BESSv2.0: A satellite-based and coupled-process model for quantifying long-term global land–atmosphere fluxes

Studies on the spatiotemporal dynamics in ecosystem carbon and water exchanges are essential in predicting the effects of climate change on regional carbon and energy budgets. Using the eddy covariance technique, carbon and water fluxes were observed in a typical winter wheat ecosystem (WWE) and an agroforest ecosystem (AFE) in the southern Loess Plateau from 2004 to 2010. The seasonal and inter-annual variability in gross primary productivity (GPP), net ecosystem exchange (NEE), evapotranspiration (ET), and water use efficiency (WUE) were examined and the main influencing factors were identified using the Pearson correlation. The results indicate that the seasonal GPP and NEE showed a bimodal distribution in WWE, while this was unimodal in AFE. The sinusoidal function did well in the characterization of seasonal ET dynamics for both ecosystems, with the determination coefficients being 0.85 and 0.94, respectively. In WWE and AFE, the annual mean GPP were 724.33 and 723.08 g C m−2 a−1, respectively, and the corresponding ET were 392.22 and 410.02 mm a−1. However, the difference in NEE between the two ecosystems was obvious, NEE were −446.28 and −549.08 g C m−2 a−1, respectively, showing a stronger carbon sink in AFE. There were strong coupling relationships between the GPP and ET of both ecosystems; the overall slopes were 1.71 and 1.69, respectively. The seasonal trend of WUE was bimodal in WWE, with peak values of 3.94 and 3.65 g C kg−1 H2O, occurring in November and April, respectively. However, the monthly WUE in AFE had one single peak of 4.07 g C kg−1 H2O in January. Photosynthetically active radiation (PAR) and soil temperature (Ts) were most positively correlated with GPP, net radiation (Rn) and Ts were the major factors influencing ET, while vapor pressure deficit (VPD) and soil water content (SWC) were the major influencing factors for WUE. These results provide observational support for regional carbon neutrality simulations.

Xinjiang is the largest semi-arid and arid region in China, and drip irrigation under plastic mulch is widely used in this water-limited area. Quantifying carbon and water fluxes as well as investigating their environmental drivers over cotton fields is critical for understanding regional carbon and water budgets in Xinjiang, the largest cotton production basin of China. In this study, an eddy covariance (EC) technique was used to measure the carbon and water fluxes of cotton field under drip irrigation with plastic mulch in the growing seasons of 2009, 2010, 2012 and 2013 at Wulanwusu Agrometeorological Experiment Station, a representative oasis cropland in northern Xinjiang. The diurnal patterns of gross primary production (GPP), net ecosystem exchange (NEE) and evapotranspiration (ET) showed obviously sinusoidal variations from June to September, while the diurnal ecosystem respiration (Res) was stable between daytime and nighttime. The daytime hourly GPP and ET displayed asymptotic relationships with net solar radiation (Rnet), while showed concave patterns with raising vapor pressure deficit (VPD) and air temperature (Ta). The increases in hourly GPP and ET towards the maximum occurred over half ranges of VPD and Ta. The seasonal variations of GPP, NEE and ET were close to the cotton phenology, which almost reached the peak value in July. The cumulative GPP averaged 816.2±55.0 g C/m2 in the growing season (from April to October), and more than half of GPP was partitioned into NEE (mean value of −478.6±41.4 g C/m2). The mean seasonal ET was 501.3±13.9 mm, and the mean water use efficiency (WUE) was 1.0±0.1 (mg C/g H2O)/d. The agro-ecosystem behaved as a carbon sink from squaring to harvest period, while it acted as a carbon source before the squaring time as well as after the harvest time.

