Flux Tower Measurements Research Articles

High-resolution spatially explicit land surface model calibration using field-scale satellite-based daily evapotranspiration product

High-resolution simulation of water budgets across the agricultural landscape is critically important to a variety of applications, such as precision agriculture, water resources management, and environmental quality assessment. Model-data integration has been shown to be an effective approach to reduce model uncertainties and there is a growing opportunity to improve land surface modeling through spatially explicit calibration with satellite data in recent decades. Recently, a satellite-based daily 30-m resolution evapotranspiration (ET) product BESS-STAIR has been developed, achieving a high performance and well capturing the spatial and temporal dynamics of ET across the U.S. Corn Belt. To explore the potential of high-resolution spatially explicit calibration for advancing land surface modeling at fine scales, we carried out calibration experiments for the Noah-MP land surface model (LSM) over cropland using this newly developed BESS-STAIR ET. We first used Sobol sensitivity analysis to identify the most sensitive parameters for the Noah-MP’s ET simulation. The most sensitive vegetation (minimum stomatal resistance) and soil parameters (saturated hydraulic conductivity, saturated matric potential, and a soil pore size distribution parameter) were calibrated using BESS-STAIR ET to improve model simulation of surface water balance. We conducted calibration experiments at 8 eddy covariance flux tower sites that grew maize and soybean across the U.S. Corn Belt, as well as a regional calibration study on the Spoon River watershed in Champaign, Illinois. When benchmarked with flux tower measurements, the BESS-STAIR ET–calibrated model (driven by flux tower forcing) on average reduced the RMSE of hourly ET from 61 W/m2 to 47 W/m2 for maize, and from 66 W/m2 to 53 W/m2 for soybean, and matched the performance of directly calibrating using flux tower measured ET. The regional study found that calibration using BESS-STAIR ET also improved the simulation of long-term regional water budgets and achieved better performance of ET than traditionally lumped calibration using streamflow. Further analysis revealed that the high-resolution calibration can resolve the spatial variations of ET to a certain extent, and the accuracy of the calibration can be largely attributed to the low bias and excellent long-term correlation of the BESS-STAIR ET data itself. Our study thus demonstrates the effectiveness of high-resolution model calibration and provides important implications in field-scale hydrological modeling and precision agricultural applications.

The effects of spatiotemporal patterns of atmospheric CO2 concentration on terrestrial gross primary productivity estimation

A quantitative understanding of the global gross primary productivity (GPP) and its responses to increasing CO2 levels is critical for quantifying the feedbacks of ecosystems to climate change. This study applied the daily boreal ecosystem productivity simulator (BEPSd) model to estimate the global GPP from 2000 to 2015, compare the estimated GPP with flux tower measurements and other GPP products to verify the estimation accuracy, and analyze the CO2 fertilization effect and conducted a spatial analysis of the effects of the spatiotemporal distribution of the CO2 concentration on the estimation of the GPP. The results showed that the estimates could capture the magnitude, amplitude, distribution, and variation in the GPP well compared with the flux tower measurements and other GPP products. In general, the terrestrial GPP increased as the atmospheric CO2 concentrations increased; however, the CO2 fertilization effect varied based on time and location and was constrained by climatic conditions. The increases in the lower latitudes were more significant than those in the middle and higher latitudes, and seasonal variation characteristics were observed in the middle and higher latitudes. Not considering the CO2 fertilization effect could underestimate the global GPP and its trend, while not considering the spatiotemporal distribution of the CO2 concentration could overestimate the global annual GPP. These results increase our understanding of the variations in carbon flux under future climate change, especially under the conditions of a changing atmospheric CO2 concentration.

