Flux Tower Observations Research Articles

A Modified Vegetation Photosynthesis and Respiration Model (VPRM) for the Eastern USA and Canada, Evaluated With Comparison to Atmospheric Observations and Other Biospheric Models

AbstractAtmospheric CO2 measurements from a dense surface network can help to evaluate terrestrial biosphere model (TBM) simulations of Net Ecosystem Exchange (NEE) with two key benefits. First, gridded CO2 flux estimates can be evaluated over regional scales, not possible using flux tower observations at discrete locations for model evaluation. Second, TBM ability to explain atmospheric CO2 fluctuations due to the biosphere can be directly tested, an important objective for anthropogenic emissions monitoring using atmospheric observations. Here, we customize the Vegetation Photosynthesis and Respiration Model (VPRM) for an eastern North American domain with strong biological activity upwind of urban areas. Parameters are optimized using flux tower observations from a historical database with sites in (and near) the domain. In addition, the respiration model (originally a linear function of temperature) is modified to account for impacts of changing foliage, non‐linear temperature, and water stress. Flux estimates from VPRM, the Carnegie‐Ames‐Stanford Approach (CASA) model and the Simple Biosphere Model v4 (SiB4), are convolved with footprints from atmospheric transport models for evaluation with CO2 observations at 21 towers in the domain, with roughly half of the towers used here for the first time. Results show that the new respiration model in VPRM helps to correct a growing season sink bias in the atmosphere associated with underestimated summertime respiration using the original model with annual parameters. The new VPRM also better explains fine‐scale atmospheric CO2 variability compared to other TBMs, due to higher resolution diagnostic phenology, the new respiration model, domain‐specific parameters, and high‐quality input data sets.

Simulated effects of plastic film-mulched soil on surface energy fluxes based on optimized TSEB model in a drip-irrigated cotton field

The value-added by plastic film mulches are numerous in agricultural productivity and its global acceptance is unprecedented. However, its disturbing effects on the natural water cycling, surface energy fluxes and partitioning of evapotranspiration (ET) in drip-irrigated fields with plastic film mulch are often overlooked or not investigated due to deficiencies in existing process-based ET models. This study optimized the simulation of plastic film-mulched soil effects on surface energy fluxes by incorporating a thermal contact resistance component into a satellite derived Land Surface Temperature-based Two-Source Energy Balance (i.e.,TSEBPM) model, in a drip-irrigated cotton field for two consecutive growing seasons (2009 and 2010). The optimized model agrees better with Eddy Covariance (EC) flux tower observation and showed reduced error biases than the original TSEB model. The RMSE values decreased for the net radiation, latent, sensible and ground heat fluxes by 10.85, 1.93, 4.84, 0. 81 (Wm−2), respectively. Moreover, ET partitioning by TSEBPM revealed the actual contribution of evaporation (E) and transpiration (T) in cotton water use at different growth stages. When cotton growth reaches the ball-opening stage (i.e., harvesting period or cotton canopy coverage > 75%), the mean E/ET value was 0.20 ± 0.05, while T/ET was 0.79 ± 0.05 during the irrigation seasons. Meanwhile, the plastic mulching altered the reflected shortwave radiation, thereby impacting on surface albedo reflectance under well saturated cotton canopy. As such, the transpiration sub-model parameters such as fractionalvegetationcover and vegetationgreenfraction had influential effects, and are the most sensitive in the TSEBPM model parameterization. This study showed that TSEBPM can provide robust insights into the effects of plastic mulching on surface energy fluxes and cotton ET, therefore can be explored further across arid agroecosystems.

