Eddy Covariance System Research Articles

Characteristic time scales of evaporation from a subarctic reservoir

AbstractWater bodies such as lakes and reservoirs affect the regional climate by acting as heat sinks and sources through the evaporation of substantial quantities of water over several months of the year. Unfortunately, energy exchange observations between deep reservoirs and the atmosphere remain rare in northeastern North America, which has one of the highest densities of water bodies in the world. This study characterizes the dynamics of turbulent heat fluxes by analysing in‐situ observations of a compact and dimictic reservoir (50.69° N, 63.24° W) located in a subarctic environment. The reservoir is characterized by a mean depth of 44 m and a surface area of 85 km2. Two eddy covariance (EC) systems, one on a raft and one onshore, were deployed from 27 June 2018 to 12 June 2022. The thermal regime of the reservoir was monitored using two vertical chains of thermistors. Results indicate a mean annual evaporation rate of 590 ± 66 mm, which is equivalent to ≈51% of the annual precipitation, with 84% of the evaporation occurring at a high rate from August to freeze‐up in late December through episodic pulses. It was difficult to close the energy balance because of the magnitude and the large time lag of the heat storage term. To circumvent this problem, we opted to perform calculations for a year that started from the first of March, as the heat storage in the water column was at its lowest at that point and could thus be ignored. From June to December, monthly Bowen ratios increased from near‐zero negative values to about 1.5. After September, due to smaller vapour pressure deficits, latent heat fluxes steadily decreased until the reservoir had a complete ice cover. Opposite diurnal cycles of sensible and latent heat fluxes were revealed during the open water period, with sensible heat fluxes peaking at night and latent heat fluxes peaking in the afternoon.

Dataset of Comparative Observations for Land Surface Processes over the Semi-Arid Alpine Grassland against Alpine Lakes in the Source Region of the Yellow River

Thousands of lakes on the Tibetan Plateau (TP) play a critical role in the regional water cycle, weather, and climate. In recent years, the areas of TP lakes underwent drastic changes and have become a research hotspot. However, the characteristics of the lake-atmosphere interaction over the high-altitude lakes are still unclear, which inhibits model development and the accurate simulation of lake climate effects. The source region of the Yellow River (SRYR) has the largest outflow lake and freshwater lake on the TP and is one of the most densely distributed lakes on the TP. Since 2011, three observation sites have been set up in the Ngoring Lake basin in the SRYR to monitor the lake-atmosphere interaction and the differences among water-heat exchanges over the land and lake surfaces. This study presents an eight-year (2012–19), half-hourly, observation-based dataset related to lake–atmosphere interactions composed of three sites. The three sites represent the lake surface, the lakeside, and the land. The observations contain the basic meteorological elements, surface radiation, eddy covariance system, soil temperature, and moisture (for land). Information related to the sites and instruments, the continuity and completeness of data, and the differences among the observational results at different sites are described in this study. These data have been used in the previous study to reveal a few energy and water exchange characteristics of TP lakes and to validate and improve the lake and land surface model. The dataset is available at National Cryosphere Desert Data Center and Science Data Bank.

Temporal Dynamics of CO2 Fluxes Measured with Eddy Covariance System in Maize, Winter Oilseed Rape and Winter Wheat Fields

