Eddy Covariance Measurements Research Articles

Measuring Turbulent Water Vapor Fluxes Using a Tunable Diode Laser-Based Open-Path Gas Analyzer

The reliable observation and accurate estimates of land–atmosphere water vapor (H2O) flux is essential for ecosystem management and the development of Earth system models. Currently, the most direct measurement method for H2O flux is eddy covariance (EC), which depends on the development of fast-response H2O sensors. In this study, we presented a cost-efficient open-path H2O analyzer (model: HT1800) based on the tunable diode laser absorption spectroscopy (TDLAS) technique, and investigated its applicability for measuring atmospheric turbulent flux of H2O using the EC method. We prepared two HT1800 analyzers with lasers that operate at wavelengths of 1392 nm and 1877 nm, respectively. The field performance of the two analyzers was evaluated through inter-comparative experiments with LI-7500RS and IRGASON, two of the most commonly used H2O analyzers in the EC community. Water vapor densities measured by the three types of analyzers had high overall agreement with the reference sensor; however, they all experienced drift. The mean density drifts of HT1800, LI-7500 and IRGASON were 3.7–5.2%, 4.0% and 3.8%, respectively. Even so, the half-hourly H2O fluxes measured by HT1800 were highly consistent with those by LI-7500RS and IRGASON (with a difference of less than 2%), suggesting that HT1800 can obtain H2O fluxes with high confidence. The HT1800 was also proved to be suitable for EC application in terms of data availability, flux detection limit and response to the high-frequency turbulent variation. Furthermore, we investigated how the spectroscopic effect influences the measurements of H2O density and flux. Despite the fact that the 1392 nm laser was much more susceptible to the spectroscopic effect, the fluxes after correcting for this bias showed excellent agreement with the IRGASON fluxes. Considering the cost advantage in laser and photodetector, the HT1800 analyzer using a 1392 nm infrared laser is a promising and economical solution for EC measurement studies of water vapor.

On the importance of the humidity flux for the surface mass balance in the accumulation zone of the Greenland Ice Sheet

Abstract. It is highly uncertain how the humidity flux between the snow surface and the atmosphere contributes to the surface mass balance (SMB) of the interior Greenland Ice Sheet (GrIS). Due to sparse observations, evaluations of the simulated humidity flux are limited. Model-based estimates of the humidity flux contribution to the SMB are, therefore, unconstrained and even disagree in magnitude and sign. In this study, we evaluate the regional climate model MAR at the EGRIP (East Greenland Ice-Core Project) site in the accumulation zone of the GrIS. We use a combined dataset of continuous one-level bulk estimates of the humidity flux covering the period of May 2016–August 2019 and eddy-resolving eddy-covariance humidity flux measurements from all four summer seasons. In summer, we document a bias of too little sublimation (−1.3 W m−2, −1.65 mm w.e.) caused by a cold bias in both air and surface temperature, leading to a reduced humidity gradient. In winter, MAR overestimates vapor deposition by about 1 order of magnitude. This is a consequence of an overestimated temperature gradient in too stable atmospheric conditions compared to observations. Both systematic errors cause a large discrepancy in the annual net humidity flux between the model and observations of −9 mm w.e. yr−1. Remarkably, the simulated net annual humidity flux contributes positively to the SMB, contrary to observations documenting a net sublimation flux. We correct the systematic errors by applying a simple but effective correction function to the simulated latent heat flux. Using this correction, we find that 5.1 % of the annual mass gain at the EGRIP site sublimates again, and 4.3 % of the total mass gain is deposited vapor from the near-surface air. The estimated net humidity flux contribution to the annual SMB is about −1 % (net sublimation) compared to +5.6 % for the uncorrected simulation. In summer, the corrected MAR simulation shows that vapor deposition accounts for 9.6 % of the total mass gain and that 31 % of the total mass gain at the EGRIP site sublimates again. The net fluxes contribute to −32 % of the summer SMB. These results demonstrate that the humidity flux is a major driver of the summer SMB in the accumulation zone of the GrIS and highlight that even small changes could increase its importance for the annual SMB in a warming climate.

