Eddy Covariance Latent Heat Fluxes Research Articles

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.

The Effect of Relative Humidity on Eddy Covariance Latent Heat Flux Measurements and its Implication for Partitioning into Transpiration and Evaporation

The Effect of Relative Humidity on Eddy Covariance Latent Heat Flux Measurements and its Implication for Partitioning into Transpiration and Evaporation

Attributing the energy imbalance by concurrent lysimeter and eddy covariance evapotranspiration measurements

The commonly observed lack of energy balance closure at eddy covariance flux tower sites represents an outstanding problem in micrometeorology and significantly compromises the value of eddy covariance latent and sensible heat flux measurements. Here we used concurrent lysimeter and eddy covariance evapotranspiration measurements to correct for the energy imbalance attributable to the eddy covariance latent heat flux measurements (32%) and then, by assuming that the Bowen ratio is correctly quantified by the eddy covariance method, attributed the remainder of the energy balance to the sensible heat flux (10%) and the available energy (58%). We discuss our findings with respect to the ongoing discussion on the causes of the energy imbalance and approaches to force energy balance closure.

Effect of Mesoscale Land Use Change on Characteristics of Convective Boundary Layer: Semi-Idealized Large Eddy Simulations over Northwest China

Although large-scale topography and land use have been properly considered in weather and climate models, the effect of mesoscale and microscale heterogeneous land use on convective boundary layer (CBL) has not been fully understood yet. In this study, the influence of semi-idealized strip-like patches of oases and deserts, which resemble irrigated land use in Northwest China, on the CBL characteristics, is investigated based on the Weather Research and Forecasting (WRF)-large eddy simulation (LES) driven by observed land surface data. The influences of soil water content in oases on aloft CBL flow structure, stability, turbulent kinetic energy (TKE), and vertical fluxes are carefully examined through a group of sensitivity experiments. The results show that secondary circulation (SC)/turbulent organized structures (TOS) is the strongest/weakest when soil water content in oases is close to saturation (e.g., when the oases are irrigated). With the decrease of soil water content in oases (i.e., after irrigation), SC (TOS) becomes weak (strong) in the lower and middle CBL, the flux induced by SC and TOS becomes small (large), which has a dramatic impact on point measurement of eddy covariance (EC) fluxes. The flux induced by SC and TOS has little influence on EC sensible heat flux, but great influence on EC latent heat flux. Under this circumstance, the area averaged heat flux cannot be represented by point measurement of flux by the EC method, especially just after irrigation in oases. Comparison of imbalance ratio (i.e., contribution of SC and TOS to the total flux) reveals that increased soil moisture in oases leads to a larger imbalance ratio as well as enhanced surface heterogeneity. Moreover, we found that the soil layer configuration at different depths has a negligible impact on the CBL flux properties.

On the Use of the Eddy Covariance Latent Heat Flux and Sap Flow Transpiration for the Validation of a Surface Energy Balance Model

Actual evapotranspiration is assessed via surface energy balance at an hourly rate. However, a robust estimation of daily evapotranspiration from hourly values is required. Outcomes of surface energy balance are frequently determined via measures of eddy covariance latent heat flux. Surface energy balance can be applied on images acquired at different times and spatial resolutions. In addition, hourly actual evapotranspiration needs to be integrated at a daily rate for operational uses. Questions arise whether the validation of surface energy balance models can benefit from complementary in situ measures of latent heat flux and sap flow transpiration. Here, validation was driven by image acquisition time, spatial resolution, and temporal integration. Thermal and optical images were collected with a proximity-sensing platform on an olive orchard at different acquisition times. Actual latent heat fluxes from canopy and sap flux at tree trunks were measured with a flux tower and heat dissipation probes. The latent heat fluxes were then further analyzed. A surface energy balance was applied over proximity sensing images re-sampled at different spatial resolutions with resulting latent heat fluxes compared to in situ ones. A time lag was observed and quantified between actual latent heat fluxes from canopy and sap flux at the tree trunk. Results also indicate that a pixel resolution comparable to the average canopy size was suitable for estimating the actual evapotranspiration via a single source surface energy balance model. Images should not be acquired at the beginning or the end of the diurnal period. Findings imply that sap flow transpiration can be used to measure surface energy balance at a daily rate or when images are found at an hourly rate near noon, and the existing time lag between the latent heat flux at the canopy and the sap flow at the trunk does not need to be taken into account.

