Covariance Measurements Research Articles

Biodiversity skepticism and measurement practices

AbstractThis paper challenges “biodiversity skepticism:” an inferential move that acknowledges the proliferation, heterogeneity, and lack of covariance of biodiversity measurements, and concludes that we should doubt the scientific validity of the biodiversity concept. As a way out of skepticism, philosophers have advocated for eliminating “biodiversity” from scientific inquiry, revising it, or deflating its meaning into a single measurable dimension. I present a counterargument to the inferential move of the skeptic by revealing how it stands on two unstated premises, namely a reflective view of measurements and the unidirectional dynamics between definitional and measurement practices, and corollary assumptions. These premises and assumptions are misaligned with a richer theoretical understanding of measurement and are sometimes inconsistent with how science operates. A more nuanced view of measurement could better explain measurement proliferation while being consistent with new ways in which the general biodiversity concept could be useful. To conclude, I urge philosophers of measurement and conceptual engineers to collaborate in tackling the interplay between conceptual change and measurement practices.

Quality control of eddy covariance fluxes of two ecosystem types with local flux-variance similarity functions in West Africa

Ecosystem capacity studies for carbon sequestration and water extraction are largely conditioned by Eddy Covariance (EC) measurement availability and quality. Our study aims at evaluating several procedures of EC data qualification. Indeed, quality control of EC data is generally ensured by stationarity and well-developed turbulence tests. The latter is based on integral turbulence characteristic (ITC) models. In this study, we revisited the quality control procedure using locally established ITC models for wind speed components and atmospheric scalars (temperature, humidity and carbon dioxide molar densities) in unstable and stable conditions. We also considered the interdependence of flux qualities because of the corrections applied to fluxes during their preprocessing. The analyses were carried out using one year of data collected above a woodland forest and mixed crop sites, both situated in Northern Benin, West Africa. From these analyzes we note that the quality flags vary depending on the use of local or existing ITC models. Also, the test of turbulence development on atmospheric scalars, previously neglected, is of great importance in the quality control of data. Thus, taking into account the ITC models associated with the atmospheric scalars established locally in the well-developed turbulence test as well as taking into account the interdependence of the flux quality have considerably improved the standard quality control procedure used in effectively filtering unrealistic flux data while keeping a considerable percentage of good and medium quality data.

Exploring and closing the energy balance of eddy covariance measurements along a land use gradient in the West African Sudanian savanna

A good understanding of land-atmosphere exchange processes is essential for developing sustainable land management practices in Africa, in order to enhance food security and strengthen the resilience against climate change and extremes in this vulnerable region. In this study, we explore the energy balance closure (EBC) of three eddy covariance (EC) sites implemented along a land use gradient (pristine savanna forest, cropland, and degraded grassland) in the Sudanian savanna of West Africa. Our results show that the EBC strongly varies over the monsoon season and the EC sites. However, the best EBC is observed at the pristine site, which has the most homogenous vegetation. Thus, landscape heterogeneity seems to play an important role in the quality of the EC measurements. Moreover, we develop a novel post-closure method based on a quantile-mapping technique conditioned on monsoonal circulation patterns specifically determined for the West African Monsoon. This method is also compared to two well-established methods, the Bowen-ratio (BR) correction and a pure quantile-mapping using various bias measures. Our results show that the novel post-closure method outperforms the other methods and, therefore, leads to better elimination of the underestimation of the turbulent fluxes at the three savanna sites. In addition, specific characteristics of turbulent fluxes, like their strong diurnal cycle, are well represented by the new correction method.

Assessing causality in deprescribing studies: A focus on adverse drug events and adverse drug withdrawal events.

Generating real-world evidence about the effect of medication discontinuation or dose reduction on outcomes, such as reduction of adverse drug effects (ADE; intended benefit) and occurrence of adverse drug withdrawal events (ADWE; unintended harm), is crucial to informing deprescribing decisions. Determining the causal effects of deprescribing is difficult for many reasons, including lack of randomization in real-world study designs and other design and measurement issues that pose threats to internal validity. The inherent challenge is how to identify the effects, both intended benefits and unintended harms, of a new medication stoppage or reduction when implemented in patients with many potential clinical and social risks that may influence the likelihood of deprescribing as well as outcomes. We discuss methodological issues of estimating the effect of medication discontinuation or reduction on risk of ADEs and ADWEs considering: (1) sampling study populations of sufficient size with the potential to demonstrate clinically meaningful and quantifiable outcomes, (2) accurate and appropriately timed measurement of covariates, outcomes, and discontinuation, and (3) statistical approaches to managing confounding and other biases inherent in long-term medication use by individuals with multiple morbidities. Designing rigorous deprescribing studies that address internal validity threats will support evidence generation by improving the ability to assess benefits and harms when the exposure of interest is the absence of a medication. Iterative learnings about data quality, variable definition, variable measurement, and exposure-outcome associations will inform strategies to improve the causal inferences possible in real-world deprescribing studies.

