Eddy Covariance Flux Measurements Research Articles

Intercomparison of fast airborne ozone instruments to measure eddy covariance fluxes: spatial variability in deposition at the ocean surface and evidence for cloud processing

Abstract. The air–sea exchange of ozone (O3) is controlled by chemistry involving halogens, dissolved organic carbon, and sulfur in the sea surface microlayer. Calculations also indicate faster ozone photolysis at aqueous surfaces, but the role of clouds as an ozone sink is currently not well established. Fast-response ozone sensors offer opportunities to measure eddy covariance (EC) ozone fluxes in the marine boundary layer. However, intercomparisons of fast airborne O3 sensors and EC O3 fluxes measured on aircraft have not been conducted before. In April 2022, the Technological Innovation Into Iodine and GV Environmental Research (TI3GER) field campaign deployed three fast ozone sensors (gas chemiluminescence and a combination of UV absorption with coumarin chemiluminescence detection, CID) together with a fast water vapor sensor and anemometer to study iodine chemistry in the troposphere and stratosphere over Colorado and over the Pacific Ocean near Hawaii and Alaska. Here, we present an instrument comparison between the NCAR Fast O3 instrument (FO3, gas-phase CID) and two KIT Fast AIRborne Ozone instruments (FAIRO, UV absorption and coumarin CID). The sensors have comparable precision < 0.4 % Hz−0.5 (0.15 ppbv Hz−0.5), and ozone volume mixing ratios (VMRs) generally agreed within 2 % over a wide range of environmental conditions: 10 < O3 < 1000 ppbv, below detection < NOx < 7 ppbv, and 2 ppmv < H2O < 4 % VMR. Both instrument designs are demonstrated to be suitable for EC flux measurements and were able to detect O3 fluxes with exchange velocities (defined as positive for upward) as slow as −0.010 ± 0.004 cm s−1, which is in the lower range of previously reported measurements. Additionally, we present two case studies. In one, the direction of ozone and water vapor fluxes was reversed (vO3 = +0.134 ± 0.005 cm s−1), suggesting that overhead evaporating clouds could be a strong ozone sink. Further work is needed to better understand the role of clouds as a possibly widespread sink of ozone in the remote marine boundary layer. In the second case study, vO3 values are negative (varying by a factor of 6–10 from −0.036 ± 0.006 to −0.003 ± 0.004 cm s−1), while the water vapor fluxes are consistently positive due to evaporation from the ocean surface and spatially homogeneous. This case study demonstrates that the processes governing ozone and water vapor fluxes can become decoupled and illustrates the need to elucidate possible drivers (physical, chemical, or biological) of the variability in ozone exchange velocities on fine spatial scales (∼ 20 km) over remote oceans.

Use of light response curve parameters to estimate gross primary production capacity from chlorophyll indices of global observation satellite and flux data

The photosynthetic rate has a nonlinear relationship with PAR during the day. We previously developed an algorithm for estimating GPP capacity, which is defined GPP under low-stress condition, using light response curves (LRCs). In this study, we studied the characteristics of LRC parameters of the initial slope and the maximum gross photosynthesis rate (Pmax), and formulas to calculate Pmax from the relationship between the chlorophyll index of the green and near-infrared (NIR) bands (CIgreen) and the GPP capacity at PAR = 2000 μmol m−2 s−1 (GP2000) for nine vegetation types spanning tropical to subarctic climates on the Eurasian and North American continents using eddy covariance flux measurements and Moderate Resolution Imaging Spectrometer (MODIS) data. The slope of the relationship between CIgreen and GP2000 was highest for sites dominated by herbaceous plants such as open shrubland, savanna, and cropland (rice paddy); it was lower at sites dominated by woody plants. The yearly GPP/GPP capacity ratio was close to one in flux data. When the method was applied to satellite data, the daily GPP capacity exhibited a similar seasonal pattern to that of the Flux GPP and MODIS GPP products. Under high dryness conditions, Flux GPP showed the drop from the GPP capacity estimated from CIgreen and diurnal PAR data around noon, and they were nearly identical during the early morning and late afternoon. The instantaneous GPP capacity could be considered the baseline of the instantaneous GPP with stress-free conditions and important for quantifying midday depression at the sub-day scale.