Evaluation of eddy covariance latent heat fluxes with independent lysimeter and sapflow estimates in a Mediterranean savannah ecosystem

We evaluated the underlying causes of differences between latent heat (LE) fluxes measured with two enclosed-path eddy covariance systems (EC) at two measurement levels and independent estimates in an open oak-tree grass savannah over almost one year. Estimates of LE of the well-stablished underlying grass by replicated weighable tension-controlled lysimiters (LELys) provided a robust baseline against which to compare EC LE measured at 1.6m above ground (LE_1.6). Similarly and at the ecosystem level, LE up-scaled using independent measurements (LEupscaled=sap flow+lysimeter) was benchmarked with 3 EC-derived LE estimates: 1) LE measured by a EC tower at 15m above ground (LE_15), 2) LE_15 adjusted to close the energy balance by using the Bowen ratio method (LEBowen=(Rn −G)/(1+β)), and 3) LE derived from the energy budget residual (LEresidual=Rn −G−H_15). The sensitivity of EC LE to the correction method applied (i.e. corrections for low-pass filtering effects on water vapor fluctuations and the so-called angle-of-attack correction) and its impact on the energy balance closure (EBC) were also evaluated.Comparison of EC LE between 1.6m- and 15m-heights showed that grass dominated annual evaporative loss from 69 to 87% depending upon the spectral correction method applied. Results revealed substantial underestimation of LE_1.6 (up to 35%) compared to LELys, which mostly occurred during the growing season. However those differences were remarkably lower when likening LE_15 versus LEupscaled (14%) suggesting that the dampening of the water vapor fluctuations due to low-pass filtering effects is more pronounced near the surface. Interestingly, a diagnostic evaluation of the errors with a random forest model showed that differences followed quite structured patterns and were associated with certain atmospheric conditions: turbulent mixing deficiencies and or stable atmospheric stratification. In addition, the model showed that differences increased with increasing relative humidity (RH) and soil moisture. Our results revealed that the degree of EBC is highly sensitive to the flux correction method applied, in particular when correcting for flow distortion effects. Typically, turbulent fluxes fell below the measured available energy (slope 0.92) but the slope switched abruptly when the angle-of-attack correction was applied (slope 1.07). Consistent with the EBC, independent LE estimates matched well with LEBowen and the EBC gap decreased when LEupscaled was used (slope 0.96). The use of independent estimates of LE together with machine learning methods are proposed as a powerful means to diagnose the complexity behind LE errors and give insights into the energy imbalance problem. In addition to inherent randomness of EC LE data, accounting for uncertainties associated with the appropriateness of the correction method applied is highly recommended.

Extending periodic eddy covariance latent heat fluxes through tree sap-flow measurements to estimate long-term total evaporation in a peat swamp forest