Turbulent Energy and Carbon Fluxes in an Andean Montane Forest—Energy Balance and Heat Storage

High mountain rainforests are vital in the global energy and carbon cycle. Understanding the exchange of energy and carbon plays an important role in reflecting responses to climate change. In this study, an eddy covariance (EC) measurement system installed in the high Andean Mountains of southern Ecuador was used. As EC measurements are affected by heterogeneous topography and the vegetation height, the main objective was to estimate the effect of the sloped terrain and the forest on the turbulent energy and carbon fluxes considering the energy balance closure (EBC) and the heat storage. The results showed that the performance of the EBC was generally good and estimated it to be 79.5%. This could be improved when the heat storage effect was considered. Based on the variability of the residuals in the diel, modifications in the imbalances were highlighted. Particularly, during daytime, the residuals were largest (56.9 W/m2 on average), with a clear overestimation. At nighttime, mean imbalances were rather weak (6.5 W/m2) and mostly positive while strongest underestimations developed in the transition period to morning hours (down to −100 W/m2). With respect to the Monin–Obukhov stability parameter ((z − d)/L) and the friction velocity (u*), it was revealed that the largest overestimations evolved in weak unstable and very stable conditions associated with large u* values. In contrast, underestimation was related to very unstable conditions. The estimated carbon fluxes were independently modelled with a non-linear regression using a light-response relationship and reached a good performance value (R2 = 0.51). All fluxes were additionally examined in the annual course to estimate whether both the energy and carbon fluxes resembled the microclimatological conditions of the study site. This unique study demonstrated that EC measurements provide valuable insights into land-surface–atmosphere interactions and contribute to our understanding of energy and carbon exchanges. Moreover, the flux data provide an important basis to validate coupled atmosphere ecosystem models.

Turbulent Heat Fluxes over Arctic Sea Ice: Measurements and Evaluation of Recent Parameterizations

We present direct eddy covariance measurements of the surface heat flux in sea ice over a wide range of conditions across the Arctic Ocean made during two research cruises. Photographic imagery of the surface around the ship provides a local, in situ estimate of the ice fraction. Aerodynamically rough conditions prevail for the majority of the time in the consolidated pack ice. The results are analyzed in the framework of a recently-developed parameterization scheme in which the exchange coefficients over ice are functions of a roughness Reynolds number, R*, hence account for aerodynamic roughness variability. This parameterization accurately represents the measured fluxes under all conditions, while under aerodynamically rough conditions the existing parameterizations from both the Met Office Unified Model, and ECMWF Integrated Forecast System overestimate the fluxes. The results corroborate those of a previous airborne study over the marginal ice zone, and encompass a wider range of atmospheric stability conditions.

Global warming impacts of carbon dioxide, methane, and albedo in an island forest nature reserve

Abstract Forest ecosystems influence climate by sequestering carbon from the atmosphere and by altering the surface energy balance. However, the combined global warming impacts (GWIs), contribution from carbon dioxide (CO2) fluxes, methane (CH4) fluxes, and albedo changes (Δα) remain poorly understood. Here, we reported the combined GWIs of CO2, CH4, and albedo with eddy covariance (EC) measurements during 2020–2022 in a subtropical island forest located in the Nanji Islands National Marine Protected Area in Southern China. We suggested that the island forest acted as a significant carbon sink, with annual CO2 and CH4 fluxes of −548.6 ± 11.1 and −5.67 ± 1.1 g C m−2 yr−1, respectively, while the daily albedo varied within the range of 0.03–0.15. By converting the radiative forcing induced by CH4 and albedo change in the forest to CO2 equivalents, we analyzed the three contributors to the combined GWI. The annual averages GWI of CO2 uptake, CH4 uptake, and Δα were −2 011.6 ± 40.6, -211.3 ± 1.1, and 0.03 ± 4.5 g CO2-eq m−2 yr−1, respectively, with a mean combined GWI of −2 223 ± 40.8 g CO2-eq m−2 yr−1. During 2020–2022, the contributions of CO2 uptake, CH4 uptake, and Δα to the combined GWI were 89.7% to 91.4%, 9.4% to 9.6% and -1.0 to 0.9%, respectively. Nanji island forest had a strong positive effect on climate change mitigation, with CO2 and CH4 uptake greatly enhancing its cooling benefits. Using Pearson correlation and path analysis, we found photosynthetically active radiation, precipitation, soil water content were the primary factors controlling the GWI dynamics, mainly driving the changes in CO2 fluxes. This study provided novel insights into the establishment of the overall evaluation framework for ecosystem-scale GWIs of CO2 and CH4 fluxes, and albedo based on long-term EC measurements in an island forest.