Gap-Filling Eddy Covariance Latent Heat Flux: Inter-Comparison of Four Machine Learning Model Predictions and Uncertainties in Forest Ecosystem

Environmental monitoring using satellite remote sensing is challenging because of data gaps in eddy-covariance (EC)-based in situ flux tower observations. In this study, we obtain the latent heat flux (LE) from an EC station and perform gap filling using two deep learning methods (two-dimensional convolutional neural network (CNN) and long short-term memory (LSTM) neural networks) and two machine learning (ML) models (support vector machine (SVM), and random forest (RF)), and we investigate their accuracies and uncertainties. The average model performance based on ~25 input and hysteresis combinations show that the mean absolute error is in an acceptable range (34.9 to 38.5 Wm−2), which indicates a marginal difference among the performances of the four models. In fact, the model performance is ranked in the following order: SVM > CNN > RF > LSTM. We conduct a robust analysis of variance and post-hoc tests, which yielded statistically insignificant results (p-value ranging from 0.28 to 0.76). This indicates that the distribution of means is equal within groups and among pairs, thereby implying similar performances among the four models. The time-series analysis and Taylor diagram indicate that the improved two-dimensional CNN captures the temporal trend of LE the best, i.e., with a Pearson’s correlation of >0.87 and a normalized standard deviation of ~0.86, which are similar to those of in situ datasets, thereby demonstrating its superiority over other models. The factor elimination analysis reveals that the CNN performs better when specific meteorological factors are removed from the training stage. Additionally, a strong coupling between the hysteresis time factor and the accuracy of the ML models is observed.

Testing stomatal models at the stand level in deciduous angiosperm and evergreen gymnosperm forests using CliMA Land (v0.1)

Abstract. At the leaf level, stomata control the exchange of water and carbon across the air–leaf interface. Stomatal conductance is typically modeled empirically, based on environmental conditions at the leaf surface. Recently developed stomatal optimization models show great skills at predicting carbon and water fluxes at both the leaf and tree levels. However, how well the optimization models perform at larger scales has not been extensively evaluated. Furthermore, stomatal models are often used with simple single-leaf representations of canopy radiative transfer (RT), such as big-leaf models. Nevertheless, the single-leaf canopy RT schemes do not have the capability to model optical properties of the leaves nor the entire canopy. As a result, they are unable to directly link canopy optical properties with light distribution within the canopy to remote sensing data observed from afar. Here, we incorporated one optimization-based and two empirical stomatal models with a comprehensive RT model in the land component of a new Earth system model within CliMA, the Climate Modelling Alliance. The model allowed us to simultaneously simulate carbon and water fluxes as well as leaf and canopy reflectance and fluorescence spectra. We tested our model by comparing our modeled carbon and water fluxes and solar-induced chlorophyll fluorescence (SIF) to two flux tower observations (a gymnosperm forest and an angiosperm forest) and satellite SIF retrievals, respectively. All three stomatal models quantitatively predicted the carbon and water fluxes for both forests. The optimization model, in particular, showed increased skill in predicting the water flux given the lower error (ca. 14.2 % and 21.8 % improvement for the gymnosperm and angiosperm forests, respectively) and better 1:1 comparison (slope increases from ca. 0.34 to 0.91 for the gymnosperm forest and from ca. 0.38 to 0.62 for the angiosperm forest). Our model also predicted the SIF yield, quantitatively reproducing seasonal cycles for both forests. We found that using stomatal optimization with a comprehensive RT model showed high accuracy in simulating land surface processes. The ever-increasing number of regional and global datasets of terrestrial plants, such as leaf area index and chlorophyll contents, will help parameterize the land model and improve future Earth system modeling in general.

Mapping Daily Evapotranspiration at Field Scale Using the Harmonized Landsat and Sentinel-2 Dataset, with Sharpened VIIRS as a Sentinel-2 Thermal Proxy