The full understanding of variation and temporal changes in carbon dioxide (CO2) fluxes in cropland may contribute to a reduction in CO2 emissions from agriculture. The aim of this study was to determine the CO2 exchange intensity in the three most popular crops in Poland. The CO2 fluxes in summer maize, winter oilseed rape and winter wheat fields were measured using the eddy covariance system. The seasonal dynamics of CO2 fluxes for all studied crops varied from each other due to individual dynamics in atmospheric CO2 assimilation of each species through the growing season. The weighted average values of CO2 fluxes calculated for the entire vegetation period were −22.22 µmol CO2 m−2 s−1, −14.27 µmol CO2 m−2 s−1 and −11.95 µmol CO2 m−2 s−1 for maize, oilseed rape and wheat, respectively. All the studied agro-ecosystems were carbon sinks during the growing season. The highest negative values of CO2 fluxes (−36.31 µmol CO2 m−2 s−1 and −33.56 µmol CO2 m−2 s−1) were observed in the maize field due to the high production of biomass. However, the maize field was also the most significant carbon source due to slow growth of plants at the beginning of the growing season, and due to leaving the field fallow after harvest until the next sowing. In these two periods, the CO2 fluxes ranged from 0.59 µmol CO2 m−2 s−1 to 3.72 µmol CO2 m−2 s−1. CO2 exchange over wheat and oilseed rape fields was less intense, but more even throughout the growing season. In the wheat field, the CO2 fluxes ranged from −1.70 µmol CO2 m−2 s−1 to −23.49 µmol CO2 m−2 s−1 and in the oilseed rape field they ranged from −1.40 µmol CO2 m−2 s−1 to −22.08 µmol CO2 m−2 s−1. In addition, the catch crop in the oilseed rape field contributed to the intensive absorption of CO2 after harvesting the main crop.

Estimating Carrot Gross Primary Production Using UAV-Based Multispectral Imagery

Gross primary productivity (GPP) is an essential parameter to assess the efficiency of terrestrial ecosystems on carbon transfer. Although GPP is regularly measured with eddy covariance (EC) systems, these are restricted to the tower footprint area, and remote sensing (RS) products have estimated GPP using multispectral vegetation indexes (VIs) from farms to whole ecosystems. Indeed, nowadays, unmanned aerial vehicle (UAV)-based RS technology is becoming more accessible. Accordingly, we propose the estimation of GPP using VIs at high spatial resolutions using UAVs and multi-spectral cameras. A small typical farm in Colombia was cultivated with carrot as our base crop. An EC system was installed to estimate GPP and was used as a reference. A total of 24 VIs from UAV-based RS products were selected and compared with the GPP of the EC system. A cross-validation process was performed, and seven VIs obtained a high R2 score (0.76–0.78). The accumulated GPP, estimated with the best index (NIRv) was 520.3 g C m−2, while the GPP-EC estimate was 580.4 g C m−2 (10.3% error). This work showed that it is possible to estimate the GPP of carrot crops using UAV-based RS, VIs, and linear regression models, which can be used in further research on GPP using UAVs.

Random Errors in the Stable Boundary Layer: Implications for Modern Observational Techniques

Abstract For decades, stable boundary layer (SBL) turbulence has proven challenging to measure, parameterize, simulate, and interpret. Uncrewed aircraft systems (UAS) are becoming a reliable method to sample the atmospheric boundary layer, offering new perspectives for understanding the SBL. Moreover, continual computational advances have enabled the use of large-eddy simulations (LES) to simulate the atmosphere at ever-smaller scales. LES is therefore a powerful tool in establishing a baseline framework to understand the extent to which vertical profiles from UAS can represent larger-scale SBL flows. To quantify the representativeness of observations from UAS profiles and eddy-covariance observations within the SBL, we performed a random error analysis using a suite of six large-eddy simulations for a wide range of stabilities. We combine these random error estimates with emulated observations of a UAS and eddy-covariance systems to better inform future observational studies. For each experiment, we estimate relative random errors using the so-called relaxed filtering method for first- and second-order moments as functions of height and averaging time. We show that the random errors can be on the same order of magnitude as other instrument-based errors due to bias or dynamic response. Unlike instrument errors, however, random errors decrease with averaging time. For these reasons, we recommend coupling UAS observations with other ground-based instruments as well as dynamically adjusting the UAS vertical ascent rate to account for how errors change with height and stability. Significance Statement Weather-sensing uncrewed aircraft systems are rapidly being realized as effective tools to collect valuable observations within the atmospheric boundary layer. To fully capitalize on this novel observational technique, it is necessary to develop an understanding of how well their observations can represent the surrounding atmosphere across various spatial and temporal scales. In this study we quantify the representativeness of atmospheric observations in the stable boundary layer by evaluating the random errors for parameters such as temperature, wind speed, and fluxes as estimated from a suite of large-eddy simulations. Our results can better inform future studies utilizing uncrewed aircraft systems by highlighting how random errors in their observations relate to vertical ascent rate, atmospheric stability, and measurement height.

