Search Results (81)

Understanding the sensitivity of ecosystem respiration (ER) to increasing temperature is crucial to predict how the terrestrial carbon sink responds to a warming climate. The temperature sensitivity of ER may vary on a diurnal basis but is poorly understood due to the paucity of observational sites documenting real ER during daytime at a global scale. Here, we used an improved flux partitioning approach to estimate the apparent temperature sensitivity of ER during the daytime (E_0,day) and nighttime (E_0,night) derived from multiyear observations of 189 FLUXNET sites. Our results demonstrated that E_0,night is significantly higher than E_0,day across all biomes, with significant seasonal variations in the day–night discrepancy in the temperature sensitivity of ER (ΔE₀ = E_0,night/E_0,day) except for evergreen broadleaf forest and savannas. Such seasonal variations in ΔE₀ mainly result from the effect of temperature and the seasonal amplitude of NDVI. We predict that future warming will decrease ΔE₀ due to the reduced E_0,night by the end of the century in most regions. Moreover, we further find that disregarding the ΔE₀ leads to an overestimation of annual ER by 10~80% globally. Thus, our study highlights that the divergent temperature dependencies between day- and nighttime ER should be incorporated into Earth system models to improve predictions of carbon–climate change feedback under future warming scenarios. Full article

Actual evapotranspiration (ET_a) is an important component of the surface water cycle. The geeSEBAL model is increasingly being used to estimate ET_a using high-resolution remote-sensing data (Landsat 4/5/7/8). However, due to surface heterogeneity, there is significant uncertainty. By optimizing the quantile values of the reverse-modelling automatic calibration algorithm (CIMEC) endpoint-component selection algorithm under extreme conditions through 212 global flux sites, we obtained the optimized quantile values of 11 vegetation types of cold- and hot-pixel endpoint components (T_s and NDVI). Based on the observation data of the global FLUXNET tower, the sensitivity of 20 parameters in the improved geeSEBAL model was determined through Sobol’s sensitivity analysis. Among them, the parameters dT and SAVI_,hot were confirmed as the most sensitive parameters of the algorithm. Subsequently, we used the differential evolution Markov chain (DE-MC) method to analyse the uncertainty of the parameters in the geeSEBAL model used the posterior distribution of the parameters to modify the sensitive parameter values or ranges in the improved geeSEBAL model and to simulate the daily ET_a. The results indicate that by analysing the end element components of the geeSEBAL model (T_s and NDVI), quantile numerical optimization and parameter optimization can be performed. Compared with the original algorithm, the improved geeSEBAL model has significantly improved simulation performance, as shown by higher R² values, higher NSE values, smaller bias values, and lower RMSE values. The most suitable values of the predefined parameter Z_oh were determined, and the reanalysis of meteorological data inputs (relative humidity (RH), temperature (T), wind speed (WS), and net radiation (R_n)) was also found to be an important source of uncertainty for the accurate estimation of ET_a. This study indicates that optimizing the quantiles and key parameters of the model end component has certain potential for further improving the accuracy of the geeSEBAL model based on high-resolution remote-sensing data in estimating the ET_a for various vegetation types. Full article

Forests play a vital role in atmospheric CO₂ sequestration among terrestrial ecosystems, mitigating the greenhouse effect induced by human activity in a changing climate. The LUE (light use efficiency) model is a popular algorithm for calculating terrestrial GPP (gross primary production) based on physiological mechanisms and is easy to implement. Different versions have been applied for many years to simulate the GPP of different ecosystem types at regional or global scales. For estimating forest GPP using different approaches, we implemented five LUE models (EC-LUE, VPM, GOL-PEM, CASA, and C-Fix) in forests of type DBF, EBF, ENF, and MF, using the FLUXNET2015 dataset, remote sensing observations, and Köppen–Geiger climate zones. We then fused these models to additionally improve the ability of the GPP estimation using an RF (random forest) and an SVM (support vector machine). Our results indicated that under a unified parameterization scheme, EC-LUE and VPM yielded the best performance in simulating GPP variations, followed by GLO-PEM, CASA, and C-fix, while MODIS also demonstrated reliable GPP estimation ability. The results of the model fusion across different forest types and flux net sites indicated that the RF could capture more GPP variation magnitudes with higher R² and lower RMSE than the SVM. Both RF and SVM were validated using cross-validation for all forest types and flux net sites, showing that the accuracy of the GPP simulation could be improved by the RF and SVM by 28% and 27%. Full article

