Latent Heat Flux Estimates Research Articles

Predicting evapotranspiration from drone-based thermography – a method comparison in a tropical oil palm plantation

Abstract. For the assessment of evapotranspiration, near-surface airborne thermography offers new opportunities for studies with high numbers of spatial replicates and in a fine spatial resolution. We tested drone-based thermography and the subsequent application of the DATTUTDUT energy balance model using the widely accepted eddy covariance technique as a reference method. The study site was a mature oil palm plantation in lowland Sumatra, Indonesia. For the 61 flight missions, latent heat flux estimates of the DATTUTDUT (Deriving Atmosphere Turbulent Transport Useful To Dummies Using Temperature) model with measured net radiation agreed well with eddy covariance measurements (r2 = 0.85; MAE = 47; RMSE = 60) across variable weather conditions and times of day. Confidence intervals for slope and intercept of a model II Deming regression suggest no difference between drone-based and eddy covariance methods, thus indicating interchangeability. The DATTUTDUT model is sensitive to the configuration of the net radiation assessment. Overall, we conclude that drone-based thermography with energy balance modeling is a reliable method complementing available methods for evapotranspiration studies. It offers promising, additional opportunities for fine grain and spatially explicit studies.

Evaluation of alternative two-source remote sensing models in partitioning of land evapotranspiration

Remote sensing (RS)-based two-source models have been widely used to separate soil evaporation (E) and vegetation transpiration (T) in ecosystems. Alternative model assumptions and physical mechanisms employed in different two-source models cause the difference in model performance. Understanding of the characteristics and limitations of alternative two-source models is critical to ensure the most effective applications in various climatic zones and underlying surfaces. This study investigated two types of land surface temperature (LST) decomposition based two-source models, namely Two-Source Energy Balance Model (TSEB) and Modified Pixel Component Arranging and Comparing Algorithm (M-PCACA); as well as a meteorological factors-based Penman-Monteith type model (PM-MU) in the partitioning of E and T. The three models were compared at 21 flux stations across China, which included seven types of underlying surfaces and one isotope station. The study demonstrated that all three models were applicable in a large scale study for latent heat flux (LE) estimation. Overall, M-PCACA performed the best with average root-mean-square errors (RMSE) of 38.7 W/m2, while PM-MU performs the worst with average RMSE of 61.4 W/m2. For ET partitioning, the two models based on LST decomposition (TSEB and M-PCACA) gave more reliable estimates compared with stable water isotope observations. In addition, verification results for the seven types of underlying surfaces showed that the TSEB gave the best accuracy in the areas with high leaf area index (LAI) and vegetation height (e.g. forest), while the PM-MU had an advantage in the areas without vegetation (e.g. deserts and water bodies). Due to the characteristics of the land surface temperature-vegetation coverage (LST-fc) trapezoidal framework, the M-PCACA model had the most stable model performance for various underlying surfaces and gave the best estimates in grassland, wetland, and farmland areas. Using a thorough discussion, this study identified the sources of discrepancies induced by different physical mechanisms of three remote sensing-based two-source models. This investigation provided insights to better understand the alternative two-source models and provides guidance for model application.

Pixel-Based Calibration and Atmospheric Correction of a UAS-Mounted Thermal Camera for Land Surface Temperature Measurements

