Latent Research Articles

The spatiotemporal variation of land surface heat fluxes in Tibetan Plateau during 2001–2022

The surface energy budget is important for understanding of energy and water cycle processes in the Tibetan Plateau (TP). In this study, the daily sensible (SH) and latent (LE) heat fluxes at the horizontal resolution of 1° are first estimated using the maximum entropy production (MEP) model (hereinafter SHMEP and LEMEP) in the entire TP during 2001–2022. The MEP model is built on physical and statistical principles to simulate surface heat fluxes. The surface net radiation, soil moisture (SM), and land surface temperature (LST) are the main driving variables for MEP model. To select the relatively accurate MEP input data, the merged surface net radiation (Rn–merged) under all-sky conditions are generated from CERES, ISCCP-FH, and ERA5 using the Bayesian Model Averaging scheme. Besides, the TP SM and LST from various data sources are evaluated using the in-situ observations at site scale. Based on the daily Rn–merged, ERA5 SM, CERES LST, and the MEP model, the daily SH and LE are estimated in the entire TP. The results show the daily SHMEP and LEMEP perform well at the validation sites, with the regional mean correlation coefficient (R) above 0.7, root-mean-square error (RMSE) of <19 W m−2, absolute value of bias and mean absolute error (MAE) below 11 W m−2. The monthly SHMEP has the regional mean R of 0.96 and RMSE of 6.30 W m−2 at all the measurement stations. For LEMEP, the regional mean R and RMSE values are 0.93 and 10.01 W m−2, respectively. The MEP simulation results are superior to the SH and LE in ERA5, ERA-Interim, MERRA-2, and JRA-55 reanalysis datasets and previous studies, especially for LE. Based on this new dataset, the spatial and temporal varying characteristics of SH and LE in the TP are analyzed. The annual mean SHMEP value are large in the western TP, the Qaidam Basin in the northern TP, and the Himalaya ranges, and small in the southeastern TP. The annual mean LEMEP has the maximum value in southeastern TP, and minimum value in western TP and the Qaidam Basin. The annual mean SHMEP and LEMEP over the entire TP are 34.79 W m−2 and 20.16 W m−2, with the significant declining trends of −0.17 W m−2 year−1 and − 0.052 W m−2 year−1 during the study period, respectively. The spatial distributions of the MEP surface heat fluxes and their trends are mainly influenced by the model inputs of Rn and SM in the TP.

Sensible, latent heat and store energy fluxes in the suburban São Paulo Megacity: seasonal and interannual variations and empirical modeling

Half-hourly values of turbulent sensible (H) and latent (LE) heat fluxes, net radiation (𝑄∗) at the surface, and heat store in the canopy (𝛥𝑄𝑆) are used to characterize seasonal and interannual changes during three not-consecutive years (2009–12) in a suburban area (LCZ 6) of the megacity of São Paulo. Turbulent fluxes are estimated applying eddy covariance method to turbulence measurements performed with sampling rate of 10 Hz, between 25 and 26 m above the surface, and enforcing quality control procedures. The diurnal evolution of monthly average hourly values of 𝐻, 𝐿𝐸, 𝑄∗ and 𝛥𝑄𝑆 observed in 2012 indicate a seasonal variation with a daytime maximum with 𝐻 varying from a maximum of 180 ± 12 W m-2 in October to a minimum of 80.1 ± 9 W m2 in June, 𝐿𝐸 varying from a maximum of 152.0 ± 7.6 W m-2 in February to a minimum of 66.4 ± 7.6 W m-2 in September, -𝑄∗ varying from a maximum of 554 ± 14 W m-2 in February to a minimum of 369±13 W m-2 in July, and -𝛥𝑄𝑆 varying from a maximum of 249± 47 W m-2 in April to a minimum of 181 ± 58 W m-2 in July. Monthly average daily values of H, LE and 𝑄∗ corroborate seasonal pattern displayed by monthly average hourly values, with maximum of 5 ± 0.5 MJ m-2 day-1 for H and 5.3 ± 0.6 MJ m-2 day-1 for LE in November (summer), and minimum of 1.3 ± 0.2 MJ m-2 day-1 for H in June and 1.9 ± 0.2 MJ m-2 day-1 for LE in August. −𝑄∗ shows a maximum of 11.6 ± 1.1 MJ m2 day-1 in March and a minimum of 4.5±0.9 MJ m-2 day-1 in June. Similar patterns was observed during 2009 and 2010. An empirical model based on Modified Priestly-Taylor Method coupled to Objective Hysteresis Method was validated with observations carried out during May and June (2009–2010, 2012) and applied successfully to estimate monthly average hourly values of 𝐻, LE and 𝛥𝑄𝑆 during February–April and August–Novemb 2012.