Abstract. A combination of measurement and modelling was used to find a pragmatic solution to estimate the annual total evaporation from the rare and indigenous Nkazana Peat Swamp Forest (PSF) on the east coast of Southern Africa to improve the water balance estimates within the area. Actual total evaporation (ETa) was measured during three window periods (between 7 and 9 days each) using an eddy covariance (EC) system on a telescopic mast above the forest canopy. Sap flows of an understory tree and an emergent tree were measured using a low-maintenance heat pulse velocity system for an entire hydrological year (October 2009 to September 2010). An empirical model was derived, describing the relationship between ETa from the Nkazana PSF and sap-flow measurements. These overlapped during two of the window periods (R2 = 0.92 and 0.90), providing hourly estimates of ETa from the Nkazana PSF for a year, totalling 1125 mm (while rainfall was 650 mm). In building the empirical model, it was found that to include the understory tree sap flow provided no benefit to the model performance. In addition, the relationship between the emergent tree sap flow with ETa between the two field campaigns was consistent and could be represented by a single empirical model (R2 = 0.90; RMSE = 0.08 mm h−1). During the window periods of EC measurement, no single meteorological variable was found to describe the Nkazana PSF ETa satisfactorily. However, in terms of evaporation models, the hourly FAO Penman–Monteith reference evaporation (ETo) best described ETa during the August 2009 (R2 = 0.75), November 2009 (R2 = 0.85) and March 2010 (R2 = 0.76) field campaigns, compared to the Priestley–Taylor potential evaporation (ETp) model (R2 = 0.54, 0.74 and 0.62 during the respective field campaigns). From the extended record of ETa (derived in this study from sap flow) and ETo, a monthly crop factor (Kc) was derived for the Nkazana PSF, providing a method of estimating long-term swamp forest water-use from meteorological data. The monthly Kc indicated two distinct periods. From February to May, it was between 1.2 and 1.4 compared with June to January, when the crop factor was 0.8 to 1.0. The derived monthly Kc values were verified as accurate (to one significant digit) using historical data measured at the same site, also using EC, from a previous study. The measurements provided insights into the microclimate within a subtropical peat swamp forest and the contrasting sap flow of emergent and understory trees. They showed that expensive, high-maintenance equipment can be used during manageable window periods in conjunction with low-maintenance systems, dedicated to individual trees, to derive a model to estimate long-term ETa over remote heterogeneous forests. In addition, the contrast in annual ETa and rainfall emphasised the reliance of the Nkazana PSF on groundwater.

Uncertainty due to hygrometer sensor in eddy covariance latent heat flux measurements

Half-hour latent heat flux (LE) was measured over an early-maturing peach orchard (Prunus persica L.) by two different hygrometers: (1) infrared gas analyzer (IRGA) (LI-COR, model LI-7500) and (2) ultraviolet hygrometer (Campbell, KH20). A good agreement between LE obtained with the IRGA (LEIRGA) and the KH20 (LEKH20) hygrometers was observed. During rainy periods, LEIRGA and LEKH20 were not reliable due to failure of the instruments caused by water drops standing over the sensors heads. Filtering out rainy periods improved the similarity between LEIRGA and LEKH20: mean estimation error, 6.2Wm−2; root mean square error, 21.3Wm−2; and refined index of agreement, 0.919. Even though the IRGA hygrometer is generally recommended, when economic constraints exist, the KH20 hygrometer can be used with similar confidence.

A gap-filling model for eddy covariance latent heat flux: Estimating evapotranspiration of a subtropical seasonal evergreen broad-leaved forest as an example

Summary In this paper we present a semi-parametric multivariate gap-filling model for tower-based measurement of latent heat flux ( LE ). Two statistical techniques, the principal component analysis (PCA) and a nonlinear interpolation approach were integrated into this LE gap-filling model. The PCA was first used to resolve the multicollinearity relationships among various environmental variables, including radiation, soil moisture deficit, leaf area index, wind speed, etc. Two nonlinear interpolation methods, multiple regressions (MRS) and the K -nearest neighbors (KNNs) were examined with random selected flux gaps for both clear sky and nighttime/cloudy data to incorporate into this LE gap-filling model. Experimental results indicated that the KNN interpolation approach is able to provide consistent LE estimations while MRS presents over estimations during nighttime/cloudy. Rather than using empirical regression parameters, the KNN approach resolves the nonlinear relationship between the gap-filled L E flux and principal components with adaptive K values under different atmospheric states. The developed LE gap-filling model (PCA with KNN) works with a RMSE of 2.4 W m −2 (∼0.09 mm day −1 ) at a weekly time scale by adding 40% artificial flux gaps into original dataset. Annual evapotranspiration at this study site were estimated at 736 mm (1803 MJ) and 728 mm (1785 MJ) for year 2008 and 2009, respectively.

