Flux Footprint Research Articles

Wind Tunnel Experiment on the Footprint of a Block-Arrayed Urban Model in a Neutrally Stratified Boundary Layer

This study addresses the need to investigate footprint function features in urban areas and establish a validation database for numerical methods. Concentration and its flux footprints of a block-arrayed urban model were measured in a wind tunnel with a neutrally stratified boundary layer. The velocity and concentration were simultaneously measured by an X-probe hot wire anemometer and a fast-response flame ionization detector to evaluate the vertical flux. Experimental results highlighted the influence of the measurement heights on footprint distributions. Because the sensors were immersed in the roughness sublayer, their footprints showed strong heterogeneity across horizontal positions caused by building configurations. It was found that turbulent flux contributes up to 70% of total flux footprints, emphasizing the importance of accurate turbulent dispersion estimation in numerical methods. Furthermore, measured footprints were compared to those modeled by a widely used analytical method (Kormann and Meixner in Boundary-Layer Meteorol 99:207–224, 2001, https://doi.org/10.1023/ A:1018991015119). The measured footprints extended further along the streamwise direction and their spanwise dispersions were constrained by the rows of blocks, which failed to be reproduced in the analytical method. This indicates the significant effects of building configurations on footprint functions in urban areas.

Transfer Efficiency and Organization in Turbulent Transport over Alpine Tundra

The exchange of momentum, heat and trace gases between atmosphere and surface is mainly controlled by turbulent fluxes. Turbulent mixing is usually parametrized using Monin–Obukhov similarity theory (MOST), which was derived for steady turbulence over homogeneous and flat surfaces, but is nevertheless routinely applied to unsteady turbulence over non-homogeneous surfaces. We study four years of eddy-covariance measurements at a highly heterogeneous alpine valley site in Finse, Norway, to gain insights into the validity of MOST, the turbulent transport mechanisms and the contributing coherent structures. The site exhibits a bimodal topography-following flux footprint, with the two dominant wind sectors characterized by organized and strongly negative momentum flux, but different anisotropy and contributions of submeso-scale motions, leading to a failure of eddy-diffusivity closures and different transfer efficiencies for different scalars. The quadrant analysis of the momentum flux reveals that under stable conditions sweeps transport more momentum than the more frequently occurring ejections, while the opposite is observed under unstable stratification. From quadrant analysis, we derive the ratio of the amount of disorganized to organized structures, that we refer to as organization ratio (OR). We find an invertible relation between transfer efficiency and corresponding organization ratio with an algebraic sigmoid function. The organization ratio further explains the scatter around scaling functions used in MOST and thus indicates that coherent structures modify MOST. Our results highlight the critical role of coherent structures in turbulent transport in heterogeneous tundra environments and may help to find new parametrizations for numerical weather prediction or climate models.

Matching Satellite Sun-Induced Chlorophyll Fluorescence to Flux Footprints Improves Its Relationship with Gross Primary Productivity