Impact of Mask Type as Training Target for Speech Intelligibility and Quality in Cochlear-Implant Noise Reduction

The selection of a target when training deep neural networks for speech enhancement is an important consideration. Different masks have been shown to exhibit different performance characteristics depending on the application and the conditions. This paper presents a comprehensive comparison of several different masks for noise reduction in cochlear implants. The study incorporated three well-known masks, namely the Ideal Binary Mask (IBM), Ideal Ratio Mask (IRM) and the Fast Fourier Transform Mask (FFTM), as well as two newly proposed masks, based on existing masks, called the Quantized Mask (QM) and the Phase-Sensitive plus Ideal Ratio Mask (PSM+). These five masks are used to train networks to estimate masks for the purpose of separating speech from noisy mixtures. A vocoder was used to simulate the behavior of a cochlear implant. Short-time Objective Intelligibility (STOI) and Perceptual Evaluation of Speech Quality (PESQ) scores indicate that the two new masks proposed in this study (QM and PSM+) perform best for normal speech intelligibility and quality in the presence of stationary and non-stationary noise over a range of signal-to-noise ratios (SNRs). The Normalized Covariance Measure (NCM) and similarity scores indicate that they also perform best for speech intelligibility/gauging the similarity of vocoded speech. The Quantized Mask performs better than the Ideal Binary Mask due to its better resolution as it approximates the Wiener Gain Function. The PSM+ performs better than the three existing benchmark masks (IBM, IRM, and FFTM) as it incorporates both magnitude and phase information.

Generation and evaluation of energy and water fluxes from the HOLAPS framework: Comparison with satellite-based products during extreme hot weather

Improving our understanding of the energy and water exchanges between the land surface and the lower atmosphere (i.e. land–atmosphere interactions), and how climate change may affect them, is crucial to predict changes in temperature and precipitation extremes. Observations of energy and water fluxes at the land surface are typically retrieved from the eddy covariance method, which presents limitations related to spatial and temporal gaps, and the non-closure of the energy and water balances. Meanwhile, soil moisture (SM) products derived from satellite data have been widely used at regional and global scales, but they are limited to capture only surface soil water content and variations. The combination of remote sensing (RS) data and modelling frameworks is called to be the solution to improve the spatial coverage and vertical resolution of land–atmosphere interactions data, ensuring the energy and water balance closure. Here, we explore the combination of remote sensing and meteorological data with a physical-based modelling framework, the High resOlution Land Atmosphere Parameters from Space (HOLAPS). We used HOLAPS to produce hourly consistent estimates of energy and water fluxes over Europe at 5 km resolution. HOLAPS and other satellite-based evapotranspiration and sensible heat flux products from the literature are evaluated against the water balance method and eddy covariance measurements. HOLAPS SM estimates together with other RS-modelling products are also evaluated against ground-based measurements at the surface and in the root zone. The evaluation of HOLAPS ET estimates show similar performance to the other products with Kling–Gupta efficiency (KGE) ¿ -0.41 in comparison with eddy covariance measurements from FLUXNET in all seasons but in boreal winter. The simulation of H is more uncertain than for ET with KGE values ranging from -2.5 to 0.8 along the products and stations at monthly scales. HOLAPS reaches slightly better results than the rest of ET and H products at daily scales in summer (KGE ¿ 0.3 for ET and KGE ¿ 0.0 for H) and during hot conditions (KGE ¿ 0.2 for ET and KGE ¿-0.2 for H), while the state-of-the-art products show KGE ¿ 0.1 for ET and KGE ¿ -0.41 for H in summer and KGE ¿ -0.1 for ET and KGE ¿ -0.6 for H during hot conditions. All products evaluated here yield a reasonable performance (KGE ¿-0.41 at most sites) in simulating SM at the surface and in the root zone. Our results expose the need for further investigating and improving product performances during extreme conditions. The good performance of HOLAPS together with its inherent advantages (RS data driven, high temporal and spatial resolution, spatial and temporal continuity, soil moisture at different depths and long-term consistent evapotranspiration and sensible heat flux estimates) support its use for agricultural and forest management activities as well as to study land–atmosphere interactions based on Earth Observations.