Comparison of sensible and latent heat fluxes from optical-microwave scintillometers and eddy covariance systems with respect to surface energy balance closure

The eddy-covariance (EC) method has been widely used to measure sensible (H) and latent (LE) heat fluxes, however, it has limited spatial representativeness (hundreds of meters’ scale). Optical-microwave scintillometers (OMS) can directly measure kilometer-scale H and LE, which can be used to validate satellite remote sensing products and model simulations. Therefore, quantitative comparisons and analyses between H and LE measured by OMS and EC are important. In this study, the H and LE values measured by OMS and EC were compared and analyzed over two different surfaces: alpine meadow (homogeneous underlying surface) and irrigated cropland (heterogeneous underlying surface). Results showed that the H (LE) difference between OMS and EC measurements was approximately 17% (18%) and 27% (37%) over the homogeneous and heterogeneous underlying surfaces during the vegetation growing season, respectively. Furthermore, the difference in measured LE from OMS and EC (ΔLEOMS−EC) is caused by the combination of the difference in land surface hydrothermal conditions (LSHCs) and the different principles of OMS and EC measurements. On the one hand, ΔLEOMS−EC varied with the changing LSHCs within the instruments’ source areas over the heterogeneous surface, and the value could be decreased by irrigation and precipitation events. On the other hand, OMS averages large-scale measurements over both space and time and does not need to capture all-scale eddies (e.g., large-scale eddies), so the occurrence of advection increases ΔLEOMS−EC. Finally, compared with that of EC system, the energy balance closure ratio of OMS system was similar over the homogeneous underlying surface but significantly increased by 26% over the heterogeneous underlying surface. These results indicate that OMS has a potential advantage in measuring sensible and latent heat fluxes, particularly over heterogeneous underlying surfaces.

On securing continuity of eddy covariance flux time-series after changing the measurement height: Correction for flux differences due to the footprint difference

During long-term flux monitoring, we occasionally encounter situations where the existing measurement setup must be changed. Considering that one of the main purposes of long-term flux monitoring is identifying long-term variability and trends, which are relatively small, it is important to understand or correct for flux differences caused by the measurement setting changes. In this study, before changing the flux measurement height of the CRK (Cheorwon Rice paddy, Korea) site from 10 m to 5 m, we installed another eddy covariance system at 5 m and compared the sensible and H2O/CO2/CH4 fluxes at both heights from April 2020 to April 2021. Although random errors could explain substantial portions of the flux differences between the two heights, it was confirmed that systematic errors also existed because the means of the flux differences were not zero, and the distributions of the flux differences were also significantly skewed. Further analysis showed that the primary cause of these systematic errors was the footprint differences rather than the difference in turbulent transport between the two heights. Based on the results, we proposed a strategy for securing the continuity of the flux time-series, which is necessary to understand the long-term variability using all data before and after a measurement change, and discussed the necessity of such a comparative observation.

Development of a Benchmark Eddy Flux Evapotranspiration Dataset for Evaluation of Satellite-Driven Evapotranspiration Models Over the CONUS

A large sample of ground-based evapotranspiration (ET) measurements made in the United States, primarily from eddy covariance systems, were post-processed to produce a benchmark ET dataset. The dataset was produced primarily to support the intercomparison and evaluation of the OpenET satellite-based remote sensing ET (RSET) models and could also be used to evaluate ET data from other models and approaches. OpenET is a web-based service that makes field-delineated and pixel-level ET estimates from well-established RSET models readily available to water managers, agricultural producers, and the public. The benchmark dataset is composed of flux and meteorological data from a variety of providers covering native vegetation and agricultural settings. Flux footprint predictions were developed for each station and included static flux footprints developed based on average wind direction and speed, as well as dynamic hourly footprints that were generated with a physically based model of upwind source area. The two footprint prediction methods were rigorously compared to evaluate their relative spatial coverage. Data from all sources were post-processed in a consistent and reproducible manner including data handling, gap-filling, temporal aggregation, and energy balance closure correction. The resulting dataset included 243,048 daily and 5,284 monthly ET values from 194 stations, with all data falling between 1995 and 2021. We assessed average daily energy imbalance using 172 flux sites with a total of 193,021 days of data, finding that overall turbulent fluxes were understated by about 12% on average relative to available energy. Multiple linear regression analyses indicated that daily average latent energy flux may be typically understated slightly more than sensible heat flux. This dataset was developed to provide a consistent reference to support evaluation of RSET data being developed for a wide range of applications related to water accounting and water resources management at field to watershed scales.