Sun-induced chlorophyll fluorescence (SIF) holds enormous potential for accurately estimating terrestrial gross primary productivity (GPP). However, current studies often overlook the spatial representativeness of satellite SIF and GPP observations. This research downscaled TROPOMI SIF (TROPOSIF) and its enhanced product (eSIF) in China’s Saihanba Forest Region to obtain high-resolution SIF data. SIF was simulated using the SCOPE model, and the downscaled SIF’s reliability was validated at two forest eddy covariance (EC) sites (SHB1 and SHB2) in the study area. Subsequently, the downscaled SIF data were matched to the EC footprint of the two forest sites, and the relationship between SIF and GPP was compared at various observational scales. Additionally, the ability of downscaled TROPOSIF and eSIF to track GPP was compared, along with the correlations among several vegetation indices (VIs) and GPP. The findings reveal the following: (1) Downscaled TROPOSIF and eSIF showed a strong linear relationship with SCOPE-modeled SIF (R2 ≥ 0.86). The eSIF closely matched the SCOPE simulation (RMSE: 0.06 mw m−2 nm−1 sr−1) and displayed a more consistent seasonal variation pattern with GPP. (2) Comparisons among coarse-resolution SIF, EC footprint-averaged SIF (SIFECA), and EC footprint-weighted SIF (SIFECW) demonstrated significant improvements in the linear relationship between downscaled SIF and GPP (the R2 increased from the range of 0.47–0.65 to 0.78–0.85). SIFECW exhibited the strongest relationship with GPP, indicating that matching SIF to flux footprints improves their relationship. (3) As the distance from the flux tower increased, the relationship between SIF and GPP weakened, reaching its lowest point beyond 1 km from the tower. Moreover, in the highly heterogeneous landscape of the SHB2 site, the relationship between VIs and GPP was poor, with no clear pattern as distance from the flux tower increased. In conclusion, the strong spatial dependency of SIF and tower-based GPP emphasizes the importance of using high-resolution SIF to accurately quantify their relationship.

Assessing Spatial Representativeness of Global Flux Tower Eddy-Covariance Measurements Using Data from FLUXNET2015

Large datasets of carbon dioxide, energy, and water fluxes were measured with the eddy-covariance (EC) technique, such as FLUXNET2015. These datasets are widely used to validate remote-sensing products and benchmark models. One of the major challenges in utilizing EC-flux data is determining the spatial extent to which measurements taken at individual EC towers reflect model-grid or remote sensing pixels. To minimize the potential biases caused by the footprint-to-target area mismatch, it is important to use flux datasets with awareness of the footprint. This study analyze the spatial representativeness of global EC measurements based on the open-source FLUXNET2015 data, using the published flux footprint model (SAFE-f). The calculated annual cumulative footprint climatology (ACFC) was overlaid on land cover and vegetation index maps to create a spatial representativeness dataset of global flux towers. The dataset includes the following components: (1) the ACFC contour (ACFCC) data and areas representing 50%, 60%, 70%, and 80% ACFCC of each site, (2) the proportion of each land cover type weighted by the 80% ACFC (ACFCW), (3) the semivariogram calculated using Normalized Difference Vegetation Index (NDVI) considering the 80% ACFCW, and (4) the sensor location bias (SLB) between the 80% ACFCW and designated areas (e.g. 80% ACFCC and window sizes) proxied by NDVI. Finally, we conducted a comprehensive evaluation of the representativeness of each site from three aspects: (1) the underlying surface cover, (2) the semivariogram, and (3) the SLB between 80% ACFCW and 80% ACFCC, and categorized them into 3 levels. The goal of creating this dataset is to provide data quality guidance for international researchers to effectively utilize the FLUXNET2015 dataset in the future.

Disaggregating the Carbon Exchange of Degrading Permafrost Peatlands Using Bayesian Deep Learning

AbstractExtensive regions in the permafrost zone are projected to become climatically unsuitable to sustain permafrost peatlands over the next century, suggesting transformations in these landscapes that can leave large amounts of permafrost carbon vulnerable to post‐thaw decomposition. We present 3 years of eddy covariance measurements of CH4 and CO2 fluxes from the degrading permafrost peatland Iškoras in Northern Norway, which we disaggregate into separate fluxes of palsa, pond, and fen areas using information provided by the dynamic flux footprint in a novel ensemble‐based Bayesian deep neural network framework. The 3‐year mean CO2‐equivalent flux is estimated to be 106 gCO2 m−2 yr−1 for palsas, 1,780 gCO2 m−2 yr−1 for ponds, and −31 gCO2 m−2 yr−1 for fens, indicating that possible palsa degradation to thermokarst ponds would strengthen the local greenhouse gas forcing by a factor of about 17, while transformation into fens would slightly reduce the current local greenhouse gas forcing.