Reconciling Top‐Down and Bottom‐Up Estimates of Ecosystem Respiration in a Mature Eucalypt Forest

AbstractEcosystem respiration (Reco) arises from interacting autotrophic and heterotrophic processes constrained by distinct drivers. Here, we evaluated up‐scaling of observed components of Reco in a mature eucalypt forest in southeast Australia and assessed whether a land surface model adequately represented all the fluxes and their seasonal temperature responses. We measured respiration from soil (Rsoil), heterotrophic soil microbes (Rh), roots (Rroot), and stems (Rstem) in 2018–2019. Reco and its components were simulated using the CABLE–POP (Community Atmosphere‐Biosphere Land Exchange–Population Orders Physiology) land surface model, constrained by eddy covariance and chamber measurements and enabled with a newly implemented Dual Arrhenius and Michaelis‐Menten (DAMM) module for soil organic matter decomposition. Eddy‐covariance based Reco (Reco.eddy, 1,439 g C m−2 y−1) was slightly higher than the sum of the respiration components (Reco.sum, 1,295 g C m−2 y−1) and simulated Reco (1,297 g C m−2 y−1). The largest mean contribution to Reco was from Rsoil (64%) across seasons. The measured contributions of Rh (49%), Rroot (15%), and Rstem (22%) to Reco.sum were very close to model outputs of 46%, 11%, and 22%, respectively. The modeled Rh was highly correlated with measured Rh (R2 = 0.66, RMSE = 0.61), empirically validating the DAMM module. The apparent temperature sensitivities (Q10) of Reco were 2.22 for Reco.sum, 2.15 for Reco.eddy, and 1.57 for CABLE‐POP. This research demonstrated that bottom‐up respiration component measurements can be successfully scaled to eddy covariance‐based Reco and help to better constrain the magnitude of individual respiration components as well as their temperature sensitivities in land surface models.

Divergent apparent temperature sensitivity of forest-floor respiration across a managed boreal forest landscape

Understanding the apparent temperature sensitivity (Q10) of forest floor respiration (Q10Rff) and its plant autotrophic (Q10Ra) and soil heterotrophic (Q10Rh) components is critical for determining the strength and direction of forest carbon cycle-climate feedbacks. However, the spatial variability of Q10Rff and its controlling factors across the spatially heterogeneous boreal forest landscape remains poorly understood. In this study, we used chamber-based respiration measurements conducted on paired natural and trenching plots over three growing seasons (2016–2018) to determine Q10Rff, Q10Rh and Q10Ra to soil temperature across 50 diverse forest stands (ranging 5–211 years old) in a managed landscape in northern Sweden. Additionally, we estimated ecosystem- and landscape-scale Q10 based on conventional and tall tower eddy covariance measurements, respectively, conducted over the same landscape. Our results suggest manifold variations (i.e., 10th to 90th percentiles) of Q10Rff (1.7 to 6.6), with Q10Ra (0.9 to 7.4) showing a greater range than Q10Rh (1.6 to 5.3). Forest-floor understory biomass was the main control of variations in Q10Ra, whereas soil properties explained best those of Q10Rh. Furthermore, a seasonal hysteresis of Q10Rff was observed, with higher values mostly occurring during the late growing season. The magnitude of this hysteresis scaled with stand age in response to concurrent changes in net primary production. The Q10Rff to air temperature declined with increasing stand age, which was attributed to the increased decoupling of air and soil temperatures with canopy development. Despite the considerable stand-level variations, when averaged over the 50 forest stands, Q10Rff to air temperature converged with that of both ecosystem- and landscape-scale respiration. In conclusion, our study highlights the complexity of Q10Rff, which needs to be considered in predictions of the interactions of climate change and management with the carbon cycle of managed boreal forests.