HighlightsA novel pixel-based calibration algorithm and an atmospheric correction method are developed.Application of the calibration methods reduces the RMSE of measurements to less than 1.32°C.The calibrations facilitate stitching of images together to form whole-field mosaics.Abstract. Thermal imagery can be used to provide insight into the water stress status and evapotranspiration demand of crops, but satellite-based sensors are generally too coarse spatially and too infrequent temporally to provide information of use for the management of specific fields. Thermal cameras mounted on small unmanned aerial systems (UAS) have potential to provide canopy temperature information at high spatial and temporal resolutions useful for crop management; however, without appropriate camera corrections, the measurement biases of these uncooled thermal cameras can be larger than ±5°C. Such uncertainty can render such camera measurements useless. In this research, a pixel-based (non-uniformity) calibration algorithm and an atmospheric correction method based on in-field approximate blackbody sources (water targets) were developed for a thermal camera. The objective was to improve the temperature measurement accuracy of the thermal camera on various land surfaces including soil and vegetation. With sufficient accuracy, temperature measurements can be used for the estimation of latent heat flux of field crops in the future. The thermal camera was first calibrated in a laboratory setting where the camera and environmental conditions were controlled. The results indicated that in the range between 10°C and 45°C, the calibrated temperatures were accurate, with an average bias of 1.76°C, and had a high linear correlation with reference temperatures (water target temperatures) (R2 > 0.99). Variability of measurements was also better constrained. In-field atmospheric correction is also important for obtaining high-accuracy thermal imagery. By applying both pixel-based calibration and atmospheric corrections, the RMSE (root mean square error) of validation targets from two dates in 2017 was reduced from 4.56°C and 6.36°C before calibration to 1.32°C and 1.24°C after calibration. The calibration process also increased the range of temperatures in the imagery, which enhanced contrast and may help with identification of tie-points and stitching of images together to form whole-field mosaics. Keywords: Atmospheric correction, Pixel-based calibration, Thermal remote sensing, UAS, Water targets.

Identification of Dominant Warm-Season Latent Heat Flux Patterns in the Lower Mississippi River Alluvial Valley

Warm-season precipitation in the Lower Mississippi River Alluvial Valley (LMRAV) is heavily dominated by the rates of evapotranspiration and surface heat fluxes and is a primary water resource for agriculture. However, the stochastic nature of LMRAV warm-season thunderstorms makes precipitation forecasts challenging. The Weather Research and Forecasting Hydrologic (WRF-Hydro) model, coupled with the multi-parameter Noah land surface (Noah-MP) model, has improved estimates of important warm-season precipitation process. Given the widespread agriculture and dominance of crop and forested landscapes over the region, proper assessment of land use / land cover (LULC) is critical in predicting warm-season precipitation patterns. The objective of this study is to quantify simulated latent heat flux sensitivity (important for warm-season precipitation) to temporally updated LULC datasets. Both the model default and annually updated LULC conditions were used to initialize a 16-year WRF-Hydro simulation from which warm-season latent heat flux estimates were obtained. Annual root mean square difference was computed at each gridpoint. Cluster analysis preprocessed with kernel principal component analysis was used to identify spatial RMSD structures that quantified sensitivity to updated LULC conditions. Results showed the largest impacts occurred directly in the LMRAV and for points slightly east and revealed a meteorological link between these regions.

Energy balance closure and advective fluxes at ADVEX sites

When measuring the energy balance at the earth’s surface using the Eddy covariance technique, the obtained budgets seldom produce a closed energy balance. The measurements often miss some of the energy fluxes. A possible reason is the neglect of non-turbulent surface fluxes of latent heat and sensible heat, i.e. advective fluxes of these quantities. We present estimates of advective latent and sensible heat fluxes for three different sites across Europe based on the ADVEX dataset. The obtained horizontal and vertical advective fluxes were site-specific and characterized by large scatter. In relative terms, the data indicated that the sensible heat budget was less affected by advection than the latent heat budget during nighttime; this is because vertical turbulent latent heat fluxes were very small or close to zero during the night. The results further showed that the additional energy gain by sensible heat advection might have triggered enhanced evaporation for two sites during nighttime. Accounting for advective fluxes improved the energy balance closure for one of the three ADVEX sites. However, the energy balance closure of the other two sites did not improve overall. A comparison with energy balance residuals (energy missed by the measurements without accounting for advection) indicated a large influence of systematic errors. An inspection of the energy balance for the sloped site of the ADVEX dataset underlined the necessity of slope-parallel measurement of radiation.

