Tower Data Research Articles

The role of root dynamics on the climate sensitivity of ecohydrological processes of over- and understory in a semi-arid groundwater-dependent ecosystem

Plant roots connect belowground moisture with aboveground vegetation functionalities, making root plasticity critical for drought resilience. This study employs an enhanced land surface ecohydrological model, Duke Coupled Hydrology Model with Vegetation and Dynamic Roots (DCHM-VDR), to investigate how root dynamics affect vegetation adaptation under a range of climate conditions. DCHM-VDR features a moisture-driven root parameterization that simulates dynamic root profiles and hydraulic redistribution (HR). Applied to a semiarid woodland with groundwater-dependent mesquites and shallow-rooted shrubs, model results compare well against AmeriFlux tower data and capture observed soil moisture patterns tied to root water uptake, including mesquite’s dimorphic root profile and shifts in water source and the direction of HR. Accounting for HR lowers overall water use efficiency (WUE) by more than 50% in the dry season due to release of deep root water uptake to moisten dry soil layers. Dynamic root profiles reduce water demand by avoiding dry soil patches and utilizing moist layers with increased WUE, especially for shrubs. Mesquites with a dimorphic root profile show a 3-fold annual transpiration (Tr) increase from 183 mm to 629 mm/year and reduced HR/Tr from 34% to 6%, along with a small reduction of around 10% for shrubs, reflecting the importance of groundwater sourcing and the codependence of shrubs on mesquite for water stress resilience. Future climate scenarios were examined using the Thermodynamic Global Warming downscaled data. Mesquites and shrubs respond positively to wetter winters, albeit with opposite water use strategies in the drier growing seasons due to different rooting depths and HR modulation of soil moisture. Mesquites increase transpiration by adjusting root fraction, water uptake, and HR closer to the saturated zone, while shrubs reduce transpiration and increase WUE by 17% compared to a decrease of 13% in mesquite WUE under the driest scenario. The intertwined water use strategies of mesquites and shrubs expressed by the root water uptake dynamics determine ecosystem response aboveground under more extreme climate conditions, highlighting the importance of modeling root architecture dynamics and co-adaptive ecohydrological processes in predicting ecosystem responses to climate change.

Damage quantification using spectral response of a multi-degree-of-freedom system with spatial and temporal stiffness variations: Application to shear-type frames

The present study is aiming to develop a spectral response-based closed-form expression of maximum damage intensity in a multi-degree-of-freedom system considering temporal and spatial variations of stiffness. The time-dependent variation of stiffness is incorporated in the formulation to simulate opening–closing mechanism of crack. In addition, an expression for multiple damage severity at consecutive stories is also derived. For the validation of the present method, numerical model of a shear-type frame is thoroughly investigated. It is observed from the analysis that incorporation of spectral amplitudes at modulated frequencies in the formulation, improves the assessment of damage severity. Further, an experiment on a scale-down 6-story steel building is performed for practical insights followed by two case studies with real data of a full-scale tower and an instrumented building. As the assumed temporal variation of stiffness is not generic, it may mislead the accuracy of estimated damage quantity in other incompatible scenarios.

Deducing Aerodynamic Roughness Length From Abundant Anemometer Tower Data to Inform Wind Resource Modeling

AbstractAerodynamic roughness length () fundamentally affects land surface momentum loss and wind resource simulation, but ground truth data of are sparse in space, causing datasets used in atmospheric models are empirically estimated from land cover types through a look‐up table. In this study, we derived values from 101 anemometer towers in China. Taking them as ground truth, we show that existing gridded datasets determined from either a look‐up table or a machine‐learning method contain considerable uncertainty and fail to capture the variability of within each land cover type, although the latter performs better. Even for the widely used ERA5, its is overestimated in wind‐rich regions of China, causing an underestimation of near‐surface wind speed. This highlights the necessity to improve data in atmospheric models. Current rapidly expanding anemometer towers may substantially enrich truth data and thus provide potential to improve wind resource modeling.