The effect of pixel heterogeneity on surface heat and water vapor flux estimated by the remote sensing-based model coupled with deep learning

Accurate quantification of sensible (H) and latent (LE) heat fluxes are desperately crucial for agricultural irrigation strategy, drought monitoring, water resource management, and climate change. Regional-scale H and LE were derived generally from satellite remote sensing data using relevant modeling methods. However, one of the challenges of modeling methods is to deal with sub-pixel heterogeneity, because the model usually assumes homogeneous conditions within a pixel. In this study, a hybrid model, which coupled deep neural network (DNN) and the remote sensing-based two-source energy balance (TSEB) model, was developed to estimate H and LE at multiple spatial resolutions to further quantify the influence of pixel size on the modeled fluxes. First, the DNN-based land surface temperature (LST) estimation model was developed and validated. LST at multiple spatial resolutions (i.e., 1, 5, 10, 30, 100, and 1000 m) were generated by the DNN method using multi-source satellite data including the GaoFen-2 (GF-2, a Chinese high-resolution optical remote-sensing satellite), Sentinel-2, and Landsat-8 images, respectively. Then, the hybrid DNN-TSEB model was used to estimate H and LE at multi-pixel sizes. Finally, modeled H and LE at 1 ∼ 1000 m were validated by eddy covariance (EC) and optical-microwave scintillometer (OMS) flux observation. Results revealed that the DNN-based LST model was a potential approach to generate LST at multiple pixel sizes. The hybrid DNN-TSEB model can produce more accurate H and LE when different land cover types were discriminated over heterogeneous farmland (e.g., the average bias for H and LE at a spatial resolution of 1 m were −9 and −6 W/m−2, respectively). Besides, the deviation of modeled fluxes from the observations increased as the spatial resolution became coarser, particularly the pixel size was greater than 30 m (average bias for H and LE were −34 and 30 W m−2, respectively). However, the magnitude of bias of fluxes estimates remained unchanged when the pixel sizes of satellite data were between 100 and 1000 m. The more important finding was that the hybrid model was a promising approach, which can obtain accurate estimates of H and LE at a high spatial resolution. In addition, the performance of the model based on GF-2 data was better than that based on Sentinel-2 and Landsat-8 data. The results suggest that pixel heterogeneity should be considered when estimating H and LE over heterogeneous landscapes with various surface types.

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

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

Detailed insitu leaf energy budget permits the assessment of leaf aerodynamic resistance as a key to enhance non-evaporative cooling under drought.

The modulation of the leaf energy budget components to maintain optimal leaf temperature are fundamental aspects of plant functioning and survival. Better understanding these aspects becomes increasingly important under a drying and warming climate when cooling through evapotranspiration (E) is suppressed. Combining novel measurements and theoretical estimates, we obtained unusually comprehensive twig-scale leaf energy budgets under extreme field conditions in droughted (suppressed E) and non-droughted (enhanced E) plots of a semi-arid pine forest. Under the same high mid-summer radiative load, leaf cooling shifted from relying on nearly equal contributions of sensible (H) and latent (LE) energy fluxes in non-droughted trees to relying almost exclusively on H in droughted ones, with no change in leaf temperature. Relying on our detailed leaf energy budget, we could demonstrate that this is due to a 2× reduction in leaf aerodynamic resistance. This capability for LE-to-H shift in leaves of mature Aleppo pine trees under droughted field conditions without increasing leaf temperature is likely a critical factor in the resilience and relatively high productivity of this important Mediterranean tree species under drying conditions.