A model for estimating transpiration from remotely sensed solar-induced chlorophyll fluorescence

Terrestrial evapotranspiration (ET) is an important flux that links global cycles of carbon, water and energy and is largely driven by transpiration (T) through leaf stomata in vegetated areas during the growing season. ET, however, remains one of the most uncertain hydrological variables at the global scale. In this study, we proposed a semi-mechanistic model for estimating terrestrial T by deriving an analytical solution between solar-induced chlorophyll fluorescence (SIF) and stomatal conductance (gc) as well as vapor pressure deficit (VPD), combining theories on the photosynthetic pathway and optimal stomatal behavior. The relationships of SIF-ETR and ETR-gc·VPD0.5 was calibrated by the Soil-Canopy Observation of Photosynthesis and Energy (SCOPE) model. This model was validated by hourly canopy SIF and concurrent eddy covariance flux observations at both forest and cropland ecosystems. Results showed that the SIF combined with VPD can better predict gc than using SIF alone with a more consistent seasonal trends found in both SIF and gc·VPD0.5. The correlation between gc·VPD0.5 and SIF was stronger than those between gc and SIF and between gc and VIs. Canopy T was accurately predicted from SIF at both hourly (R2 > 0.65) and daily (R2 > 0.76) scales and was also successfully estimated using SIF observations from the TROPOspheric Monitoring Instrument (TROPOMI) at cropland ecosystems. In comparison with empirical relationships of directly linking gc with SIF or VIs, the proposed model produced latent heat flux (λE) estimation in best agreement with measured values at all three sites. Our model could be a step forward in understanding the coupling of carbon and water cycles and may be used in ecosystem models for improving ET estimation over large areas.

Current and future impacts of drought and ozone stress on Northern Hemisphere forests.

Rising ozone (O3 ) concentrations, coupled with an increase in drought frequency due to climate change, pose a threat to plant growth and productivity which could negatively affect carbon sequestration capacity of Northern Hemisphere (NH) forests. Using long-term observations of O3 mixing ratios and soil water content (SWC), we implemented empirical drought and O3 stress parameterizations in a coupled stomatal conductance-photosynthesis model to assess their impacts on plant gas exchange at three FLUXNET sites: Castelporziano, Blodgett and Hyytiälä. Model performance was evaluated by comparing model estimates of gross primary productivity (GPP) and latent heat fluxes (LE) against present-day observations. CMIP5 GCM model output data were then used to investigate the potential impact of the two stressors on forests by the middle (2041-2050) and end (2091-2100) of the 21st century. We found drought stress was the more significant as it reduced model overestimation of GPP and LE by ~11%-25% compared to 1%-11% from O3 stress. However, the best model fit to observations at all the study sites was obtained with O3 and drought stress combined, such that the two stressors counteract the impact of each other. With the inclusion of drought and O3 stress, GPP at CPZ, BLO and HYY is projected to increase by 7%, 5% and 8%, respectively, by mid-century and by 14%, 11% and 14% by 2091-2100 as atmospheric CO2 increases. Estimates were up to 21% and 4% higher when drought and O3 stress were neglected respectively. Drought stress will have a substantial impact on plant gas exchange and productivity, off-setting and possibly negating CO2 fertilization gains in future, suggesting projected increases in the frequency and severity of droughts in the NH will play a significant role in forest productivity and carbon budgets in future.

Uncertainties in Ocean Latent Heat Flux Variations over Recent Decades in Satellite-Based Estimates and Reduced Observation Reanalyses

