–Stanford Approach (CASA) and light‐use efficiency model to achieve summer maize (Zea mays L.) yield estimation. The results showed that the fused normalized difference vegetation index (NDVI) could inherit the spatial Sentinel‐2A NDVI details and express the spatial differences between smaller features more effectively. The STNLFFM NDVI curve was consistent with the actual summer maize growth condition, which accurately reflects the NDVI trend and local abrupt change information during the summer maize growth period. Moreover, the fused NDVI was influenced by topographic differences and artificial irrigation factors, whereas the summer maize yield in mountainous and plateau areas of the Yuncheng Basin was <5,000 kg ha−1 and those in the alluvial plain of the Sushui River reached 8,000 kg ha−1. The accuracy of the yield estimation model constructed based on STNLFFM NDVI (mean absolute percentage error [MAPE] = 5.47%, −13.74% ≤ relative error [RE] ≤0.12%) was significantly higher than that of the model based on MODIS NDVI (MAPE = 15.65%, −19.67% ≤ RE ≤ 20.88%), indicating that the use of spatio‐temporal fusion technology can effectively improve the summer maize yield estimation accuracy. STNLFFM was used to obtain high spatial and temporal resolution images. NPP in summer maize planting area was simulated using CASA in combination with STNLFFM. The yield estimation accuracy of STNLFFM was higher than that of MODIS.
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Forest ecosystems play an important role in the global carbon cycle. Clarifying the large-scale dynamics of net primary productivity (NPP) and its correlation with climatic factors is essential for national forest ecology and management. Hence, this study aimed to explore the effects of major climatic factors on the Carnegie–Ames–Stanford Approach (CASA) model-estimated NPP of the entire forest and all its corresponding vegetation types in China from 1982 to 2015. The spatiotemporal patterns of interannual variability of forest NPP were illustrated using linear regression and geographic information system (GIS) spatial analysis. The correlations between forest NPP and climatic factors were evaluated using partial correlation analysis and sliding correlation analysis. We found that over thirty years, the average annual NPP of the forests was 887 × 1012 g C/a, and the average annual NPP per unit area was 650.73 g C/m2/a. The interannual NPP of the entire forest and all its corresponding vegetation types significantly increased (p < 0.01). The increase in the NPP of evergreen broad-leaved forests was markedly substantial among forest types. From the spatial perspective, the NPP of the entire forest vegetation gradually increased from northwest to southeast. Over the years, the proportions of the entire forest and all its corresponding vegetation types with a considerable increase in NPP were higher than those with a significant decrease, indicating, generally, improvements in forest NPP. We also found climatic factors variably affected the NPP of forests over time considering that the rise in temperature and solar radiation improved the interannual forest NPP, and the decline in precipitation diminished the forest NPP. Such varying strength of the relationship between the interannual forest NPP and climatic factors also varied across many forest types. Understanding the spatiotemporal pattern of forest NPP and its varying responses to climatic change will improve our knowledge to manage forest ecosystems and maintain their sustainability under a changing environment.
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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.
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Knowing the freeze-thaw (FT) state of the land surface is essential for many aspects of weather forecasting, climate, hydrology, and agriculture. Microwave L-band emission contains rather direct information about the FT-state because of its impact on the soil dielectric constant, which determines microwave emissivity and the optical depth profile. However, current L-band-based FT algorithms need reference values to distinguish between frozen and thawed soil, which are often not well known. We present a new FT-state-detection algorithm based on the daily variation of the H-polarized brightness temperature of the SMAP L3c FT global product for the northern hemisphere, which is available from 2015 to 2021. Exploiting the daily variation signal allows for a more reliable state detection, particularly during the transition periods, when the near-surface soil layer may freeze and thaw on sub-daily time scales. The new algorithm requires no reference values; its results agree with the SMAP FT state product by up to 98% in summer and up to 75% in winter. Compared to the FT state inferred indirectly from the 2-m air temperature and collocated soil temperature at 0–7 cm of the ERA5-land reanalysis, the new FT algorithm has a similar performance to the SMAP FT product. The most significant differences occur over the midlatitudes, including the Tibetan plateau and its downstream area. Here, daytime surface heating may lead to daily FT transitions, which are not considered by the SMAP FT state product but are correctly identified by the new algorithm. The new FT algorithm suggests a 15 days earlier start of the frozen-soil period than the ERA5-land’s estimate. This study is expected to extend the L-band microwave remote sensing data for improved FT detection.