Comparison of three modified models in evapotranspiration and its components over a rainfed spring maize cropland on the Loess Plateau, China

Accurately estimated evapotranspiration (ET) and its components in the growing season of cropland are required to optimize agricultural practices and improve water use efficiency. In this study, we developed a modified Shuttleworth–Wallace (SWm) model, a modified Priestley-Taylor (PTm) model, and a modified dual crop coefficient (DKm) model based on the characteristics of a rainfed spring maize cropland on the Loess Plateau. The estimated ET and its components by the three models were compared with the measured ET and its components using an eddy covariance system and microlysimeters. The results indicated that the three models performed well in estimating the 30 min ET and daily ET and its components during the growing seasons of the rainfed spring maize field in 2015 and 2016. The diurnal course of the measured 30 min ET was closest to that of the estimated 30 min ET from the DKm model, and this model performed best to estimate 30 min ET with a regression coefficient (b0) of 1.01 (R2 = 0.89) in 2015 and 0.97 (R2 = 0.88) in 2016. According to statistical analysis, the PTm model outperformed the other two modified models on estimating daily ET with b0 of 0.97 (R2 = 0.89) and 0.94 (R2 = 0.89) and daily plant transpiration (Tp) with b0 of 1.07 (R2 = 0.93) and 1.11 (R2 = 0.93) during the both growing seasons. The performance of the three modified models on daily soil evaporation (Es) was not as good as that of daily ET and Tp, and the DKm model performed best to reproduce daily Es with a b0 of 0.91 (R2 = 0.37) and 0.88 (R2 = 0.54) in 2015 and 2016, respectively. As the PTm model required the fewest input variables among the three modified models, this model was finally recommended for rainfed spring maize on the Loess Plateau, China.

Different spatial and temporal arrangements for validating the latent heat flux obtained using the MOD16 product in a forest in the Western Amazon

The Amazon rainforest is an important source of evapotranspiration and is essential in the global atmospheric circulation and hydrological cycle. However, measurements on vegetated surfaces are difficult, and methods such as remote sensing are promising in the micrometeorological data area. We aimed to assess the applicability of the MOD16 product to estimate evapotranspiration in a vegetated area. MOD16 data (LEMOD) derive from the Moderate Resolution Imaging Spectroradiometer sensor. For comparison, we used data from the eddy covariance system (LEEC) of the tower of the Large-scale Biosphere-Atmosphere Program in the Amazon of the Jaru Biological Reserve. To measure the linearity among variables, the Pearson correlation test (α=0.05) was used, and the T test was applied to assess the statistical significance between LEMOD and LEEC means (α=0.05); the root mean square error was also calculated. Regarding LEMOD, a similarity was found between the annual means of model and data, in which the best LEMOD estimates were obtained for annual averaging, when mean values were 96.51 W m–2 (0.7% lower than LEEC). LEMOD overestimates ranged from 1.05 W m–2 (1%) in 2007 to 15.78 W m–2 (18%) in 2005. Variations in short periods were not represented by the LEMOD product.

Methane dynamics from a mixed plantation of north China: Observation using closed-path eddy covariance method.