Accurate and frequent monitoring of evapotranspiration (ET) at sub-field scales can provide valuable information for agricultural water management, quantifying crop water use and stress toward the goal of increasing crop water use efficiency and production. Using land-surface temperature (LST) data retrieved from Landsat thermal infrared (TIR) imagery, along with surface reflectance data describing albedo and vegetation cover fraction, surface energy balance models can generate ET maps down to a 30 m spatial resolution. However, the temporal sampling by such maps can be limited by the relatively infrequent revisit period of Landsat data (8 days for combined Landsats 7 and 8), especially in cloudy areas experiencing rapid changes in moisture status. The Sentinel-2 (S2) satellites, as a good complement to the Landsat system, provide surface reflectance data at 10–20 m spatial resolution and 5 day revisit period but do not have a thermal sensor. On the other hand, the Visible Infrared Imaging Radiometer Suite (VIIRS) provides TIR data on a near-daily basis with 375 m resolution, which can be refined through thermal sharpening using S2 reflectances. This study assesses the utility of augmenting the Harmonized Landsat and Sentinel-2 (HLS) dataset with S2-sharpened VIIRS as a thermal proxy source on S2 overpass days, enabling 30 m ET mapping at a potential combined frequency of 2–3 days (including Landsat). The value added by including VIIRS-S2 is assessed both retrospectively and operationally in comparison with flux tower observations collected from several U.S. agricultural sites covering a range of crop types. In particular, we evaluate the performance of VIIRS-S2 ET estimates as a function of VIIRS view angle and cloud masking approach. VIIRS-S2 ET retrievals (MAE of 0.49 mm d−1 against observations) generally show comparable accuracy to Landsat ET (0.45 mm d−1) on days of commensurate overpass, but with decreasing performance at large VIIRS view angles. Low-quality VIIRS-S2 ET retrievals linked to imperfect VIIRS/S2 cloud masking are also discussed, and caution is required when applying such data for generating ET timeseries. Fused daily ET time series benefited during the peak growing season from the improved multi-source temporal sampling afforded by VIIRS-S2, particularly in cloudy regions and over surfaces with rapidly changing vegetation conditions, and value added for real-time monitoring applications is discussed. This work demonstrates the utility and feasibility of augmenting the HLS dataset with sharpened VIIRS TIR imagery on S2 overpass dates for generating high spatiotemporal resolution ET products.

Sensitivity analysis of hydrological processes to perturbed climate in a southern boreal forest basin

Hydrological processes over and through frozen and unfrozen ground were simulated in the well instrumented boreal forest basin of White Gull Creek, Saskatchewan, Canada using a model created using the flexible Cold Regions Hydrological Modelling (CRHM) platform. The CRHM-created Boreal Hydrology Model was structured and initially parameterized using decades of process hydrology research in the southern boreal forest with minor parameter calibration, and generally produced quite good performance on simultaneously reproducing the measurements of runoff, snow water equivalent (SWE), soil liquid water content and eddy correlation flux tower observations of evapotranspiration (ET) over two decades. To examine the sensitivity of basin hydrology to perturbed climate inputs, air temperature (T) inputs were set up by linear increments in the reference observation of up to +6 ℃, and precipitation (P) inputs were generated by multiplying the reference observed P from 70% to 130%. The model results showed that the basin hydrological variables showed quite different sensitivities to perturbations of P and T. The volume of annual runoff and the annual runoff coefficient increased more rapidly with rising P, at rates of 31% and 16% per 10% increase in P, but decreased by only 3.8% and 4.7% per 1 ℃ of warming. Annual ET increased rapidly with temperature, by 7% per 1 ℃ of warming and therefore drove the streamflow volumetric changes with warming, but increased only 1% per 10% increase in P. Perturbations of P and T had distinctively different influences on the streamflow regime. Increased P enhanced the intra- and inter-annual variabilities of basin runoff, reduced the relative contribution of winter runoff to annual runoff and increased the relative contribution of summer runoff; whilst rising T resulted in the inverse changes in the streamflow regime. Effects of warming on some hydrological processes could be compensated for to varying degrees by the effects of increases in P. Reductions in the annual runoff volume and runoff coefficient caused by warming up to 6 ℃ could be compensated for by increases of <20% in P. However, the maximum increase in P (+30%) examined could only compensate for the changes in snow processes caused by warming of less than 4 ℃ and snow-cover duration decreases with 1 ℃ warming could not be compensated for by any precipitation increase considered. These results inform the vulnerability of boreal forest hydrology to the first-order changes in P and T and provide guidance for further climate impact assessments for hydrology in the southern boreal forest in Canada.