Interannual, sub-seasonal and spatial variations in growing season surface heat fluxes for the U.S. Corn Belt

Spatiotemporal variations in surface latent (LE) and sensible (H) heat fluxes from croplands and patches of remnant deciduous forest in the Midwest U.S. Corn Belt have been proposed to trigger “nonclassical mesoscale circulations” (NCMCs) in the atmosphere's boundary layer and moist convection, under suitable synoptic conditions. However, prior to evaluating the link between land use and land cover (LULC), NCMCs, and precipitation for the Corn Belt, it is necessary to better understand the interannual, sub-seasonal, and spatial variations in surface heat fluxes. Here, a nine-year warm season “climatology” (1999–2007) of daily afternoon and early evening averages of LE and H from two flux towers, representative of cropland (Bondville, IL) and deciduous forest (Morgan Monroe State Forest, IN), is analyzed for sub-seasonal and interannual variations in the convective fluxes. Wilcoxon signed rank tests show significant growing season and sub-seasonal anomalies in LE and H at both flux tower sites. Structural equation modeling relates these inter-growing season anomalies in LE and H to variations in net radiation, vapor pressure deficit, and temperature, while MODIS Normalized Difference Vegetation Index data link some of the sub-seasonal variations in the surface energy balance to phenology at the start of the growing season. Additional composite analysis of LE and H from the North American Regional Reanalysis shows characteristic spatial heterogeneities and significant differences in LE and H for different synoptic circulation patterns related to the variations in net radiation and LULC. These results highlight the importance of considering the sub-seasonal and spatial variations in surface heat fluxes of the Corn Belt for the range of synoptic conditions, within the context of this region's recent interannual climate variations and will help inform the search for LULC-influenced moist convection.

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

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

Vulnerability of Primary Productivity and Its Carbon Use Efficiency to Unfavorable Climatic Conditions in Jambi Province, Indonesia

Climatic conditions and land cover play crucial roles in influencing the process of carbon uptake through vegetation. This study aimed to analyze the effect of climate variability on carbon uptake of four different land covers in Jambi Province, Indonesia. The four land cover types studied were: forest, shrub, grass, and irrigated soybean, based on Community Land Model version 5. Forest was found to have the highest net primary production (NPP) compared to the other land covers. Seasonal climate variability showed no major effect on NPP and gross primary production (GPP). However, GPP and NPP experienced significant declines during El Niño Southern Oscillation (ENSO), particularly in 2015. Carbon use efficiency (CUE = NPP/GPP) was also affected by ENSO, where CUE decreased during El Niño, particularly in October and November with an increased number of days without rainfall. In addition, the difference between latent (LE) and sensible heat (H) flux, denoted as (LE-H), decreased from August to November. This difference was highly correlated with NPP. This result indicates that when water supply is low, stomata will close, thereby reducing photosynthesis and transpiration, and allocating more of the available energy to sensible heat flux rather than latent heat flux.

Understanding and reducing the uncertainties of land surface energy flux partitioning within CMIP6 land models

Land surfaces dissipate energy through latent (LE) and sensible (H) heat fluxes that modulate atmospheric temperature and humidity, which in return affect land surface vegetation and soil processes. Within this two-way land-atmosphere coupling, surface energy partitioning (LE versus H) plays a central role in connecting the land and atmosphere states and fluxes. However, considerably large uncertainties still exist in earth system land models, i.e. the phase 6 of the Coupled Model Intercomparison Project (CMIP6). Further, the underlying controls from climate and biological factors on surface energy partitioning over different biome types are not well understood. In this study, we combined machine learning (ML) and causal inference models to investigate and reduce the uncertainties (i.e., parametric, structural, and forcing uncertainties) of CMIP6 simulated evaporative fraction (defined as LE / (LE + H)) across 64 FLUXNET sites that cover five major biomes. We found that CMIP6 model ensembles overestimated evaporative fraction with considerable spread at deciduous broadleaf forest, evergreen needleleaf forest, and savanna sites. Accounting for the biases from all related surface climate and biological driving variables, the CMIP6 model simulated EF could be largely improved (e.g., R between model and observed EF improved from 0.47 to 0.66), with leaf area index, vapor pressure deficit, and precipitation dominated the model improvement. Furthermore, ML-based parameterization generally showed a promising opportunity to further reduce model biases (e.g., R improved from 0.66 to 0.80) in spite of the limited improvement at evergreen broadleaf forest sites where model bias may be dominated by structural imperfection. This study provided an effective framework to understand and reduce model uncertainties in simulating land surface energy flux partitioning and, more importantly, highlighted the need of effective model structure improvement for the next generation earth system land model development.