Plant stomata regulate transpiration (T) and CO₂ assimilation, essential for the water–carbon cycle. Quantifying how environmental factors influence stomatal conductance will provide a scientific basis for understanding the vegetation–atmosphere water–carbon exchange process and water use strategies. Based on eddy covariance and hydro-metrological observations from FLUXNET sites with four plant functional types and using three widely applied methods to estimate ecosystem T from eddy covariance data, namely uWUE, Perez-Priego, and TEA, we quantified the regulation effect of environmental factors on canopy stomatal conductance (G_s). The environmental factors considered here include radiation (net radiation and solar radiation), water (soil moisture, relative air humidity, and vapor pressure deficit), temperature (air temperature), and atmospheric conditions (CO₂ concentration and wind speed). Our findings reveal variation in the influence of these factors on G_s across biomes, with air temperature, relative humidity, soil water content, and net radiation being consistently significant. Wind speed had the least influence. Incorporating the leaf area index into a Random Forest model to account for vegetation phenology significantly improved model accuracy (R² increased from 0.663 to 0.799). These insights enhance our understanding of the primary factors influencing stomatal conductance, contributing to a broader knowledge of vegetation physiology and ecosystem functioning. Full article

Accurate and reliable estimation of actual evapotranspiration (AET) is essential for various hydrological studies, including drought prediction, water resource management, and the analysis of atmospheric–terrestrial carbon exchanges. Gridded AET products offer potential for application in ungauged areas, but their uncertainties may be significant, making it difficult to identify the best products for specific regions. While in situ data directly estimate gridded ET products, their applicability is limited in ungauged areas that require FLUXNET data. This paper employs an Extended Triple Collocation (ETC) method to estimate the uncertainty of Global Land Evaporation Amsterdam Model (GLEAM), Famine Early Warning Systems Network (FLDAS), and Maximum Entropy Production (MEP) AET product without requiring prior information. Subsequently, a merged ET product is generated by combining ET estimates from three original products. Furthermore, the study quantifies the uncertainty of each individual product across different vegetation covers and then compares three original products and the Merged ET with data from 645 in situ sites. The results indicate that GLEAM covers the largest area, accounting for 39.1% based on the correlation coefficient criterion and 39.9% based on the error variation criterion. Meanwhile, FLDAS and MEP exhibit similar performance characteristics. The merged ET derived from the ETC method demonstrates the ability to mitigate uncertainty in ET estimates in North American (NA) and European (EU) regions, as well as tundra, forest, grassland, and shrubland areas. This merged ET could be effectively utilized to reduce uncertainty in AET estimates from multiple products for ungauged areas. Full article

The sea ice albedo can amplify global climate change and affect the surface energy in the Arctic. In this paper, the data from Visible and Infra-Red Radiometer (VIRR) onboard Fengyun-3C satellite are applied to derive the Arctic sea ice albedo. Two radiative transfer models, namely, 6S and FluxNet, are used to simulate the reflectance and albedo in the shortwave band. Clear sky sea ice albedo in the Arctic region (60°~90°N) from 2016 to 2019 is derived through the physical process, including data preprocessing, narrowband to broadband conversion, anisotropy correction, and atmospheric correction. The results are compared with aircraft measurements and AVHRR Polar Pathfinder-Extended (APP-x) albedo product and OLCI MPF product. The bias and standard deviation of the difference between VIRR albedo and aircraft measurements are −0.040 and 0.071, respectively. Compared with APP-x product and OLCI MPF product, a good consistency of albedo is shown. And analyzed together with melt pond fraction, an obvious negative relationship can be seen. After processing the 4-year data, an obvious annual trend can be observed. Due to the influence of snow on the ice surface, the average surface albedo of the Arctic in March and April can reach more than 0.8. Starting in May, with the ice and snow melting and melt ponds forming, the albedo drops rapidly to 0.5–0.6. Into August, the melt ponds begin to freeze and the surface albedo increases. Full article