Although an important greenhouse gas, methane flux in hilly forest ecosystems remains unclear. By using closed-path eddy covariance systems, methane flux was measured continuously from 2017 to 2019 in a mixed plantation in the Xiaolangdi area of the Yellow River in North China. The methane flux footprint and its diurnal and monthly variations were analysed, and its characteristics on rainy days are discussed. The results showed that: (a) the observation data were reliable with good spatial representation (b) The methane flux in the mixed plantation ecosystem had obvious diurnal and seasonal variations: the monthly average diurnal variation of the methane flux had a single-peak; the methane flux value was source in the daytime and sink at night. The daily mean maximum value of methane flux in growing season was lower than that in non-growing season with the maximum value appearing in March, and the minimum value in October. (c) The forest is an atmospheric CH4 source with the annual emission in 2017 of (3.31g C·m-2·year -1) >2019 (2.94g C·m-2·year-1) >2018 (2.81g C·m-2·year -1), and the main influencing factor was precipitation. Rainfall affected CH4 flux with a lag period of approximately three days. Rainfall also changed the balance of CH4 flux between sink or source according to precipitation intensity and frequency.

The effect of relative humidity on eddy covariance latent heat flux measurements and its implication for partitioning into transpiration and evaporation

While the eddy covariance (EC) technique is a well-established method for measuring water fluxes (i.e., evaporation or 'evapotranspiration’, ET), the measurement is susceptible to many uncertainties. One such issue is the potential underestimation of ET when relative humidity (RH) is high (>70%), due to low-pass filtering with some EC systems. Yet, this underestimation for different types of EC systems (e.g. open-path or closed-path sensors) has not been characterized for synthesis datasets such as the widely used FLUXNET2015 dataset. Here, we assess the RH-associated underestimation of latent heat fluxes (LE, or ET) from different EC systems for 163 sites in the FLUXNET2015 dataset. We found that the LE underestimation is most apparent during hours when RH is higher than 70%, predominantly observed at sites using closed-path EC systems, but the extent of the LE underestimation is highly site-specific. We then propose a machine learning based method to correct for this underestimation, and compare it to two energy balance closure based LE correction approaches (Bowen ratio correction, BRC, and attributing all errors to LE). Our correction increases LE by 189% for closed-path sites at high RH (>90%), while BRC increases LE by around 30% for all RH conditions. Additionally, we assess the influence of these corrections on ET-based transpiration (T) estimates using two different ET partitioning methods. Results show opposite responses (increasing vs. slightly decreasing T-to-ET ratios, T/ET) between the two methods when comparing T based on corrected and uncorrected LE. Overall, our results demonstrate the existence of a high RH bias in water fluxes in the FLUXNET2015 dataset and suggest that this bias is a pronounced source of uncertainty in ET measurements to be considered when estimating ecosystem T/ET and WUE.

True eddy accumulation – Part 2: Theory and experiment of the short-time eddy accumulation method

Abstract. A new variant of the eddy accumulation method for measuring atmospheric exchange is derived, and a prototype sampler is evaluated. The new method, termed short-time eddy accumulation (STEA), overcomes the requirement of fixed accumulation intervals in the true eddy accumulation method (TEA) and enables the sampling system to run in a continuous flow-through mode. STEA enables adaptive time-varying accumulation intervals, which improves the system's dynamic range and brings many advantages to flux measurement and calculation. The STEA method was successfully implemented and deployed to measure CO2 fluxes over an agricultural field in Braunschweig, Germany. The measured fluxes matched very well against a conventional eddy covariance system (slope of 1.04, R2 of 0.86). We provide a detailed description of the setup and operation of the STEA system in the continuous flow-through mode, devise an empirical correction for the effect of buffer volumes, and describe the important considerations for the successful operation of the STEA method. The STEA method reduces the bias and uncertainty in the measured fluxes compared to conventional TEA and creates new ways to design eddy accumulation systems with finer control over sampling and accumulation. The results encourage the application of STEA for measuring fluxes of more challenging atmospheric constituents such as reactive species. This paper is Part 2 of a two-part series on true eddy accumulation.