AbstractFour state-of-the-art satellite-based estimates of ocean surface latent heat fluxes (LHFs) extending over three decades are analyzed, focusing on the interannual variability and trends of near-global averages and regional patterns. Detailed intercomparisons are made with other datasets including 1) reduced observation reanalyses (RedObs) whose exclusion of satellite data renders them an important independent diagnostic tool; 2) a moisture budget residual LHF estimate using reanalysis moisture transport, atmospheric storage, and satellite precipitation; 3) the ECMWF Reanalysis 5 (ERA5); 4) Remote Sensing Systems (RSS) single-sensor passive microwave and scatterometer wind speed retrievals; and 5) several sea surface temperature (SST) datasets. Large disparities remain in near-global satellite LHF trends and their regional expression over the 1990–2010 period, during which time the interdecadal Pacific oscillation changed sign. The budget residual diagnostics support the smaller RedObs LHF trends. The satellites, ERA5, and RedObs are reasonably consistent in identifying contributions by the 10-m wind speed variations to the LHF trend patterns. However, contributions by the near-surface vertical humidity gradient from satellites and ERA5 trend upward in time with respect to the RedObs ensemble and show less agreement in trend patterns. Problems with wind speed retrievals from Special Sensor Microwave Imager/Sounder satellite sensors, excessive upward trends in trends in Optimal Interpolation Sea Surface Temperature (OISST AVHRR-Only) data used in most satellite LHF estimates, and uncertainties associated with poor satellite coverage before the mid-1990s are noted. Possibly erroneous trends are also identified in ERA5 LHF associated with the onset of scatterometer wind data assimilation in the early 1990s.

ANN-Based Estimation of Low-Latitude Monthly Ocean Latent Heat Flux by Ensemble Satellite and Reanalysis Products.

Ocean latent heat flux (LHF) is an essential variable for air–sea interactions, which establishes the link between energy balance, water and carbon cycle. The low-latitude ocean is the main heat source of the global ocean and has a great influence on global climate change and energy transmission. Thus, an accuracy estimation of high-resolution ocean LHF over low-latitude area is vital to the understanding of energy and water cycle, and it remains a challenge. To reduce the uncertainties of individual LHF products over low-latitude areas, four machine learning (ML) methods (Artificial Neutral Network (ANN), Random forest (RF), Bayesian Ridge regression and Random Sample Consensus (RANSAC) regression) were applied to estimate low-latitude monthly ocean LHF by using two satellite products (JOFURO-3 and GSSTF-3) and two reanalysis products (MERRA-2 and ERA-I). We validated the estimated ocean LHF using 115 widely distributed buoy sites from three buoy site arrays (TAO, PIRATA and RAMA). The validation results demonstrate that the performance of LHF estimations derived from the ML methods (including ANN, RF, BR and RANSAC) were significantly better than individual LHF products, indicated by R2 increasing by 3.7–46.4%. Among them, the LHF estimation using the ANN method increased the R2 of the four-individual ocean LHF products (ranging from 0.56 to 0.79) to 0.88 and decreased the RMSE (ranging from 19.1 to 37.5) to 11 W m−2. Compared to three other ML methods (RF, BR and RANSAC), ANN method exhibited the best performance according to the validation results. The results of relative uncertainty analysis using the triangle cornered hat (TCH) method show that the ensemble LHF product using ML methods has lower relative uncertainty than individual LHF product in most area. The ANN was employed to implement the mapping of annual average ocean LHF over low-latitude at a spatial resolution of 0.25° during 2003–2007. The ocean LHF fusion products estimated from ANN methods were 10–30 W m−2 lower than those of the four original ocean products (MERRA-2, JOFURO-3, ERA-I and GSSTF-3) and were more similar to observations.

Estimation of High-Resolution Global Monthly Ocean Latent Heat Flux from MODIS SST Product and AMSR-E Data

Accurate estimation of satellite-derived ocean latent heat flux (LHF) at high spatial resolution remains a major challenge. Here, we estimate monthly ocean LHF at 4 km spatial resolution over 5 years using bulk algorithm COARE 3.0, driven by satellite data and meteorological variables from reanalysis. We validated the estimated ocean LHF by multiyear observations and by comparison with seven ocean LHF products. Validation results from monthly observations at 96 widely distributed buoy sites from three buoy site arrays (TAO, PIRATA, and RAMA) indicated a bias of less than 7 W/m2 with R2 of more than 0.80 (p<0.01) and with a King–Gupta efficiency (KGE) of over 0.84. Our estimated ocean LHF also performs well in simulating annual variability and predicting between-site variability, as indicated by a bias of lower than 6 W/m2 and an R2 of more than 0.84 (p<0.01). Overall, the average KGE for estimated ocean LHF increased by 18%–23% compared to other LHF products, indicating robust LHF estimation performance. Importantly, our estimated annual ocean LHF has similar global spatial distribution compared to other LHF products, although there are general differences in LHF values due to the difference in the models and the spatial resolution.

