Search

Diagnosing and predicting evaporation through satellite‐based surface energy balance (SEB) and land surface models (LSMs) faces challenges due to the non‐linear responses of aerodynamic (ga) and stomatal conductance (gcs) to concurrent soil and atmospheric drought. Despite a soaring popularity to refine gcs formulation in LSMs by integrating soil‐plant hydraulics, SEB models often overlook the utility of gcs. This oversight is attributed to the overriding emphasis on reducing ga uncertainties and the lack of coordination between these two modeling communities. This disengagement between modeling communities poses a persistent challenge in understanding divergent evaporation estimates during intense soil‐atmospheric drought. Here we conducted a theoretical experiment over two contrasting European forest sites to examine the sensitivity of conductances and evaporative fluxes to a water‐stress factor (β‐factor), coupled with land surface temperature (LST) and vapor pressure deficit (representing soil and atmospheric drought proxy). Utilizing a non‐parametric diagnostic model (Surface Temperature Initiated Closure, STIC) and a prognostic model (Community Land Model, CLM5.0), the analysis revealed that the β‐factor, alongside different functional forms of conductances and the loose coupling of CLM5.0 conductances to LST, significantly influenced the response of the two models to soil and atmospheric drought. These discrepancies propagated in the estimates of evaporative fluxes between STIC and CLM5.0. The analysis reaffirms the need for a consensus on theory and models capturing the sensitivity of biophysical conductances to soil‐atmospheric drought interplay. It emphasizes the need for fostering collaboration between modeling communities to enhance the prediction of evaporation in complex environmental conditions. Plain Language Summary: The regulation of plant water loss through evaporation is governed by two key physical and biological attributes: aerodynamic and stomatal conductance. The magnitude and variability of these conductances and their degree of regulation on evaporation is heavily dependent on how the conductances respond to the conjugate dryness from the soil and the atmosphere. Because these conductances cannot be typically measured at a large scale, the majority of the global evaporation models use different mechanistic functions to estimate them, which involves many empirical parameters. Such methods do not fully capture the evaporation variability of ecosystems during water stress, leading to large errors in water cycle monitoring. Our model‐based synthetic experiment shows how two structurally different models with different functional forms of the conductances respond very differently to emerging soil‐atmospheric water stress and produce divergent estimates of evaporation in a variety of dry and wet conditions. This study not only provides insights into the role of conjugate effects of soil and atmospheric drought in explaining the conductances and evaporation variability but also suggests a novel perspective for reconciling predictive and remote sensing evaporation models, benefiting water management, testing plant water use theories, and understanding land‐atmosphere interactions. Key Points: Diagnostic (STIC) and prognostic (CLM5.0) evaporation models show distinct levels of sensitivity to water stressSTIC and CLM5.0 evaporation models better agree in simulating the energy fluxes as compared to underlying biophysical conductanceMajor differences in the simulated stomatal conductance are due to divergences in the physiological assumptions of the two evaporation modeling approaches [ABSTRACT FROM AUTHOR]

Capturing the spatial and temporal dynamics of global gross primary production (GPP) and its underlying mechanisms is important for understanding the global carbon cycle and climate change. Satellite remote sensing enables continuous observation of vegetation dynamics over large regions, providing valuable opportunities for investigating spatial and temporal changes in GPP on a global scale. However, different GPP products typically exhibit significant disparities in terms of global GPP estimations and have different sensitivities to environmental factors due to model differences. Therefore, this study compared five global GPP products to identify the temporal and spatial trends of global GPP in recent years and the sensitivity to environmental factors. Results showed that although the annual mean value of terrestrial GPP exhibited an upward trend over the past three decades, their trends and growth rates on a global scale were not entirely consistent for the periods before and after 2000; they even exhibited opposing trends among different products in tropical regions. Although the spatial pattern of major environmental impact factors for different GPP products was consistent across the globe, some significant differences were observed among the different products. Clarifying the trends of GPP and its response to environmental factors under the current scenario of climate change needs to be further addressed. Plain Language Summary: Gross primary production (GPP) represents the growth characteristics and health status of the terrestrial ecosystem and provides the terrestrial ecosystem with the ability to partially offset anthropogenic carbon dioxide emissions. We compared five global GPP products to identify the temporal and spatial trends of global GPP in recent years and the sensitivity to environmental factors. Results showed that the annual mean value of terrestrial GPP exhibited an upward trend over the past three decades, however, trends and growth rates of GPP on a global scale were not entirely consistent for the periods before and after 2000. Furthermore, different products even exhibited opposing trends among different products in tropical regions after 2000. The findings are important for understanding the global carbon cycle and climate change. Key Points: Trends and growth rates of gross primary production (GPP) are not fully consistent before and after 2000After 2000, different GPP products show opposite growth trends in the tropics [ABSTRACT FROM AUTHOR]

The Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) is a critical land surface variable for carbon cycle modeling and ecological monitoring. Several global FAPAR products have been released and have become widely used; however, spatiotemporal inconsistency remains a large issue for the current products, and their spatial resolutions and accuracies can hardly meet the user requirements. An effective solution to improve the spatiotemporal continuity and accuracy of FAPAR products is to take better advantage of the temporal information in the satellite data using deep learning approaches. In this study, the latest version (V6) of the FAPAR product with a 250-m resolution was generated from Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance data and other information, as part of the Global Land Surface Satellite (GLASS) products suite. In addition, it was aggregated to multiple coarser resolutions (up to 0.25° and monthly). Three existing global FAPAR products (MODIS Collection 6, GLASS V5, and PROBA-V V1) were used to generate the time series training samples, which were used to develop a Bidirectional Long Short-Term Memory (Bi-LSTM) model. Direct validation using high-resolution FAPAR maps from the Validation of Land European Remote sensing Instrument (VALERI) and ImagineS networks revealed that the GLASS V6 FAPAR product has a higher accuracy than PROBA-V, MODIS, and GLASS V5, with an R2 value of 0.80 and Root Mean Square Errors (RMSEs) of 0.10-0.11 at the 250-m, 500-m, and 3-km scales, and a higher percentage (72%) of retrievals for meeting the accuracy requirement of 0.1. Global spatial evaluation and temporal comparison at the Ameriflux and National Ecological Observatory Network (NEON) sites revealed that the GLASS V6 FAPAR has a greater spatiotemporal continuity and reflects the variations in the vegetation better than the GLASS V5 FAPAR. The higher quality of the GLASS V6 FAPAR is attributed to the ability of the Bi-LSTM model, which involved highquality training samples and combines the strengths of the existing FAPAR products, as well as the temporal and spectral information from the MODIS surface reflectance data and other information. [ABSTRACT FROM AUTHOR]

Plants primarily conduct photosynthesis in the daytime, offering an opportunity to increase photosynthesis and carbon sink by providing light at night. We used a fully coupled Earth System Model to quantify the carbon sequestration and climate effects of a novel carbon removal proposal: lighting up tropical forests at night via lamp networks above the forest canopy. Simulation results show that additional light increased tropical forest carbon sink by 10.4 ± 0.05 petagrams of carbon per year during a 16-year lighting experiment, resulting in a decrease in atmospheric CO2 and suppression of global warming. In addition, local temperature and precipitation increased. The energy requirement for capturing one ton of carbon is lower than that of Direct Air Carbon Capture. When the lighting experiment was terminated, tropical forests started to release carbon slowly. This study suggests that lighting up tropical forests at night could be an emergency solution to climate change, and carbon removal actions focused on enhancing ecosystem productivity by altering environmental factors in the short term could induce post-action CO2 outgassing. [ABSTRACT FROM AUTHOR]

The increasing severity and prevalence of powdery mildew aided by extensive use of semi-dwarf cultivars and high levels of nitrogenous fertilizers are causing significant yield losses in wheat. Resistant cultivars are the most cost-effective and environmentally friendly approach to manage the disease. The objective of this study was to identify quantitative trait loci (QTL) for powdery mildew resistance in a doubled haploid (DH) population from a cross between leading Chinese cultivars, Yangmai 16 and Zhongmai 895. A high-density genetic map comprising of 14,480 non-redundant markers (equal to 148,179 SNPs) in 21 wheat chromosomes was constructed by genotyping the population with the Wheat 660 K SNP array. The DH population was phenotyped for powdery mildew resistance at the adult plant stage in multiple field trials, including four environments in the 2016–2017 cropping season and two environments in 2017–2018. Composite interval mapping detected six stable QTL explaining 3.8–23.6% of the phenotypic variance across environments. QPmyz.caas-5DS, QPmyz.caas-6BL and QPmyz.caas-7BS, are probably new QTL for powdery mildew resistance. One SNP marker closely linked to QPmyz.caas-6BL, the most stable QTL, was converted into a Kompetitive Allele-Specific PCR marker (K_AX-94973433) and validated on 103 commercial wheat cultivars. Significantly lower maximum disease severities of cultivars with the resistance-associated allele than those with the susceptibility-associated allele at QPmyz.caas-6BL in some environments indicated partial effectiveness of the marker. The novel QTL and their closely linked markers identified in the present study should facilitate development of cultivars with improved powdery mildew resistance. [ABSTRACT FROM AUTHOR]