Quantifying winter wheat evapotranspiration and crop coefficients under sprinkler irrigation using eddy covariance technology in the North China Plain

Sprinkler irrigation has been widely used for winter wheat crops in the North China Plain (NCP) to maintain high crop yield and enhance water use efficiency because water resources are scarce. Determining the accurate crop evapotranspiration (ETc) and crop coefficient (Kc) of winter wheat is critical for optimizing sprinkler irrigation management. In this study, an eddy covariance system was deployed in a sprinkler irrigation field to measure the energy balance components (net radiation Rn, soil heat flux G, sensible heat H, latent heat LE) and calculate ETc and Kc. The wheat growth parameters and yield were also investigated. The results showed that the energy balance closure rate, the rate of daily (LE + H) to (Rn - G), was 0.89, indicating a sufficient energy balance closure. Seasonal total LE flux accounted for 67% of the total available energy (Rn - G). The seasonal ETc values were 434 and 396 mm during the 2019–2020 and 2020–2021 growing seasons, respectively, which are comparable to those (400–470 mm) under surface irrigation. The mean daily ETc was 0.87, 0.46, 3.24, 4.81, and 3.94 mm day−1 in the early growth, overwintering, rapid growth, middle, and late growth stages, respectively. The corresponding Kc values were 0.77, 0.47, 1.09, 1.14 and 0.70. The Kc values were close to those reported in the FAO 56 paper. The Kc is positively related to the leaf area index (LAI), and their relationship was fitted as Kc=0.87+0.14lnLAI(R2=0.96) for an LAI range of 1–7, where ln is a natural logarithm function. The measured wheat grain yield was 9100 and 9857 kg ha−1, and the water productivity (WP) was 2.07 and 2.49 kg m−3 in the two seasons, respectively. Both factors are approximately 30% higher than those (7200 kg ha−1, 1.68 kg m−3) reported by other studies. Considering the high wheat yield and comparable crop ETc, sprinkler irrigation is recommended for application on the NCP. The seasonal ETc of 400–430 mm and the obtained Kc values in this study can be used to make sprinkler irrigation scheduling for winter wheat on the NCP.

A dataset of carbon and water fluxes and micrometerological elements in Danzhou rubber plantation (2010-2018)

Rubber plantation is one of the main artificial vegetation types in Hainan Island. As an important forest ecosystem in China’s tropical regions, rubber plantation plays an important role in the regional carbon and water cycle. Measuring the carbon and water fluxes is critical for estimating the carbon and water balance and energy exchange of rubber plantation. Since November 2009, near-surface CO2 and H2O fluxes observation have been carried out in Danzhou rubber plantation, including eddy covariance system and meteorological observation system. Up to now, it has accumulated flux and meteorological observation data for 12 years. This dataset organizes the rubber plantation flux and conventional meteorological data in Danzhou from 2010 to 2018, including net ecosystem productivity, ecosystem respiration, gross ecosystem productivity, latent heat flux, sensible heat flux, evapotranspiration, air temperature, air relative humidity, vapor pressure, wind speed, wind direction, atmospheric pressure, total radiation, net radiation, photosynthetically active radiation, soil temperature, soil water content and precipitation. This dataset can provide basis and reference for ecosystem evaluation and sustainable development planning in economic and social.

Net Ecosystem Exchange of Carbon Dioxide in Rice Summer – Autumn crop of the lower Mekong delta, Vietnam

This study describes the net ecosystem exchange of CO2 in a rice summer-autumn growth season in the lower Mekong Delta, Vietnam. Eddy Covariance and bio-meteorological systems were used to continually record the net CO2 exchange between the paddy field and the atmosphere, together with environmental variables affecting the exchange rate. Rice plant growth (height, above-, and belowground biomass) was measured every 15 days during the entire crop. Our study paddy field emitted CO2 at the mean rate of 5.06 ± 2.49 µmol m-2 s-1, while the rate of CO2 capturing was 18.65 ± 11.76 µmol m-2 s-1 throughout the entire crop. The paddy field acted as a CO2 sink almost during the crop. The quick increase of leaf area from the 15th to the 60th day led to the rise in CO2 influx. The decrease in influx rate after the 60th day resulted from the senescence and death of the aboveground biomass. The drainage in the last ten days of the crop probably contributed to the increase in CO2 effluxes and hence, turned the paddy field into a CO2 source. Ambient temperature was an important factor affecting the rate of CO2 exchange affects the photosynthesis, plant respiration, photorespiration, and organic matter decomposition.