Modeling gas exchange and biomass production in West African Sahelian and Sudanian ecological zones

Abstract. West African Sahelian and Sudanian ecosystems provide essential services to people and also play a significant role within the global carbon cycle. However, climate and land use are dynamically changing, and uncertainty remains with respect to how these changes will affect the potential of these regions to provide food and fodder resources or how they will affect the biosphere–atmosphere exchange of CO2. In this study, we investigate the capacity of a process-based biogeochemical model, LandscapeDNDC, to simulate net ecosystem exchange (NEE) and aboveground biomass of typical managed and natural Sahelian and Sudanian savanna ecosystems. In order to improve the simulation of phenology, we introduced soil-water availability as a common driver of foliage development and productivity for all of these systems. The new approach was tested by using a sample of sites (calibration sites) that provided NEE from flux tower observations as well as leaf area index data from satellite images (MODIS, MODerate resolution Imaging Spectroradiometer). For assessing the simulation accuracy, we applied the calibrated model to 42 additional sites (validation sites) across West Africa for which measured aboveground biomass data were available. The model showed good performance regarding biomass of crops, grass, or trees, yielding correlation coefficients of 0.82, 0.94, and 0.77 and root-mean-square errors of 0.15, 0.22, and 0.12 kg m−2, respectively. The simulations indicate aboveground carbon stocks of up to 0.17, 0.33, and 0.54 kg C ha−1 m−2 for agricultural, savanna grasslands, and savanna mixed tree–grassland sites, respectively. Carbon stocks and exchange rates were particularly correlated with the abundance of trees, and grass biomass and crop yields were higher under more humid climatic conditions. Our study shows the capability of LandscapeDNDC to accurately simulate carbon balances in natural and agricultural ecosystems in semiarid West Africa under a wide range of conditions; thus, the model could be used to assess the impact of land-use and climate change on the regional biomass productivity.

Parsimony vs predictive and functional performance of three stomatal optimization principles in a big‐leaf framework

Stomatal optimization models can improve estimates of water and carbon fluxes with relatively low complexity, yet there is no consensus on which formulations are most appropriate for ecosystem-scale applications. We implemented three existing analytical equations for stomatal conductance, based on different water penalty functions, in a big-leaf comparison framework, and determined which optimization principles were most consistent with flux tower observations from different biomes. We used information theory to dissect controls of soil water supply and atmospheric demand on evapotranspiration in wet to dry conditions and to quantify missing or inadequate information in model variants. We ranked stomatal optimization principles based on parameter uncertainty, parsimony, predictive accuracy, and functional accuracy of the interactions between soil moisture, vapor pressure deficit, and evapotranspiration. Performance was high for all model variants. Water penalty functions with explicit representation of plant hydraulics did not substantially improve predictive or functional accuracy of ecosystem-scale evapotranspiration estimates, and parameterizations were more uncertain, despite having physiological underpinnings at the plant level. Stomatal optimization based on water use efficiency thus provided more information about ecosystem-scale evapotranspiration compared to those based on xylem vulnerability and proved more useful in improving ecosystem-scale models with less complexity.

Sun-induced fluorescence and near-infrared reflectance of vegetation track the seasonal dynamics of gross primary production over Africa