Water use efficiency and its drivers in four typical agroecosystems based on flux tower measurements

Understanding the interactive responses of agroecosystem water use efficiency (WUE) to climatic and vegetation factors is significant for revealing how ecohydrological processes vary between the carbon and water cycles in the changing climate. Based on data from 11 eddy covariance flux sites for a total of 32 site-years in four typical agroecosystems, including winter wheat (Triticum aestivum L.), paddy rice (Oryza sativa L.), soybean (Glycine max L. Merr.) and summer maize (Zea mays L.), evapotranspiration (ET) of the four crops was partitioned based on the theory of underlying WUE. Seasonal dynamics of transpiration (T), ET, gross primary production (GPP), T-based WUE at the canopy level (WUEc) and ET-based WUE at the agroecosystem level (WUEe) were investigated. A series of climatic and vegetation factors affecting WUEc and WUEe were quantified using the path analysis method. T/ET was 0.67 ± 0.04 for summer maize, followed by winter wheat, soybean and paddy rice, with values of 0.65 ± 0.07, 0.60 ± 0.05 and 0.57 ± 0.02, respectively. T, ET, GPP and WUEe increased as crops grew but decreased as crops matured. However, WUEc did not change as apparently as WUEe since T and GPP changed synchronously throughout the growing season. Summer maize possessed the largest WUEc of 5.69 ± 0.29 g C kg−1 H2O, followed by winter wheat, soybean and paddy rice, with values of 4.47 ± 0.87, 3.79 ± 0.20 and 2.60 ± 0.17 g C kg−1 H2O, respectively. WUEe followed the same order as that of WUEc among the four croplands, with values of 3.32 ± 0.71, 2.73 ± 0.81, 1.90 ± 0.10 and 1.47 ± 0.09 g C kg−1 H2O, respectively. WUEc was mainly regulated by canopy water consumption than carbon sequestration, while carbon sequestration regulated WUEe more than water consumption at the ecosystem scale. WUEc was mainly controlled by climatic factors (vapor pressure deficit, net radiation, air temperature and soil temperature) in the croplands, while the leaf area index exerted stronger controls on WUEe variations than climatic factors by its strong regulation on T/ET in the four croplands.

Evapotranspiration Partitioning at Field Scales Using TSEB and Multi-Satellite Data Fusion in The Middle Reaches of Heihe River Basin, Northwest China

Daily evapotranspiration (ET) and its components of evaporation (E) and transpiration (T) at field scale are often required for improving agricultural water management and maintaining ecosystem health, especially in semiarid and arid regions. In this study, multi-year daily ET, E, and T at a spatial resolution of 100 m in the middle reaches of Heihe River Basin were computed based on an ET partitioning method developed by combing remote sensing-based ET model and multi-satellite data fusion methodology. Evaluations using flux tower measurements over irrigated cropland and natural desert sites indicate that this method can provide reliable estimates of surface flux partitioning and daily ET. Modeled daily ET yielded root mean square error (RMSE) values of 0.85 mm for cropland site and 0.84 mm for desert site, respectively. The E and T partitioning capabilities of this proposed method was further assessed by using ratios E/ET and T/ET derived from isotopic technology at the irrigated cropland site. Results show that apart from early in the growing season when the actual E was reduced by plastic film mulching, the modeled E/ET and T/ET agree well with observations in terms of both magnitude and temporal dynamics. The multi-year seasonal patterns of modeled ET, E, and T at field scale from this ET partitioning method shows reasonable seasonal variation and spatial variability, which can be used for monitoring plant water consumption in both agricultural and natural ecosystems.

Evaluation of atmospheric and terrestrial effects in the carbon cycle for forest and grassland ecosystems using a remote sensing and modeling approach