Characterizing energy balance closure over a heterogeneous ecosystem using multi-tower eddy covariance

Single point eddy covariance measurements of the Earth’s surface energy budget frequently identify an imbalance between available energy and turbulent heat fluxes. While this imbalance lacks a definitive explanation, it is nevertheless a persistent finding from single-site measurements; one with implications for atmospheric and ecosystem models. This has led to a push for intensive field campaigns with temporally and spatially distributed sensors to help identify the causes of energy balance non-closure. Here we present results from the Chequamegon Heterogeneous Ecosystem Energy-balance Study Enabled by a High-density Extensive Array of Detectors 2019 (CHEESEHEAD19)—an observational experiment designed to investigate how the Earth’s surface energy budget responds to scales of surface spatial heterogeneity over a forest ecosystem in northern Wisconsin. The campaign was conducted from June–October 2019, measuring eddy covariance (EC) surface energy fluxes using an array of 20 towers and a low-flying aircraft. Across the domain, energy balance residuals were found to be highest during the afternoon, coinciding with the period of surface heterogeneity-driven mesoscale motions. The magnitude of the residual varied across different sites in relation to the vegetation characteristics of each site. Both vegetation height and height variability showed positive relationships with the residual magnitude. During the seasonal transition from latent heat-dominated summer to sensible heat-dominated fall the magnitude of the energy balance residual steadily decreased, but the energy balance ratio remained constant at 0.8. This was due to the different components of the energy balance equation shifting proportionally, suggesting a common cause of non-closure across the two seasons. Additionally, we tested the effectiveness of measuring energy balance using spatial EC. Spatial EC, whereby the covariance is calculated based on deviations from spatial means, has been proposed as a potential way to reduce energy balance residuals by incorporating contributions from mesoscale motions better than single-site, temporal EC. Here we tested several variations of spatial EC with the CHEESEHEAD19 dataset but found little to no improvement to energy balance closure, which we attribute in part to the challenging measurement requirements of spatial EC.

Modeling sugarcane development and growth within ECOSMOS biophysical model

Sugarcane plays an important role in electricity and sugar production and is a viable biofuel. Developing and optimizing a mechanism that can predict crop growth and yield at different spatiotemporal scales can promote the understanding of the effects of cultivation on the ecosystem, while providing options for optimizing management measures and improving the operational procedures of sugarcane growers. The main objective of this study is to integrate the sugarcane module into the ECOSystem MOdel Simulator (ECOSMOS) model and calibrate a parameter set for sugarcane genotypes groups (using different datasets); the model supports datasets that vary in complexity (from flux tower experiments to operational plots), while accounting for high genotype-by-environment-by-management (GxExM) variability. First, we calibrated the ECOSMOS biophysical and physiological parameters for the sugarcane module using two micrometeorological experimental sites, based on eddy-covariance and biomass measurements. Second, sugarcane genotypes located in different regions of contrasting climate conditions were split into two groups based on their period of harvest, i.e., early or mid-to-late harvest season, and two parameter sets were proposed. The sugarcane module was used to estimate the yield of numerous plots, using two different parameter sets, namely, the general and regionally-specific parameter sets. The model could successfully simulate the biophysical and physiological processes of the biomass of stalks and leaves, energy and carbon fluxes, and soil-water dynamics; for Experimental Site 2, the Nash-Sutcliffe efficiency (NSE) was 0.14–0.86 and the relative root mean square error (RRMSE) was 13–112. However, the generic parameter set did not perform well in all production environments, and the difference between the observed and simulated yields ranged from 0.9 to 14.5 (Mg ha-1). Hence, a novel calibration approach adopted in this study improved the module’s accuracy, while improving the performances for all five production environments, with the difference between the observed and simulate yields being 0.3–2.2 (Mg ha-1). Although the two parameter sets can be used as a reference for sugarcane plantations in Brazil, we recommend recalibrating the model (for ensuring higher accuracy) before operational applications. Notably, the ECOSMOS-sugarcane model is emerging as a complex ecosystem model that can support the quantifications and evaluations of the effects of sugarcane plantations on the carbon and water balances in different environmental conditions, particularly in tropical regions.

Temporal Error Correlations in a Terrestrial Carbon Cycle Model Derived by Comparison to Carbon Dioxide Eddy Covariance Flux Tower Measurements