Footprints in an urban array model under multiple wind directions: A wind tunnel experiment investigation

This study investigates the footprint distributions at various measurement positions in an urban array model, considering two incident wind directions, 22.5° and 45°, by a wind tunnel experiment. The airflow velocity and tracer concentration are simultaneously measured by an X-probe hot wire anemometer and a flame ionization detector, respectively, to assess the vertical flux of tracer gas. The experiment findings reveal a unified relationship between footprint distributions and measurement heights, as well as pronounced heterogeneity in footprints across horizontal positions in the urban model. The concentration footprints in both wind directions exhibit local patterns, primarily influenced by the local canopy vortex between buildings, and global patterns, arising from the large-scale building configuration within the urban model. Notably, the local pattern demonstrates a significant spanwise tilt in the 22.5° case and wave-like propagation along the streamwise direction in the 45° case, while the global pattern, characterized by contour lines developing along rows of blocks, remains consistent across both scenarios. Moreover, the flux footprint has constrained source areas compared to the concentration footprint, with its peak coinciding with areas exhibiting local patterns, where turbulent flux contribution exceeds 70%. These findings emphasize the importance of predicting the dispersion driven by local canopy vortex in the numerical modeling of urban footprint applications.

Evapotranspiration partitioning by integrating eddy covariance, micro-lysimeter and unmanned aerial vehicle observations: A case study in the North China Plain

Partitioning of evapotranspiration (ET) into soil evaporation (E) and plant transpiration (T) poses a significant challenge. This study proposed a novel approach that combines eddy covariance, micro-lysimeter, and high-resolution unmanned aerial vehicle (UAV) images within the flux footprint to optimize the canopy and soil resistances of the Shuttleworth-Wallace model (S-W) using a multi-objective optimization scheme. A two-year experiment was conducted at the Yucheng and Luancheng sites to investigate the effectiveness of the proposed method for the summer maize-wheat winter system in the North China Plain. Our results showed that the S-W model with multi-objective optimization by using high-resolution UAV images within the flux footprint significantly improved the estimation accuracy of ET components compared with the traditional one- and two-objective optimization schemes. The estimation of ET, T, and E of our method had an average root mean square error of 0.67 mm day−1, 0.66 mm day−1, and 0.28 mm day−1, respectively. The optimized S-W model using high-resolution UAV data within the flux footprint produced better ET partitioning than that optimized using Moderate Resolution Imaging Spectroradiometer (MODIS) data. We then applied the proposed method to estimate ET and ET components in the North China Plain. This study highlighted the importance of utilizing multi-source data (especially high-resolution UAV images) and multi-objective optimization for accurate ET partitioning.

Analysis of flux footprints in fragmented, heterogeneous croplands

An accurate quantification of fluxes from heterogeneous sites and further bifurcation into contributing homogeneous fluxes is an active field of research. Among such sites, fragmented croplands with varying surface roughness characteristics pose formidable challenges for footprint analysis. We conducted two flux monitoring experiments in fragmented croplands characterized by two dissimilar surfaces with objectives to: (i) evaluate the performance of two analytical footprint models in heterogeneous canopy considering aggregated roughness parameters and (ii) analyze the contribution of fluxes from individual surfaces under changing wind speed. A set of three eddy covariance (EC) towers (one each capturing the homogenous fluxes from individual surfaces and a third, high tower capturing the heterogeneous mixed fluxes) was used for method validation. High-quality EC fluxes that fulfill stationarity and internal turbulence tests were analyzed considering daytime, unstable conditions. In the first experiment, source area contribution from a surface is gradually reduced by progressive cut, and its effect on high-tower flux measurements is analyzed. Two footprint models (Kormann and Meixner ‘KM’; analytical solution to Lagrangian model ‘FFP’) with modified surface roughness parameters were applied under changing source area contributions. FFP model has consistently over predicted the footprints (RMSEFFP = 0.31 m−1, PBIASFFP = 19.00), whereas KM model prediction was gradually changed from over prediction to under prediction towards higher upwind distances (RMSEKM = 0.02 m−1, PBIASKM = 8.50). Sensitivity analysis revealed that the models are more sensitive to turbulent conditions than surface characteristics. This motivated to conduct the second experiment, where the fractional contribution of individual surfaces (α and β) to the heterogeneous fluxes measured by the high tower (T3) was estimated using the principle of superposition (FT3 = α FT1 + β FT2). Results showed that α and β are dynamic during daylight hours and strongly depend on mean wind speed (U) and friction velocity (u*). The contribution of fluxes from adjoining fields [1 − (α + β)] is significant beyond 80% isopleth. Our findings provide guidelines for future analysis of fluxes in heterogeneous, fragmented croplands.