Influence of short-term surface temperature dynamics on tree orchards energy balance fluxes

Land surface temperature (LST) plays an essential role in developing and applying precision agriculture protocols, especially for calculating crop evapotranspiration (ETc) by surface energy balance (SEB) approaches; and for determining crop water status. However, LST is quite dependent on the meteorological conditions, which can rapidly vary. This variability, together with the limited meterological data acquisition frequency in most weather stations, can lead to the miscalculation of the SEB components, especially relevant when used for irrigation purposes.The present study assessed the temporal dynamic of LST in a very short period of time (20-minutes) through the acquisition of multiple thermal imagery. Additionally, a combination of SEB approach with Eddy Covariance technique was performed for quantifying the effect that LST variations have on the sensible (H) and latent (LE) heat fluxes.Even under steady meteorological conditions, temporal variations in LST of 3.5 and 4.0 K were observed for tree canopy and sunny bare soil surfaces, respectively. These LST oscillations reached values of about 7.8 and 17.9 K for tree canopies and bare soil when heterogeneous meteorological conditions were observed (i.e. cloud presence). Such LST differences translated into H and LE differences of about 26 and 19%, respectively; with variations up to 5 (for H) and 2.7 times (for LE) under fast-varying meteorological conditions.The obtained results suggest the necessity of acquiring thermal imagery when steady meteorological conditions exist or, otherwise, ensuring the collection of instantaneous meteorological data for applying post-processing corrections. This is of importance when incorporating the obtained ETc maps into precision irrigation protocols.

Energy Exchange and Evapotranspiration over the Ejina Oasis Riparian Forest Ecosystem with Different Land-Cover Types

Investigating the energy and water vapor exchange in oasis riparian forest ecosystems is of significant importance to improve scientific understanding of land surface processes in extreme arid regions. The Heihe Watershed Allied Telemetry Experimental Research (HiWATER) provided many observations of water vapor and heat fluxes from riparian forest ecosystem by using a network of eddy-covariance (EC) systems installed over representative surfaces in the Ejina Oasis, which is located in the downstream areas of the Heihe River Basin, northwestern China. Based on EC flux measurements and meteorological data performed at five stations and covering representative surface types of Populus euphratica tree with associated Tamarix chinensis shrub, Tamarix chinensis shrubland, cantaloupe cropland, and barren-land, this study explored the spatio-temporal patterns of heat and water vapor fluxes over the Ejina Oasis riparian forest ecosystem with five different surface types over the course of a growing season in 2014. Energy balance closure of the flux data was evaluated; footprint analysis for each EC site was also performed. Results showed that energy balance closure for the flux data was reasonably good, with average energy balance ratio (EBR) of 1.03. The seasonal variations in net radiation (Rn), latent (LE), and sensible heat flux (H) over the five contrasting surfaces were similar, and a reverse seasonal change was observed in energy partitioning into LE and H. Remarkable differences in Rn, LE, and H between the five surfaces were explored preliminarily, associated closely with the soil properties and foliage phenology. Over the growing season (May–October) in 2014, the total ET ranged 622–731 mm for mixed forest of P. euphratica trees with associated T. chinensis shrubs with average daily ET of 3.6–4.2 mm; ET from T. chinensis shrubland was about 541 mm, with average daily ET of 3.6 mm. ET for barren-land was 195 mm. The total ET in irrigated cantaloupe cropland with plastic mulch was 431 mm for its four-month growing period with a total average of 3.8 mm d−1. Determination of ET over riparian forest ecosystem helps to improve reasonable use of limited water resource in the Ejina Oasis.

Estimation of Turbulent Heat Fluxes and Gross Primary Productivity by Assimilating Land Surface Temperature and Leaf Area Index

AbstractIn this study, land surface temperature (LST) and leaf area index (LAI) observations are merged with a coupled two‐source surface energy budget–vegetation dynamic model (TSEB–VDM) via a variational data assimilation (VDA) system to predict turbulent heat fluxes and gross primary productivity (GPP). The TSEB and VDM are coupled by relating photosynthesis in the VDM to transpiration in the TSEB equation. Unknown parameters of the VDA approach are the neutral bulk heat transfer coefficient (CHN), evaporative fractions for soil and canopy (EFS and EFC), and specific leaf area (cg). The VDA approach is evaluated at six AmeriFlux sites with distinct vegetative and climatic characteristics. The modeled sensible (H) and latent (LE) heat fluxes, and GPP agree well with the corresponding eddy covariance measurements in different environmental conditions. The six‐site average root mean square error (RMSE) of estimated daily H, LE, and GPP is 42.2 W m−2, 51.5 W m−2, and 1.8 gC m−2 d−1, respectively. The outcomes show that the developed VDA approach is able to exploit the implicit information in the sequences of LST and LAI measurements to estimate H, LE, and GPP. Our findings also indicate that the estimates of the H and LE are more sensitive to uncertainties in LST measurements, while the GPP retrievals are more affected by uncertainties in the LAI observations.