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The “Kökyar Greening Project” in the suburb of Aksu, Xinjiang, is a model of large-area artificial afforestation in an environment of drought and water scarcity. As an important part of the “3-North Shelter Forest Program”, it plays an important role in promoting the economic development and the environmentally friendly construction of Aksu and even of the whole Xinjiang region. Based on multisource remote-sensing data and meteorological observation data, this study explored the temporal and spatial changes in the vegetation parameters (FVC, NPP, and VEQI) and the ecological parameters (RSEI and LULC) in the Kökyar Project Area from 2000 to 2021. Based on the Theil–Sen median and TSS-RESTREND, this study investigated the path of mutual influence among the FVC, NPP, VEQI, and RSEI, as well as their responses to climate change and human activities. The results show that: (1) from 2000 to 2021, the FVC, NPP, VEQI, and RSEI in the Kökyar Project Area showed a significant upward trend and showed the distribution characteristics of “high in the south and low in the north”. (2) Over the past 22 years, the RSEI has shown a significant increase with the FVC, NPP and VEQI (p < 0.001), indicating that the “Kökyar Greening Project” has achieved significant ecological benefits. (3) The changes in the vegetation parameters and RSEI in the Kökyar Project Area were dominated by human activities. (4) The Kökyar Project Area has caused great changes to the ecosystem pattern of the region, and the vegetation parameters and RSEI in the Kökyar Project Area have increased, mainly in the form of cropland and grassland expansion over the past 22 years.
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Gross Primary Productivity (GPP) is the amount of sequestered CO2 during plant photosynthesis. GPP is an important indicator of ecosystem health in various ecologies and to assess climate change. The objective of the present work is to propose a machine learning based GPP estimation model using remote sensing (RS) data in combination with meteorological data (MET) and topographical data (TOPO) for prediction of GPP, which can be upscaled in temporal and spatial resolution. Random Forest Regression (RFR) is proposed for this using the Fluxnet2015 GPP dataset for the Australian region. This model has attained a very high accuracy with an R2 value of 0.82, as estimated by 10-fold cross-validation. The model has been compared with state-of-the-art machine learning models and found to be performing better than others. Different feature sets like MET-features and TOPO-features were evaluated in combination with RS-features. The results exhibited that the RFR model performed better when MET and TOPO features are combined with RS-features. GPP prediction for the year 2014, in 8 days temporal and 500m spatial resolution for the Australian region for different plant function types is demonstrated using the proposed model and produced very high value of R2 (0.84), when compared to ground truth. Thus, the proposed approach of the RFR model for GPP estimation showed significant improvement in regional carbon cycle studies and can also be employed for simulating GPP for the future under different climate scenarios.
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The underground river watersheds in karst areas are undergoing the degradation of ecosystem functions, thus hindering the development of benefits to humans. Taking the Nandong underground river watershed (NURW) as a test case in southern China, this study evaluated net primary productivity (NPP), water yield (WY), and soil retention (SR) and the relationships between the three factors from 2000 to 2018. The results showed that (1) NPP exhibited a continuously rising trend from 2000 to 2018, and the WY and SR also increased in general; (2) land use was an important influencing factor for ecosystem services, and the synergy between the two improved since 2006; (3) the ecosystem synergy was poor in the NURW, and the conflicts among ecosystem services were especially severe in areas with a high incidence of rocky desertification. This study will provide a reference for regional governments by clarifying the possible future state of the ecosystem.