Spatial and temporal variation of three Eddy-Covariance flux footprints in a Tropical Dry Forest

Tropical Dry Forest (TDF) is a distinctive ecosystem characterized by distinct seasonal changes, primarily composed of deciduous trees adapted to endure prolonged drought periods. Obtaining ecosystem-scale flux measurements using the Eddy Covariance (EC) technique is crucial for comprehending the spatiotemporal dynamics of interactions between the land and the atmosphere. It is imperative to have information about the variability in the contribution source area of the Flux Footprint Area (FFA) to assess the impact of changing microclimate variables on the EC FFA. In this study, we describe and evaluate the influence of various phases of the El Niño Southern Oscillation (ENSO) on the spatial and temporal climatology of FFA at three EC sites within the TDF of the Santa Rosa National Park-Environmental Super Site (SRNP-EMSS). Our analysis covered the period from 2014 to 2021 on a seasonal scale and employed a 2D model. Our findings indicate that: a) seasonal climatology FFAs exhibit variation across locations and over time due to factors such as forest canopy height, site topography, wind direction, and atmospheric stability conditions; b) FFAs are smaller during dry seasons compared to wet seasons; and c) multiple ENSO phases (cold, neutral, and warm) significantly affect the seasonal climatology of FFAs. Notably, during the moderate La Niña phase (2020/2021), the mean FFA size was 4.20 km2, which was 23.80% larger than the mean FFA during the intense El Niño phase (2015/2016) at 3.20 km2. Additionally, during La Niña phases, FFAs were more asymmetrically distributed around the EC tower compared to El Niño phases, with mean symmetric indices of 0.35 and 0.29, respectively. Finally, the overlap among the three EC climatology FFAs during La Niña phases was 35% greater than that during the El Niño phases. Our study highlights how variations in FFAs can significantly impact intra-annual flux estimates.

Development of a footprint description tool utilizing SMEAR Estonia eddy-covariance data and footprint modelling in combination with remote sensed forest species and land cover data

Abstract Understanding how forest ecosystems respond to environmental factors, particularly in the context of global climate change, is essential for devising effective mitigation strategies. This study focuses on quantifying the interaction between forest ecosystems and atmospheric gases. To achieve our objectives, we are using the eddy covariance (EC) flux method to measure air turbulence and gas concentrations above the forest canopy at the Station for Measuring Ecosystem-Atmosphere Relations (SMEAR) in southern Estonia. We apply a flux footprint (FFP) model to describe the spatial extent and position of the surface area contributing to the turbulent flux measurements. The FFP analysis provides valuable insights into the long-term changes in SMEAR Estonia, the FFP and its relationship with forest management and land use changes. Our findings reveal that the FFP area varies from year to year due to changes in wind speed and direction, affecting the contribution of different land cover elements to the overall FFP. The average changes in the FFP area at a height of 30 meters were approximately 4.9%, while those at a height of 70 meters were only 1.6%. Moreover, human activities, such as thinning and clear-cutting, influence the growing stock and increment of forest stands.