The exchange of moisture and energy between the land and the atmosphere plays a crucial role in terrestrial hydrological cycle and climate change. However, existing studies on the retrieval of surface water and heat flux tend to overlook the dynamic changes in surface vegetation and atmospheric aerosols, which directly affect surface energy and indirectly alter various meteorological factors, including cloud, precipitation, and temperature. In this study, we assess the machine-learning retrieval method for surface fluxes that takes into account vegetation changes and aerosol effects, using FLUXNET observations and remote sensing data to retrieve latent heat flux (LE) and sensible heat flux (H). We constructed four sets of deep neural network models: (a) The first set considers only meteorological factors, (b) the second set considers meteorological factors and aerosols, (c) the third set considers meteorological factors and vegetation changes, and (d) the fourth set comprehensively considers meteorological factors, aerosols, and vegetation changes. All model performances were evaluated using statistical indicators. ERA5 reanalysis and remote sensing data were used to drive the models and retrieve daily H and LE. The retrieved results were validated against ground observation sites that were not involved in model training or the FLUXCOM product. The results show that the model that considers meteorological factors, aerosols, and vegetation changes has the smallest errors and highest correlation for retrieving H and LE (R_H = 0.85, RMSE_H = 24.88; R_LE = 0.88, RMSE_LE = 22.25). The ability of the four models varies under different vegetation types. In terms of seasons, the models that consider meteorological factors and vegetation changes, as well as those that comprehensively consider meteorological factors, aerosols, and vegetation changes, perform well in retrieving the surface fluxes. As for spatial distribution, when atmospheric aerosols are present in the region, the model that considers both meteorological factors and aerosols retrieves higher values of H compared to the model that considers only meteorological factors, while the LE values are relatively lower. The model that considers meteorological factors and vegetation changes, as well as the model that comprehensively considers meteorological factors, aerosols, and vegetation changes, retrieves lower values in most regions. Through the validation of independent observation sites and FLUXCOM products, we found that the model, considering meteorological factors, aerosols, and vegetation changes, was generally more accurate in the retrieval of surface fluxes. This study contributes to improving the retrieval and future prediction accuracy of surface fluxes in a changing environment. Full article

Forest ecosystems in the mid-latitudes of the Northern Hemisphere are significantly affected by frequent extreme weather events. How different forest ecosystems respond to these changes is a major challenge. This study aims to assess differences in the response of daily net ecosystem exchange (NEE) of CO₂ and latent heat flux (LE) between different boreal and temperate ecosystems and the atmosphere to extreme weather events (e.g., anomalous temperature and precipitation). In order to achieve the main objective of our study, we used available reanalysis data and existing information on turbulent atmospheric fluxes and meteorological parameters from the global and regional FLUXNET databases. The analysis of NEE and LE responses to high/low temperature and precipitation revealed a large diversity of flux responses in temperate and boreal forests, mainly related to forest type, geographic location, regional climate conditions, and plant species composition. During the warm and cold seasons, the extremely high temperatures usually lead to increased CO₂ release in all forest types, with the largest response in coniferous forests. The decreasing air temperatures that occur during the warm season mostly lead to higher CO₂ uptake, indicating more favorable conditions for photosynthesis at relatively low summer temperatures. The extremely low temperatures in the cold season are not accompanied by significant NEE anomalies. The response of LE to temperature variations does not change significantly throughout the year, with higher temperatures leading to LE increases and lower temperatures leading to LE reductions. The immediate response to heavy precipitation is an increase in CO₂ release and a decrease in evaporation. The cumulative effect of heavy precipitations is opposite to the immediate effect in the warm season and results in increased CO₂ uptake due to intensified photosynthesis in living plants under sufficient soil moisture conditions. Full article