AbstractAtmospheric CO2 flux inversions require as input an estimate of spatial and temporal correlations of errors in their estimate of the prior mean. Some previous studies have used the differences in CO2 daily average flux estimates produced by terrestrial carbon cycle models and eddy covariance measurements to constrain the flux error correlations. Since inversions are starting to resolve the daily cycle, we set out to examine the correlations at sub‐daily time scales, as well as the correlations across years. To this end, we examine the autocorrelations in the difference between net ecosystem‐atmosphere exchange measurements from 75 AmeriFlux towers and temporally downscaled high‐spatial‐resolution flux estimates from the Carnegie‐Ames‐Stanford Approach (CASA) terrestrial carbon cycle model. We find that the daily cycle is prominent in these hourly autocorrelations and that these autocorrelations persist across years. We propose a family of functions to model these temporal correlations in atmospheric inversions, and use cross validation to determine which of the correlation functions best fits autocorrelation data from towers not in the training set. Correlation functions with a component that attempts to model the daily cycle in the differences match correlations from other towers better than those without. Those models that reproduce the same correlation structures at 1‐year intervals while modulating the amplitudes of the correlations between those intervals improve the fit still further.

Temperature threshold for seagrass (Zostera marina) ecosystem production derived from in situ aquatic eddy covariance measurements

As seagrass meadows are increasingly threatened by warming oceans and extreme heating events, it is critical that we enhance our understanding of their ecosystem response to heat stress. This study relied on our extensive database of hourly eelgrass Zostera marina ecosystem metabolism to determine, for the first time, the temperature stress threshold (Tth) of Z. marina meadows under naturally varying in situ conditions. Eelgrass ecosystem metabolism was measured using the aquatic eddy covariance technique in a 20 km2 meadow at the Virginia Coast Reserve (USA). We constructed and fitted a non-linear multivariate model to identify 28.6°C as the threshold above which substantial negative effects on net photosynthesis occur. On average, daytime oxygen fluxes decreased by 50% on afternoons when Tth was exceeded, which shifted daily net ecosystem metabolism from metabolic balance to net heterotrophy and therefore a loss in carbon. This study highlights the vulnerability of eelgrass meadows to future warming projections.

Dynamics of evapotranspiration from concurrent above- and below-canopy flux measurements in a montane Sierra Nevada forest

Evapotranspiration (ET) from the land surface to the atmosphere is a major component of Earth's water cycle, and comprises both transpiration (T) of xylem water from plants and evaporation (E) of water from soils and vegetation surfaces. These two component fluxes respond differently to changes in temperature, water availability and atmospheric CO2 concentrations. Concurrent eddy covariance (EC) measurements above and below forest canopies provide a promising approach to partition ET into E and T. However, below-canopy EC measurements are rare, and questions remain regarding their spatial variability, canopy coupling, and temporal dynamics. To address these challenges, we measured and partitioned ET over more than three years, using concurrent above- and below-canopy EC towers in a montane forest at Sagehen Creek in California's Sierra Nevada mountains. This is the establishing study for the AmeriFlux site US-SHC. The main environmental control for ET was available energy; other important controls were canopy & soil temperature, soil moisture, vapor pressure deficit, and wind speed. Below-canopy measurements at two locations within the above-canopy footprint were similar to one another, suggesting low spatial heterogeneity in understory ET near the creek at our Sagehen site. We observed a total forest ET of 606 ± 50 mm yr−1 with 275 ± 17 mm yr−1 measured in the understory (all mean ± SD) during the water years 2018–2020. Interannual variability in ET and T was small despite large variability in precipitation totals; thus the P–ET water balance was mainly driven by variations in water supply. Partitioning the components of total forest ET at Sagehen with concurrent EC measurements showed that on average, 67–74% of ET originated from T (47% from trees and 20–27% from understory vegetation), while 26–33% were from E (mostly from the understory). Our results demonstrate the potential of concurrent above- and below-canopy EC measurements for ET partitioning.

Using foliar δ13C from high‐Andean plants (Silala River basin) as a measure of potential evapotranspiration through water use efficiency

AbstractThe water‐dependent nature of arid ecosystems is closely related to the coupling between energy input through photosynthesis and the loss of water through transpiration (Tr), which can be expressed as water use efficiency (WUE). The relationship, however, between environmental factors and plant physiology in controlling evapotranspiration is not well understood in high‐altitude arid environments. Here, we review the use of carbon isotope fractionation (δ13C) to indirectly track fluctuations in WUE and the use of the portable chamber method to partition landscape actual evapotranspiration (ETa) into Tr and bare soil evaporation (Ebs) in the alluvial deposits of the Silala River, a high elevation watershed located in northern Chile. Landscape ETa was also measured with Eddy covariance (EC) systems in the basin's riparian wetland and alluvial hillslope deposits. Carbon isotope results were consistent with what is known from the literature regarding these high‐elevation ecosystems. WUE, as estimated by carbon isotope discrimination values, decreased in summer (the wet season), and increased in winter at all sites. These results were consistent with the EC measurements. Changes in WUE were much greater in the valley wetlands than along the hillslopes, most likely due to a large drop in available soil moisture along the valley bottom during the dry season. Portable chamber results obtained during summer and winter field campaigns showed that at the landscape scale, hillslope ETa was mainly dominated by bare soil evaporation; and ETa partitioning into Tr and Ebs had a seasonal change.This article is categorized under: Engineering Water > Methods Science of Water > Hydrological Processes