Post-fire co-stimulation of gross primary production and ecosystem respiration in a meadow grassland on the Tibetan Plateau

The spatial and temporal variability of net ecosystem exchange (NEE) of CO2 and evapotranspiration (ET) of a karst-hole sphagnum peat mire situated at the boundary between broad-leaved and forest–steppe zones in the central part of European Russia in the Tula region was described using results from field measurements. NEE and ET were measured using a portable measuring system consisting of a transparent ventilated chamber combined with an infrared CO2/H2O analyzer, LI-840A (Li-Cor, USA) along a transect from the southern peripheral part of the mire to its center under sunny clear-sky weather conditions in the period from May to September of 2012 and in May 2013. The results of the field measurements showed significant spatial and temporal variability of NEE and ET that was mainly influenced by incoming solar radiation and ground water level. The seasonal patterns of NEE and ET within the mire were quite different. During the entire growing season the central part of the mire was a sink of CO2 for the atmosphere. NEE reached maximal values in June–July (−6.8 ± 4.2 μmol m−2 s−1). The southern peripheral part of the mire, due to strong shading by the surrounding forest, was a sink of CO2 for the atmosphere in June–July only. ET reached maximal values in the well-lighted central parts of the mire in May (0.34 ± 0.20 mm h−1) mainly because of high air and surface temperatures and the very wet upper peat horizon and sphagnum moss. Herbaceous species made the maximum contribution to the total gross primary production (GPP) in both the central and the peripheral parts of the mire. The contribution of sphagnum to the total GPP of these plant communities was relatively small and ranged on sunny days of July–August from −1.1 ± 1.1 mgC g−1 of dry weight (DW) per hour in the peripheral zone of the mire to −0.6 ± 0.2 mgC g−1 DW h−1 at the mire center. The sphagnum layer made the maximum contribution to total ET at the mire center (0.25 ± 0.10 mm h−1) and the herbaceous species on the peripheral part of the mire (0.03 ± 0.03 mm h−1).

Satellite-based modeling of gross primary production in an evergreen needleleaf forest

Gross Primary Production (GPP) and evapotranspiration (ET) play a critical role of the global carbon, water and energy cycle. Accurate quantification of the global GPP and ET could improve our ability to understand global climate change and energy budget. However, most of the GPP and ET remote sensing models fail to take the coupled relationship between vegetation transpiration (Et) and photosynthesis into consideration. More importantly, these models might ignore the difference of transpiration and photosynthesis rate in different groups of leaves (sunlit and shaded). Here, we coupled the estimates of daily GPP and ET at 84 global forest sites based on the Two-Leaf Light Use Efficiency model and the Penman-Monteith equation that were linked by the Ball-Berry conductance model. The developed model was well calibrated with the root mean square error (RMSE) and the coefficient of determination (R2) being 1.97 gC/m2 day and 0.77 for GPP respectively, and being 21.91 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and 0.65 for ET, respectively. In the meantime, the validation results demonstrated the good performance of the coupled model, with the RMSE and R2 being 1.95 gC/m2 day and 0.77 for GPP, respectively, and being 21.34 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and 0.66 for ET, respectively.

Reliable modeling of carbon and water fluxes is essential for understanding the terrestrial carbon and water cycles and informing policy strategies aimed at constraining carbon emissions and improving water use efficiency. We used an assimilation framework (LPJ-Vegetation and soil moisture Joint Assimilation, or LPJ-VSJA) to improve gross primary production (GPP) and evapotranspiration (ET) estimates globally. The terrestrial biosphere model that we used is the integrated model – LPJ-PM coupled from the Lund-Potsdam-Jena Dynamic Global Vegetation Model (LPJ-DGVM) and a hydrology module (i.e., the updated Priestley–Taylor Jet Propulsion Laboratory model, PT-JPLSM). Satellite-based soil moisture products derived from the Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active and Passive (SMAP) and leaf area index (LAI) from the global Land and Ground satellite (GLASS) product were assimilated into LPJ-PM to improve GPP and ET simulations using a Proper Orthogonal Decomposition-based ensemble four-dimensional variational assimilation method (PODEn4DVar). The joint assimilation framework LPJ-VSJA achieved the best model performance (with an R2 of 0.91 and 0.81 and an RMSD reduced by 50.4 % and 38.4 % for GPP and ET, respectively, compared with those of LPJ-DGVM at the monthly scale). The assimilated GPP and ET demonstrated a better performance in the arid and semiarid regions (GPP: R2 = 0.73, ubRMSD = 1.05 g C m−2 d−1; ET: R2 = 0.73, ubRMSD = 0.61 mm d−1) than in the humid and sub-dry humid regions (GPP: R2 = 0.61, ubRMSD = 1.23 g C m−2 d−1; ET: R2 = 0.66; ubRMSD = 0.67 mm d−1). The ET simulated by LPJ-PM that assimilated SMAP or SMOS had a slight difference, and the ET that assimilated SMAP soil moisture data was more improved than that assimilated SMOS data. Our global simulation modeled by LPJ-VSJA was compared with several global GPP and ET products (e.g., GLASS GPP, GOSIF GPP, GLDAS ET, GLEAM ET) using the triple collocation (TC) method. Our products, especially ET, exhibited advantages in the overall error distribution (estimated error (μ): 3.4 mm month−1; estimated standard deviation of μ: 1.91 mm month−1). Our research showed that the assimilation of multiple datasets could reduce model uncertainties, while the model performance differed across regions and plant functional types. Our assimilation framework (LPJ-VSJA) can improve the model simulation performance of daily GPP and ET globally, especially in water-limited regions.