Spatial and Temporal Variations of Carbon Dioxide Fluxes in Urban Ecosystems of Changsha, China

Understanding the spatial and temporal variations of urban carbon dioxide fluxes (FCO2) and their influencing factors is crucial for solving urban climate problems and promoting the development of low-carbon cities. In this study, the carbon dioxide flux (FCO2) in Changsha City, China, was analyzed using the eddy covariance technique and flux footprint model. The results showed that the extent of the flux footprint within the observation site was mostly limited to 500 m. Diurnal variation of FCO2 showed a regular pattern influenced by plant photosynthesis and traffic flow. Meanwhile, photosynthesis was directly regulated by photosynthetically active radiation and indirectly regulated by air temperature and water vapor pressure differences. The average value of FCO2 was lower during the daytime than at night, indicating the high vegetation cover (43.5%) in the study area. In addition, there were spatial characteristics of FCO2 in each wind direction due to different surface land use in the study area. Notably, a decreasing trend in carbon dioxide content was observed after the area covered by vegetation was 1.8 times the area of buildings and major roads combined. These findings guide climate management, urban planning, and sustainable development toward a low-carbon society.

Seasonal Dynamics of Flux Footprint for a Measuring Tower in Southern Taiga via Modeling and Experimental Data Analysis

This paper reports on the location of sources contributing to a point flux measurement in the southern taiga, Russia. The measurement tower is surrounded by a coniferous forest with a mean aerodynamically active height of 27 m (h). Aerodynamical parameters of the forest, such as displacement height d and aerodynamic roughness z0, derived from wind speed profile measurements for 2017–2019, were used to estimate the seasonal and daily behavior of the flux footprint. Two analytical footprint models by Schuepp et al. and by Kormann and Meixner driven by d and z0 were used to estimate the footprint for canopy sources. The Lagrangian simulation (LS) approach driven by flow statistics from measurements and modeling was used to estimate the footprint for ground-located sources. The Flux Footprint Prediction (FFP) tool for assessing canopy flux footprint (Kljun et al.) applied as the option in the EddyPro v.7 software was inspected against analytical and LS methods. For model comparisons, two parameters from estimated footprint functions were used: the upwind distance (fetch) of the peak contribution in the measured flux (Xmax) and the fetch that contributed to 80% of the total flux (CF80). The study shows that Xmax varies slightly with season but relies on wind direction and time of day. All methods yield different Xmax values but fall in the same range (60–130 m, around 2–5 h); thus, they can estimate the maximum influence distance with similar confidence. The CF80 values provided by the FFP tool are significantly lower than the CF80 values from other methods. For instance, the FFP tool estimates a CF80 of about 200 m (7 h), whereas other methods estimate a range of 600–1100 m (25–40 h). The study emphasizes that estimating the ground source footprint requires either the LS method or more complex approaches based on Computational Fluid Dynamics (CFD) techniques. These findings have essential implications in interpreting eddy-flux measurements over the quasi-homogeneous forest.

Measurements and modeling of type-I and type-II ELMs heat flux to the DIII-D divertor

Type-I and type-II edge-localized-modes (ELMs) heat flux profiles measured at the DIII-D divertor feature a peak in the vicinity of the strike-point and a plateau in the scrape-off-layer (SOL), which extends to the first wall. The plateau is present in attached and detached divertors and it is found to originate with plasma bursts upstream in the SOL. The integrated ELM heat flux is distributed at ∼65% in the peak and ∼35% in this plateau. The parallel loss model, currently used at ITER to predict power loads to the walls, is benchmarked using these results in the primary and secondary divertors with unprecedented constraints using experimental input data for ELM size, radial velocity, energy, electron temperature and density, heat flux footprints and number of filaments. The model can reproduce the experimental near-SOL peak within ∼20%, but cannot match the SOL plateau. Employing a two-component approach for the ELM radial velocity, as guided by intermittent data, the full radial heat flux profile can be well matched. The ELM-averaged radial velocity at the separatrix, which explains profile widening, increases from ∼0.2 km s−1 in attached to ∼0.8 km s−1 in detached scenarios, as the ELM filaments’ path becomes electrically disconnected from the sheath at the target. The results presented here indicate filaments fragmentation as a possible mechanism for ELM transport to the far-SOL and provide evidence on the beneficial role of detachment to mitigate ELM flux in the divertor far-SOL. However, these findings imply that wall regions far from the strike points in future machines should be designed to withstand significant heat flux, even for small-ELM regimes.