Abstract. The carbon cycle of tropical terrestrial vegetation plays a vital role in the storage and exchange of atmospheric CO2. But large uncertainties surround the impacts of land-use change emissions, climate warming, the frequency of droughts, and CO2 fertilization. This culminates in poorly quantified carbon stocks and carbon fluxes even for the major ecosystems of Africa (savannas and tropical evergreen forests). Contributors to this uncertainty are the sparsity of (micro-)meteorological observations across Africa's vast land area, a lack of sufficient ground-based observation networks and validation data for CO2, and incomplete representation of important processes in numerical models. In this study, we therefore turn to two remotely sensed vegetation products that have been shown to correlate highly with gross primary production (GPP): sun-induced fluorescence (SIF) and near-infrared reflectance of vegetation (NIRv). The former is available from an updated product that we recently published (Sun-Induced Fluorescence of Terrestrial Ecosystems Retrieval – SIFTER v2), which specifically improves retrievals in tropical environments. A comparison against flux tower observations of daytime-partitioned net ecosystem exchange from six major biomes in Africa shows that SIF and NIRv reproduce the seasonal patterns of GPP well, resulting in correlation coefficients of &gt;0.9 (N=12 months, four sites) over savannas in the Northern and Southern hemispheres. These coefficients are slightly higher than for the widely used Max Planck Institute for Biogeochemistry (MPI-BGC) GPP products and enhanced vegetation index (EVI). Similarly to SIF signals in the neighboring Amazon, peak productivity occurs in the wet season coinciding with peak soil moisture and is followed by an initial decline during the early dry season, which reverses when light availability peaks. This suggests similar leaf dynamics are at play. Spatially, SIF and NIRv show a strong linear relation (R&gt;0.9; N≥250 pixels) with multi-year MPI-BGC GPP even within single biomes. Both MPI-BGC GPP and the EVI show saturation relative to peak NIRv and SIF signals during high-productivity months, which suggests that GPP in the most productive regions of Africa might be larger than suggested.

An improved remote sensing based approach for predicting actual Evapotranspiration by integrating LiDAR

Spatially distributed information of actual Evapotranspiration (ET) is essentially required for various applications in agriculture, as well as studies related to water balance. An Energy Balance model that uses optical and thermal remote sensing datasets is appropriate for the spatially distributed estimation of ET, which is primarily controlled by local environmental conditions. In this study, we use the Surface Energy Balance System (SEBS), which is an Energy Balance model, to estimate an improved actual ET, by integrating canopy height information obtained from ICESat/GLAS, the first space-borne LiDAR satellite. Our model estimates latent heat flux (LE) as a residual of energy balance, while sensible heat flux (H) is estimated based on Monin–Obukhov similarity theory. The spatial variability of H and LE estimated from Landsat TM/ETM+ images were compared with eddy covariance (EC) based in situ flux tower observations, in order to document the SEBS model uncertainties in three forest sites in the East and Southeast Asian ecosystem. We identified that canopy height (hc) is one of the critical parameters that can induce uncertainties in SEBS model estimations. Therefore, hc obtained from LiDAR was introduced in parameterization of the roughness length for momentum transfer (z0m). In situ based hc obtained from flux tower stations, and the normalized difference vegetation index based formulation of hc proposed by Su (2001), were also introduced in the SEBS z0m parameterization. Root mean square error of LiDAR integrated SEBS (SEBSLiDar) for estimated H values reduced by (20, 45, and 23)% compared to Landsat integrated SEBS (SEBSLandsat), and (18, 7, and 9)% compared to In situ integrated SEBS (SEBSInsitu) at the three selected forest sites (SMC, QYZ, and SMF), respectively, while the mean coefficient of determination values for the estimated LE improved by (9.09 and 5.88)% compared to SEBSLandsat and SEBSInsitu, respectively, with reference to ground-based flux tower observations. Our analysis reveals that LiDAR-based parameterization of z0m can significantly increase the accuracy of estimated roughness lengths within an ET model, which can therefore improve the accuracy of the estimated turbulent heat fluxes. SEBSLiDAR was further evaluated for the spatially distributed mapping of actual ET. We have observed that ET estimated by SEBSLandsat overestimated in forest sites compared to EC observations; however, LiDAR-integrated SEBS marginally improved the accuracy of ET, thereby leading to an improvement in the mean correlation value of 0.87 and mean normalized standard deviation value of 0.93 approaching unity based on Taylor diagram, which more closely approximates the actual ground-based measurements. The proposed methodology overcomes the current limitation in the energy balance models in estimating roughness lengths, by providing accurate formulation of hc by specifically incorporating LiDAR measurements in areas of tall vegetation canopies. The operational improvement in roughness lengths by next-generation SEBSLiDAR model provides an opportunity to improve the estimation accuracy of water and energy fluxes, specifically ET. Overall, our proposed SEBSLiDAR model provides a benchmark that is useful to policy makers and water resource managers in devising a plan for sustainable use of water resources in the East Asian region, as well as other similar ecosystems.