Estimation of Urban Greenhouse Gas Fluxes from Mole Fraction Measurements Using Monin–Obukhov Similarity Theory

Abstract The purpose of this study is to determine whether urban greenhouse gas (GHG) fluxes can be quantified from tower-based mole fraction measurements using Monin–Obukhov similarity theory (MOST). Tower-based GHG mole fraction networks are used in many cities to quantify whole-city GHG emissions. Local-scale, micrometeorological flux estimates would complement whole-city estimates from atmospheric inversions. CO2 mole fraction and eddy-covariance flux data at an urban site in Indianapolis, Indiana, from October 2020 through January 2022 are analyzed. Using MOST flux–variance and flux–gradient relationships, CO2 fluxes were calculated using these mole fraction data and compared to the eddy-covariance fluxes. MOST-based fluxes were calculated using varying measurement heights and methods of estimating stability. The MOST flux–variance relationship method showed good temporal correlation with eddy-covariance fluxes at this site but overestimated flux magnitudes. Fluxes calculated using flux–gradient relationships showed lower temporal correlation with eddy-covariance fluxes but closer magnitudes to eddy-covariance fluxes. Measurement heights closer to ground level produce more precise flux estimates for both MOST-based methods. For flux–gradient methods, flux estimates are more accurate and precise when low-altitude measurements are combined with a large vertical separation between measurement heights. When stability estimates based on eddy-covariance flux measurements are replaced with stability estimates based on the weather station or net radiation data, the MOST-based fluxes still capture the temporal patterns measured via eddy covariance. Based on these results, MOST can be used to estimate the temporal patterns in local GHG fluxes at mole fraction tower sites, complementing the small number of eddy-covariance flux measurements available in urban settings.

A comparative analysis of OpenET for evaluating evapotranspiration in California almond orchards

The almond industry in California faces water management challenges that are being exacerbated by droughts, climate change, and groundwater sustainability legislation. The Tree-crop Remote sensing of Evapotranspiration eXperiment (T-REX) aims to explore opportunities to improve precision irrigation management for woody perennial cropping systems. Almond orchards in the California Central Valley were equipped with eddy covariance flux measurements to evaluate satellite remote sensing-based evapotranspiration (RSET) models. OpenET provides high-resolution (30-m spatial and daily temporal) RSET data, synthesizing decades of research for practical water management. This study provides an evaluation of OpenET performance at six almond sites covering a large range in soils, age, and variety. It also compares OpenET ensemble evapotranspiration (ET) data with applied irrigation and precipitation records over an additional 148 almond orchards located in the Central Valley of California. Results show OpenET models, including the ensemble ET value, produced reasonable and actionable ET values, with overall coefficient of determination (R2) and mean absolute error values of 0.73- and 0.95-mm d−1 at the daily time step, respectively. However, given the temporal sampling of Landsat (8-day revisit) and the interpolation methods used, the assessed ET models had difficulty in capturing short-term variability in almond ET; for example, the rapid decline in measured ET observed as a response to lack of irrigation preceding and during almond harvest. The study also drew attention to the spatial complexity in scenarios where irrigated orchards are surrounded by hot/dry areas, causing discrepancies between measured and modeled ET values. In comparison with irrigation records, OpenET ensemble ET was capable of quantifying water input (applied irrigation + precipitation) in almond orchards to within 13 % when evaluating monthly data. Initial results presented here reinforce the idea that RSET models, such as in OpenET, are powerful tools, yet their application requires nuanced understanding and careful consideration of local conditions.

Using eddy-covariance to measure the effects of COVID-19 restrictions on CO2 emissions in a neighborhood of Indianapolis, IN

Eddy-covariance (EC) flux measurements in Indianapolis were used to quantify the impact of the COVID-19 lockdown on CO and CO2 emissions from a highway and a suburban neighborhood. CO2 fluxes were measured for 6 weeks pre-lockdown (January 22, 2020–March 3, 2020) and during lockdown (March 25, 2020– May 5, 2020) using EC instrumentation at 41 m AGL. Fossil fuel CO2 emissions (CO2ff) were estimated by calculating eddy diffusivity to obtain CO flux and then scaling by the CO:CO2ff emissions ratio (RCO). Flux measurements segregated by wind direction were compared to hourly emissions from the 2020 Hestia inventory model. The lockdown CO2ff average weekday emissions from the highway estimated by EC decreased by 51.5 ± 10.9% (11.2 ± 2.2 µmol m−2 s−1) compared to pre-lockdown, similar to Hestia’s estimate 56 ± 7% (12 ± 1 µmol m−2 s−1). The EC measurements detected a significant (2.2 ± 0.7 µmol m−2 s−1) but smaller magnitude decrease in CO2ff emissions from the suburban neighborhood. The daily cycles of CO2ff emissions were significantly correlated with Hestia estimates from the highway but not from the suburbs. This study demonstrates that EC flux towers and high-resolution inventory models in regions with mixed and spatially heterogeneous sources can quantify abrupt changes in sector- and source-specific CO2 fluxes.