COVID-19 pandemic underscores role of green space in urban carbon dynamics

For Detroit Michigan the arrival of COVID-19 led to intensive measures to prevent further spread of the virus resulting in consequent changes in traffic and energy use. We take advantage of these different emission scenarios to explore CO2 dynamics in a postindustrial city with a declining population and increasing green space. We present atmospheric CO2 concentration and net urban ecosystem exchange of CO2 (NUE) from a typical eddy covariance system and canopy greenness from a field camera on the Wayne State University campus in midtown Detroit. We categorized our study period (January 18, 2020–July 31, 2020) into three subperiods associated with the state-wide shelter-in-place order. Our results support that the city was a net carbon source throughout the period, particularly during the shelter-in-place period, although reduced traffic lowered CO2 concentrations and NUE. However, during the post-order period when traffic was highest, atmospheric CO2 concentrations and NUE were lowest, suggesting that the greening of urban vegetation may have greater carbon mitigation potential than lowering anthropogenic carbon emissions through traffic reductions.

Characteristics of Evapotranspiration and Crop Coefficient Correction at a Permafrost Swamp Meadow in Dongkemadi Watershed, the Source of Yangtze River in Interior Qinghai–Tibet Plateau

The Qinghai–Tibet Plateau (QTP), known as the Earth’s third pole, is highly sensitive to climate change. Various environmental degradation has occurred due to the effects of climate warming such as the degradation of permafrost and the thickening of active layers. Evapotranspiration, as a key element of hydrothermal coupling, has become a key factor of the plateau environment for deciphering deterioration, and the FAO P-M model has a good physical foundation and simple model data requirements as a primary tool to study the plateau evapotranspiration. There has been a large research base, but the estimation of evapotranspiration in alpine regions is still subject to many uncertainties. This is reflected in the fact that the classification of underlying surface types has not been sufficiently detailed and the evapotranspiration characteristics of some special underlying surface types are still unclear. Therefore, in this work, we modified the FAO P-M coefficients based on the characteristics of actual evapotranspiration measured by the Eddy covariance system and the key influencing factors to better simulate the actual evapotranspiration in alpine swamp meadow. The results were as follows: (1) Both ETa measured by the Eddy covariance system and ET0 calculated by FAO P-M showed the same trend at the daily and annual scales and hysteresis was confirmed to exist, so the error caused by hysteresis should be considered in further research. (2) The annual ETa was 566.97 mm and annual ETa/P was 0.76, and about 11.19% of ETa occurred during the night. The ETa was 2.15 during the non-growing seasons, implying that a large amount of soil water was released into the air by evapotranspiration. (3) The evapotranspiration characteristics of alpine swamp meadow are formed under the following conditions: control of net radiation (Rn) affected by VPD during the growing season and affected by soil temperature and humidity during the non-growing season. Precipitation and soil water content are no longer the main controlling factors of evapotranspiration during the growing season at the alpine swamp meadow as the volume soil water content tends to saturate. (4) The basic corrected Kc was 1.14 during the initial and mid-growing season, 1.05 during the subsequent growing season, and 0–0.25 during the non-growing season, and the correction factor process can also provide ideas for correcting the Kc of other vegetation.

Assessment of crop evapotranspiration and deep percolation in a commercial irrigated citrus orchard under semi-arid climate: Combined Eddy-Covariance measurement and soil water balance-based approach