The impact of trends in South Asia Low pressure center on North Indian Ocean air-sea heat fluxes

This paper examines the interannual variability of latent (LE) and sensible (H) heat fluxes over the North Indian Ocean for the summer season (June, July, August), that show positive linear trends during 1958–2017. Using Air-Sea Flux (OAFlux) from NCEP/NCAR reanalysis-I, the variability and trends in LE and H heat fluxes are compared with changes in the position and pressure of the South Asia Low (SAL). Investigation of interactions between the air-sea heat fluxes and SAL pressure center through correlations analysis and with analysis of composites of humidity gradient, temperature gradient and vector wind fields is carried out. The results show that the SAL plays an important role in the variability of air-sea heat fluxes over North Indian Ocean. There is a negative correlation between SAL pressure and its latitude position. A change of the SAL from a north location to a south location induces changes in North Indian Ocean surface winds, humidity and temperature. These, combined with sea surface warming trends, produce trends in summer latent and sensible heat fluxes.

Energy flux observation in a shrub ecosystem of a gully region of the Chinese Loess Plateau

Flux observations are important for understanding the basic processes of carbon and water cycling in an ecosystem. In 2020 (1 January 2020 to 31 December 2020), we continuously monitored the surface energy fluxes of a shrub ecosystem using an eddy covariance system. Additionally, meteorological variables, such as temperature and rainfall, were measured. The study site was located in Chunhua of China, which is a gully region of the Chinese Loess Plateau. The diurnal and seasonal dynamic changes in the energy balance of this region were analysed, Bowen and energy balance ratios were calculated, and environmental factors affecting the variations in latent (LE) and sensible heat fluxes (H) were explored. The results showed that the annual net radiation (Rn), LE, H, and soil heat flux (G) were 2385.6, 1059.1, 787.6, and - 48.3 MJ/m2, respectively, with 490.58 MJ/m2 energy remaining undetected. The main part of the annual energy of the shrub ecosystem was latent heat, accounting for 44.4% of Rn. A seasonal variation in each energy component was evident, and the diurnal variation showed a single peak curve. LE in the growing season was dominant, and the proportion of LE and H in the net radiation in the non-growing season showed little difference. The Bowen ratio changed monthly and significantly fluctuated from 0.2 in July to 4.0 in December. It was high in winter and low in summer, with little fluctuation. The annual energy balance ratio was 0.8. The energy balance closure in the non-growing season was greater than that in the growing season. Both H and LE were significantly correlated with net radiation, and H was negatively correlated with rainfall, soil moisture, and soil temperature. Unlike H, LE was significantly positively correlated with soil temperature. In addition, LE was positively correlated with air humidity gradient and water vapour pressure difference.

Temporal variability in evapotranspiration and energy partitioning over a seasonally flooded scrub forest of the Brazilian Pantanal

Brazilian Pantanal is the largest seasonal wetland in the world and it plays an important role in regulating the local and global water cycle and energy balance. The Pantanal landscape consists of a mosaic of temporary and permanent aquatic habitats, floodable and non-floodable pastures, and forests; however, patterns of energy exchange are poorly understood, especially for seasonally flooded forests. Thus, the main objective of this research was to assess the variability of evapotranspiration (ET) and to evaluate biophysical controls on ET for a seasonally flooded scrub-forest. We used eddy covariance to measure latent (LE) and sensible (H) heat fluxes and linked both micrometeorological and remote sensing data to assess the connection between water dynamics and phenology. Our data suggest that 72% of the net radiation (Rn) was consumed by LE + H, even during the dry season, indicating that trees can access deep water when surface water is limiting. The decoupling factor (0.63 ± 0.08) suggests that the canopy is decoupled from the atmosphere and ET is essentially sensitive to Rn. The patterns of ET and vapor pressure deficit suggest that ET was more dependent on the Rn received and less dependent on stomatal control, but still exhibited a strong relationship with biotic factors such as enhanced vegetation index (EVI), which is sensitive to seasonal variations in leaf area index and canopy cover. Stomatal limitations of this seasonally flooded forest appear to be minimal, even in the dry season, presumably due to the relatively high-water table, the ability of trees to acquire subsurface water, and the predominance of secondary species with high rates of leaf gas exchange. These data indicate that seasonally flooded tropical forests of the Pantanal are consistent sources of water vapor to the atmosphere even during seasonal drought.