Winter harvesting reduces methane emissions and enhances blue carbon potential in coastal phragmites wetlands

Enhancing the ability of coastal blue carbon to accumulate and store carbon and reduce net greenhouse gas emissions is an essential component of a comprehensive approach for tackling climate change. The annual winter harvesting of Phragmites is common worldwide. However, the effects of harvesting on methane (CH4) emissions and its potential as an effective blue carbon management strategy have rarely been reported. In this study, the effects of winter Phragmites harvesting on the CH4 and carbon dioxide (CO2) fluxes and the underlying mechanisms in coastal Phragmites wetlands were investigated by comparing the eddy covariance flux measurements for three coastal wetlands with different harvesting and tidal flow conditions. The results show that harvesting can greatly reduce the CH4 emissions and the radiative forcing of CH4 and CO2 fluxes in coastal Phragmites wetlands, suggesting that winter Phragmites harvesting has great potential as a nature-based strategy to mitigate global warming. The monthly mean CH4 fluxes were predominantly driven by air temperature, gross primary productivity, and latent heat fluxes, which are related to vegetation phenology. Additionally, variations in the salinity and water levels exerted strong regulation effects on CH4 emissions, highlighting the important role of proper tidal flow restoration and resalinization in enhancing blue carbon sequestration potential. Compared with the natural, tidally unrestricted wetlands, the CH4 fluxes in the impounded wetland were less strongly correlated with hydrometeorological variables, implying the increased difficulties of predicting CH4 variations in impounded ecosystem. This study facilitates the improved understanding of carbon exchange in coastal Phragmites wetlands with harvesting or impoundment, and provides new insights into effective blue carbon management strategies beyond tidal wetland restoration for mitigating the effects of climate change.

Methodological advancement in deriving primary productivity and ecosystem respiration fluxes across different biomes

In this paper, we introduce a methodology that can improve the estimations of Gross Primary Productivity (GPP) and ecosystem Respiration (Reco) processes at a regional scale. This method is based on a satellite data-driven approach which is suitable for regions like India where there exists a serious shortage of ground-based observations of biospheric carbon fluxes (e.g., Eddy Covariance (EC) flux measurements). We relied on the Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance for capturing vegetation dynamics in the Light-Use Efficiency (LUE)-based vegetation model. Further, we utilised recently available satellite-based Solar-Induced Fluorescence (SIF) and other variables such as Soil Moisture (SM) and Soil Temperature (ST) to refine the predictions of GPP and Reco. The methodology involves establishing a relationship between SIF and GPP for different vegetation classes over India. The SIF-GPP relationship established across the biomes was then used to correct the GPP fluxes simulated by the LUE-based model. Similarly, the ecosystem respiration estimations by the model have undergone refinement by incorporating ST and SM information. This innovative method shows remarkable potential to improve biospheric CO2 uptake and release, especially for in situ data-constrained regions like India.• SIF-based information is introduced to a light-use efficiency-based vegetation model.• SIF-GPP relationship is established for major biomes across India.• SM and ST information is incorporated into the Reco simulations in the model.

A Novel Method for Characterizing the Inter‐ and Intra‐Lake Variability of CH4 Emissions: Validation and Application Across a Latitudinal Transect in the Alpine Region

AbstractLakes in the Alpine region are recognized as critical methane (CH4) emitters, but a robust characterization of the magnitude and variability of CH4 fluxes is missing. We developed a mobile platform for CH4 eddy covariance (EC) flux measurements to tackle this gap. The mobile system was deployed at nine lakes across a latitudinal transect in the Alps and validated by comparing the measured fluxes with fixed on‐shore EC station and to chamber and boundary layer flux estimates. Our approach was shown to be well suited to capture different CH4 emission pathways and to integrate the within lake variability, therefore, overcoming the limitations of other methods (e.g., boundary layer method). Additionally, this mobile system offers a tool to characterize inter‐lake variability of fluxes with consistent measurements. Methane fluxes were explained by dissolved nitrogen, total phosphorus, dissolved oxygen, seston and lake size. The highest fluxes and most substantial seasonal variability were found in a shallow low‐altitude lake in the Southern Alps. We suggest that characterizing the intra‐lake emission heterogeneity and consistent measurements for a better understanding of inter‐lake emission differences is fundamental for a solid estimate of freshwater CH4 budgets.