An accurate estimate of crop coefficient (Kc) values at different development stages (Kcini, Kcmid, and Kcend) is crucial for assessing crop water requirements in semi-arid regions. The objectives of this study were first to quantify the reference evapotranspiration (ETo) and to calculate the actual evapotranspiration (ETa) over citrus in a semi-arid climate under drip irrigation. For this purpose, a site of a citrus orchard in Souss-Massa, planted with the Esbal variety of clementine, was equipped with an Eddy-Covariance (EC) system, and sensors to measure radiation, soil heat flux, and micrometeorological forcing data, during 2020 and 2021 seasons. Also, the soil moisture content at various soil depths in the root zone near the EC tower was monitored. The energy balance closure (EBC) approach was adopted for flux assessment to ensure a quality check for the EC measurements. The obtained EBCs were about 82% and 79% for the daily measurements in 2020 and 2021, respectively, which can be considered acceptable considering the nature of the citrus orchard (relatively tall and sparse). Second, the study aimed to estimate actual Kc act values for citrus under the same irrigation strategy. The derived values were compared to different recommended Kc values in the literature. In the third stage, this work aimed to offer an alternative plan to sustainable irrigation management by elaborating an irrigation schedule for citrus crops in the region using the FAO-56 simple approach to avoid water stress and deep percolation (i.e., Ks = 1 and DP = 0). Eventually, an irrigation schedule was drawn following the crop’s phenological stages. The seasonal mean citrus evapotranspiration (ETa) values are 1.68, 3.02, and 1.86 mm/day for the initial, mid, and end-season. The seasonal actual Kc act values were 0.64, 0.58, and 0.64 for Kcini, Kcmid, and Kcend, respectively. Additionally, the application of the water balance equation revealed that a large quantity of water is lost through deep percolation (52% of total water supplied). The study focuses on Citrus trees being a strategic crop with important socio-economic values in the Souss-Massa region. Thus, the results should support both scientists and farmers in planning and strategy development.

Accuracies of field CO2–H2O data from open-path eddy-covariance flux systems: assessment based on atmospheric physics and biological environment

Abstract. Ecosystem CO2–H2O data measured by infrared gas analyzers in open-path eddy-covariance (OPEC) systems have numerous applications, such as estimations of CO2 and H2O fluxes in the atmospheric boundary layer. To assess the applicability of the data for these estimations, data uncertainties from analyzer measurements are needed. The uncertainties are sourced from the analyzers in zero drift, gain drift, cross-sensitivity, and precision variability. These four uncertainty sources are individually specified for analyzer performance, but so far no methodology exists yet to combine these individual sources into a composite uncertainty for the specification of an overall accuracy, which is ultimately needed. Using the methodology for closed-path eddy-covariance systems, this overall accuracy for OPEC systems is determined from all individual uncertainties via an accuracy model and further formulated into CO2 and H2O accuracy equations. Based on atmospheric physics and the biological environment, for EC150 infrared CO2–H2O analyzers, these equations are used to evaluate CO2 accuracy (±1.22 mgCO2 m−3, relatively ±0.19 %) and H2O accuracy (±0.10 gH2O m−3, relatively ±0.18 % in saturated air at 35 ∘C and 101.325 kPa). Both accuracies are applied to conceptual models addressing their roles in uncertainty analyses for CO2 and H2O fluxes. For the high-frequency air temperature derived from H2O density along with sonic temperature and atmospheric pressure, the role of H2O accuracy in its uncertainty is similarly addressed. Among the four uncertainty sources, cross-sensitivity and precision variability are minor, although unavoidable, uncertainties, whereas zero drift and gain drift are major uncertainties but are minimizable via corresponding zero and span procedures during field maintenance. The accuracy equations provide rationales to assess and guide the procedures. For the atmospheric background CO2 concentration, CO2 zero and CO2 span procedures can narrow the CO2 accuracy range by 40 %, from ±1.22 to ±0.72 mgCO2 m−3. In hot and humid weather, H2O gain drift potentially adds more to the H2O measurement uncertainty, which requires more attention. If H2O zero and H2O span procedures can be performed practically from 5 to 35 ∘C, the H2O accuracy can be improved by at least 30 %: from ±0.10 to ±0.07 gH2O m−3. Under freezing conditions, the H2O span procedure is impractical but can be neglected because of its trivial contributions to the overall uncertainty. However, the zero procedure for H2O, along with CO2, is imperative as an operational and efficient option under these conditions to minimize H2O measurement uncertainty.