Accurate quantification of ecosystem water use efficiency (eWUE) over agroecosystems is crucial for managing water resources and assuring food security. Currently, the uncoupled Moderate Resolution Imaging Spectroradiometer (MODIS) product is the most widely applied dataset for simulating local, regional, and global eWUE across different plant functional types. However, it has been rarely investigated as to whether the coupled product can outperform the uncoupled product in eWUE estimations for specific C4 and C3 crop species. Here, the eWUE as well as gross primary production (GPP) and evapotranspiration (ET) from the uncoupled MODIS product and the coupled Penman–Monteith–Leuning version 2 (PMLv2) product were evaluated against the in-situ observations on eight-day and annual scales (containing 1902 eight-day and 61 annual samples) for C4 maize and C3 soybean at the five cropland sites from the FLUXNET2015 and AmeriFlux datasets. Our results show the following: (1) For GPP estimates, the PMLv2 product showed paramount improvements for C4 maize and slight improvements for C3 soybean, relative to the MODIS product. (2) For ET estimates, both products performed similarly for both crop species. (3) For eWUE estimates, the coupled PMLv2 product achieved higher-accuracy eWUE estimates than the uncoupled MODIS product at both eight-day and annual scales. Taking the result at an eight-day scale for example, compared to the MODIS product, the PMLv2 product could reduce the root mean square error (RMSE) from 2.14 g C Kg⁻¹ H₂O to 1.36 g C Kg⁻¹ H₂O and increase the coefficient of determination (R²) from 0.06 to 0.52 for C4 maize, as well as reduce the RMSE from 1.33 g C Kg⁻¹ H₂O to 0.89 g C Kg⁻¹ H₂O and increase the R² from 0.05 to 0.49 for C3 soybean. (4) Despite the outperformance of the PMLv2 product in eWUE estimations, both two products failed to differentiate C4 and C3 crop species in their model calibration and validation processes, leading to a certain degree of uncertainties in eWUE estimates. Our study not only provides an important reference for applying remote sensing products to derive reliable eWUE estimates over cropland but also indicates the future modification of the current remote sensing models for C4 and C3 crop species. Full article

This paper investigates the use of soil moisture data from satellites and a hydrological model as inputs to a simplified CH₄ emission model (MeSMOD) for estimating CH₄ emissions from boreal and pan-Arctic regions between 2015 and 2021. MeSMOD is calibrated using FLUXNET—CH₄ sites and the predictive performance is evaluated using several metrics, including the Nash-Sutcliffe efficiency (NSE). Using satellite soil moisture with 100 m resolution, MeSMOD has the highest performance (NSE = 0.63) compared with using satellite soil moisture of 10 km and hydrological model soil moisture of 10 km and 50 km (NSE = 0.59, 0.56, and 0.53, respectively) against site-level CH₄ flux. This study has upscaled the estimates to the pan-Arctic region using MeSMOD, resulting in comparable mean annual estimates of CH₄ emissions using satellite soil moisture of 10 km (33 Tg CH₄ yr⁻¹) and hydrological model soil moisture of 10 km (39 Tg CH₄ yr⁻¹) compared with previous studies using random forest technique for upscaling (29.5 Tg CH₄ yr⁻¹), LPJ-wsl process model (30 Tg CH₄ yr⁻¹), and CH₄ CAMS inversion (34 Tg CH₄ yr⁻¹). MeSMOD has also accurately captured the high methane emissions observed by LPJ-wsl and CAMS in 2016 and 2020 and effectively caught the interannual variability of CH₄ emissions from 2015 to 2021. The study emphasizes the importance of using high-resolution satellite soil moisture data for accurate estimation of CH₄ emissions from wetlands, as these data directly reflect soil moisture conditions and lead to more reliable estimates. The approach adopted in this study helps to reduce errors and improve our understanding of wetlands’ role in CH₄ emissions, ultimately reducing uncertainties in global CH₄ budgets. Full article