Sea spray emissions from the Baltic Sea: comparison of aerosol eddy covariance fluxes and chamber-simulated sea spray emissions

Abstract. To compare in situ and laboratory estimates of sea spray aerosol (SSA) production fluxes, we conducted two research campaigns in the vicinity of an eddy covariance (EC) flux tower on the island of Östergarnsholm in the Baltic Sea during May and August 2021. To accomplish this, we performed EC flux measurements for particles with diameters between 0.25 and 2.5 µm simultaneously with laboratory measurements using a plunging jet sea spray simulation chamber containing local seawater sampled close to the footprint of the flux tower. We observed a log-linear relationship between wind speed and EC-derived SSA emission fluxes, a power-law relationship between significant wave height and EC-derived SSA emission fluxes, and a linear relationship between wave Reynolds number and EC-derived SSA emission fluxes, all of which are consistent with earlier studies. Although we observed a weak negative relationship between particle production in the sea spray simulation chamber and seawater chlorophyll-α concentration and a weak positive relationship with the concentration of fluorescent dissolved organic matter in seawater, we did not observe any significant impact of dissolved oxygen on particle production in the chamber. To obtain an estimate of the size-resolved emission spectrum for particles with dry diameters between 0.015 and 10 µm, we combined the estimates of SSA particle production fluxes obtained using the EC measurements and the chamber measurements in three different ways: (1) using the traditional continuous whitecap method, (2) using air entrainment measurements, and (3) simply scaling the chamber data to the EC fluxes. In doing so, we observed that the magnitude of the EC-derived emission fluxes compared relatively well to the magnitude of the fluxes obtained using the chamber air entrainment method as well as the previous flux measurements of Nilsson et al. (2021) and the parameterizations of Mårtensson et al. (2003) and Salter et al. (2015). As a result of these measurements, we have derived a wind-speed-dependent and wave-state-dependent SSA parameterization for particles with dry diameters between 0.015 and 10 µm for low-salinity waters such as the Baltic Sea, thus providing a more accurate estimation of SSA production fluxes.

Water-balance-based evapotranspiration for 56 large river basins: A benchmarking dataset for global terrestrial evapotranspiration modeling

A thorough validation of global terrestrial evapotranspiration (ET) models requires reliable ground-observed ET data. While the open-access eddy-covariance flux measurements are widely used for such a purpose, they have certain drawbacks, and thus, other alternative publicly available reference datasets are urgently needed. Using remote sensing and ground-based observational data, this study provides water-balance-based evapotranspiration (ETwb) estimates for 56 large (>105 km2) river basins of the world over the 1983–2016 period. For each basin, the observed runoff and four different precipitation (Prec) data sources were combined with three types of terrestrial water storage change (dS) estimates, yielding altogether 12 annual ETwb time-series. An optimally merged ETwb time-series was eventually produced using the Bayesian-based three-cornered hat method. The relative uncertainty in the ETwb estimates is less than 10 % in most basins and it stems primarily from the uncertainty in Prec. In summary, this new ETwb dataset has the following advantages: i) The gauge undercatch in Prec was corrected, thus mitigating the well-known general underestimation of ETwb in mid- and high-latitudes; ii) Multiple Prec and dS datasets were combined to account for the uncertainty in the water balance approach, thereby enabling the quantification of the uncertainty in ETwb and its sources, and; iii) The ETwb dataset stretches more than three decades, making it appropriate for evaluating long-term trends in global ET models. This ETwb dataset is publicly available (https://data.tpdc.ac.cn/en/data/e010cd0d-0881-4e7e-9d63-d36992750b04) and may serve as a benchmarking tool to calibrate/validate large-scale ET models for an improved understanding of regional- and/or global-scale ET processes.