Functional convergence of biosphere–atmosphere interactions in response to meteorological conditions

Abstract. Understanding the dependencies of the terrestrial carbon and water cycle with meteorological conditions is a prerequisite to anticipate their behaviour under climate change conditions. However, terrestrial ecosystems and the atmosphere interact via a multitude of variables across temporal and spatial scales. Additionally these interactions might differ among vegetation types or climatic regions. Today, novel algorithms aim to disentangle the causal structure behind such interactions from empirical data. The estimated causal structures can be interpreted as networks, where nodes represent relevant meteorological variables or land-surface fluxes and the links represent the dependencies among them (possibly including time lags and link strength). Here we derived causal networks for different seasons at 119 eddy covariance flux tower observations in the FLUXNET network. We show that the networks of biosphere–atmosphere interactions are strongly shaped by meteorological conditions. For example, we find that temperate and high-latitude ecosystems during peak productivity exhibit biosphere–atmosphere interaction networks very similar to tropical forests. In times of anomalous conditions like droughts though, both ecosystems behave more like typical Mediterranean ecosystems during their dry season. Our results demonstrate that ecosystems from different climate zones or vegetation types have similar biosphere–atmosphere interactions if their meteorological conditions are similar. We anticipate our analysis to foster the use of network approaches, as they allow for a more comprehensive understanding of the state of ecosystem functioning. Long-term or even irreversible changes in network structure are rare and thus can be indicators of fundamental functional ecosystem shifts.

Can Land Surface Models Capture the Observed Soil Moisture Control of Water and Carbon Fluxes in Temperate‐To‐Boreal Forests?

AbstractThe reservoir of soil water is exchanged with the atmosphere through evaporative processes, which are mediated via vegetation through transpiration. Carbon uptake is closely coupled to transpiration, and most process‐based models link transpiration and photosynthesis explicitly. Here, we evaluate the simulation of water and carbon fluxes in forested areas of North America using point‐based simulations of the Community Land Model version 5 (CLM5) with Soil Moisture Active/Passive (SMAP) satellite derived soil moisture measurements and in situ measurements at eight Fluxnet and two United States Climate Research Network (CRN) sites. Compared to observations, there is a broad site‐to‐site variability in simulated soil moisture, with some sites exhibiting wet biases and others dry biases. The bias sign does not depend on ecosystem or other environmental drivers such as radiation and temperature. Compared to Fluxnet latent heat (LH) and gross primary production (GPP) flux tower observations, simulated LH flux biases at deciduous broadleaf forests are linked with soil moisture biases, and the model captures the observed seasonal cycle and parabolic seasonal relationship with soil moisture. The parabolic shape is driven by high soil moisture and low LH fluxes in June, peak LH and drier soil conditions in July, followed by further soil moisture drawdown in August. GPP is underestimated at most sites, and the model exhibits a linear relationship between soil moisture and GPP. Because the photosynthesis parameterizations are similar in most Earth system models, further model development that incorporates observations and observed relationships is needed to accurately capture the GPP‐soil moisture relationship.