Remote sensing based evapotranspiration and runoff modeling of agricultural, forest and urban flux sites in Denmark: From field to macro-scale

Evapotranspiration ( E ) and runoff ( R T) was modeled for the island of Sjælland (≈7330 km 2) in Denmark at multiple spatial scales encompassing agricultural, forest and urban land surfaces. National data were used to represent spatial variations in climate, soil properties and lower boundary conditions, and the EOS/MODIS Normalized Difference Vegetation Index ( NDVI) was used to map (a) the temporal development in leaf area index for agricultural fields, (b) a dynamic “canopy” coefficient ( K c) of forests being scaled between its minimum and maximum values for use in the FAO Penman–Monteith equation, and (c) the impervious land cover fraction of urban regions. At field level, the use of local-scale model parameters, NDVI time series and site-specific methodologies to simulate E of the 3 major land surface types (agricultural land, forests and urban regions) explained 67–79% of the observed variability in eddy covariance latent heat fluxes. The “effective” spatial resolution needed to adopt local-scale model parameters for spatial-deterministic hydrological modeling was assessed using a high-spatial resolution (30 m) variogram analysis of the NDVI. The use of the NDVI variogram to evaluate land surface heterogeneity is based on the assumption that sub-class soil heterogeneity can be indirectly represented by the observed spatial variations in NDVI due to its close affiliation with vegetation growth, soil water uptake and evapotranspiration. Multiple spatial resolution water balance simulations were compared to validate the identified effective spatial resolution (500 m) model representation of land cover, NDVI and drainage pattern. Simulated R T of 30 catchments were compared with the fast-flow component of stream discharge data ( Q − Q b) which is insensitive to groundwater abstraction and most sensitive to the spatial land surface representation. A good agreement was observed in the timing and size of peak flows in catchment dominated by agricultural, forest and urban land uses in periods when E has important control on the water balance and soil water percolation to groundwater is negligible (Days 125–300). The presence/absence of pipe drains, urban surface runoff and forest parameterization cause very large differences in the water balance of agricultural, forest and urban regions. The results show that the use of local-scale standard model parameters and NDVI time series representing agricultural, forest and urban land surfaces in physically based hydrological modeling makes it possible to reproduce much of the observed variability (48–73%) in stream flow ( Q − Q b) when data and modeling is applied at an effective spatial resolution capable of representing land surface heterogeneity. In order to further improve the results, (1) advanced spatial parameterization methods are needed to improve the modeling of bare soil E of agricultural fields, (2) the impact of local conditions, such as tree age and nutrient levels, should be used to parameterize the maximum K c used for forest E modeling, (3) high accuracy remote sensing based estimation of vegetation parameters is particularly important during sparsely vegetated conditions, and (4) the use of component stream flow data to evaluate the physical consistency of spatial-deterministic models appears to be feasible and should be further explored.

Adequacy of Villalobos method to adjust eddy covariance latent heat flux

Latent (LE) and two sets of sensible (H) heat fluxes (H ec and H t 2) were measured with an eddy covariance system (sonic anemometer, krypton hygrometer, two fine-wire thermocouples) over grass (6 days, summer 1997) and wheat (7 days, late spring 1999) in NE Spain. The objective was to evaluate a method to correct eddy covariance underestimation due to horizontal sensor displacement, based upon the similarity of covariance loss for both LE and H. First, this assumption was examined by regression analyses of measured lysimeter LE (LE lys ) versus eddy covariance LE (LE ec ) and H ec versus H t 2. In general, regression slopes of LE and H were broadly similar, although this similarity depended upon the atmospheric stability conditions. For grass, LE ec was significantly lower than LE lys , while for wheat there was a close agreement between LE lys and LE ec . Ratios of horizontal sensor displacement to measurement sensor height above zero plane displacement (s:z d ) were about 0.36–0.40 for grass and 0.11 for wheat. A further regression analysis was performed to compare LE lys with corrected eddy covariance LE (LE ecv ) values. That correction significantly improved eddy covariance LE values measured over grass but only in some cases, mostly under unstable (near to neutral) atmospheric conditions. It can be concluded that low s:z d ratios (less than 0.1) are preferable to reduce loss in covariance of LE. If the use of higher s:z d ratios cannot be avoided, the Villalobos method may reduce the expected loss in covariance but only under limited conditions. In these situations other solutions to reduce loss in covariance should be investigated further.