Modeling transpiration using solar-induced chlorophyll fluorescence and photochemical reflectance index synergistically in a closed-canopy winter wheat ecosystem

The joint use of solar-induced chlorophyll fluorescence (SIF) and the photochemical reflectance index (PRI) has been shown to improve gross primary productivity (GPP) estimation across various plant functional types. However, the utility of PRI in combination with SIF for transpiration (T) estimation has not yet been explored. Additionally, current SIF-driven transpiration models including linear models, semi-mechanical models (combination of canopy conductance, gC, derived from a SIF and vapor pressure deficit, VPD, driven linear model with the Penman-Monteith model), and hybrid models (combination of gC derived from a SIF and VPD driven machine learning model with the Penman-Monteith model) have rarely been mutually assessed. Based on concurrent remotely sensed SIF and PRI, and eddy covariance flux measurements during one growing season for a winter wheat ecosystem in northern China, we investigated the mediating effect of PRI on SIF-driven T estimation under different VPD conditions and compared the performance of linear, semi-mechanical, and hybrid models in estimating T. Our results showed that the mediating effect of PRI on T described in the SIF-driven linear, semi-mechanistic, and hybrid models was significant under high VPD conditions rather than under low VPD conditions. Specifically, based on T partitioned using an underlying water use efficiency method as a benchmark, the root mean square error (RMSE) value of the PRI-mediated linear, semi-mechanistic, and hybrid models was 28.01 W/m2, 22.25 W/m2, and 28.71 W/m2 lower, respectively, than those of the corresponding models without PRI when VPD was >1.5 kPa. Based on T partitioned using a transpiration estimation algorithm as a benchmark, these three models also exhibited a significant reduction in RMSE under high VPD conditions after considering PRI. The main rationale behind the PRI improvement is that PRI can track photosynthetic dynamics under high VPD conditions. Based on the simulation results of the Soil-Canopy-Observation of Photosynthesis and Energy fluxes (SCOPE) model, PRI can serve as an indicator for non-photochemical quenching (NPQ) within this ecosystem. Consequently, PRI can enhance the capability of SIF to characterize the energy dissipation of photosynthetically active radiation and help SIF to yield more accurate information on GPP and gc under high VPD conditions. Finally, the order of model performance in estimating T was generally hybrid model > semi-mechanistic model > linear model. Our findings show the effectiveness of PRI for improving SIF-driven transpiration estimation under high VPD conditions and provide a new hybrid model for estimating T from SIF.

Impact of meteorological variability on diurnal and seasonal net ecosystem productivity in a desert riparian forest ecosystem.

The desert riparian forests are susceptible to meteorological changes and contribute significantly to the net ecosystem productivity (NEP) variations of arid ecosystems. However, the responsive patterns of their NEP variations to the meteorological variabilities remain inadequately comprehended. To address this gap, we utilized seven years of eddy covariance flux measurements in a representative desert riparian forest to investigate the NEP variations and its response to changing meteorological factors across diverse temporal scales. The results revealed significant periodic variations in half-hourly NEP, with dominant cycles spanning from five hours to one year, with a principal oscillation period of one day. Key meteorological factors including global solar radiation (Rg), relative humidity (RH), air temperature (Ta), soil temperature (Ts), and vapor pressure deficit (VPD) exhibited synchronization with NEP on daily scales. This synchronization, coupled with the observed one-day periodic NEP variations, provides robust evidence supporting the existence of a circadian rhythm in the ecosystem carbon exchange of desert riparian forest regulated by meteorological conditions. Seasonal patterns were significant in the impact of Rg phase, Ta diurnal amplitude, and VPD diurnal amplitude on NEP diurnal amplitude and phase. The NEP diurnal amplitude significantly, directly, and positively affected daily NEP in both the dormant and growing seasons, whereas its phase yielded significant negative effects (P< 0.05). The averages, amplitudes, and phases of diurnal meteorological conditions controlled the daily NEP by regulating NEP diurnal amplitude and phase. These findings provide evidence that the variability in circadian rhythms, caused by the increase in diurnal Ta and VPD, significantly impact the daily NEP at an ecosystem scale. This study enriches our comprehension of the meteorological mechanisms governing diurnal and seasonal carbon uptake dynamics within desert riparian forests, providing fresh insights into the direct and indirect roles of climate change in shaping patterns of ecosystem carbon exchange.