Uncertainties Caused by Resistances in Evapotranspiration Estimation Using High-Density Eddy Covariance Measurements

AbstractQuantifying the uncertainties caused by resistance parameterizations is fundamental for understanding, improving, and developing terrestrial evapotranspiration (ET) models. Using high-density eddy covariance (EC) tower observations in a heterogeneous oasis in northwest China, this study evaluates the impacts of resistances on the estimation of latent heat flux (LE), the energy equivalent of ET, by comparing resistance parameterizations with different complexities under one- and two-source Penman–Monteith (PM) equations. The results showed that the mean absolute percent error (MAPE) for the LE estimates from the one- and two-source PM equations varied from 32% to 53%, and the uncertainties were caused mainly by the resistance parameterizations. Calibrating the parameters required in the resistance estimations could improve the performance of the PM equations; specifically, the MAPEs for the one-source PM equations were approximately 16%, whereas they were 38% for the two-source PM equations, emphasizing that multiple resistances result in increased uncertainties. The following conclusions were reached: 1) the empirical and biophysical parameters required in resistance estimations were responsible for the uncertainty; 2) increasingly complex resistance parameterizations resulted in greater uncertainties in LE estimates; and 3) models without resistance parameterizations exhibited reduced uncertainties in LE estimates.

Spatial Estimation of the Latent Heat Flux in a Tropical Dry Forest by Using Unmanned Aerial Vehicles

In this paper, we address the retrieval of spatially distributed latent heat flux ( λ E) over a tropical dry forest using multi-spectral and thermal unmanned aerial vehicle (UAV) imagery. The study was carried out in the Santa Rosa National Park Environmental Monitoring Super-Site, Costa Rica, in June 2016. The triangle method was used to derive λ E from the UAV imagery and the results were compared to λ E measurements of an eddy covariance system within the coincident eddy flux tower footprint. The tower footprint was derived using a two-dimensional parameterization model for flux footprint prediction. The comparisons with the flux tower measurements showed a mean relative difference of 10.98% with a slight overestimation of the UAV-based flux retrievals by nearly 7.7 Wm − 2 . The results are in good agreement with satellite-based retrievals, as provided by the literature, for which the triangle method was initially developed and mostly used so far. This study proved to be a promising approach for transferring the triangle method to UAV imagery in ecosystems such as tropical dry forests. With the presented approach, new details in spatially distributed latent heat flux estimates at ultra-high resolution are now possible, thereby potentially closing the gap in spatial resolution between satellites and flux towers. Even more, it allows tracing the latent heat flux from single trees at leaf level. Besides, this approach also opens new perspectives for the monitoring of latent heat fluxes in tropical dry forests.

Estimation of Daily Terrestrial Latent Heat Flux with High Spatial Resolution from MODIS and Chinese GF-1 Data.

Reliable estimates of terrestrial latent heat flux (LE) at high spatial and temporal resolutions are of vital importance for energy balance and water resource management. However, currently available LE products derived from satellite data generally have high revisit frequency or fine spatial resolution. In this study, we explored the feasibility of the high spatiotemporal resolution LE fusion framework to take advantage of the Moderate Resolution Imaging Spectroradiometer (MODIS) and Chinese GaoFen-1 Wide Field View (GF-1 WFV) data. In particular, three-fold fusion schemes based on Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) were employed, including fusion of surface reflectance (Scheme 1), vegetation indices (Scheme 2) and high order LE products (Scheme 3). Our results showed that the fusion of vegetation indices and further computing LE (Scheme 2) achieved better accuracy and captured more detailed information of terrestrial LE, where the determination coefficient (R2) varies from 0.86 to 0.98, the root-mean-square error (RMSE) ranges from 1.25 to 9.77 W/m2 and the relative RSME (rRMSE) varies from 2% to 23%. The time series of merged LE in 2017 using the optimal Scheme 2 also showed a relatively good agreement with eddy covariance (EC) measurements and MODIS LE products. The fusion approach provides spatiotemporal continuous LE estimates and also reduces the uncertainties in LE estimation, with an increment in R2 by 0.06 and a decrease in RMSE by 23.4% on average. The proposed high spatiotemporal resolution LE estimation framework using multi-source data showed great promise in monitoring LE variation at field scale, and may have value in planning irrigation schemes and providing water management decisions over agroecosystems.