Soil moisture (SM) and vapor pressure deficit (VPD) are key variables that affect the carbon, water, and energy cycles. Very few studies have been conducted to evaluate their impacts on site-specific ecosystems, especially in mountainous regions and plains using an integrated remote sensing and modeling approach. This study used two flux tower sites, P301 (forest–ecosystem), Prairie View A&M University (PVAMU) (grassland–ecosystem) to evaluate gross primary production (GPP), sensible heat flux (H), and latent heat flux (LE). The community land model (CLM4) and soil moisture active passive (SMAP) carbon product were employed and evaluated using the flux tower data from 2016 to 2018. From the CLM4 and SMAP estimation of GPP, the CLM4 showed better accuracy at P301, whereas SMAP performed better at PVAMU. The SMAP overestimated GPP at P301, which could be due to the coarse footprint of SMAP (9 × 9 km). CLM4 overestimated GPP during the growing season at PVAMU, which could be due to the structural and parametric uncertainties. The H at P301 showed good agreement between the CLM4 results and flux tower measurements. However, H at the PVAMU site was most likely affected by the precipitation. Moreover, at P301, VPD was effective in controlling the carbon and water fluxes (GPP and LE) with a positive partial correlation (p–value < 0.05). However, at PVAMU, SM (terrestrial control) showed substantial control over the fluxes due to year-round precipitation. Overall, the results showed that the California site was affected due to low precipitation and could be vulnerable to climate change due to a reduction in SM and high VPD during the summers. In contrast, the Texas site had high SM due to frequent and intense rainfall events due to the changing climate. For the future study, a detailed analysis of SM and VPD control at regional and global scales for various land cover types is needed.

Examining the link between vegetation leaf area and land–atmosphere exchange of water, energy, and carbon fluxes using FLUXNET data

Abstract. Vegetation regulates the exchange of water, energy, and carbon fluxes between the land and the atmosphere. This regulation of surface fluxes differs with vegetation type and climate, but the effect of vegetation on surface fluxes is not well understood. A better knowledge of how and when vegetation influences surface fluxes could improve climate models and the extrapolation of ground-based water, energy, and carbon fluxes. We aim to study the link between vegetation and surface fluxes by combining the yearly average MODIS leaf area index (LAI) with flux tower measurements of water (latent heat), energy (sensible heat), and carbon (gross primary productivity and net ecosystem exchange). We show that the correlation of the LAI with water and energy fluxes depends on the vegetation type and aridity. Under water-limited conditions, the link between the LAI and the water and energy fluxes is strong, which is in line with a strong stomatal or vegetation control found in earlier studies. In energy-limited forest we found no link between the LAI and water and energy fluxes. In contrast to water and energy fluxes, we found a strong spatial correlation between the LAI and gross primary productivity that was independent of vegetation type and aridity. This study provides insight into the link between vegetation and surface fluxes. It indicates that for modelling or extrapolating surface fluxes, the LAI can be useful in savanna and grassland, but it is only of limited use in deciduous broadleaf forest and evergreen needleleaf forest to model variability in water and energy fluxes.

Satellite-based reflectances capture large fraction of variability in global gross primary production (GPP) at weekly time scales

A perception has emerged, based on several studies, that satellite-based reflectances are limited in terms of their ability to predict gross primary production (GPP) globally at weekly temporal scales. The basis for this inference is in part that reflectances, particularly expressed in the form of vegetation indices (VIs), convey information about potential rather than actual photosynthesis, and they are sensitive to non-green substances (e.g., soil, woody branches, and snow) as well as to chlorophyll. Previous works have suggested that processing and quality control of satellite-based reflectance data play an important role in their interpretation. In this study, we use high quality reflectance data from the MODerate-resolution Imaging Spectroradiometer (MODIS) data to train neural networks that are used to upscale GPP estimated from eddy covariance flux tower measurements globally. We quantify the ability of the machine learning approaches to capture GPP variability at daily to interannual time scales. Our results show that MODIS reflectances, when paired only with potential short-wave radiation, are able to capture a large fraction of GPP variability (approximately 77%) at daily to weekly time scales. Additional meteorological information (temperature, water vapor deficit, soil water content, ET, and incident radiation) captures only a few more percent of the GPP variability. The meteorological information is used most effectively when information about plant functional type and climate classification is included. We show that machine learning can be a useful tool for estimating GPP uncertainties as well as GPP itself from upscaling methods. Our estimated global annual mean GPP for 2007 is 142.5 ± 7.7 Pg C y−1, which is higher than some other satellite-based estimates but within the range of other reported observation-, model-, and hybrid-based values.

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.