Seasonal and interannual patterns of carbon and water fluxes of a poplar plantation under peculiar eco-climatic conditions

Carbon budget measurement over 12 years at a crop production site in the silty-loam region in Belgium

<strong class="journal-contentHeaderColor">Abstract.</strong> Earth's drylands are home to more than two billion people, provide key ecosystem services, and exert a large influence on the trends and variability in Earth's carbon cycle. However, modeling dryland carbon and water fluxes with remote sensing suffers from unique challenges not typically encountered in mesic systems, particularly in capturing soil moisture stress. Here, we develop and evaluate an approach for the joint modeling of dryland gross primary production (GPP), net ecosystem exchange (NEE), and evapotranspiration (ET) in the western United States (US) using a suite of AmeriFlux eddy covariance sites spanning major functional types and aridity regimes. We use artificial neural networks (ANNs) to predict dryland ecosystem fluxes by fusing optical vegetation indices, multitemporal thermal observations, and microwave soil moisture and temperature retrievals from the Soil Moisture Active Passive (SMAP) sensor. Our new dryland ANN (DrylANNd) carbon and water flux model explains more than 70 % of monthly variance in GPP and ET, improving upon existing MODIS GPP and ET estimates at most dryland eddy covariance sites. DrylANNd predictions of NEE were considerably worse than its predictions of GPP and ET likely because soil and plant respiratory processes are largely invisible to satellite sensors. Optical vegetation indices, particularly the normalized difference vegetation index (NDVI) and near-infrared reflectance of vegetation (NIR<span class="inline-formula">_v</span>), were generally the most important variables contributing to model skill. However, daytime and nighttime land surface temperatures and SMAP soil moisture and soil temperature also contributed to model skill, with SMAP especially improving model predictions of shrubland, grassland, and savanna fluxes and land surface temperatures improving predictions in evergreen needleleaf forests. Our results show that a combination of optical vegetation indices and thermal infrared and microwave observations can substantially improve estimates of carbon and water fluxes in drylands, potentially providing the means to better monitor vegetation function and ecosystem services in these important regions that are undergoing rapid hydroclimatic change.

Agriculture plays a crucial role as a carbon sink in the atmosphere, contributing to a climate-neutral economy, which requires a comprehensive understanding of Earth’s complex biogeochemical processes. This study aims to quantify, for the first time, Gross Primary Productivity (GPP) and ecosystem water use efficiency (eWUE) in almond orchards during their vegetative phase. The study was conducted over six growing seasons (2017–2022) across two drip-irrigated commercial almond groves located in Albacete, SE Spain. Eddy covariance flux tower systems were used to measure Net Ecosystem Exchange (NEE) and evapotranspiration (ET), which were then used to calculate GPP and eWUE. A novel approach was developed to estimate eWUE by integrating the Normalized Difference Vegetation Index (NDVI), reference ET, and air temperature. The results show similar almond orchard carbon-fixing capacity rates to those of other natural and agro-ecosystems. Seasonal and interannual variability in GPP and eWUE were observed. The NDVI-ET combination proved to be effective for GPP estimations (regression coefficient of 0.78). Maximum carbon-fixing values were observed at ET values of around 4–5 mm/d. In addition, a novel method was developed to estimate eWUE from NDVI, reference ET and air temperature (RMSE of 0.38 g·C/kg·H2O). This study highlights the carbon capture potential of almond orchards during their vegetative phase and introduces a novel approach for eWUE monitoring, with the intention of underscoring their significance in a climate change context and to encourage further research.