Terminal pose control method of power tower climbing robot for job safety

Abstract With the increasing demand for power system maintenance, the safety and efficiency of power tower climbing operations have become the focus of industry attention. This article proposes an end pose control method for power tower climbing robots aimed at job safety, targeting the problem of end pose control for power tower climbing robots. Firstly, the motion data of the power tower climbing robots is collected by deploying sensors. Subsequently, preprocessing and feature extraction are performed on the collected data to ensure its accuracy and availability. On this basis, an accurate end pose control model for climbing robots is constructed, which can adjust the robot’s motion parameters in real time to adapt to the complex changes in the tower structure. The experimental results show that the control method is effective and stable in actual climbing operations, significantly improving the efficiency and safety of climbing robots. This provides an innovative solution for the automation maintenance of power towers, which has important theoretical value and practical application prospects.

Sensitivity and Uncertainty Analysis of the GeeSEBAL Model Using High-Resolution Remote-Sensing Data and Global Flux Site Data

Actual evapotranspiration (ETa) is an important component of the surface water cycle. The geeSEBAL model is increasingly being used to estimate ETa 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 (Ts 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 ETa. The results indicate that by analysing the end element components of the geeSEBAL model (Ts 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 Zoh were determined, and the reanalysis of meteorological data inputs (relative humidity (RH), temperature (T), wind speed (WS), and net radiation (Rn)) was also found to be an important source of uncertainty for the accurate estimation of ETa. 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 ETa for various vegetation types.

Are the ecosystem-level evaporative stress indices representative of evaporative stress of vegetation?

Evaporative Stress Index (ESI), also sometimes referred as Evaporative Stress Ratio (ESR), has been widely used as an indicator of vegetation evaporative stress, and is often used to track forest and agriculture droughts. Lower the stress, higher is the value of ESI or ESR. The goal of this study is to assess the suitability of these indices for tracking vegetation evaporative stress. As the dynamics of water loss from vegetation through transpiration (T) can be different than that of evapotranspiration (ET) from the ecosystem, it is hypothesized that ESI or ESR may not be sufficiently representative of the vegetation evaporative stress. Using eddy covariance flux tower data of 518 site years, distributed across 49-sites and 9 land covers globally, our findings reveal underestimation of vegetation evaporative stress by ESI during periods of high vapor pressure deficit (VPD) and overestimation during dry, low-VPD periods. The results highlight the need to improve representativeness of ESI for monitoring vegetation evaporative stress. Notably, this may entail accurate estimation of ecosystem T in systems lacking in-situ data, a challenge that warrants further attention.

Better representation of vegetation phenology improves estimations of annual gross primary productivity

Carbon uptake by vegetation plays a vital role in the global carbon cycle. Annual gross primary productivity (AGPP) represents the total amount of carbon compounds produced by vegetation photosynthesis over a year and is a crucial metric for quantifying vegetation carbon uptake. Although some theories have been developed to explain the spatiotemporal variation in AGPP, they often overlook the seasonal differences in photosynthesis across vegetation phenological periods. This gap highlights the need for a more reasonable representation of vegetation phenology in AGPP modelling. Therefore, we developed a novel theoretical AGPP model that decomposes AGPP into detailed vegetation phenological periods (green-up, maturation, and senescence) and investigated the effects of climatic factors on the seasonal components of AGPP. Compared with existing models, our model considers the length of the multiple vegetation phenological periods, rather than just the length of the carbon uptake period. When evaluated against flux tower data, our model outperformed the comparative model, with a higher determination coefficient and lower root mean square error. The analysis also demonstrated that the developed model can reproduce spatial and temporal variations in satellite-based global AGPP. In addition, we identified distinct differences in the responses of AGPP's seasonal components to climatic factors: shortwave radiation predominantly affected the seasonal component of AGPP during senescence, air temperature during green-up, and vapor pressure deficit during maturation. Our study proposes a potential mechanism for AGPP estimation and highlights the importance of accurately representing vegetation phenology in AGPP modelling.