Comparative analysis of FAO dual Kc, Priestley‐Taylor and flux variance similarity methods in partitioning evapotranspiration from flood‐irrigated rice fields

AbstractAccurate quantification of evapotranspiration (ET) and its bifurcation into productive transpiration (T) and unproductive evaporation (E) are essential for the successful implementation of sustainable irrigation practices. This study compares the performances of three distinct methods for partitioning ET into E and T fluxes from flood‐irrigated rice fields. These methods include: i) the FAO dual Kc‐ETo model, which utilizes Penman–Monteith (PM) estimates of reference ET and crop‐specific scaling factors; ii) the Priestley‐Taylor (PT) method, which relies on radiation‐driven energy balance at the canopy surface; and iii) the flux variance similarity (FVS) method, which utilizes high‐frequency eddy covariance (EC) measurements of carbon and water vapour fluxes. Meteorological, phenological and hydrological parameters were monitored over two winter seasons in a paddy field, replicating site‐specific management strategies. The cumulative ETs obtained from the PM, PT and FVS methods were 445 mm, 300 mm and 356 mm, respectively, for season 1 and 428 mm, 321 mm and 375 mm, respectively, for season 2. The accuracy of the ET estimation and partition methods was assessed against independent measurements of ET and E using a lysimeter and a microlysimeter. The results demonstrated that the EC‐based FVS method offers superior accuracy in both quantifying (R2 = 0.67, RMSE = 0.70 mm) and partitioning (R2 = 0.51, RMSE = 0.76 mm) ET fluxes, followed by the PM‐based FAO dual Kc‐ETo and PT methods. Evaporation accounted for 29 ± 5% of consumptive water use, highlighting an opportunity for the implementation of water‐saving strategies, particularly during vegetative and transplantation stages. The findings of the path analysis indicate that vegetative and radiation factors influence ET variation, while meteorological and soil factors significantly impact T variation.

Spatial variations of annual net ecosystem productivity and its trend over Chinese terrestrial ecosystems based on spatial downscaling.

Annual net ecosystem productivity (NEP), the amount of net carbon sequestration during a year, serves as the basis of terrestrial carbon sink. Quantifying the spatial variations of NEP and its trend would enhance our understandings on the response and adaption of ecosystems to environmental change, which also serves for the regional carbon management targeting at carbon neutrality. Based on process-based model and data-driven model simulating NEP, we selected the optimal simulating NEP mostly representing NEP spatial variations with multiple site eddy covariance measurements to develop the spatial downscaling method and generate high resolution NEP data of China, which was used to examine the spatial variations of NEP and its trend and driving factors during 2000-2017. Compared with process-based model results, data-driven model simulating NEP could mostly represent the spatial variation of site measurements. The random forest regression based on climate, soil, and biological data combining with the simple scaling could successfully downscale NEP to a high spatial resolution. From 2000 to 2017, the total amount of NEP in China was (1.30±0.03) Pg C·a-1, showing a decreasing-increasing pattern with the inflection point in 2009. Chinese NEP decreased from southeast to northwest, showing a descending latitudinal distribution and an ascending longitudinal distribution, with the combined effects of climate and biotic factors. NEP trend decreased from east towards west, which was only accompanied with a slightly ascending longitudinal distribution, while photosynthetically active radiation and soil organic carbon content dominated the spatial variations of NEP trend. Therefore, the spatial patterns of generated NEP obviously differed from those of NEP trend, suggesting the obvious difference between the responses and adaptions of ecosystems to environmental changes.

Effects of Biophysical Factors on Light Use Efficiency at Multiple Time Scales in a Chinese Cork Oak Plantation Ecosystem