Two-level eddy covariance measurements reduce bias in land-atmosphere exchange estimates over a heterogeneous boreal forest landscape

Estimates of land-atmosphere exchanges of carbon, energy, water vapor, and other greenhouse gases based on the eddy covariance (EC) technique rely on the fundamental assumption that the flux footprint area is homogeneous. We investigated the impact of source area heterogeneity on flux estimates in single-level EC measurements over a managed boreal forest landscape. For this purpose, we compared single-level measurements with those from a two-level approach consisting of concurrent EC measurements at 60 and 85 m above the ground. This two-level set-up provided a unique opportunity to obtain nearly congruent diel footprint areas by combining data from the higher and lower levels during day- and nighttime, respectively.We found that the variation in the averaged footprint area between day- and nighttime was reduced by up to 89% in the two-level approach compared to the single-level data at the higher level (85 m). Considering spring, summer, and fall months, the resulting relative potential bias in flux observations due to landscape heterogeneity was highest at short time steps (≤ daily) ranging between 35% and 325% for half-hourly data. During winter months, when stable atmospheric regimes prevailed during day and night, the footprints within the diel course nearly overlapped also at a given single level and hence no improvement of flux estimates was found. The absolute cumulated sums for the study period (excluding winter months) of gross primary production, ecosystem respiration, latent heat, and sensible heat flux were underestimated by about 28%, 52%, 5%, and 3%, respectively, whereas that of net ecosystem CO2 exchange was overestimated by about 109% in the single-level approach. Overall this study suggests that footprint heterogeneity may introduce considerable bias in single-level flux estimates — particularly at short time scales — with large implications for model-data fusion studies, site comparisons, and up- or downscaling of land-atmosphere exchange processes.

Complexity of carbon dioxide flux in urban areas: A comparison with natural surfaces

The characteristics of turbulent CO2 transport and its dissimilarity with heat and water vapor are investigated over both natural and urban areas. A novel index TS is proposed to effectively quantify the transport similarity between two scalars. By comparison, it is found that the transport of CO2 shows great complexity in urban areas. It is ideal in natural areas that heat, water vapor, and CO2 are efficiently transported by thermal plumes (i.e., the dominant coherent structures under unstable conditions), and that the transport similarity among them becomes increasingly evident with the increase of atmospheric instability. However, in urban areas, the transport of CO2 shows significant dissimilarity from that of heat and water vapor, and it is hard to detect the role of thermal plumes. Furthermore, it is observed that the sector-average CO2 flux in urban areas changes largely with the wind blowing from different urban functional areas. Specially, for a given direction, there might be contrasting characteristics in CO2 transport under different unstable conditions. These features can be explained by the flux footprint. Since the CO2 sources and sinks are distributed heterogeneously in urban areas, the variation of footprint areas with wind direction or atmospheric instability, causes the alternation between source-dominated (i.e., upward) and sink-dominated (i.e., downward) CO2 transport. Therefore, the role of coherent structures in CO2 transport is substantially confused by spatially-confined sources/sinks in urban areas, leading to significant transport dissimilarity between CO2 and heat or water vapor and thus the great complexity in CO2 transport. The findings in this study are helpful to promote the understanding of the global carbon cycle in depth.