Ecosystem carbon exchange across China's coastal wetlands: Spatial patterns, mechanisms, and magnitudes

Coastal wetlands are of great importance for global carbon cycle and climate mitigation because of their strong carbon uptake capacity. However, the national-scale ecosystem carbon exchange dynamics in coastal wetlands, including magnitudes, spatial patterns, and controlling mechanisms, remains poorly understood. In this study, we utilized eddy covariance measurements from China's coastal wetlands to quantify carbon fluxes, assess their spatial patterns, and explore the controlling mechanisms. Integrating climate, vegetation, and soil factors, we constructed a cascaded relationship network to reveal the potential controlling mechanisms of spatial variations in carbon fluxes. The results revealed that gross primary production (GPP) and ecosystem respiration (ER) were 1405 ± 656 (mean ± sd) gC m−2 yr−1and 893 ± 465, respectively. Net ecosystem production (NEP) in coastal wetlands (567 ± 348 gC m−2 yr−1) exceeded China's terrestrial and marine ecosystems by 2–8 times, highlighting their significant carbon sink capacity. The carbon sink capacity in mangroves was significantly higher than salt marsh, exhibiting a twofold difference in NEP. Spatially, carbon fluxes displayed negative correlations with latitude, indicating the influence of climate features. Although tropical climate zone exhibited significantly higher carbon fluxes than temperate zone, no differences were observed between subtropics zone with others due to the distribution of mixed plants and largest area. The structural equation model (SEM) showed that the climate factors, including temperature, precipitation, and net radiation indirectly promoted GPP and ER through regulating the physiological process of mangrove and salt marsh, as well as soil carbon production and consumption. The cascaded relationship of climate-vegetation-soil showed in SEM explained 71–85 % of the spatial variations in GPP and ER, and ultimately accounted for 85 % of NEP. The carbon consumption efficiency (ER/GPP) in coastal vegetations was of 0.6, lower than that of global and China's terrestrial ecosystems, suggesting their strong efficiency in carbon fixation in coastal wetlands. Lastly, the scenario simulation results implied the importance of coastal vegetation restoration on enhancing blue carbon benefits.

Machine-learned actual evapotranspiration for an irrigated pecan orchard in Northwest Mexico

Accurate field-scale estimates of the actual evapotranspiration (ETact) based on readily available input data is indispensable to optimize irrigation in (semi-)arid regions. In this study, at an irrigated pecan orchard in Northwest Mexico we explored the potential for three different machine learning algorithms to improve upon the 30-min ETact estimates provided by the FAO Penman-Monteith method (FAO-PM) when trained and tested on multi-year eddy-covariance measurements. We found that all three machine learning algorithms performed well (R2>0.9), with the Random Forest (RF) giving the best overall performance. RF consistently outperformed FAO-PM on sub-daily scales in the growing season, showing its potential to further improve sub-daily ETact estimates. In addition, RF 1) shows a clear improvement (R2 increase up to 0.168) compared to a typical FAO-PM estimate based on the procedures described in FAO report 56, and 2) mostly performed similar as FAO-PM with a highly accurate locally fitted crop factor on the seasonal scale. The latter highlights its ability to infer the correct seasonal trend through input variables that reflect the underlying physical processes, giving potential for application at other similar fields in the region with limited local tuning. To assess whether incorporating existing (semi-)physical parameterizations could improve the performance, we additionally combined our machine learning models with FAO-PM both with and without locally fitted crop factor. Overall, we found this resulted in a modest improvement in performance in most cases (R2 increase up to 0.01). Notably, incorporating the locally calibrated crop factor and FAO-PM each reduced the errors made by the RF in the second half of the growing season. This implies that it is still useful to incorporate FAO-PM and a crop factor in the machine learning model, especially when the crop factor can be determined with high accuracy.