The effects of anomalous weather conditions (such as extreme temperatures and precipitation) on CO₂ flux variability in different tropical ecosystems were assessed using available reanalysis data, as well as information about daily net CO₂ fluxes from the global FLUXNET database. A working hypothesis of the study suggests that the response of tropical vegetation can differ depending on local geographical conditions and intensity of temperature and precipitation anomalies. The results highlighted the large diversity of CO₂ flux responses to the fluctuations of temperature and precipitation in tropical ecosystems that may differ significantly from some previously documented relationships (e.g., higher CO₂ emission under the drier and hotter weather, higher CO₂ uptake under colder and wetter weather conditions). They showed that heavy precipitation mainly leads to the strong intensification of mean daily CO₂ release into the atmosphere at almost all stations and in all types of study biomes. For the majority of considered tropical ecosystems, the intensification of daily CO₂ emission during cold and wet weather was found, whereas the ecosystems were predominantly served as CO₂ sinks from the atmosphere under hot/dry conditions. Such disparate responses suggested that positive and negative temperature and precipitation anomalies influence Gross Primary Production (GPP) and Ecosystem Respiration (ER) rates differently that may result in various responses of Net Ecosystem Exchanges (NEE) of CO₂ to external impacts. Their responses may also depend on various local biotic and abiotic factors, including plant canopy age and structure, plant biodiversity and plasticity, soil organic carbon and water availability, surface topography, solar radiation fluctuation, etc. Full article

Green vegetation plays a vital role in energy flows and matter cycles in terrestrial ecosystems, and vegetation phenology may not only be influenced by, but also impose active feedback on, climate changes. The phenological events of vegetation such as the start of season (SOS), end of season (EOS), and length of season (LOS) can respond to climate changes and affect gross primary productivity (GPP). Here, we coupled satellite remote sensing imagery with FLUXNET observations to systematically map the shift of SOS, EOS, and LOS in global vegetated area, and explored their response to climate fluctuations and feedback on GPP during the last two decades. The results indicated that 11.5% of the global vegetated area showed a significantly advanced trend in SOS, and that only 5.2% of the area presented significantly delayed EOS during the past two decades, resulting in significantly prolonged LOS in 12.6% of the vegetated area. The climate factors, including seasonal temperature and precipitation, attributed to the shifts in vegetation phenology, but with high spatial and temporal difference. LOS was positively and significantly correlated with GPP in 20.2% of the total area, highlighting that longer LOS is likely to promote vegetation productivity. The feedback on GPP from the shifted vegetation phenology may serve as an adaptation mechanism for terrestrial ecosystems to mitigate global warming through improved carbon uptake from the atmosphere. Full article

The soil water supply and atmospheric humidity conditions are crucial in controlling plants’ stomatal behavior and water use efficiency. When there is water stress caused by an increase in saturated water vapor pressure (VPD) and a decrease in soil water content (SWC), plants tend to close stomata to reduce water loss. This affects the gross primary productivity (GPP) and evapotranspiration (ET), subsequently leading to changes in water use efficiency (WUE) and carbon use efficiency (CUE) in plants. However, land–atmosphere interactions mean that water vapor in the atmosphere and soil moisture content causing water stress for plants are closely related. This study aims to compare and estimate the effects of VPD and SWC on the carbon cycle and water cycle for different plant functional types. Based on the fluxnet2015 dataset from around the world, the WUE and CUE of five plant functional types (PFTs) were estimated under varying levels of VPD and SWC. The results showed that high VPD and low SWC limit the stomatal conductance (Gs) and gross primary productivity (GPP) of plants. However, certain types of vegetation (crops, broad-leaved forests) could partially offset the negative effects of high VPD with higher SWC. Notably, higher SWC could even alleviate limitations and partially promote the increase in GPP and net primary production (NPP) with increasing VPD. WUE and CUE were directly affected by Gs and productivity. In general, the increase in VPD in the five PFTs was the dominant factor in changing WUE and CUE. The impact of SWC limitations on CUE was minimal, with an overall impact of only −0.05μmol/μmol on the four PFTs. However, the CUE of savanna plants changed differently from the other four PFTs. The rise in VPD dominated the changes in CUE, and there was an upward trend as SWC declined, indicating that the increase in VPD and decrease in SWC promote the increase in the CUE of savanna plants to some extent. Full article