Predicting time series of vegetation leaf area index across North America based on climate variables for land surface modeling using attention-enhanced LSTM

ABSTRACT Ecosystem process modeling is vital for a broad range of applications. It requires a key metric to characterize vegetation canopies: the leaf area index (LAI). Many land surface models utilize satellite-derived LAI for modeling ecosystem processes. The use of satellite-derived LAI constrains the models for predicting ecosystem processes when observational data are unavailable. Enriching prognostic models for predicting the LAI is favorable to land surface studies for forecasting vegetation-related processes. An attention-enhanced long and short memory (AELSTM) model was developed to predict the vegetation LAI time series based on climatic data. The AELSTM outperformed comparative machine learning models when assessed using satellite and flux tower data. The LAI predicted by AELSTM are temporally and spatially consistent with satellite-derived LAI. The R2 values attained 0.86–0.93 across biomes. AELSTM can be coupled with the land surface models to predict gross primary productivity. Our proposed model was demonstrated to be effective in predicting the LAI time series under different Shared Socioeconomic Pathways in CMIP6. Our study demonstrated that deep learning approaches are capable of modeling and characterizing the spatial and temporal patterns of key vegetation variables such as LAI. Moreover, the study has provided references for vegetation processes in land surface studies.

Causal Drivers of Land‐Atmosphere Carbon Fluxes From Machine Learning Models and Data

AbstractInteractions among atmospheric, root‐soil, and vegetation processes drive carbon dioxide fluxes (Fc) from land to atmosphere. Eddy covariance measurements are commonly used to measure Fc at sub‐daily timescales and validate process‐based and data‐driven models. However, these validations do not reveal process interactions, thresholds, and key differences in how models replicate them. We use information theory‐based measures to explore multivariate information flow pathways from forcing data to observed and modeled hourly Fc, using flux tower data sets in the Midwestern U.S. in intensively managed corn‐soybean landscapes. We compare multiple linear regressions, long‐short term memory, and random forests (RF), and evaluate how different model structures use information from combinations of sources to predict Fc. We extend a framework for model predictive and functional performance, which examines a suite of dependencies from all forcing variables to the observed or modeled target. Of the three model types, RF exhibited the highest functional and predictive performance, correctly capturing strong dependencies between radiation and temperature variables with Fc. Regionally trained models demonstrate lower predictive but higher functional performance compared to site‐specific models, suggesting superior reproduction of observed relationships at the expense of predictive accuracy. This study shows that some metrics of predictive performance encapsulate functional behaviors better than others, highlighting the need for multiple metrics of both types. This study improves our understanding of carbon fluxes in an intensively managed landscape, and more generally provides insight into how model structures and forcing variables translate to interactions that are well versus poorly captured in models.

Technical note: Flagging inconsistencies in flux tower data

Abstract. Global collections of synthesized flux tower data such as FLUXNET have accelerated scientific progress beyond the eddy covariance community. However, remaining data issues in FLUXNET data pose challenges for users, particularly for multi-site synthesis and modelling activities. Here, we present complementary consistency flags (C2Fs) for flux tower data, which rely on multiple indications of inconsistency among variables, along with a methodology to detect discontinuities in time series. The C2F relates to carbon and energy fluxes, as well as to core meteorological variables, and consists of the following: (1) flags for daily data values, (2) flags for entire-site variables, and (3) flags at time stamps that mark large discontinuities in the time series. The flagging is primarily based on combining outlier scores from a set of predefined relationships among variables. The methodology to detect break points in the time series is based on a non-parametric test for the difference in distributions of model residuals. Applying C2F to the FLUXNET 2015 dataset reveals the following: (1) among the considered variables, gross primary productivity and ecosystem respiration data were flagged most frequently, in particular during rain pulses under dry and hot conditions. This information is useful for modelling and analysing ecohydrological responses. (2) There are elevated flagging frequencies for radiation variables (shortwave, photosynthetically active, and net). This information can improve the interpretation and modelling of ecosystem fluxes with respect to issues in the driver. (3) The majority of long-term sites show temporal discontinuities in the time series of latent energy, net ecosystem exchange, and radiation variables. This should be useful for carefully assessing the results in terms of interannual variations in and trends of ecosystem fluxes. The C2F methodology is flexible for customizing and allows for varying the desired strictness of consistency. We discuss the limitations of the approach that can present starting points for future developments.