Methane Gas Ebullition Dynamics From Different Subtropical Wetland Vegetation Communities in Big Cypress National Preserve, Florida Are Revealed Using a Multi‐Method, Multi‐Scale Approach

AbstractMethane (CH4) emissions from peat soils are highly variable in space and time and are influenced by changes in biogeochemical controls and other environmental factors. Areas or times with disproportionally high CH4 emissions in wetlands may develop where conditions are especially conducive for microbial processes like methanogenesis. Currently, eddy covariance methods are employed to quantify CH4 exchanges over several extensive subtropical forested wetland communities in the Big Cypress National Preserve, Florida. In this work, we investigate the importance of multi‐scale measurements to characterize CH4 ebullition dynamics from subtropical wetlands. Our approach uses a combination of gas traps, time‐lapse photography, and capacitance probes to characterize ebullition dynamics from two different wetland vegetation communities for comparison to eddy covariance CH4 measurements at the site. Ground‐penetrating radar surveys and soil sampling are used to assess differences in subsurface properties between sites that influence ebullition. Our results show that the mean measurement bias between fluxes measured in this study and the eddy covariance measurements over the same period was 10–14 times larger during the wet season when ebullition rates were greatest, than the dry season, when ebullition rates were smallest. This suggests that eddy covariance measurements may underestimate the CH4 contribution of ebullition across heterogeneous wetland vegetation communities and that the comparability of CH4 fluxes from methods varying in spatio‐temporal scale changes in response to subtropical Florida seasonality. Our work suggests that these methods can be used to complement eddy covariance measurements and improve the characterization of ebullition dynamics in subtropical wetlands.

Quality evaluation for measurements of wind field and turbulent fluxes from a UAV-based eddy covariance system

Abstract. Instrumentation packages for eddy covariance (EC) measurements have been developed for unoccupied aerial vehicles (UAVs) to measure the turbulent fluxes of latent heat (LE), sensible heat (H), and CO2 (Fc) in the atmospheric boundary layer. This study aims to evaluate the performance of this UAV-based EC system. First, the measurement precision (1σ) of georeferenced wind was estimated to be 0.07 m s−1. Then, the effect of the calibration parameter and aerodynamic characteristics of the UAV on wind measurement was examined by conducting a set of calibration flights. The results showed that the calibration improved the quality of the measured wind field, and the influence of upwash and the leverage effect can be ignored in wind measurement by the UAV. Third, for the measurements of turbulent fluxes, the error caused by instrumental noise was estimated to be 0.03 µmolm-2s-1 for Fc, 0.02 W m−2 for H, and 0.08 W m−2 for LE. Fourth, data from the standard operational flights were used to assess the influence of resonance on the measurements and to test the sensitivity of the measurement under the variation (±30 %) in the calibration parameters around their optimum value. The results showed that the effect of resonance mainly affected the measurement of CO2 (∼5 %). The pitch offset angle (εθ) significantly affected the measurement of vertical wind (∼30 %) and turbulent fluxes (∼15 %). The heading offset angle (εψ) mainly affected the measurement of horizontal wind (∼15 %), and other calibration parameters had no significant effect on the measurements. The results lend confidence to the use of the UAV-based EC system and suggest future improvements for the optimization of the next-generation system.

Quantifying the impact of climate smart agricultural practices on soil carbon storage relative to conventional management

Climate smart agricultural practices have received considerable attention recently for their potential for climate change mitigation through sequestering atmospheric carbon. Despite the enthusiasm for climate smart practices, there is limited evidence they are more effective at removing carbon from the atmosphere and storing it in the soil than current practices. We hypothesized that a field with aspirational (ASP) practices (i.e., no-till corn-soybean-wheat-hay rotation with cover crops) would accumulate more soil organic carbon (SOC) versus a business-as-usual (BAU) field (i.e., conventional-tillage, corn-soybean-soybean rotation). We used deep soil cores (1 m) to assess changes in soil organic carbon (ΔSOC) between 2016 and 2022 for the two fields and compare with estimates based on eddy covariance calculation of ΔSOC. We found that the ASP field had ΔSOC that was positive, and larger than the BAU field. Both the soil sample method (ΔSOCSS) and the eddy covariance method (ΔSOCEC) agreed on this point, but the magnitude of ΔSOC was much larger when estimated with soil samples (ΔSOCSS was 1.9 ± 1.7 % yr−1 and -0.7 ± 1.3 % yr−1 at ASP and BAU, respectively) than with eddy covariance (ΔSOCEC was 0.80 ± 0.09 % yr−1 and 0.12 ± 0.06 % yr−1 at ASP and BAU, respectively). Finally, we used the continuous measurements of carbon fluxes from the eddy covariance towers to examine how conservation practices (cover crops, no-till, or expanded crop rotation) led to increased carbon storage. We found that unharvested cover crops add carbon to the soil that offsets net carbon losses that otherwise reduce soil carbon storage when the field is fallow. No-till and expanded crop rotations also affect the carbon budget of the agro-ecosystems. Results from this study illustrate the value of conservation practices in a changing climate and the value of eddy covariance measurements for assessing climate smart practices.