Impact of heatwave events on latent and sensible surface heat flux and future perspectives in shallow lakes based on climate change models

According to model projections, increases in the frequency and intensity of heatwaves are expected all over the world. This study analyzed, for the first time, the effect of heatwaves events on long-term surface latent (LE) and sensible heat fluxes (H) from two shallow lakes: La Salada lake (LS - 39°27′ S, 62°42′ W) and Sauce Grande lake (SG - 38°57′ S, 61°24′ W). The main drivers of LE and H are wind speed and direction, relative humidity, and the difference between air and water temperature. We found that the daily values of both fluxes were highly fluctuating. Mean daily H values ranged between -309.4 to 200.5 W m-2 and -78.6 to 104.8 W m-2 in LS and SG, respectively. LE oscillated between -152 and 463.9 W m-2 and between -59.2 and 360.1 W m-2 in LS and SG, respectively. Both fluxes decreased with the passage of heatwaves events, presenting a high variation in its amplitude. Changes up to 96% in mean daily LE and 671% in mean daily H for LS and up to 25% in LE and 987% in H for SG were accounted in days with heatwaves respect to regular ones. We analyzed the air temperature increase for both lakes under future global warming scenarios (RCP 4.5 y RCP 8.5), expecting higher amplitudes in heat fluxes. These results contribute to the development of lake models, as well as to water resources management in the future.

Improve the Performance of the Noah‐MP‐Crop Model by Jointly Assimilating Soil Moisture and Vegetation Phenology Data

AbstractThe interactions between crops and the atmosphere significantly impact surface energy and hydrology budgets, climate, crop yield, and agricultural management. In this study, a multipass land data assimilation scheme (MLDAS) is proposed based on the Noah‐MP‐Crop model. The ensemble Kalman filter (EnKF) method is used to jointly assimilate the leaf area index (LAI), soil moisture (SM), and solar‐induced chlorophyll fluorescence (SIF) observations to predict sensible (H) and latent (LE) heat fluxes, gross primary productivity (GPP), etc. Such joint assimilation is demonstrated to be effective in constraining the model state variables (i.e., leaf biomass and SM) and optimizing key crop‐model parameters (i.e., specific leaf area [SLA], and maximum rate of carboxylation, Vcmax). The performance of the MLDAS is evaluated against observations at two AmeriFlux cropland sites, revealing good an agreement with the observed H, LE, and GPP. When using optimized model parameters (SLA and Vcmax) and jointly assimilating LAI, SM, and SIF observations, the MLDAS produces 34.28%, 26.90%, and 51.82% lower root mean square deviations for daily H, LE, and GPP estimates compared with the Noah‐MP‐Crop open loop simulation. Our findings also indicate that the H and LE predictions are more sensitive to SM measurements, while the GPP simulations are more affected by LAI and SIF observations. The results indicate that performances of physical models can be greatly improved by assimilating multi‐source observations within MLDAS.

Independent estimation of sensible and latent fluxes in a vineyard using improved surface renewal analysis

Turbulent fluxes are key components of surface energy balance. Micrometeorological techniques, such as eddy covariance (EC), are commonly used to estimate sensible (H) and latent (LE) heat fluxes, because they provide direct measurements and do not interfere with the normal crop canopy development. However, EC has technical difficulties, strict size, and surface homogeneity requirements and is relatively expensive. Surface renewal analysis (SRA) is a promising alternative for turbulent flux estimation, because it is more economical, has less-stringent fetch requirements, and can be deployed in heterogeneous canopies or markedly sloped surfaces. Castellvi (SRACast) presented a methodology to estimate H using SRA, avoiding the high-frequency wind speed records that are necessary for determining stability and friction velocity, which are then used to calibrate the α parameter (a factor that corrects the nonuniform heating of an air parcel). Instead, an iterative procedure using wind speed recorded using a simple cup anemometer can be used. Here, we estimated sensible and latent heat fluxes and latent heat flux as residual from the energy–balance equation using SRACast during January 2018 and compared the results with EC measurements in a trilled vineyard (heterogeneous canopy), in Pirque, Santiago de Chile. Values of H and LE through SRACast present a high agreement with EC, with slopes (b) of 1.11 and 0.88 and coefficients of determination (R2) of 0.97 and 0.89, respectively. LEres_SRA showed values of 1.60 and 0.80 for b and R2, respectively. Energy balance closure was slightly better for SRA than for EC (b, 0.73 and 0.71; R2, 0.94 and 0.95), proving to be a reasonably good and simpler alternative for turbulent flux estimation.