A Temperature-Domain SEBAL Model Based on a Wind Speed-Independent Theoretical Trapezoidal Space Between Fractional Vegetation Coverage and Land Surface Temperature

The surface energy balance algorithm for land (SEBAL) model is one of the most widely used methods for estimating evapotranspiration. Numerous physical studies have conducted to mitigate the limitations of spatial-domain dependence and the annoying uncertainties in visually identifying the dry/wet pixels in SEBAL; however, they are subject to the quality of wind speed ( ${u}$ ) observation, which is known as a high temporal–spatial variation and is not routinely available, especially in a heterogeneous area. In this study, we constructed a ${u}$ -independent algorithm to calculate the dry/wet endpoints for each pixel. The wet endpoint was determined by assuming no turbulent heat exchange between a water-saturated surface and atmosphere. The dry endpoint was calculated using the assumption and consensus that pixels had equivalent neutral aerodynamic resistance under given atmospheric forcing and vegetation coverage. Then, we built a temperature-domain SEBAL (TD-SEBAL) model and validated it in the MUSOEXE between May and September 2012, located at desert-oasis transition zone in the middle reaches of the Heihe watershed across eight landscapes. The results showed that TD-SEBAL could provide reasonable dry/wet endpoint retrieval, and reliable latent heat flux estimates with a mean bias of −0.25 W/m2, a root-mean-square error of 52.9 W/m2, and a coefficient of determination of 0.91.

Estimation of Sensible and Latent Heat Fluxes of an Isolated Tree in Japanese Summer

Considering that the characteristics of the heat balance between an isolated tree and the urban atmosphere have not yet been sufficiently clarified, we quantify both the sensible heat flux ($$ H_{T} $$) and latent heat flux ($$ lE_{T} $$) to and from an isolated Z. Serrata in the Japanese summer. To estimate the whole-tree transpiration rate (ET) and $$ lE_{T} $$ values, we apply a previously developed method using a weighing lysimeter. To estimate the boundary-layer heat conductance ($$ g_{hT} $$) of the total leaf area of the tree crown and the sensible heat flux ($$ H_{T} $$), we apply a scaling-up approach using the heat-balance two-state (HB-TS) method, which previously only targeted single leaves, to the tree crown. Two sample trees with similar crown shape and total leaf area are selected for the HB-TS method but under different irrigation conditions, and $$ g_{hT} $$ is estimated. The ET values of the tree change three times during the dry-down experiment (during which time irrigation was halted). We also compare HT and lET values between two different days under different irrigation and soil–water conditions. The most important result from this comparison is that the tendencies of HT and lET were reversed on these days, and the Bowen ratio ($$ \beta = H_{T} /lE_{T} $$) dramatically varies between 0.29 and 2.2. These results indicate that the Bowen ratio of isolated trees at urban sites can vary between the previously reported values for forest sites and those for artificial urban sites within a short period, owing to urban-unique conditions (e.g., limited water supply and rooting space, and artificial sealing).