Assessing the performance of the Gaussian Process Regression algorithm to fill gaps in the time-series of daily actual evapotranspiration of different crops in temperate and continental zones using ground and remotely sensed data

The knowledge of crop evapotranspiration is crucial for several hydrological processes, including those related to the management of agricultural water sources. In particular, the estimations of actual evapotranspiration fluxes within fields are essential to managing irrigation strategies to save water and preserve water resources. Among the indirect methods to estimate actual evapotranspiration, ETa, the eddy covariance (EC) method allows to acquire continuous measurement of latent heat flux (LE). However, the time series of EC measurements are sometimes characterized by a lack of data due to the sensors' malfunctions. At this aim, Machine Learning (ML) techniques could represent a powerful tool to fill possible gaps in the time series. In this paper, the ML technique was applied using the Gaussian Process Regression (GPR) algorithm to fill gaps in daily actual evapotranspiration. The technique was tested in six different plots, two in Italy, three in the United States of America, and one in Canada, with different crops and climatic conditions in order to consider the suitability of the ML model in various contexts. For each site, the climate variables were not the same, therefore, the performance of the method was investigated on the basis of the available information. Initially, a comparison of ground and reanalysis data, where both databases were available, and between two different satellite products, when both databases were available, have been conducted. Then, the GPR model was tested. The mean and the covariance functions were set by considering a database of climate variables, soil water status measurements, and remotely sensed vegetation indices. Then, five different combinations of variables were analyzed to verify the suitability of the ML approach when limited input data are available or when the weather variables are replaced with reanalysis data. Cross-validation was used to assess the performance of the procedure. The model performances were assessed based on the statistical indicators: Root Mean Square Error (RMSE), coefficient of determination (R2), Mean Absolute Error (MAE), regression coefficient (b), and Nash-Sutcliffe efficiency coefficient (NSE). The quite high Nash Sutcliffe Efficiency (NSE) coefficient, and the root mean square error (RMSE) low values confirm the suitability of the proposed algorithm.

Aerodynamic effects cause higher forest evapotranspiration and water yield reductions after wildfires in tall forests.

Wildfires are increasing in frequency, intensity, and extent globally due to climate change and they can alter forest composition, structure, and function. The destruction and subsequent regrowth of young vegetation can modify the ecosystem evapotranspiration and downstream water availability. However, the response of forest recovery on hydrology is not well known with even the sign of evapotranspiration and water yield changes following forest fires being uncertain across the globe. Here, we quantify the effects of forest regrowth after catastrophic wildfires on evapotranspiration and runoff in the world's tallest angiosperm forest (Eucalyptus regnans) in Australia. We combine eddy covariance measurements including pre- and post-fire periods, mechanistic ecohydrological modeling and then extend the analysis spatially to multiple fires in eucalypt-dominated forests in south-eastern Australia by utilizing remote sensing. We find a fast recovery of evapotranspiration which reaches and exceeds pre-fire values within 2 years after the bushfire, a result confirmed by eddy covariance data, remote sensing, and modeling. Such a fast evapotranspiration recovery is likely generalizable to tall eucalypt forests in south-eastern Australia as shown by remote sensing. Once climate variability is discounted, ecohydrological modeling shows evapotranspiration rates from the recovering forest which reach peak values of +20% evapotranspiration 3 years post-fire. As a result, modeled runoff decreases substantially. Contrary to previous research, we find that the increase in modeled evapotranspiration is largely caused by the aerodynamic effects of a much shorter forest height leading to higher surface temperature, higher humidity gradients and therefore increased transpiration. However, increases in evapotranspiration as well as decreases in runoff caused by the young forest are constrained by energy and water limitations. Our result of an increase in evapotranspiration due to aerodynamic warming in a shorter forest after wildfires could occur in many parts of the world experiencing forest disturbances.