Exploring the Potential of Long Short‐Term Memory Networks for Predicting Net CO2 Exchange Across Various Ecosystems With Multi‐Source Data

AbstractUpscaling flux tower measurements based on machine learning (ML) algorithms is an essential approach for large‐scale net ecosystem CO2 exchange (NEE) estimation, but existing ML upscaling methods face some challenges, particularly in capturing NEE interannual variations (IAVs) that may relate to lagged effects. With the capacity to characterize temporal memory effects, the Long Short‐Term Memory (LSTM) networks are expected to help solve this problem. Here we explored the potential of LSTM for predicting NEE across various ecosystems using flux tower data over 82 sites in North America. The LSTM model with differentiated plant function types (PFTs) demonstrates the capability to explain 79.19% (R2 = 0.79) of the monthly variations in NEE within the testing set, with RMSE and Mean Absolute Error values of 0.89 and 0.57 g C m−2 d−1 respectively (r = 0.89, p < 0.001). Moreover, the LSTM model performed robustly in predicting cross‐site variability, with 67.19% of the sites that can be predicted by both LSTM models with and without distinguished PFTs showing improved predictive ability. Most importantly, the IAV of predicted NEE highly correlated with that in flux observations (r = 0.81, p < 0.001), clearly outperforming that by the random forest model (r = −0.21, p = 0.011). Among all nine PFTs, solar‐induced chlorophyll fluorescence, downward shortwave radiation, and leaf area index are the most important variables for explaining NEE variations, collectively accounting for approximately 54.01% in total. This study highlights the great potential of LSTM for improving carbon flux upscaling with multi‐source remote sensing data.

Ground-Based NDVI Network: Early Validation Practice with Sentinel-2 in South Korea.

As satellite launching increases worldwide, uncertainty quantification for satellite data becomes essential. Misunderstanding satellite data uncertainties can lead to misinterpretations of natural phenomena, emphasizing the importance of validation. In this study, we established a tower-based network equipped with multispectral sensors, SD-500 and SD-600, to validate the satellite-derived NDVI product. Multispectral sensors were installed at eight long-term ecological monitoring sites managed by NIFoS. High correlations were observed between both multispectral sensors and a hyperspectral sensor, with correlations of 0.76 and 0.92, respectively, indicating that the calibration between SD-500 and SD-600 was unnecessary. High correlations, 0.8 to 0.96, between the tower-based NDVI with Sentinel-2 NDVI, were observed at most sites, while lower correlations at Anmyeon-do, Jeju, and Wando highlighting challenges in evergreen forests, likely due to shadows in complex canopy structures. In future research, we aim to analyze the uncertainties of surface reflectance in evergreen forests and develop a biome-specific validation protocol starting from site selection. Especially, the integration of tower, drone, and satellite data is expected to provide insights into the effect of complex forest structures on different spatial scales. This study could offer insights for CAS500-4 and other satellite validations, thereby enhancing our understanding of diverse ecological conditions.