Spatial Variability of Albedo and Net Radiation at Local Scale Using UAV Equipped with Radiation Sensors

Energy balance closure is an important feature in studies of ecosystem exchanges of energy and greenhouse gases using the eddy covariance method. Previous analyses show that this is still a problem with imbalances in the order of 0.6–0.7 to full closure (for only a few sites). It has been suggested that mesoscale transport processes that are not captured by the eddy covariance measurements are the main reason behind the closure problem. So far, very little action has been taken to investigate another potential cause of the problem, namely, the role of spatial variation in net radiation at the scale of typical flux footprints. The reason for this knowledge gap is mainly due to the lack of suitable methods to perform such investigations. Here, we show that such measurements can be performed with an unmanned aerial vehicle equipped with radiation sensors. A comparison using a reference radiometer on a fixed mast with a hovering UAV equipped with pyranometers for incoming and outgoing shortwave radiation and an infrared thermometer for surface temperature measurements shows that incoming and outgoing shortwave radiation can be measured with a standard error of 7.4 Wm−2 and 1.8 Wm−2, respectively. An application of the system was made over a five-year-old forest flux site in Sweden. Here, the net longwave radiation was estimated from the measured surface temperature and the calculated incoming longwave radiation. The results show that during the mission around noon on a clear day, distinct ‘hotspots’ existed over the plantation with the albedo varying between 15.5 and 17.9%, the surface temperature varying between 22.2 and 25.5 °C and the net radiation varying between 330 and 380 Wm−2. These variations are large enough to have a significant impact on the energy balance closure problem. Our conclusion is that we now have the tools to investigate the spatial variability of the radiation regime over flux sites and that this should be given more attention in the future.

Backward-Eulerian Footprint Modelling Based on the Adjoint Equation for Atmospheric and Urban-Terrain Dispersion

This study developed a backward-Eulerian footprint modelling method based on an adjoint equation for atmospheric boundary-layer flows. In the proposed method, the concentration footprint can be obtained directly by numerical simulation with the adjoint equation, and the flux footprints can be estimated using the adjoint concentration based on the gradient diffusion hypothesis. We first tested the proposed method by estimating the footprints for an ideal three-dimensional boundary layer with different atmospheric stability conditions based on the Monin–Obukhov profiles. It was indicated that the results were similar to the FFP method (Kljun et al. in Boundary-Layer Meteorol 112:503–523, 2004, https://doi.org/10.1023/B:BOUN.0000030653.71031.96; Geosci Model Dev 8:3695–3713, 2015, https://doi.org/10.5194/gmd-8-3695-2015) for convective conditions and the K–M method (Kormann and Meixner in Boundary-Layer Meteorol 99:207–224, 2001, https://doi.org/10.1023/A:1018991015119) for stable conditions. The proposed method was then coupled with the Reynolds averaged Navier–Stokes model to calculate the footprints for a block-arrayed urban canopy. The results were qualitatively compared to the results from the Lagrangian-Large-Eddy-Simulation (LL) method (Hellsten et al. in Boundary-Layer Meteorol 157:191–217, 2015, https://doi.org/10.1007/s10546-015-0062-4). It was shown that the proposed method reproduced the main features of footprints for different sensor positions and measurement heights. However, it is necessary to simulate the adjoint equation with a more sophisticated turbulence model in the future to better capture turbulent effects in the footprint modelling.

Quantifying the Spatial Representativeness of Carbon Flux Footprints of a Grassland Ecosystem in the Semi‐Arid Region

AbstractQuantifying the single‐site representativeness of carbon flux footprints plays a crucial role in land‐atmosphere interaction, especially in semi‐arid regions with high‐frequency turbulence. In this study, we used multi‐platform datasets, including observational data derived from the eddy covariance flux monitoring systems of the Semi‐Arid Climate and Environment Observatory of Lanzhou University (SACOL) and multivariate satellite remote sensing, to explore the distribution characteristics of carbon flux footprints during 2007–2016, using a Flux Footprint Prediction model. The relative importance of atmospheric boundary layer factors and vegetation factors on the area of carbon flux footprints was quantified by correlation analysis, multiple stepwise regression, and random forest. Physical mechanisms affecting the differences in the spatial distribution of carbon flux footprints were also analyzed. The results show that the carbon flux footprints are usually distributed on the prevailing wind side of the station, whose main contributing source area is the relatively flat grassland at the summit range of SACOL, with a spatial representativeness of 67.4–507.2 m in length and 54,982–105,329 m2 in area. Mechanism studies have shown that the frictional velocity, vegetation Index, atmospheric instability parameters, and wind speed have significant importance on the area of carbon flux footprints due to dynamic and thermal forcing, with frictional velocity being dominant, while wind direction has a major effect on the profile shape. This study provides references for the representativeness of site fluxes for semi‐arid regions dominated by grassland ecosystems where observations are often scarce.