Monitoring gross primary production (GPP) is necessary for quantifying the terrestrial carbon balance. The near-infrared reflectance of vegetation (NIRv) has been proven to be a good predictor of GPP. Given that radiation powers photosynthesis, we hypothesized that (i) the addition of photosynthetic photon flux density (PPFD) information to NIRv would improve estimates of GPP and that (ii) a further improvement would be obtained by incorporating the estimates of radiation distribution in the canopy provided by the foliar clumping index (CI). Thus, we used GPP data from FLUXNET sites to test these possible improvements by comparing the performance of a model based solely on NIRv with two other models, one combining NIRv and PPFD and the other combining NIRv, PPFD and the CI of each vegetation cover type. We tested the performance of these models for different types of vegetation cover, at various latitudes and over the different seasons. Our results demonstrate that the addition of daily radiation information and the clumping index for each vegetation cover type to the NIRv improves its ability to estimate GPP. The improvement was related to foliage organization, given that the foliar distribution in the canopy (CI) affects radiation distribution and use and that radiation drives productivity. Evergreen needleleaf forests are the vegetation cover type with the greatest improvement in GPP estimation after the addition of CI information, likely as a result of their greater radiation constraints. Vegetation type was more determinant of the sensitivity to PPFD changes than latitude or seasonality. We advocate for the incorporation of PPFD and CI into NIRv algorithms and GPP models to improve GPP estimates. Full article

Carbon and water fluxes in ecosystems are tightly coupled by gas diffusion through stomata. However, carbon–water (C–W) relationships vary largely across time scales, vegetation types, and regions. Subtropical forests in China play an important role in the global carbon and water cycles, yet studies of C–W relationships in this region remain limited. Here, we investigated summer-time C–W relationships in this region at two subtropical sites: the evergreen broadleaved forest at Dinghushan (23.17° N, 112.53° E, 300 m) and the evergreen coniferous forest at Qianyanzhou (26.74° N, 115.06° W, 106 m), using the flux tower data from the FLUXNET2015. The C–W relationship was examined using two measures. The first was daily water use efficiency (WUE), which is the ratio of daily gross primary productivity (GPP) to evapotranspiration (ET). The second was the correlation coefficient (r) of hourly GPP and ET. Our analysis showed that the daily WUE in the two forests ranged over 4–14 mg CO₂ per g H₂O, higher in the coniferous forest than in the broadleaved forest. The mean values of r for hourly C–W coupling were similar at the two forests, being 0.5–0.6, which suggests asynchronous diurnal variations in GPP and ET. Both daily WUE and r were modulated by meteorological conditions. In general, high radiation, air temperature, and humidity can reduce WUE at both sites. For the broadleaved forest, the most influential factor on WUE was VPD, followed by radiation, while in the coniferous forest, VPD, air temperature, and radiation were almost equally important. For hourly C–W coupling, VPD plays a significant role. The drier the air is, the weaker the coupling in the two forests. The daily WUE and hourly C–W coupling reflect the C–W relationship from different perspectives. Both showed the strongest response to VPD but with different sensitivity. Full article