AgriCarbon-EO v1.0.1: large-scale and high-resolution simulation of carbon fluxes by assimilation of Sentinel-2 and Landsat-8 reflectances using a Bayesian approach

Abstract. Soil organic carbon storage is a well-identified climate change mitigation solution. Quantification of the soil carbon storage in cropland for agricultural policy and offset carbon markets using in situ sampling would be excessively costly, especially at the intrafield scale. For this reason, comprehensive monitoring, reporting, and verification (MRV) of soil carbon and its explanatory variables at a large scale need to rely on hybrid approaches that combine remote sensing and modelling tools to provide the carbon budget components with their associated uncertainties at intrafield scale. Here, we present AgriCarbon-EO v1.0.1: an end-to-end processing chain that enables the estimation of carbon budget components for major and cover crops at intrafield resolution (10 m) and regional extents (e.g. 10 000 km2) by assimilating remote sensing data (e.g. Sentinel-2 and Landsat8) in a physically based radiative transfer (PROSAIL) and agronomic models (SAFYE-CO2). The data assimilation in AgriCarbon-EO is based on a novel Bayesian approach that combines normalized importance sampling and look-up table generation. This approach propagates the uncertainties across the processing chain from the reflectances to the output variables. After a presentation of the chain, we demonstrate the accuracy of the estimates of AgriCarbon-EO through an application over winter wheat in the southwest of France during the cropping seasons from 2017 to 2019. We validate the outputs with flux tower data for net ecosystem exchange, biomass destructive samples, and combined harvester yield maps. Our results show that the scalability and uncertainty estimates proposed by the approach do not hinder the accuracy of the estimates (net ecosystem exchange, NEE: RMSE =1.68–2.38 gC m−2, R2=0.87–0.77; biomass: RMSE =11.34 g m−2, R2=0.94). We also show the added value of intrafield simulations for the carbon components through scenario testing of pixel and field simulations (biomass: bias =-47 g m−2, −39 % variability). Our overall analysis shows satisfying accuracy, but it also points out the need to represent more soil processes and include synthetic aperture radar data that would enable a larger coverage of AgriCarbon-EO. The paper's findings confirm the suitability of the choices made in building AgriCarbon-EO as a hybrid solution for an MRV scheme to diagnose agro-ecosystem carbon fluxes.

Simulation of gross primary productivity and impact of drought in Liulin watershed of Taihang mountains over 2000–2020

As the largest flux in the global terrestrial carbon cycle, Gross Primary Productivity (GPP) is a key factor for the terrestrial carbon budget. Based on the eddy flux tower data, remote sensing data and the meteorological precipitation data, we calibrated three parameters of maximum light use efficiency, optimum temperature, and maximum temperature in the Vegetation Photosynthesis Model (VPM) to reproduce the GPP spatiotemporal characteristic in Liulin watershed of Taihang Mountains. We further analyzed the time lag effect and cumulative effect of the standard precipitation index (SPI) on GPP by the Pearson correlation coefficients. Results show that VPM captures the GPP characteristics of eddy flux tower observation in Liulin watershed. During the calibration period (R2 = 0.94, RMSE = 0.47 g C m-2·d-1) and validation period (R2 = 0.91, RMSE = 0.60 g C m-2 d-1), the adjusted VPM model successfully reproduced the temporal dynamics of site-observed GPP. From 2000 to 2020, VPM model can effectively reproduce the long-term variations in GPP (R2 = 0.69 vs. NIRv_GPP), with the GPP ranging from 446.4 to 828.1 g C m-2 y-1 and an average value of 545.6 g C m-2 y-1 in Liulin watershed. The GPP exhibited an increasing trend from 2000 to 2020. A mutation in the GPP occurred in 2010, with an average annual increase of 13.9 g C m-2 y-1 from 2000 to 2010 (p < 0.05), followed by an average annual increase of 10.22 g C m-2 y-1 from 2011 to 2020 (p > 0.05). The SPI effectively monitored drought events in the Liulin watershed. With a cumulative effect of 9 months and a lag effect of 6–7 months, the time lag and cumulative effects of drought had a long-term effect on GPP. It is imperative to have long-term eddy flux tower data and meteorological data to better evaluate the spatiotemporal dynamics of GPP and the impact of drought.