Scalar Flux Profiles in the Unstable Atmospheric Surface Layer Under the Influence of Large Eddies: Implications for Eddy Covariance Flux Measurements and the Non‐Closure Problem

AbstractHow convective boundary‐layer (CBL) processes modify fluxes of sensible (SH) and latent (LH) heat and CO2 (Fc) in the atmospheric surface layer (ASL) remains a recalcitrant problem. Here, large eddy simulations for the CBL show that while SH in the ASL decreases linearly with height regardless of soil moisture conditions, LH and Fc decrease linearly with height over wet soils but increase with height over dry soils. This varying flux divergence/convergence is regulated by changes in asymmetric flux transport between top‐down and bottom‐up processes. Such flux divergence and convergence indicate that turbulent fluxes measured in the ASL underestimate and overestimate the “true” surface interfacial fluxes, respectively. While the non‐closure of the surface energy balance persists across all soil moisture states, it improves over drier soils due to overestimated LH. The non‐closure does not imply that Fc is always underestimated; Fc can be overestimated over dry soils despite the non‐closure issue.

Radiation and temperature drive diurnal variation of aerobic methane emissions from Scots pine canopy.

Methane emissions from plant foliage may play an important role in the global methane cycle, but their size and the underlying source processes remain poorly understood. Here, we quantify methane fluxes from the shoots of Scots pine trees, a dominant tree species in boreal forests, to identify source processes and environmental drivers, and we evaluate whether these fluxes can be constrained at the ecosystem-level by eddy covariance flux measurements. We show that shoot-level measurements conducted in forest, garden, or greenhouse settings; on mature trees and saplings; manually and with an automated CO2-, temperature-, and water-controlled chamber system; and with multiple methane analyzers all resulted in comparable daytime fluxes (0.144 ± 0.019 to 0.375 ± 0.074 nmol CH4 g-1 foliar d.w. h-1). We further find that these emissions exhibit a pronounced diurnal cycle that closely follows photosynthetically active radiation and is further modulated by temperature. These diurnal patterns indicate that methane production is associated with diurnal cycle of sunlight, indicating that this production is either a byproduct of photosynthesis-associated biochemical reactions (e.g., the methionine cycle) or produced through nonenzymatic photochemical reactions in plant biomass. Moreover, we identified a light-dependent component in stand-level methane fluxes, which showed order-of-magnitude agreement with shoot-level measurements (0.968 ± 0.031 nmol CH4 g-1 h-1) and which provides an upper limit for shoot methane emissions.

Carbon Dioxide and Heat Fluxes during Reforestation in the North Caucasus

Human impact on natural ecosystems has significantly increased in recent decades. As a result, the structure and functioning of ecosystems are seriously altered. This in turn affects regional weather and climate conditions through changes in the radiation, water, and carbon balance of ecosystems. Investigating the process of natural ecosystem restoration after disturbances is an important issue in the context of climate change. During monitoring observations of greenhouse gas (GHG) fluxes in a reforestation area in the Chechen Republic, new experimental data on their seasonal variability were obtained, and their sensitivity to changes in environmental conditions was assessed. Forest restoration and land reclamation are essential components of the low-carbon development and decarbonization strategy of the world economy. Observations of GHG fluxes were conducted in the reclaimed area, which was planted with tree seedlings. One year of eddy covariance flux measurements (May 2022–June 2023) demonstrated that CO2 uptake by the reforestation area in a humid continental climate with hot summers and cold winters is determined by the moisture conditions of the growing season. The cumulative net ecosystem exchange (NEE) for the entire measurement period at the carbon farm was 613.7 gC·m−2. The uptake of CO2 by the reforestation area was observed only due to active seedling growth during periods of sufficient soil moisture (May 2023). During this time, total NEE uptake was 48.7 gC·m−2. Sensible and latent heat fluxes were also dependent on weather conditions, primarily on incoming solar radiation and moisture conditions. For the successful implementation of climate projects aimed at the creation of artificial forest ecosystems with high CO2 uptake capacity, it is essential to develop appropriate hydro-meliorative measures that ensure a sufficient amount of available soil moisture.

Mango‐GPP: A Process‐Based Model for Simulating Gross Primary Productivity of Mangrove Ecosystems