Terrestrial water loss at night: global relevance from observations and climate models

Abstract. Nocturnal water loss (NWL) from the surface into the atmosphere is often overlooked because of the absence of solar radiation to drive evapotranspiration and the measuring difficulties involved. However, growing evidence suggests that NWL – and particularly nocturnal transpiration – represents a considerable fraction of the daily values. Here we provide a global overview of the characteristics of NWL based on latent heat flux estimates from the FLUXNET2015 dataset, as well as from simulations of global climate models. Eddy-covariance measurements at 99 sites indicate that NWL represents 6.3 % of total evapotranspiration on average. There are six sites where NWL is higher than 15 %; these sites comprise mountain forests with considerable NWL during winter that is related to snowy and windy conditions. Higher temperature, vapor pressure deficit, wind speed, soil moisture, and downward longwave radiation are related to higher NWL, although this is not consistent across all of the sites. On the other hand, the global multi-model mean of terrestrial NWL is 7.9 % of the total evapotranspiration. The spread of the model ensemble, however, is greater than 15.8 % over half of the land grid cells. Finally, NWL is projected to increase everywhere with an average of 1.8 %, although with a substantial inter-model spread. Changes in NWL contribute substantially to projected changes in total evapotranspiration. Overall, this study highlights the relevance of water loss during the night and opens avenues to explore its influence on the water cycle and the climate system under present and future conditions.

Estimating Turbulent Fluxes in the Tropical Andes

The correct estimation of Sensible Heat Flux (H) and Latent Heat Flux (LE) (i.e., turbulent fluxes) is vital in the understanding of exchange of energy and mass among hydrosphere, atmosphere, and biosphere in an ecosystem. One of the most popular methods to measure these fluxes is the Eddy Covariance (EC) technique; however, there are a number of setbacks to its application, especially in remote and topographically complex terrain such as the higher altitudes of the Andes. Efforts have been made by the scientific community to parameterise these fluxes based on other more commonly measured variables. One of the most widespread methods is the so-called bulk method, which relates average temperature, humidity, and wind vertical profiles to the turbulent fluxes. Another approach to estimate LE is the Penman-Monteith (PM) equation which uses meteorological measurements at a single level. The objective of this study was to validate these methods for the first time in the Tropical Andes in Southern Ecuador (in the páramo ecosystem at 3780 m a.s.l.) using EC and meteorological measurements. It was determined that the bulk method was the best to estimate H, although some adjustments had to be made to the typical assumptions used to estimate surface meteorological values. On the other hand, the PM equation yielded the best LE estimations. For both fluxes, the error in the estimations was within the uncertainty range of the EC measurements. It can be concluded that it is possible to accurately estimate H and LE using the methods described in this paper in this ecosystem when no direct measurements are available.

Estimation of Turbulent Heat Fluxes via Assimilation of Air Temperature and Specific Humidity into an Atmospheric Boundary Layer Model

AbstractA number of studies have used time series of air temperature and specific humidity observations to estimate turbulent heat fluxes. These studies require the specification of surface roughness lengths for heat and momentum (that are directly related to the neutral bulk heat transfer coefficientCHN) and/or ground heat flux, which are often unavailable. In this study, sequences of air temperature and specific humidity are assimilated into an atmospheric boundary layer model within a variational data assimilation (VDA) framework to estimateCHN, evaporative fraction (EF), turbulent heat fluxes, and atmospheric boundary layer (ABL) height, potential temperature, and humidity. The developed VDA approach needs neither the surface roughness parameterization (as it is optimized by the VDA approach) nor ground heat flux measurements. The VDA approach is tested over the First International Satellite Land Surface Climatology Project Field Experiment (FIFE) site in the summers of 1987 and 1988. The results indicate that the estimated sensible and latent heat fluxes agree fairly well with the corresponding measurements. For FIFE 1987 (1988), the daily sensible and latent heat fluxes estimates have a root-mean-square error of 25.72 W m−2(27.77 W m−2) and 53.63 W m−2(48.22 W m−2), respectively. In addition, the ABL height, specific humidity, and potential temperature estimates from the VDA system are in good agreement with those inferred from the radiosondes both in terms of magnitude and diurnal trend.