Estimating Fine‐Scale Transpiration From UAV‐Derived Thermal Imagery and Atmospheric Profiles

AbstractAccurate and timely observations of individual‐scale transpiration are critical for predicting ecosystem responses to climate change. Existing remote sensing methods for measuring transpiration lack the spatial resolution needed to resolve individual plants, and their sources of uncertainty are not well‐constrained. We present two novel approaches for independently quantifying fine‐scale transpiration using thermal imagery and a suite of environmental sensors mounted on an unmanned aerial vehicle (UAV) platform. The first is a surface energy balance (SEB) approach designed for fine‐scale thermal imagery; the second uses profiles of air temperature (Ta) and humidity (hr) to calculate transpiration from the Bowen Ratio. Both approaches derive the energy equivalent of transpiration, latent heat flux (λE), solely using data acquired from the UAV. We compare the two approaches and their sources of uncertainty using data from several flights at a grassland eddy covariance site in 2021 and 2022 and using typical diurnal conditions to evaluate the uncertainty of λE estimates for each approach. The SEB approach generated independent, UAV‐based estimates of λE within ∼20% of eddy covariance measurements and was most sensitive to surface temperature and resistance to heat transfer. λE calculated from the Bowen Ratio approach was ∼30% higher than tower values due to inaccuracies in Ta and hr, the main sources of uncertainty in this approach. The Bowen Ratio approach has a lower overall potential uncertainty, indicating its potential for improvement over the SEB approach. Our results are the first physically‐based observations of transpiration derived solely from a UAV platform, with no ancillary data inputs.

On the Efficacy of Monin–Obukhov and Bulk Richardson Surface-Layer Parameterizations over Drylands

Abstract Surface-layer parameterizations for heat, mass, momentum, and turbulence exchange are a critical component of the land surface models (LSMs) used in weather prediction and climate models. Although formulations derived from Monin–Obukhov similarity theory (MOST) have long been used, bulk Richardson (Rib) parameterizations have recently been suggested as a MOST alternative but have been evaluated over a limited number of land-cover and climate types. Examining the parameterizations’ applicability over other regions, particularly drylands that cover approximately 41% of terrestrial land surfaces, is a critical step toward implementing the parameterizations into LSMs. One year (1 January–31 December 2018) of eddy covariance measurements from a 10-m tower in southeastern Arizona and a 200-m tower in western Texas were used to determine how well the Rib parameterizations for friction velocity (), sensible heat flux (H), and turbulent kinetic energy (TKE) compare against MOST-derived parameterizations of these quantities. Independent of stability, wind speed regime, and season, the Rib and TKE parameterizations performed better than the MOST parameterizations, whereas MOST better represented H. Observations from the 200-m tower indicated that the parameterizations’ performance degraded as a function of height above ground. Overall, the Rib parameterizations revealed promising results, confirming better performance than traditional MOST relationships for kinematic (i.e., ) and turbulence (i.e., TKE) quantities, although caution is needed when applying the Rib H parameterizations to drylands. These findings represent an important milestone for the applicability of Rib parameterizations, given the large fraction of Earth’s surface covered by drylands. Significance Statement Weather forecasting models rely upon complex mathematical relationships to predict temperature, wind, and moisture. Monin–Obukhov similarity theory (MOST) has long been used to forecast these quantities near the land surface, even though MOST’s limitations are well known in the scientific community. Researchers have suggested an alternative to MOST called the bulk Richardson (Rib) approach. To allow for the Rib approach to be used in weather forecasting models, the approach needs to be tested over different land-cover and climate types. In this study, we applied the Rib approach to dry areas of the United States and found that the approach better represented turbulence variables than MOST relationships. These findings are an important step toward using Rib relationships in weather forecasting models.

Temporal variations of carbon and water fluxes in a subtropical mangrove forest: Insights from a decade-long eddy covariance measurement