Remote-Sensing-Based Water Balance for Monitoring of Evapotranspiration and Water Stress of a Mediterranean Oak–Grass Savanna

Mediterranean oak savannas (known as dehesas in Spain) are exposed to numerous threats from natural and economic causes. A close monitoring of the use of water resources and the status of the vegetation in these ecosystems can be useful tools for maintaining the production of ecological services. This study explores the estimation of evapotranspiration (ET) and water stress over a dehesa by integrating remotely sensed data into a water balance using the FAO-56 approach (VI-ETo model). Special attention is paid to the different phenology and contribution to the system’s hydrology of the two main canopy layers of the system (tree + grass). The results showed that the model accurately reproduced the dynamics of the water consumed by the vegetation, with RMSE of 0.47 mm day−1 and low biases for both, the whole system and the grass layer, when compared with flux tower measurements. The ET/ETo ratio helped to identify periods of water stress, confirmed for the grassland by measured soil water content. The modeling scheme and Sentinel-2 temporal resolution allowed the reproduction of fast and isolated ET pulses, important for understanding the hydrologic behavior of the system, confirming the adequacy of this sensor for monitoring grasslands water dynamics.

Inter-comparison of evapotranspiration datasets over heterogeneous landscapes across Australia

The selection of optimal actual evapotranspiration (ET) dataset for hydro-meteorological applications requires a detailed accuracy assessment and uncertainty characterization. This evaluation study was conducted at the site scale to compare the accuracies of ET measurements extracted from four widely used satellite- and model-based ET datasets (MOD16, GLEAM, GLDAS, and AWRA-L). Site-scale ET values were compared with five eddy-covariance-based in-situ flux tower measurements representing different land cover types: forest, grassland, and savanna, over heterogeneous landscapes across Australia. The results show moderate to good index of agreement >0.71, >0.88, and >0.46 for forest, grassland, and savanna, respectively, producing a root mean square errors in the range of 8.16–31.52 mm/month. AWRA-L demonstrated its best performance in forest ecosystems, GLDAS in grasslands, and GLEAM performed well in both forest and grassland biomes. Compared with the other datasets tested in this study, MOD16 performed relatively poorly across Australia. AWRA-L consistently showed the best agreement with the flux tower measurements, with a normalized standard deviation (σn) of ~0.98, followed by GLEAM (σn~0.92). Based on our accuracy assessment, agreement between the satellite- and model-based ET products and the flux tower measurements followed the order: AWRA-L > GLEAM > GLDAS > MOD16. The results of this study further reveal that existing ET datasets each have a particular advantages and limitations for a specific land cover types. Therefore, an integrated and merged ET product is needed to take advantage of the complementary strengths of each ET dataset. Overall, our quality evaluation of four widely used satellite- and model-based ET measurements can serve as a benchmark study of ET dynamics over different land cover types and be used to select an appropriate dataset for improved quantification of water consumption across Australia.

Scaling carbon fluxes from eddy covariance sites to globe: synthesis and evaluation of the FLUXCOM approach