Inside the flux footprint: The role of organized land cover heterogeneity on the dynamics of observed land-atmosphere exchange fluxes

Eddy covariance measurements quantify the magnitude and temporal variability of land-atmosphere exchanges of water, heat, and carbon dioxide (CO2) among others. However, they also carry information regarding the influence of spatial heterogeneity within the flux footprint, the temporally dynamic source/sink area that contributes to the measured fluxes. A 25 m tall eddy covariance flux tower in Central Illinois, USA, a region where drastic seasonal land cover changes from intensive agriculture of maize and soybean occur, provides a unique setting to explore how the organized heterogeneity of row crop agriculture contributes to observations of land-atmosphere exchange. We characterize the effects of this heterogeneity on latent heat (LE), sensible heat (H), and CO2 fluxes (Fc) using a combined flux footprint and eco-hydrological modeling approach. We estimate the relative contribution of each crop type resulting from the structured spatial organization of the land cover to the observed fluxes from April 2016 to April 2019. We present the concept of a fetch rose, which represents the frequency of the location and length of the prevalent upwind distance contributing to the observations. The combined action of hydroclimatological drivers and land cover heterogeneity within the dynamic flux footprint explain interannual flux variations. We find that smaller flux footprints associated with unstable conditions are more likely to be dominated by a single crop type, but both crops typically influence any given flux measurement. Meanwhile, our ecohydrological modeling suggests that land cover heterogeneity leads to a greater than 10% difference in flux magnitudes for most time windows relative to an assumption of equally distributed crop types. This study shows how the observed flux magnitudes and variability depend on the organized land cover heterogeneity and is extensible to other intensively managed or otherwise heterogeneous landscapes.

Construction of a spatially gridded heat flux map based on airborne flux Measurements using remote sensing and machine learning methods

Acquiring the relative truth values of land surface sensible (H) and latent (LE) heat fluxes at the regional scale is important for monitoring and simulating evapotranspiration at a global scale using satellite data or process-based models. However, it is difficult to directly obtain these values with fine spatial representation at regional scales using conventional ground-based measurement methods. Therefore, airborne measurements of turbulent flux are highly advantageous. By leveraging airborne measurements of H and LE fluxes collected in the Netherlands in August 2008, in this study we evaluated five machine learning methods for the construction of a regional gridded heat flux map, including artificial neural networks, boosted regression trees, random forest regression, deep neural networks, and support vector regression. The models were trained and tested using a dataset compiled from observations of H and LE, the digital elevation model, MODIS land surface temperature, enhanced vegetation index, and albedo data for each flux footprint. The best performing model was used to construct a regional gridded heat flux map over a case region located in the center of the Netherlands. The results shown that the support vector regression performed better than other models, with R2=0.91,RMSE=9.6W/m2 for H, and R2=0.89,RMSE=26.32W/m2 for LE. The constructed heat flux maps achieved a relative prediction error of ≤32.8% (R2>0.71) for H and ≤43.5% (R2>0.53) for LE compared to aircraft measurements. The constructed heat flux maps were then aggregated for each land cover type, providing individual estimates of source strength (52< H< 66 W/m2 and 108< LE< 218 W/m2) and spatial variability (52< σH< 66 W/m2 and 108< σLE< 218 W/m2) with an ensemble precision of < 6% (1σ). Finally, the limitations and future prospects of the current study were summarized and discussed.