AbstractMangrove ecosystems are becoming increasingly important in global climate mitigation. However, large gaps still exist in evaluating mangroves' gross primary productivity (GPP) due to reasons such as the specific influences, for example, temperature and salt stresses are poorly described in Earth System Models (ESMs). This study developed a process‐based biogeochemical model (Mango‐GPP) to improve the GPP simulation in natural and restored mangroves. The model integrates mangrove‐specific physiological processes, including the response to salt and temperature stresses, as well as the light‐use efficiency at different growing stages. Eddy covariance flux measurements at two natural sites and one restored site in China were used to calibrate and validate Mango‐GPP. The model was calibrated by inverse analysis approach based on two cases and independently validated against the other cases. The validation results showed that it was generally capable of simulating the seasonal and interannual GPP variations at different sites. The simulated daily and annual GPPs agreed well with the observations and yielded R2 of 0.67 and 0.96, with model efficiency of 0.64 and 0.93, respectively. In comparison, Mango‐GPP showed better performances than many current satellite‐based GPP products and ESMs. The model was more sensitive to solar radiation, carbon dioxide concentration, and leaf traits. Future improvements should focus on enhancing Mango‐GPP's descriptive power of key processes, and further simulating other carbon fluxes at regional scales. This work provides a model foundation for further simulating carbon exchanges between the atmosphere, mangrove, and ocean for studying the coastal wetland restoration on regional carbon neutrality.

Dynamics and biophysical controls of nocturnal water loss in a winter wheat-summer maize rotation cropland: a multi-temporal scale analysis

Nocturnal water loss (NWL), the evapotranspiration minus dew at night, has been identified to play a significant role in the surface water and energy balance. However, few studies have examined the variability in NWL in croplands with high water consumption across different timescales. Here, based on the eddy-covariance measurements of latent heat flux during nighttime, the dynamics and biophysical controls of NWL were examined in a winter wheat-summer maize rotation cropland in the North China Plain over a 5-year period (2008-2012) at nocturnal, seasonal and inter-annual scales. Besides, its relationship to drought was also revealed. Nocturnally, NWL showed a decreasing tendency from 20:00 to 05:00, with large diurnal variations in May. Seasonally, NWL was higher for wheat than that for maize. Interannually, the annual average daily NWL showed the same variation as the NWL during the wheat growth across the five years. The NWL was dominantly controlled by the difference between surface temperature and air temperature (Ts-Ta) nocturnally; Seasonally, NWL was influenced by vapor pressure deficit (VPD), wind speed (Ws) and site bulk surface conductance (gs) for wheat, and by VPD, gs and net radiation (Rn) for maize; NWL was mainly regulated by Ts-Ta and soil water content at 40 cm below the ground (VWC40) interannually. VPD and gs controlled NWL more during dry years. NDVI exhibited stronger control on NWL for wheat in wet years. The most favorable conditions for NWL during dry months were at Ta<16 ℃, VPD <0.8 kPa, and VWC40 <0.46 m3 m−3. NWL accounted for 4.0% of precipitation during the non-dry period but 19.6% during drought. Our results highlight the importance of biophysical factors in regulating NWL of the cropland ecosystem, and more attention should be paid to the influence of NWL on the water and carbon cycles in the context of climate change.

Direct observations of NOxemissions over the San Joaquin Valley using airborne flux measurements during RECAP-CA 2021 field campaign

Abstract. Nitrogen oxides (NOx) are principle components of air pollution and serve as important ozone precursors. As the San Joaquin Valley (SJV) experiences some of the worst air quality in the United States, reducing NOx emissions is a pressing need, yet quantifying current emissions is complicated due to a mixture of mobile and agriculture sources. We performed airborne eddy covariance flux measurements during the Re-Evaluating the Chemistry of Air Pollutants in California (RECAP-CA) field campaign in June 2021. Combining footprint calculations and land cover statistics, we disaggregate the observed fluxes into component fluxes characterized by three different land cover types. On average, we find emissions of 0.95 mg N m−2 h−1 over highways, 0.43 mg N m−2 h−1 over urban areas, and 0.30 mg N m−2 h−1 over croplands. The calculated NOx emissions using flux observations are utilized to evaluate anthropogenic emissions inventories and soil NOx emissions schemes. We show that two anthropogenic inventories for mobile sources, EMFAC (EMission FACtors) and FIVE (Fuel-based Inventory for Vehicle Emissions), yield strong agreement with emissions derived from measured fluxes over urban regions. Three soil NOx schemes, including the MEGAN v3 (Model of Emissions of Gases and Aerosols from Nature), BEIS v3.14 (Biogenic Emission Inventory System), and BDISNP (Berkeley–Dalhousie–Iowa Soil NO Parameterization), show substantial underestimates over the study domain. Compared to the cultivated soil NOx emissions derived from measured fluxes, MEGAN and BEIS are lower by more than 1 order of magnitude, and BDISNP is lower by a factor of 2.2. Despite the low bias, observed soil NOx emissions and BDISNP present a similar spatial pattern and temperature dependence. We conclude that soil NOx is a key feature of the NOx emissions in the SJV and that a biogeochemical-process-based model of these emissions is needed to simulate emissions for modeling air quality in the region.