Abstract. FLUXNET comprises globally distributed eddy-covariance-based estimates of carbon fluxes between the biosphere and the atmosphere. Since eddy covariance flux towers have a relatively small footprint and are distributed unevenly across the world, upscaling the observations is necessary to obtain global-scale estimates of biosphere–atmosphere exchange. Based on cross-consistency checks with atmospheric inversions, sun-induced fluorescence (SIF) and dynamic global vegetation models (DGVMs), here we provide a systematic assessment of the latest upscaling efforts for gross primary production (GPP) and net ecosystem exchange (NEE) of the FLUXCOM initiative, where different machine learning methods, forcing data sets and sets of predictor variables were employed. Spatial patterns of mean GPP are consistent across FLUXCOM and DGVM ensembles (R2&gt;0.94 at 1∘ spatial resolution) while the majority of DGVMs show, for 70 % of the land surface, values outside the FLUXCOM range. Global mean GPP magnitudes for 2008–2010 from FLUXCOM members vary within 106 and 130 PgC yr−1 with the largest uncertainty in the tropics. Seasonal variations in independent SIF estimates agree better with FLUXCOM GPP (mean global pixel-wise R2∼0.75) than with GPP from DGVMs (mean global pixel-wise R2∼0.6). Seasonal variations in FLUXCOM NEE show good consistency with atmospheric inversion-based net land carbon fluxes, particularly for temperate and boreal regions (R2&gt;0.92). Interannual variability of global NEE in FLUXCOM is underestimated compared to inversions and DGVMs. The FLUXCOM version which also uses meteorological inputs shows a strong co-variation in interannual patterns with inversions (R2=0.87 for 2001–2010). Mean regional NEE from FLUXCOM shows larger uptake than inversion and DGVM-based estimates, particularly in the tropics with discrepancies of up to several hundred grammes of carbon per square metre per year. These discrepancies can only partly be reconciled by carbon loss pathways that are implicit in inversions but not captured by the flux tower measurements such as carbon emissions from fires and water bodies. We hypothesize that a combination of systematic biases in the underlying eddy covariance data, in particular in tall tropical forests, and a lack of site history effects on NEE in FLUXCOM are likely responsible for the too strong tropical carbon sink estimated by FLUXCOM. Furthermore, as FLUXCOM does not account for CO2 fertilization effects, carbon flux trends are not realistic. Overall, current FLUXCOM estimates of mean annual and seasonal cycles of GPP as well as seasonal NEE variations provide useful constraints of global carbon cycling, while interannual variability patterns from FLUXCOM are valuable but require cautious interpretation. Exploring the diversity of Earth observation data and of machine learning concepts along with improved quality and quantity of flux tower measurements will facilitate further improvements of the FLUXCOM approach overall.

Generating Spatially Robust Carbon Budgets From Flux Tower Observations

AbstractEstimating global terrestrial productivity is typically achieved by rescaling individual flux tower measurements, traditionally assumed to represent homogeneous areas, using gridded remote sensing and climate data. Using 154 locations from the FLUXNET2015 database, we demonstrate that variations in spatial homogeneity and nonuniform sampling patterns introduce variability in carbon budget estimates that propagate to the biome scale. We propose a practical solution to quantify the variability of vegetation characteristics and uniformity of sampling patterns and, moreover, account for contributions of sampling variations over heterogeneous surfaces to carbon budgets from flux towers. Our proposed space‐time‐equitable budgets reduce uncertainty related to heterogeneities, allow for more accurate attribution of physiological variations in productivity trends, and provide more representative grid cell averages for linking fluxes with gridded data products.

Evaluation of MOD16 Algorithm over Irrigated Rice Paddy Using Flux Tower Measurements in Southern Brazil

Evapotranspiration (ET) is an important component of the hydrological cycle. Understanding the ET process has become of fundamental importance given the scenario of global change and increasing water use, especially in the agricultural sector. Determining ET over large agricultural areas is a limiting factor due to observational data availability. In this regard, remote sensing data has been used to estimate ET. In this study, we evaluated the Moderate-Resolution Imaging Spectroradiometer (MODIS) land surface ET product estimates (hereafter MOD16 ET – MODIS Global Terrestrial Evapotranspiration Product) over two rice paddy areas in Southern Brazil, through the ET measured using the eddy covariance technique (hereafter EC). The energy balance components were evaluated during fallow and flooded seasons showing latent heat flux dominates in both seasons. The results showed that MOD16 ET underestimated EC measurements. Overall, the RMSE (root mean square error) ranged between 13.40 and 16.35 mm 8-day−1 and percent bias (PBIAS) ranged between −33.7% and −38.7%. We also assessed the ET (measured and estimated) main drivers, with EC yielding higher correlation against observed net radiation (Rn) and global radiation (Rg), followed by air temperature (Temp) and vapor pressure deficit (VPD), whilst MOD16 ET estimates yielded higher correlation against leaf area index (LAI) and fraction of photosynthetically active radiation (fPAR). The MOD16 algorithm was forced with meteorological measurements but the results did not improve as expected, suggesting a low sensitivity to meteorological inputs. Our results indicated when a water layer was present over the soil surface without vegetation (LAI around zero), the largest differences between EC measurements and MOD16 ET were found. In this period, the expected domain of soil evaporation was not observed in MOD16 ET physical processes partition, indicating the algorithm was not able to detect areas with high soil moisture. In general, the MOD16 ET product presented low accuracy when compared against experimental measurements over flooded rice paddy, suggesting more studies are necessary, in order to reduce uncertainties associated to the land cover conditions.