Mangroves, highly efficient ecosystems in sequestering CO2, are strongly impacted by climate change. The lack of long-term observation in mangroves hinders the evaluation of seasonal and inter-annual variability in carbon and water fluxes and their responses to various environmental drivers. In this study, we measured net ecosystem CO2 exchange and evapotranspiration between the atmosphere and subtropical mangroves using the eddy covariance technique over a decade (2010–2019) in southern China. This mangrove forest acted as a strong CO2 sink, with annual net ecosystem production (NEP) ranging from 622.5 to 832.8 g C m−2 year−1. The annual evapotranspiration (ET) varied between 934.6 and 1004.9 mm year−1. During the study period, ET consistently remained higher in the wet season (May to October) compared to the dry season, while NEP did not exhibit consistent seasonal variation. Path analysis indicated that during the dry season, NEP was primarily influenced by global solar radiation and vapor pressure deficit. However, in the wet season, NEP was regulated by a combination of global solar radiation, vapor pressure deficit, air temperature, and tidal inundation time. Additionally, the promoting effect of global solar radiation on NEP decreased in the wet season, while the inhibitory influences of higher temperature and vapor pressure deficit on NEP intensified during the period. Unlike NEP, the dominant factors affecting ET (global solar radiation, air temperature, and vapor pressure deficit) and their intensities remained relatively consistent during both seasons. Furthermore, the relative importance of global solar radiation on NEP and ET increased over the decade, while the influence of tidal inundation time diminished. This study not only improves the understanding of the response of subtropical mangroves to climate change but also provides a valuable benchmark dataset to validate the interannual variability of mangrove carbon and water fluxes estimated from the models.

Accuracy evaluation of ET and its components from three remote sensing ET models and one process based hydrological model using ground measured eddy covariance and sap flow

Evapotranspiration (ET) and its components, transpiration (T) and soil evaporation (Es) are crucial to understand hydrological cycle and effective water loss in water resource management. Remote sensing models and process based hydrological models are two kinds of effective methods for evaluating ET and its partitions. Although the ability of remote sensing models to simulate ET has been widely verified, there is limited verification of ET components, especially T, which is the major component of ET. This research used ET obtained from eddy covariance system and T obtained from sap flow observation in an apricot orchard of north China to evaluate the accuracy of ET and its components from three remote sensing models (PT-JPL, PML, and SWH) and a process-based hydrological model (SHAW). The results showed that after calibrations, all the four models satisfactorily simulated total ET particularly at annual level. Among them, the PML model had the best fit efficiency, with the most satisfied R2 and NSE values. The PT-JPL estimated average ET was the most accurate, with the slope nearest to 1.0. While for the simulation of T, SHAW model had a better performance owing to the fact that SHAW model simulate plant transpiration using changes in soil water content. The average T/ET of the models for the growing season was close to the observed value of 0.70, except that the T/ET of SWH is high due to the lack of Ei calculation. Furthermore, nearly all the three remote sensing models underestimated ET in the dry spring and overestimated ET in the rainy summer. In comparison with the process-based SHAW model, it is suggested that remote sensing ET relies more heavily on the seasonal change of meteorological factors such as precipitation but less capable in reflecting the gradually delaying process of soil moisture in the dry season. Therefore, it is indicated that for future improvement of remote sensing ET models, adjustment of soil water availability in a much longer term is suggested at least for semi-humid and semi-arid ecosystems.

Ecosystem responses dominate the trends of annual gross primary productivity over terrestrial ecosystems of China during 2000–2020

The annual gross primary productivity (AGPP) forms the foundation for terrestrial carbon sink. AGPP trends stem from the combined impacts of environmental factors and ecosystem responses to those factors. These responses include model structure modifications altering both the structural properties (represented by leaf area index) and the functional properties (represented by per leaf AGPP). Understanding the contributions of ecosystem responses to AGPP trends is crucial for accurately assessing spatiotemporal variations in AGPP, which aids in effective carbon management. Based on eddy covariance measurements of AGPP in China, we generated AGPP during 2000–2020 by constructing yearly AGPP mapping schemes from site measurements and analyzed AGPP trends. The roles of ecosystem responses and their pathways were further elucidated by employing analysis of variance. Results showed that the total AGPP in China was 7.39 ± 0.62 PgC yr−1, with an increasing trend of 0.095 PgC yr−2. This increase primarily sourced from the total AGPP in Subtropical Evergreen Needleleaf Forest. AGPP trends spatially decreased from east to west, with most regions showing positive values. Structural and functional properties contributed similarly to AGPP trends, with varying regional contributions. The structural properties played a dominant role in north regions while the functional properties were more important in south and west regions. Ecosystem responses similarly determined the trends of ecosystem properties, which decreased from west to east. Ecosystem responses thus dominated AGPP trends through the structural properties in north regions but through the functional properties in south and west regions. Therefore, Chinese AGPP exhibited a spatially varied increasing trend during 2000–2020. Ecosystem responses dominated AGPP trends through divergent properties across regions. Our results highlight the significanc of ecosystem responses in AGPP trends and their divergent pathways across regions. This also provides independent data support for quantifying regional carbon budgets.