Estimation of surface heat fluxes via variational assimilation of land surface temperature, air temperature and specific humidity into a coupled land surface-atmospheric boundary layer model

Numerous studies have estimated surface heat fluxes by assimilating land surface temperature (LST) observations (as the state variable of land surface). A number of other studies have focused on the estimation of surface energy balance components by assimilating air temperature and specific humidity (as the state variables of atmosphere) into an atmospheric boundary layer model. This study advances these existing data assimilation approaches by the synergistic assimilation of LST, air temperature, and specific humidity into a coupled land surface-atmospheric boundary layer model. The unknown parameters are the neutral bulk heat transfer coefficient (CHN) and evaporative fraction (EF). CHN scales the sum of turbulent heat fluxes, and EF represents their partitioning. The developed approach is tested at the First International Satellite Land Surface Climatology Project Field Experiment (FIFE) site in the summer of 1987 and 1988. The results indicate that the new approach performs well in both wet and dry periods because it uses the implicit information in both the land surface and atmospheric state variables (i.e., LST, air temperature, and specific humidity). The root-mean-square-errors (RMSEs) of estimated daily sensible and latent heat fluxes are 21.80 Wm−2 (22.10 Wm−2) and 39.32 Wm−2 (36.89 Wm−2) for FIFE 87 (88). The corresponding mean-absolute-percentage-errors (MAPEs) are 22.16% (18.64%) and 13.98% (13.44%) for FIFE 87 (88). The new variational data assimilation (VDA) system outperforms the previous studies that assimilated either LST or air temperature/specific humidity. For FIFE 87, this study decreases the RMSEs of daily sensible and latent heat fluxes estimates by 12.5% and 24.4% compared to assimilating only LST, and by 15.2% and 26.7% compared to assimilating only air temperature and specific humidity. A similar improvement is obtained for FIFE 88. The atmospheric boundary layer height, potential temperature, and specific humidity estimates from the VDA approach are also in good agreement with the corresponding radiosonde observations, and can capture their variations during the course of the day.

Effects of Surface Heterogeneity Due to Drip Irrigation on Scintillometer Estimates of Sensible, Latent Heat Fluxes and Evapotranspiration over Vineyards

Accurate estimates of sensible (H) and latent (LE) heat fluxes and actual evapotranspiration (ET) are required for monitoring vegetation growth and improved agricultural water management. A large aperture scintillometer (LAS) was used to provide these estimates with the objective of quantifying the effects of surface heterogeneity due to soil moisture and vegetation growth variability. The study was conducted over drip-irrigated vineyards located in a semi-arid region in Albacete, Spain during summer 2007. Surface heterogeneity was characterized by integrating eddy covariance (EC) observations of H, LE and ET; land surface temperature (LST) and normalized difference vegetation index (NDVI) data from Landsat and MODIS sensors; LST from an infrared thermometer (IRT); a data fusion model; and a two-source surface energy balance model. The EC observations showed 16% lack of closure during unstable atmospheric conditions and was corrected using the residual method. The comparison between the LAS and EC measurements of H, LE, and ET showed root mean square difference (RMSD) of 25 W m−2, 19 W m−2, and 0.41 mm day−1, respectively. LAS overestimated H and underestimated both LE and ET by 24 W m−2, 34 W m−2, and 0.36 mm day−1, respectively. The effects of soil moisture on LAS measurement of H was evaluated using the Bowen ratio, β. Discrepancies between HLAS and HEC were higher at β ≤ 0.5 but improved at 1 ≥ β > 0.5 and β > 1.0 with R2 of 0.76, 0.78, and 0.82, respectively. Variable vineyard growth affected LAS performance as its footprints saw lower NDVILAS compared to that of the EC (NDVIEC) by ~0.022. Surface heterogeneity increased during wetter periods, as characterized by the LST–NDVI space and temperature vegetation dryness index (TVDI). As TVDI increased (decreased) during drier (wetter) conditions, the discrepancies between HLAS and HEC, as well as LELAS and LEEC Re decreased (increased). Thresholds of TVDI of 0.3, 0.25, and 0.5 were identified, above which better agreements between LAS and EC estimates of H, LE, and ET, respectively, were obtained. These findings highlight the effectiveness and ability of LAS in monitoring vegetation growth over heterogonous areas with variable soil moisture, its potential use in supporting irrigation scheduling and agricultural water management over large regions.