Tracking autumn photosynthetic phenology on Tibetan plateau grassland with the green–red vegetation index

Accurate monitoring autumn photosynthetic phenology is essential for understanding carbon cycles. The broadband green-red vegetation index (GRVI) derived from broadband red and green reflectance has been increasingly used in this field. However, the performance of GRVI in large areas remains unclear. We evaluated the performance of the normalized vegetation index (NDVI), normalized difference greenness index (NDGI), the near-infrared reflectance of vegetation (NIRv), solar-induced chlorophyll fluorescence (SIF) and GRVI in tracking autumn photosynthetic phenology of alpine grassland at flux sites and the entire Tibetan Plateau (TP). The results revealed that GRVI (R2 = 0.42, RMSE = 7.68 d and Bias = 4.39 d) performed comparable with SIF (R2 = 0.45, RMSE = 5.79 d and Bias = 0.71 d) in extracting the end of the photosynthetically active season (EOS) with eddy covariance flux measurements as reference. On contrary, a systematically later EOS was estimated by NDVI (Bias = 20.35 d), NDGI (Bias = 13.62 d) and NIRv (Bias = 6.56 d). The application example on the TP collaborated these findings. The divergent performances between indicators were rooted in the photosynthesis downregulation in autumn which is jointly controlled by canopy structure and physiology. Due to the fact that NDVI, NDGI and NIRv primarily depict vegetation structure, while the use of SIF, which represents vegetation photosynthetic physiology, is influenced by the limited spatial resolution and temporal coverage. Our study highlights the unique advantage of GRVI over other existing satellite indicators for estimating autumn photosynthetic phenology with high resolution and long-time span. We suggest revisiting the dynamics of autumn photosynthetic phenology using GRVI, which has significant implications on carbon uptake studies.

Snow–vegetation–atmosphere interactions in alpine tundra

Abstract. The interannual variability of snow cover in alpine areas is increasing, which may affect the tightly coupled cycles of carbon and water through snow–vegetation–atmosphere interactions across a range of spatio-temporal scales. To explore the role of snow cover for the land–atmosphere exchange of CO2 and water vapor in alpine tundra ecosystems, we combined 3 years (2019–2021) of continuous eddy covariance flux measurements of the net ecosystem exchange of CO2 (NEE) and evapotranspiration (ET) from the Finse site in alpine Norway (1210 m a.s.l.) with a ground-based ecosystem-type classification and satellite imagery from Sentinel-2, Landsat 8, and MODIS. While the snow conditions in 2019 and 2021 can be described as site typical, 2020 features an extreme snow accumulation associated with a strong negative phase of the Scandinavian pattern of the synoptic atmospheric circulation during spring. This extreme snow accumulation caused a 1-month delay in melt-out date, which falls in the 92nd percentile in the distribution of yearly melt-out dates in the period 2001–2021. The melt-out dates follow a consistent fine-scale spatial relationship with ecosystem types across years. Mountain and lichen heathlands melt out more heterogeneously than fens and flood plains, while late snowbeds melt out up to 1 month later than the other ecosystem types. While the summertime average normalized difference vegetation index (NDVI) was reduced considerably during the extreme-snow year 2020, it reached the same maximum as in the other years for all but one of the ecosystem types (late snowbeds), indicating that the delayed onset of vegetation growth is compensated to the same maximum productivity. Eddy covariance estimates of NEE and ET are gap-filled separately for two wind sectors using a random forest regression model to account for complex and nonlinear ecohydrological interactions. While the two wind sectors differ markedly in vegetation composition and flux magnitudes, their flux response is controlled by the same drivers as estimated by the predictor importance of the random forest model, as well as by the high correlation of flux magnitudes (correlation coefficient r=0.92 for NEE and r=0.89 for ET) between both areas. The 1-month delay of the start of the snow-free season in 2020 reduced the total annual ET by 50 % compared to 2019 and 2021 and reduced the growing-season carbon assimilation to turn the ecosystem from a moderate annual carbon sink (−31 to −6 gC m−2 yr−1) to a source (34 to 20 gC m−2 yr−1). These results underpin the strong dependence of ecosystem structure and functioning on snow dynamics, whose anomalies can result in important ecological extreme events for alpine ecosystems.