Light use efficiency (LUE) characterizes the efficiency of vegetation in converting photosynthetically active radiation (PAR) into biomass energy through photosynthesis and is a critical parameter for gross primary productivity (GPP) in terrestrial ecosystems. Based on the eddy covariance measurements of a Chinese cork oak plantation ecosystem in northern China, the temporal variations in LUE were investigated, and biophysical factors were examined at time scales ranging from hours to years. Our results show that diurnal LUE first increased sharply before 8:30 and then decreased gradually until 12:00, thereafter increasing gradually and reaching the maximum value at sunset during the growing season. The daily and monthly LUE first increased and then decreased within a year and showed a substantial drop around June. The annual LUE ranged from 0.09 to 0.17 g C mol photon−1, and the multiyear mean maximal LUE was 0.30 g C mol photon−1 during 2006–2019. Only GPP (positive) and clearness index (CI) (negative) had consistent effects on LUE at different time scales, and the effects of the remaining biophysical factors on LUE were different as the time scale changed. The effects of air temperature, vapor pressure deficit, precipitation, evaporative fraction, and normalized difference vegetation index on LUE were mainly indirect (via PAR and/or GPP). When CI decreased, an increased ratio of diffuse PAR to PAR produced a more uniform irradiance in the canopy, which ultimately resulted in a higher LUE. Due to climate change in our study area, the annual LUE may decrease in the future but improving management practices may slow or even reverse this trend in the annual LUE in the studied Chinese cork oak plantation.

Study of Global Navigation Satellite System Receivers' Accuracy for Unmanned Vehicles.

The development of unmanned ground vehicles and unmanned aerial vehicles requires high-precision navigation due to the autonomous motion and higher traffic intensity. The existing L1 band GNSS receivers are a good and cheap decision for smartphones, vehicle navigation, fleet management systems, etc., but their accuracy is not good enough for many civilian purposes. At the same time, real-time kinematic (RTK) navigation allows for position precision in a sub-centimeter range, but the system cost significantly narrows this navigation to a very limited area of applications, such as geodesy. A practical solution includes the integration of dual-band GNSS receivers and inertial sensors to solve high-precision navigation tasks, but GNSS position accuracy may significantly affect IMU performance due to having a great impact on Kalman filter performance in unmanned vehicles. The estimation of dilution-of-precision (DOP) parameters is essential for the filter performance as the optimality of the estimation in the filter is closely connected to the quality of a priori information about the noise covariance matrix and measurement noise covariance. In this regard, the current paper analyzes the DOP parameters of the latest generation dual-band GNSS receivers and compares the results with the L1 ones. The study was accomplished using two types of antennas-L1/L5 band patch and wideband helix antennas, which were designed and assembled by the authors. In addition, the study is extended with a comparison of GNSS receivers from different generations but sold on the market by one of the world's leading GNSS manufacturers. The analyses of dilution-of-precision (DOP) parameters show that the introduction of dual-band receivers may significantly increase the navigation precision in a sub-meter range, in addition to multi-constellation signal reception. The fast advances in the performance of the integrated CPU in GNSS receivers allow the number of correlations and tracking satellites to be increased from 8-10 to 24-30, which also significantly improves the position accuracy even of L1-band receivers.

Implications of energy balance non-closure on carbon dioxide flux uncertainties: Insights from large eddy simulations in convective boundary layers

The non-closure of surface energy balance, often encountered in eddy covariance (EC) measurements, raises a critical query: does this non-closure lead to underestimated scalar fluxes, particularly CO2 flux (Fc), when using the same theoretical framework in EC? To address this question, we utilize high-resolution large-eddy simulations (LESs) to explore correlations between energy flux imbalances and Fc imbalances in convective boundary layers, considering both homogeneous and idealized heterogeneous surfaces. Our findings reveal that the unsteady CO2 or storage represents a leading factor influencing Fc imbalance, especially notable when the entrainment ratio for Fc is large. Even in scenarios with uniform surface Fcs, heterogeneous thermally-generated turbulence resulting from variable surface sensible heat flux (H) can induce substantial horizontal flux divergence, magnifying Fc imbalance. While a linear correlation between the energy flux imbalance and Fc imbalance arises under shared causative mechanisms (e.g., storage), complex correlations emerge if their influencing factors differ, contingent upon surface heterogeneity and site location. This complexity underscores the limitations in applying the closing methods for energy flux imbalance to the Fc imbalance.

Eddy covariance measurements reveal a decreased carbon sequestration strength 2010-2022 in an African semiarid savanna.