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The Human Appropriation of Net Primary Production (HANPP) of agroecosystems is critical to food security, sustainable cropland use, and key biogeochemical processes such as carbon cycling and energy flow. However, current agroecosystem management lacks the support of high-resolution crop-type-specific HANPP information. To this end, this study integrated multi-source data of crop type, Normalized Difference Vegetation Index (NDVI) time-series, irrigation and climate, and multiple methods of the Miami model, the Carnegie-Ames-Stanford Approach (CASA) model, and process parameters to map the 30-m resolution spatial distribution of agro-ecosystem HANPP in the Heihe River Basin (HRB) in 2007 and 2012. We then analyzed the influences of climate condition, irrigation, and crop type on the HANPP. The average HANPP in the HRB decreased from 762.4 to 712.1 g C/m 2 from 2007 to 2012, with a decrease by 6.6%. The HANPP values of wheat, barley, and oilseed rape decreased by more than 10.0%, whereas that of corn only decreased by 3.1%. The ratio of HANPP to potential NPP (NPP pot) dropped from 82.7% to 81.4% and that of land-use-induced HANPP (HANPP LUC) to HANPP from 61.9% to 58.5%, whereas that of crop-harvest-induced HANPP (HANPP harv) to HANPP increased from 38.1% to 41.5%. These changes indicated that crop productivity increased whereas NPP loss decreased. Crop type conversion accounted for 84.7% of the HANPP changes in HRB, with a value of − 93.6 × 10 9 g C. Due to irrigation supplementation, the HANPP in high-temperature areas was higher than that in low-temperature areas with high precipitation. However, irrigation above 1000 mm no longer promoted HANPP, indicating that the irrigation efficiency in the HRB is low. Reducing HANPP LUC and carbon-water inputs while increasing HANPP harv is the key approach to obtain food security and sustainable agroecosystem development. Effective irrigation strategies and scientific crop planting adjustment should take into account their spatially heterogeneous and crop-specific impacts on the HANPP to help achieve these goals.
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Publisher Summary The study of leaf anatomy and of the mechanisms of the opening and closing of stomatal guard cells leads one to suppose that the stomata constitute the main or even the sole regulating system in leaf transpiration. Meteorologists have developed a wide variety of formulae for estimating evaporation from vegetation that are based entirely on weather variables and take no account at all of the species composition or stomatal properties of the transpiring vegetation. These “potential evaporation” formulae are widely and, to a large degree, successfully used for estimating evaporation from vegetation that is not water-stressed. Transpiration depends on stomatal conductance, net radiation receipt and upon air saturation deficit, temperature, and wind speed. Saturation deficit and wind speed vary through leaf boundary layers, through canopies, and through the atmosphere above the canopies. The sensitivity of saturation deficit to changes in stomatal conductance depends on where the saturation deficit is measured. If all of the stomata on a single leaf change aperture in unison, there may be a substantial change in saturation deficit measured at the leaf surface but a negligible change in saturation deficit measured a centimetre or two away, outside the leaf boundary layer.