The eddy-covariance storage term in air: Consistent community resources improve flux measurement reliability

In the widely-used eddy-covariance (EC) technique, it is often assumed that the air storage term, i.e. the change of below-turbulence-sensor scalar abundance, is negligible or comprises a small part of net surface-atmosphere exchange (NSAE). Previous studies have demonstrated that this assumption is often violated where non-turbulent processes prevail, and thus it is important to measure and calculate air storage in flux measurements. However, the implementation of air storage measurement and calculation is not ubiquitous as EC standard turbulent flux. In most cases, air storage is not a standard data product or even neglected in EC flux tower measurements. In other cases, air storage term is calculated simply using only the measurements at the tower top. This gap between the ideal initiative and actual implementation motivates us to derive and release one of the first community resources to facilitate the consistent measurement and calculation of EC air storage across sites. These resources include (i) the standardized air storage term measurement setup design at National Ecological Observatory Network (NEON) sites; (ii) the development and public release of the eddy4R.stor open-source air storage R-package; (iii) the derivation and public release of storage term data products, measured and calculated consistently across 47 NEON sites; and (iv) exploration the scientific usefulness of these resources through example use cases, specifically the exploration of the bias of the air storage term when different measurement level intensity used and exploration of the air storage term pattern. We expect the consistent air storage measurement and calculation can better serve the overall purpose of the EC technique to provide more reliable measurement of NSAE for the community. This can further benefit the community accurate depiction of the sub-daily to diurnal cycle of surface fluxes in doing carbon cycle flux partitioning, land modeling, and studying ecosystem response to weather extremes.

Carbon dioxide balance in early-successional forests after forest fires in interior Alaska

Fire is the major disturbance in North American boreal forests, and is thought to be the most important process that determines the carbon balance in North American boreal forests. This study conducted four years of tower flux measurements in a burned ecosystem from one to four years after a fire, and nine years of measurements in a young regeneration from five to 13 years after a fire in interior Alaska. The fire scar acted as a source of 248 g C m−2 yr-1 one year after the fire, and the annual CO2 emissions continuously decreased until seven years after the fire. At the final year of the study period, 13 years after the fire, the older forest became a CO2 sink. During the 13 years after the fires, the total post-fire emissions were 767 g C m−2 across both sites. Gross primary productivity (GPP) and ecosystem respiration (RE) recovered to those of mature black spruce forests 10 years after the fire. The successional recovery of GPP was mostly explained by the recovery of the leaf area index (LAI). Anomalous weather, such as a cold spring, hot summer, and high summer rainfall, increased the CO2 emissions rather than the uptake. In interior Alaska, the post-fire CO2 emissions (35–48 Tg C) were estimated to be approximately one third to fourth of the direct CO2 emissions (156 Tg C) by combustions from 1998 to 2017, which indicates that post-fire emissions are important to the regional CO2 balance. The forest successional trajectory at young age still contains large uncertainties due to lack of data, and thus adding new data improves our understanding of the post-fire CO2 balance.