Hydrometeorological Factors Affecting the Carbon Exchange of the Himalayan Pine-dominated Ecosystem

The net carbon uptake of the natural and dense Himalayan ecosystems is not well explored at a sub-daily scale due to limited data availability. Here, we present the first-ever analysis of the hydrometeorological controls of the sub-daily scale Net Ecosystem Exchange (NEE) in a Western Himalayan pine-dominated ecosystem (Pinus roxburghii). We used observed half-hourly flux tower data for 104 weeks from December 2014 to November 2016. We found that the sub-daily (6 h) scale variability of NEE contributes 20–40% to its within-week variability. We generated transfer entropy (TE)-based weekly process networks with a maximum memory of 6 h to understand sub-daily scale processes. Using weekly networks, we present consistent causal links on a seasonal scale. The hydrometeorological variables affecting the NEE and Ecosystem Respiration (RE) at a sub-daily scale are Net Solar Radiation (NSR), Relative Humidity (RH), Surface Air Temperature (TA), and Sensible Heat Flux (SH). These variables are consistently active in the networks with seasonally varying magnitudes of TE. We also found that for both monsoon and post-monsoon, NEE receives more incoming links from meteorological variables during dry weeks than wet weeks. Precipitation (P) did not send a direct causal link to NEE or RE within a sub-daily scale but strongly influenced the causal associations between hydrometeorological and carbon exchange variables. The network does not show any immediate (within 6 h) impact of P on NEE, which probably implies a delayed response of vegetation to P through soil moisture. At higher memory, P may act as an influential background variable.

Analysis of Boundary Layer Structure, Turbulence, and Flux Variations before and after the Passage of a Sea Breeze Front Using Meteorological Tower Data

Analysis of Boundary Layer Structure, Turbulence, and Flux Variations before and after the Passage of a Sea Breeze Front Using Meteorological Tower Data

Urban canopy height ozone distribution in a Chinese inland city: Effects of anthropogenic NO emissions

With the increase of urban building height, people pay more and more attention to the characteristics of pollutants in urban canopy height. This study combined the generalized additive model (GAM) and the observation-based model (OBM) to explore the vertical characteristics and drivers of ozone (O3) based on meteorology tower (200 m) data to quantify the effects of factors and photochemical reactions on O3 formation at different heights. The F values of GAM reflect the importance of each factor, indicating that NO (F is 33.99 in the peak season, 36.72 in the non-peak season) was the dominant driver of O3 and was more important in the lower layer (20–116 m). Temperature (F is 35.42) was the main contributor to O3 pollution in the peak season, especially for O3 in the upper layer (116–200 m). The net O3 production rate in the peak season was 1.47 times that in the non-peak season due to strong photochemical reactions and meteorological conditions. And the net O3 production rate decreased sharply with increasing height in the two seasons. Less net O3 production in the upper layer was accompanied by a higher O3 mixing ratio, which indicated that there was more background O3 in the upper layer. OBM model results showed that the reaction between hydroperoxyl radical (HO2) and NO was the primary contribution pathway, accounting for 54.00 % and 57.50 % in the peak and non-peak seasons, respectively. O3 formation was highly sensitive to VOCs, while NOx reduction could have positive or negative effects on O3 depending on the levels of hydroxyl radical (OH). The understanding of the formation mechanism of O3 and the influence of NO on O3 provides insights into the importance of anthropogenic activities at urban canopy heights in shaping the vertical structure of O3.