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We ran the terrestrial ecosystem model (TEM) for the globe at 0.5° resolution for atmospheric CO2 concentrations of 340 and 680 parts per million by volume (ppmv) to evaluate global and regional responses of net primary production (NPP) and carbon storage to elevated CO2 for their sensitivity to changes in vegetation nitrogen concentration. At 340 ppmv, TEM estimated global NPP of 49.0 1015 g (Pg) C yr-1 and global total carbon storage of 1701.8 Pg C; the estimate of total carbon storage does not include the carbon content of inert soil organic matter. For the reference simulation in which doubled atmospheric CO2 was accompanied with no change in vegetation nitrogen concentration, global NPP increased 4.1 Pg C yr-1 (8.3%), and global total carbon storage increased 114.2 Pg C. To examine sensitivity in the global responses of NPP and carbon storage to decreases in the nitrogen concentration of vegetation, we compared doubled CO2 responses of the reference TEM to simulations in which the vegetation nitrogen concentration was reduced without influencing decomposition dynamics ("lower N" simulations) and to simulations in which reductions in vegetation nitrogen concentration influence decomposition dynamics ("lower N+D" simulations). We conducted three lower N simulations and three lower N+D simulations in which we reduced the nitrogen concentration of vegetation by 7.5, 15.0, and 22.5%. In the lower N simulations, the response of global NPP to doubled atmospheric CO2 increased approximately 2 Pg C yr-1 for each incremental 7.5% reduction in vegetation nitrogen concentration, and vegetation carbon increased approximately an additional 40 Pg C, and soil carbon increased an additional 30 Pg C, for a total carbon storage increase of approximately 70 Pg C. In the lower N+D simulations, the responses of NPP and vegetation carbon storage were relatively insensitive to differences in the reduction of nitrogen concentration, but soil carbon storage showed a large change. The insensitivity of NPP in the N+D simulations occurred because potential enhancements in NPP associated with reduced vegetation nitrogen concentration were approximately offset by lower nitrogen availability associated with the decomposition dynamics of reduced litter nitrogen concentration. For each 7.5% reduction in vegetation nitrogen concentration, soil carbon increased approximately an additional 60 Pg C, while vegetation carbon storage increased by only approximately 5 Pg C. As the reduction in vegetation nitrogen concentration gets greater in the lower N+D simulations, more of the additional carbon storage tends to become concentrated in the north temperate-boreal region in comparison to the tropics. Other studies with TEM show that elevated CO2 more than offsets the effects of climate change to cause increased carbon storage. The results of this study indicate that carbon storage would be enhanced by the influence of changes in plant nitrogen concentration on carbon assimilation and decomposition rates. Thus changes in vegetation nitrogen concentration may have important implications for the ability of the terrestrial biosphere to mitigate increases in the atmospheric concentration of CO2 and climate changes associated with the increases.
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The global parameter fields used in the revised Simple Biosphere Model (SiB2) of Sellers et al. are reviewed. The most important innovation over the earlier SiB1 parameter set of Dorman and Sellers is the use of satellite data to specify the time-varying phonological properties of FPAR, leaf area index. and canopy greenness fraction. This was done by processing a monthly 1° by 1° normalized difference vegetation index (NDVI) dataset obtained farm Advanced Very High Resolution Radiometer red and near-infrared data. Corrections were applied to the source NDVI dataset to account for (i) obvious anomalies in the data time series, (ii) the effect of variations in solar zenith angle, (iii) data dropouts in cold regions where a temperature threshold procedure designed to screen for clouds also eliminated cold land surface points, and (iv) persistent cloud cover in the Tropics. An outline of the procedures for calculating the land surface parameters from the corrected NDVI dataset is given, and a brief description is provided of source material, mainly derived from in situ observations, that was used in addition to the NDVI data. The datasets summarized in this paper should he superior to prescriptions currently used in most land surface parameterizations in that the spatial and temporal dynamics of key land surface parameters, in particular those related to vegetation, are obtained directly from a consistent set of global-scale observations instead of being inferred from a variety of survey-based land-cover classifications.