Monitoring the changes of ecosystem functioning is pivotal for understanding the global carbon cycle. Despite its size and contribution to the global carbon cycle, Africa is largely understudied in regard to ongoing changes of its ecosystem functioning and their responses to climate change. One of the reasons is the lack of long-term insitu data. Here, we use eddy covariance to quantify the net ecosystem exchange (NEE) and its components-gross primary production (GPP) and ecosystem respiration (Reco) for years 2010-2022 for a Sahelian semiarid savanna to study trends in the fluxes. Significant negative trends were found for NEE (12.7 ± 2.8 g C m2 year-1), GPP (39.6 ± 7.9 g C m2 year-1), and Reco (32.2 ± 8.9 g C m2 year-1). We found that NEE decreased by 60% over the study period, and this decrease was mainly caused by stronger negative trends in rainy season GPP than in Reco. Additionally, we observed strong increasing trends in vapor pressure deficit, but no trends in rainfall or soil water content. Thus, a proposed explanation for the decrease in carbon sink strength is increasing atmospheric dryness. The warming climate in the Sahel, coupled with increasing evaporative demand, may thus lead to decreased GPP levels across this biome, and lowering its CO2 sequestration.

Delayed kernels for longitudinal survival analysis and dynamic prediction.

Predicting patient survival probabilities based on observed covariates is an important assessment in clinical practice. These patient-specific covariates are often measured over multiple follow-up appointments. It is then of interest to predict survival based on the history of these longitudinal measurements, and to update predictions as more observations become available. The standard approaches to these so-called 'dynamic prediction' assessments are joint models and landmark analysis. Joint models involve high-dimensional parameterizations, and their computational complexity often prohibits including multiple longitudinal covariates. Landmark analysis is simpler, but discards a proportion of the available data at each 'landmark time'. In this work, we propose a 'delayed kernel' approach to dynamic prediction that sits somewhere in between the two standard methods in terms of complexity. By conditioning hazard rates directly on the covariate measurements over the observation time frame, we define a model that takes into account the full history of covariate measurements but is more practical and parsimonious than joint modelling. Time-dependent association kernels describe the impact of covariate changes at earlier times on the patient's hazard rate at later times. Under the constraints that our model (a) reduces to the standard Cox model for time-independent covariates, and (b) contains the instantaneous Cox model as a special case, we derive two natural kernel parameterizations. Upon application to three clinical data sets, we find that the predictive accuracy of the delayed kernel approach is comparable to that of the two existing standard methods.

Temporal Variations in Methane Emissions from a Restored Mangrove Ecosystem in Southern China

The role of coastal mangrove wetlands in sequestering atmospheric carbon dioxide has been increasingly investigated in recent years. While studies have shown that mangroves are weak sources of methane (CH4) emissions, measurements of CH4 fluxes from these ecosystems remain scarce. In this study, we examined the temporal variation and biophysical drivers of ecosystem-scale CH4 fluxes in China’s northernmost mangrove ecosystem based on eddy covariance measurements obtained over a 3-year period. In this mangrove, the annual CH4 emissions ranged from 6.15 to 9.07 g C m−2 year−1. The daily CH4 flux reached a peak of over 0.07 g C m−2 day−1 during the summer, while the winter CH4 flux was negligible. Latent heat, soil temperature, photosynthetically active radiation, and tide water level were the primary factors controlling CH4 emissions. This study not only elucidates the mechanisms influencing CH4 emissions from mangroves, strengthening the understanding of these processes but also provides a valuable benchmark dataset to validate the model-derived carbon budget estimates for these ecosystems.

Observed surface heat fluxes partitioning during the local growing season over the Tibetan Plateau

Turbulent heat fluxes across the surface are an important mechanism of land-atmosphere coupling. But there is still a lack of sufficient observational measurements, particularly over the climate sensitive Tibetan Plateau (TP). This paper examines the partitioning between sensible and latent heat fluxes during growing season using the Bowen ratio as a diagnostic based on eddy covariance measurements from 12 observational sites located on the TP with the altitudes ranging between 3327 m and 4509 m above sea level. The results show an average Bowen ratio of 1.13, indicating that sensible heat flux is only slightly dominant in surface energy balance. For different climate zones, Bowen ratio varies from 0.56 to 1.05 in the semi-arid zone, 0.53 and 0.57 in the subhumid region, and 2.73 to 3.11 in the arid zone. The Bowen ratio shows sensitivity to soil dry and wet condition, with higher values during drier soil conditions. In the arid to semi-arid regions, the Bowen ratio shows a clear positive correlation with the Drought Soil Index (DSI) and a negative correlation with the Normalized Difference Vegetation Index (NDVI), suggesting sensitivity to soil moisture conditions. In the sub-humid climate zone, vapor pressure deficit (VPD) is the dominant factor influencing the Bowen ratio. In the wet and dry transition zone, soil moisture, VPD, NDVI and land-air temperature difference all have a role to play.