Evaluating the Met Office Unified Model land surface temperature in Global Atmosphere/Land 3.1 (GA/L3.1), Global Atmosphere/Land 6.1 (GA/L6.1) and limited area 2.2 km configurations

Abstract. A limitation of the Met Office operational data assimilation scheme is that surface-sensitive infrared satellite sounding channels cannot be used during daytime periods where numerical weather prediction (NWP) model background land surface temperature (LST) biases are greater than 2 K in magnitude. The Met Office Unified Model (UM) has a significant cold LST bias in semi-arid regions when compared with satellite observations; a range of UM configurations were assessed with different model resolutions, land surface cover datasets and bare soil parameterisations. UM LST biases were evaluated at global resolution and in a limited area model (LAM) at a 2.2 km resolution over the SALSTICE (Semi-Arid Land Surface Temperature and IASI Calibration Experiment) experimental domain in south-eastern Arizona. This validation is in conjunction with eddy-covariance flux tower measurements. LST biases in the Global Atmosphere/Land 3.1 (GA/L3.1) configuration were largest in the mid-morning with respect to Moderate Resolution Imaging Spectroradiometer (MODIS) Terra (-13.6±2.8 K at the Kendall Grassland site). The diurnal cycle of LST in Global Atmosphere/Land 6.1 (GA/L6.1) showed a significant improvement relative to GA/L3.1 with the cold LST biases reduced to -1.4±2.7 K and -3.6±3.0 K for Terra and Aqua overpasses, respectively. The higher-resolution LAM showed added value over the global configurations. The spatial distribution of the LST biases relative to MODIS and the modelled bare soil cover fraction were found to be moderately correlated (0.61±0.08) during the daytime, which suggests that regions of cold LST bias are associated with low bare soil cover fraction. Coefficients of correlation with the shrub surface fractions followed the same trend as the bare soil cover fraction, although with a less significant correlation (0.36±0.09), and indicated that the sparse vegetation canopies in south-eastern Arizona are not well represented in UM ancillary datasets. The x component of the orographic slope was positively correlated with the LST bias (0.41±0.05 for MODIS Aqua) and identified that regions of cold model LST bias are found on easterly slopes, and regions of warm model LST bias are found on westerly slopes. An overestimate in the modelled turbulent heat and moisture fluxes at the eddy-covariance flux sites was found to be coincident with an underestimate in the ground heat flux.

Estimation of hourly land surface heat fluxes over the Tibetan Plateau by the combined use of geostationary and polar-orbiting satellites

Abstract. Estimation of land surface heat fluxes is important for energy and water cycle studies, especially on the Tibetan Plateau (TP), where the topography is unique and the land–atmosphere interactions are strong. The land surface heating conditions also directly influence the movement of atmospheric circulation. However, high-temporal-resolution information on the plateau-scale land surface heat fluxes has been lacking for a long time, which significantly limits the understanding of diurnal variations in land–atmosphere interactions. Based on geostationary and polar-orbiting satellite data, the surface energy balance system (SEBS) was used in this paper to derive hourly land surface heat fluxes at a spatial resolution of 10 km. Six stations scattered throughout the TP and equipped for flux tower measurements were used to perform a cross-validation. The results showed good agreement between the derived fluxes and in situ measurements through 3738 validation samples. The root-mean-square errors (RMSEs) for net radiation flux, sensible heat flux, latent heat flux and soil heat flux were 76.63, 60.29, 71.03 and 37.5 W m−2, respectively; the derived results were also found to be superior to the Global Land Data Assimilation System (GLDAS) flux products (with RMSEs for the surface energy balance components of 114.32, 67.77, 75.6 and 40.05 W m−2, respectively). The diurnal and seasonal cycles of the land surface energy balance components were clearly identified, and their spatial distribution was found to be consistent with the heterogeneous land surface conditions and the general hydrometeorological conditions of the TP.

Modulation of the urban boundary‐layer heat budget by a heatwave

Previous studies on urban heatwaves (HWs) often focused on the surface and near‐surface thermal conditions, but the urban boundary layer under HW conditions is less studied. This study examines the urban boundary‐layer heat budget over the Phoenix metropolitan area throughout a HW event in June 2016 using the Weather Research and Forecasting (WRF) model. The simulations are first validated against a wide range of observational data, including radiosonde, aircraft, weather station, and flux tower measurements. Then the boundary‐layer heat budget integrated over the urban area is examined. It is found that the surface sensible‐heat flux plays a major role in heating the urban boundary layer during daytime. The warming rate of the urban boundary layer is modulated by the large‐scale subsidence and the horizontal advection throughout the HW period, which however largely cancel each other due to the mass balance constraint. Our findings suggest that the traditional one‐dimensional convective boundary‐layer model may be more applicable than its original derivation implies.