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Terrestrial net primary production (NPP) is sensitive to a number of controls, including aspects of climate, topography, soils, plant and microbial characteristics, disturbance, and anthropogenic impacts. Yet, at least at the global scale, models based on very different types and numbers of parameters yield similar results. Part of the reason for this is that the major NPP controls influence each other, resulting, under current conditions, in broad correlations among controls. NPP models that include richer suites of controlling parameters should be more sensitive to conditions that disrupt the broad correlations, but the current paucity of global data limits the power of complex models. Improved data sets will facilitate applications of complex models, but many of the critical data are very difficult to produce, especially for applications dealing with the past or future. It may be possible to overcome some of the challenges of data availability through increased understanding and modeling of ecological processes that adjust plant physiology and architecture in relation to resources. The CASA (Carnegie, Stanford, Ames Approach) model introduced by Potter et al. (1993) and expanded here uses a combination of ecological principles, satellite data, and surface data to predict terrestrial NPP on a monthly time step. CASA calculates NPP as a product of absorbed photosynthetically active radiation, APAR, and an efficiency of radiation use, {var_epsilon}. The underlying postulate is that some of the limitations on NPP appear in each. CASA estimates annual terrestrial NPP to be 48 Pg and the maximum efficiency of PAR utilization ({var_epsilon}*) to be 0.39 g C MJ{sup {minus}1} PAR. Spatial and temporal variation in APAR is more than fivefold greater than variation in {var_epsilon}.
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A global primary productivity and phytogeography model is described. The model represents the biochemical processes of photosynthesis and the dependence of gas exchange on stomatal conductance, which in turn depends on temperature and soil moisture. Canopy conductance controls soil water loss by evapotranspiration. The assignment of nitrogen uptake to leaf layers is proportional to irradiance, and respiration and maximum assimilation rates depend on nitrogen uptake and temperature. Total nitrogen uptake is derived from soil carbon and nitrogen and depends on temperature. The long-term average annual carbon and hydrological budgets dictate canopy leaf area. Although observations constrain soil carbon and nitrogen, the distribution of vegetation types is not specified by an underlying map. Variables simulated by the model are compared to experimental results. These comparisons extend from biochemical processes to the whole canopy, and the comparisons are favorable for both current and elevated CO{sub 2} atmospheres. The model is used to simulate the global distributions of leaf area index and annual net primary productivity. These distributions are sufficiently realistic to demonstrate that the model is useful for analyzing vegetation responses to global environmental change. 116 refs., 11 figs.
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Progress in modeling the global carbon cycle is inhibited by the lack of a high-quality data set based upon field observations of net primary productivity (NPP) with which to calibrate, parameterize, and evaluate terrestrial biosphere models. Under the aus- pices of the Global Primary Production Data Initiative (GPPDI), an activity endorsed by the International Geosphere-Biosphere Program's Data and Information System, a small international workshop was held in Cincinnati, Ohio, USA, in December 1996 to address the problem of extrapolating sparse field observations of NPP to produce a consistent database representative of major biomes. We report the conclusions of this workshop and the goals of GPPDI—to further expand the existing data compilation, to agree upon con- sistent standards for cross-site comparisons and allometric relationships for various biome types, and to document methodologies for spatial extrapolation from point measurements to grid cells. The resulting NPP database will also have intrinsic value: global data are important for many ecological problems, and NPP is a kind of ''pathfinder'' for other ecological data sets.
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A nine-year (1982–1990) global normalized difference vegetation index (NDVI) data set with a spatial resolution of 1° by 1° and a temporal resolution of one month was compiled for use in climate studies. This data set was derived from higher resolution (5–8 km) monthly continental NDVI data sets that have been processed and archived by the Global Inventory Monitoring and Modelling Studies (GIMMS) group at NASA/Goddard Space Flight Center. The continental GIMMS NDVI data sets were calculated from Global Area Coverage (GAC) data collected at daily intervals by the Advanced Very High Resolution Radiometer (AVHRR) onboard the NOAA-7, -9 and -11 satellites
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The equilibrium terrestrial biosphere model BIOME3 simulates vegetation distribution and biogeochemistry, and couples vegetation distribution directly to biogeochemistry. Model inputs consist of latitude, soil texture class, and monthly climate (temperature, precipitation, and sunshine) data on a 0.5° grid. Ecophysiological constraints determine which plant functional types (PFTs) may potentially occur. A coupled carbon and water flux model is then used to calculate, for each PFT, the leaf area index (LAI) that maximizes net primary production (NPP), subject to the constraint that NPP must be sufficient to maintain this LAI. Competition between PFTs is simulated by using the optimal NPP of each PFT as an index of competitiveness, with additional rules to approximate the dynamic equilibrium between natural disturbance and succession driven by light competition. Model output consists of a quantitative vegetation state description in terms of the dominant PFT, secondary PFTs present, and the total LAI and NPP for the ecosystem. Canopy conductance is treated as a function of the calculated optimal photosynthetic rate and water stress. Regional evapotranspiration is calculated as a function of canopy conductance, equilibrium evapotranspiration rate, and soil moisture using a simple planetary boundary layer parameterization. This scheme results in a two-way coupling of the carbon and water fluxes through canopy conductance, allowing simulation of the response of photosynthesis, stomatal conductance, and leaf area to environmental factors including atmospheric CO2. Comparison with the mapped distribution of global vegetation shows that the model successfully reproduces the broad-scale patterns in potential natural vegetation distribution. Comparison with NPP measurements, and with an FPAR (fractional absorbed photosynthetically active radiation) climatology based on remotely sensed greenness measurements, provides further checks on the model's internal logic. The model is envisaged as a tool for integrated analysis of the impacts of changes in climate and CO2 on ecosystem structure and function.