Improving the ability of solar-induced chlorophyll fluorescence to track gross primary production through differentiating sunlit and shaded leaves

Recently, solar-induced chlorophyll fluorescence (SIF) is a promising tool to estimate gross primary production (GPP). Photosynthesis gradually saturates with the increasing light, but fluorescence tends to keep increasing, leading to a nonlinear SIF-GPP relationship. This nonlinearity occurs for sunlit leaves but not for shaded leaves for which photosynthesis is light-limited. However, the separation of sunlit and shaded SIF has not been systematically investigated when estimating GPP from SIF. Therefore, it is promising to develop a model for GPP estimation considering such differences. This study proposed an approach to separate the total canopy SIF emission (SIFtotal) from TROPOspheric Monitoring Instrument (TROPOMI) SIF into their sunlit and shaded components (SIFsun and SIFshade). The nonlinearity and linearity in SIF-GPP relationships for sunlit and shaded leaves were incorporated into a two-leaf hybrid model, which was fitted using flux tower data and then evaluated using leave-one-site-out crossing validation. We also elucidated the distinct SIF-GPP relationships between sunlit and shaded leaves using the Soil-Canopy-Observation of Photosynthesis and the Energy balance (SCOPE) model simulation. Compared to previously used linear (R2 = 0.68, RMSE = 2.13 gC·m−2·d−1) or hyperbolic (R2 = 0.72, RMSE = 2.01 gC·m−2·d−1) model based on the big-leaf assumption, our proposed two-leaf hybrid model has the best performance on GPP estimation (R2 = 0.77, RMSE = 1.79 gC·m−2·d−1). We also applied this two-leaf hybrid model to estimate the global GPP during the main growing season in Northern Hemisphere, which were highly correlated with several existing GPP products, with R2 ranging from 0.79 to 0.88. These results will improve our understanding of the relationship between SIF and GPP for sunlit and shaded leaves and will advance application of satellite SIF data to GPP estimation.

A soil-air temperature model to determine the start of season phenology of deciduous forests

Forest ecosystems play a major role in sequestering atmospheric carbon dioxide, which can help offset the detrimental effects of anthropogenic carbon emissions. However, climate change has and will continue to affect the phenology of forest ecosystems’ carbon uptake, changing both the “carbon uptake transition date” - when forests shift from being a net carbon source to sink - and the “green-up date” reflecting the onset of bud burst. Previous studies have shown that a forest's carbon uptake transition date correlates to the date when soil temperature warms enough to surpass mean annual air temperature (soil-air temperature model). However, we still don't know if this simple relationship holds across different sites or over longer time periods. In this study, we explore the relationship between climate and both types of phenological transition dates using over 200 site years of data between 1997 and 2022. Using flux tower data from 18 sites across North America and Europe, we derive three potential carbon uptake transition dates corresponding to the dates when 10%, 25%, and 50% of seasonal net ecosystem exchange (NEE) amplitude is reached. Using PhenoCam data, we then derive three potential green-up dates corresponding to when 10%, 25%, and 50% of total seasonal green chromatic coordinate (GCC) is reached (the greenness model). We evaluate our model estimates using concordance coefficients, a metric of agreement between two measures, to determine which process, carbon uptake or budburst, is best predicted by the soil-air temperature model and to what extent. We find that variation in phenological relationships can be attributed to different regional and bioclimatic groups, highlighting potential biome-specific strengths and limitations of the soil-air temperature model. This model offers a simple approach to better understand phenological transitions and identify potential and limitations for a simple universal SOS prediction approach in deciduous forests.

Trends and Patterns of Daily Maximum, Minimum and Mean Temperature in Brazil from 2000 to 2020

According to data obtained from meteorological towers, Brazil has significantly increased temperature in the past 20 years, particularly in the North and Midwest regions. Vapor pressure deficit and evapotranspiration were also analyzed, showing an increase across the entire country, confirming that the air is becoming drier. This warming trend is part of the global climate change phenomenon caused by the rise of greenhouse gases in the atmosphere, fires, poor soil management practices, deforestation, and logging. The increase in temperature and dryness has profoundly impacted Brazil’s climate and ecosystems, leading to intensified extreme weather events and changes in the distribution of both animal and plant species. This study highlights the importance of utilizing meteorological tower data to monitor and understand the effects of climate change in Brazil. It emphasizes the need for immediate action to address its causes and mitigate its negative impacts.