High Spatial and Temporal Resolution Energy Flux Mapping of Different Land Covers Using an Off-the-Shelf Unmanned Aerial System

With the development of low-cost, lightweight, integrated thermal infrared-multispectral cameras, unmanned aerial systems (UAS) have recently become a flexible complement to eddy covariance (EC) station methods for mapping surface energy fluxes of vegetated areas. These sensors facilitate the measurement of several site characteristics in one flight (e.g., radiometric temperature, vegetation indices, vegetation structure), which can be used alongside in-situ meteorology data to provide spatially-distributed estimates of energy fluxes at very high resolution. Here we test one such system (MicaSense Altum) integrated into an off-the-shelf long-range vertical take-off and landing (VTOL) unmanned aerial vehicle, and apply and evaluate our method by comparing flux estimates with EC-derived data, with specific and novel focus on heterogeneous vegetation communities at three different sites in Germany. Firstly, we present an empirical method for calibrating airborne radiometric temperature in standard units (K) using the Altum multispectral and thermal infrared instrument. Then we provide detailed methods using the two-source energy balance model (TSEB) for mapping net radiation (Rn), sensible (H), latent (LE) and ground (G) heat fluxes at &lt;0.82 m resolution, with root mean square errors (RMSE) less than 45, 37, 39, 52 W m−2 respectively. Converting to radiometric temperature using our empirical method resulted in a 19% reduction in RMSE across all fluxes compared to the standard conversion equation provided by the manufacturer. Our results show the potential of this UAS for mapping energy fluxes at high resolution over large areas in different conditions, but also highlight the need for further surveys of different vegetation types and land uses.

On the Utility of High-Resolution Soil Moisture Data for Better Constraining Thermal-Based Energy Balance over Three Semi-Arid Agricultural Areas

Over semi-arid agricultural areas, the surface energy balance and its components are largely dependent on the soil water availability. In such conditions, the land surface temperature (LST) retrieved from the thermal bands has been commonly used to represent the high spatial variability of the surface evaporative fraction and associated fluxes. In contrast, however, the soil moisture (SM) retrieved from microwave data has rarely been used thus far due to the unavailability of high-resolution (field scale) SM products until recent times. Soil evaporation is controlled by the surface SM. Moreover, the surface SM dynamics is temporally related to root zone SM, which provides information about the water status of plants. The aim of this work was to assess the gain in terms of flux estimates when integrating microwave-derived SM data in a thermal-based energy balance model at the field scale. In this study, SM products were derived from three different methodologies: the first approach inverts SM, labeled hereafter as ‘SMO20’, from the backscattering coefficient and the interferometric coherence derived from Sentinel-1 products in the water cloud model (WCM); the second approach inverts SM from Sentinel-1 and Sentinel-2 data based on machine learning algorithms trained on a synthetic dataset simulated by the WCM noted ‘SME16’; and the third approach disaggregates the soil moisture active and passive SM at 100 m resolution using Landsat optical/thermal data ‘SMO19’. These SM products, combined with the Landsat based vegetation index and LST, are integrated simultaneously within an energy balance model (TSEB-SM) to predict the latent (LE) and sensible (H) heat fluxes over two irrigated and rainfed wheat crop sites located in the Haouz Plain in the center of Morocco. H and LE were measured over each site using an eddy covariance system and their values were used to evaluate the potential of TSEB-SM against the classical two source energy balance (TSEB) model solely based on optical/thermal data. Globally, TSEB systematically overestimates LE (mean bias of 100 W/m2) and underestimates H (mean bias of −110 W/m2), while TSEB-SM significantly reduces those biases, regardless of the SM product used as input. This is linked to the parameterization of the Priestley Taylor coefficient, which is set to αPT = 1.26 by default in TSEB and adjusted across the season in TSEB-SM. The best performance of TSEB-SM was obtained over the irrigated field using the three retrieved SM products with a mean R2 of 0.72 and 0.92, and a mean RMSE of 31 and 36 W/m2 for LE and H, respectively. This opens up perspectives for applying the TSEB-SM model over extended irrigated agricultural areas to better predict the crop water needs at the field scale.