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A satellite-based 1° by 1° normalized difference vegetation index (NDVI) data set has been processed to derive land surface parameters for general circulation models of the atmosphere (GCMs). Prior to calculation of the land surface parameters, corrections were applied to the source NDVI data set to account for (i) obvious anomalies in the data time-series, (ii) the effect of variations in solar zenith angle, (iii) data dropouts in cold regions where a temperature threshold procedure designed to screen for clouds also eliminates cold land surface points, and (iv) persistent cloud cover in the tropics. An outline of the procedures for calculating land surface parameters from the corrected NDVI data set is given, and a brief description is provided of source material that was used in addition to the NDVI data. The data sets summarized in this paper should represent improvements over prescriptions currently used in land surface parameterizations in that the spatial and temporal dynamics of key land surface parameters, in particular of those related to vegetation, are obtained from direct measurements rather than indirectly inferred from survey-based land cover classifications.
Article
Two simulations of the seasonal variation of the global atmospheric CO2 distribution are obtained by combining an atmospheric transport model, two parameterizations of soil heterotrophic respiration (SHR), and a mechanistic model of carbon assimilation in the biosphere (CARAIB) that estimates the net primary production (NPP) of continental vegetation. The steady state hypothesis of the biosphere allows the spatial distribution and the global content of the soil carbon to be expressed as a function of the root fractions of soil respiration under forested and herbaceous vegetation covers. The sensitivity of the modeled CO2 signal to the wind field does not exceed the observed interannual variability. The influence of the various vegetation zones is quantified by the Fourier analysis of the modeled atmospheric signal. In the northern hemisphere, the temperate ecosystems dominate the seasonal atmospheric signal of the extratropical latitudes. The ecosystems of the tropical northern zone determine the local signal, while the southern tropical ecosystems influence largely the signal in the whole southern hemisphere. The results give credence to the mechanistic modeling of NPP since the simulated atmospheric signal is comparable with that obtained with normalized difference vegetation index (NDVI) based diagnostic models coupled with a parameterization of SHR fitted to optimize the atmospheric signal.
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The effect of doubling atmospheric CO2 concentration (Ca) on climate and vegetation is investigated using a combined climate-vegetation model. The vegetation model predicts the response of leaf area index, canopy transpiration (ET) and whole-plant carbon balance to changes in climate, soil moisture, and atmospheric CO2 forcing. This model has been embedded in the UK Meteorological Office Single Column Model (SCM), which provides the climate feedback to the vegetation. The vegetation model uses an optimisation approach to predict stomatal resistance, a biochemical model to predict photosynthesis and a simple carbon balance model to predict leaf area. Respiration is calculated as a function of leaf area and vegetation height. Clouds are assumed to be radiatively passive in the SCM to avoid unrealistic feedbacks.