Modeling carbonyl sulfide and carbon dioxide fluxes in a northern boreal coniferous forest using memory-based deep learning

Abstract. Improving the accuracy of monitoring cropland CO2 exchange at heterogeneous spatial scales is of great importance for reducing spatial and temporal uncertainty in estimating terrestrial carbon (C) dynamics. In this study, an approach to estimate daily cropland C fluxes is developed and tested by combining time series of field-scale eddy covariance (EC) CO2 flux data and Sentinel-2 satellite-based vegetation indices (VIs) after appropriately accounting for the spatial alignment between the two time series datasets. The study was carried out for an agricultural field (118 ha) in the lowlands of northeastern Germany. The ability of different VIs to estimate daily net ecosystem exchange (NEE) and gross primary productivity (GPP) based on linear regression models was assessed. Most VIs showed high (&gt;0.9) and statistically significant (p&lt;0.001) correlations with GPP and NEE, although some VIs deviated from the seasonal pattern of CO2 exchange. By contrast, correlations between ecosystem respiration (Reco) and VIs were weak and not statistically significant, and no attempt was made to estimate Reco from VIs. Linear regression models explained generally more than 80 % and 70 % of the variability in NEE and GPP, respectively, with high variability among the individual VIs. The performance in estimating daily C fluxes varied among VIs depending on the C flux component (NEE or GPP) and observation period. Root mean square error (RMSE) values ranged from 1.35 g C m−2 d−1 using the green normalized difference vegetation index (GNDVI) for NEE to 5 g C m−2 d−1 using the simple ratio (SR) for GPP. This equated to an underestimated net C uptake of only 41 g C m−2 (18 %) and an overestimation of gross C uptake of 854 g C m−2 (73 %). Differences between the measured and estimated C fluxes were mainly explained by the diversion of the C flux and VI signal during winter when C uptake remained low, while VI values indicated an increased C uptake due to relatively high crop leaf area. Overall, the results exhibited similar error margins to mechanistic crop models. Thus, they indicated the suitability and expandability of the proposed approach for monitoring cropland C exchange with satellite-derived VIs.

&amp;lt;p&amp;gt;Drought is not only a multiscale (e.g., temporal, spatial) but also a multidimensional (e.g., onset, offset, duration, frequency, magnitude, intensity) phenomenon, and ecosystem production and respiration may respond to each drought dimension differently. &amp;amp;#160;Although multiple reports exist in literature on the drought impact on ecosystem productivity, it remains unclear how each component of drought impacts ecosystem gross primary production (GPP), ecosystem respiration (R&amp;lt;sub&amp;gt;ECO&amp;lt;/sub&amp;gt;), and net ecosystem exchange (NEE) and how the different drought dimensions interacted with each other on their impacts. In this study, we conducted a comprehensive drought impact assessment on forest GPP, NEE, and R&amp;lt;sub&amp;gt;ECO&amp;lt;/sub&amp;gt; including all the drought dimensions using FLUXNET observations and multiple time-scales of Standardized Precipitation-Evapotranspiration Index (SPEI). Our results indicated that while most earlier drought studies focused on simultaneous and post-drought conditions, the cumulative drought impacts and drought timing are more significantly impacting forest carbon uptake than simultaneous drought severity. Temporal standardization based meteorological drought indices could be used to accurately reflect plant water stress if antecedent and cumulative drought conditions are considered.&amp;lt;/p&amp;gt;

Integrating leaf functional traits improves modelled estimates of carbon and water fluxes at a subtropical evergreen conifer forest