Using Very High Resolution Thermal Infrared Imagery for More Accurate Determination of the Impact of Land Cover Differences on Evapotranspiration in an Irrigated Agricultural Area

Land cover has a strong effect on the evapotranspiration (ET) and the hydrologic cycle. Urbanization alters the land cover affecting the surface energy balance and ET by, for example, urban encroachment in agricultural areas. This study investigates the potential utility of high resolution ET in determining more accurately the impact of land cover on water use for an agricultural area. The approach was to apply the physically based two-source energy balance (TSEB) model to very high resolution (~8 m) aircraft thermal data and compare the ET pattern and distribution to TSEB output using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data acquired on 2 August 2012. Modeled flux components were validated using measurements collected from a network of 16 eddy covariance (EC) towers at the study site. The modeled ET using the aircraft data agreed satisfactorily with the flux tower measurements and had better performance than the TSEB model applied to the ASTER data. The percent errors between ET closed by the Bowen ratio (BR) and residual (RE) approaches were 3 and 1%, respectively. It is shown that the high resolution aircraft ET can more accurately determine the change in ET magnitude by having pure pixels of the main land cover types, namely urban, agriculture, and natural vegetation. As a result, the ET histogram exhibits a significant bi-modal distribution which can be used to accurately distinguish the impact on ET from urban versus agricultural land cover areas and potentially monitor the effect on ET over a landscape due to small changes in land cover. At the coarser 90 m resolution of ASTER, the TSEB ET estimates are more often a combination of urban and agricultural land cover ET near the urban-agriculture land cover boundaries. As a result, the bi-modal distribution in ET is almost nonexistent. This study demonstrates the potential utility of high resolution ET mapping for more accurately determining the magnitude of the ET differences between cropland and urban land cover. It also suggests that, with high resolution thermal imagery, TSEB is a potential tool for monitoring the impact on ET due to relatively small changes in land cover as a result of urban expansion. Such a tool would be useful for watershed management.

The spatial variability of actual evapotranspiration across the Amazon River Basin based on remote sensing products validated with flux towers

Actual evapotranspiration (ET) is a major component of the water balance. While several international flux measurement programs have been executed in the tropical rain forest of the Amazon, those measurements represent the evaporative process at a few selected sites only. The aim of this study is to obtain the spatial distribution of ET, using remote sensing techniques, across the entire Amazon River Basin. Results from six global ET products based on remote sensing techniques (GLEAM, SEBS, ALEXI, CMRSET, MOD16, and SSEBop) were merged to obtain an ensemble prediction of the ET rates for the complex and in-accessible environment of the Amazon at a spatial resolution of 250 m. The study shows that the basin-wide average ET is 1316 mm/year with a standard deviation of 192 mm/year. This new ET-Amazon product was validated against seven different historic flux tower measurements. The energy balance closure of the in situ measurements varied between 86 and 116%. Only months with more than 70% completeness of in situ measurements were considered for validation. Different procedures for closure correction were included in the analyses. The correlation between measured and remotely sensed ET is good (R2 > 0.97 for consecutive periods of 2 to 12 months), and the bias correction is negligible for the energy balance residual method, which seemed most favorable. Monthly ET values have more uncertainty. The monthly RMSE values vary between 7.4 and 27.8 mm/month (the average RMSE is 22.2 mm/month), and the coefficient of determination (R2) varies between 0.48 and 0.87 (the average R2 is 0.53). The ET from the water balance is 1380 mm/year, being − 64 mm/year difference and 4.6% less than ET derived from the water balance. The evaporation from the Amazon basin inside Brazil is 5063 km3/year, followed by Peru with 1165 km3/year. ET-Amazon shows more spatial details and accuracy than alternative global ET products such as LandFlux-EVAL, Model Tree Ensemble (MTE), and WACMOS-ET. This justifies the development of new regional ET products.