A physical-based two-source evapotranspiration model with Monin–Obukhov similarity theory

ABSTRACT Accurate quantification of energy budget components and its partitioning between canopy and soil is essential for improving water resource management. During the last few decades, several two-source evapotranspiration (ET) models have been developed by exploiting satellite datasets in conjunction with meteorological observations to estimate fluxes at different spatio-temporal scales. However, the complex parameterization scheme intrinsic to those models as well as the need for a large number of input datasets hampers their applications in a broader hydro-meteorological studies. In this study, we formulated a Leaf Area Index (LAI)-based methodology to develop a Two-Source Surface Energy Balance System (TS-SEBS) based on Monin–Obukhov similarity theory and Su (2002) principles, which uses fewer input datasets from satellite and meteorological observations to obtain fluxes on local as well as regional scales. Specifically, 97 medium-resolution Landsat TM/ETM+ images have been used to validate the TS-SEBS model estimations with (a) Eddy Covariance (EC)-based in-situ flux tower observations and (b) one-source SEBS (Surface Energy Balance System) model estimations in East Asian ecosystems. The mean bias and RMSE were 10.90 and 58.95 Wm−2 for instantaneous net radiation, 35.14, and 63.35 Wm−2 for instantaneous net radiation over the soil, and −25.06 and 45.93 Wm−2 for instantaneous net radiation over the canopy, whereas the ground heat flux showed a mean bias and RMSE of 45.60 and 59.21 Wm−2, respectively, over six selected sites. The results of sensible heat flux (H) for SEBS revealed its underestimation pattern (mean bias value of −20.68 Wm−2), whereas TS-SEBS tended to slightly overestimate values (mean bias value of 2.32 Wm−2) compared with EC observations. The TS-SEBS exhibited relatively better approximations of H (mean RMSE of 57.83 Wm−2) compared with SEBS (mean RMSE of 79.47 Wm−2) by reducing the error by ~27%. In terms of spatially distributed estimation of ET, the TS-SEBS outperformed SEBS with a mean normalized standard deviation value of 1.08 compared with 1.25 for SEBS, a reduction of ~14%. A mean Pearson’s correlation coefficient value >0.73 was obtained for both selected models based on a Taylor diagram, and these statistics more closely approximate the ground-based EC observations. The better performance of TS-SEBS compared with SEBS is due to the incorporation of improved LAI-based formulations in its structural algorithm to partition fluxes between soil and canopy, incorporation of improved wind speed profile extinction coefficient, and separate calculations of aerodynamic resistance for both soil and canopy components. The proposed methodology in the next-generation TS-SEBS model provides an unprecedented opportunity for quantifying improved energy budgets and partitioning fluxes between soil and canopy in an accurate and computationally efficient approach for better plant-soil-atmosphere interaction studies.

Modeling Evapotranspiration of Winter Wheat Using Contextual and Pixel-Based Surface Energy Balance Models

HighlightsThree contextual-based (CB) and two pixel-based (PB) models were evaluated to estimate ET of rainfed winter wheat.Instantaneous available energy estimation and ET upscaling impacted model performance.The CB models performed better at instantaneous and daily scales compared to the PB models.ET estimation biases increased during low vegetation and drier conditions, especially for the PB models.Abstract. Surface energy balance (SEB) models based on thermal remote sensing data are widely used in research applications to map evapotranspiration (ET) across various landscapes. However, their ability to capture ET from winter wheat remains underexplored, especially in practical applications such as integrated resource management and drought preparedness. Investigating winter wheat ET dynamics is important in agricultural regions such as the Southern Great Plains of the U.S., where winter wheat is extensively cultivated. The goal of this study was to evaluate the performance of five fully automated SEB models, three contextual-based (CB) and two pixel-based (PB), in estimating instantaneous and daily ET of winter wheat by comparing the model results with flux tower observations. The CB models included Surface Energy Balance Algorithm for Land (SEBAL), Mapping Evapotranspiration at high Resolution with Internalized Calibration (METRIC), and Triangular Vegetation Temperature (TVT). The PB models included Surface Energy Balance System (SEBS) and Two-Source Energy Balance (TSEB). Model evaluation during two winter wheat growing seasons (2016-2018) using 28 Landsat images showed that the instantaneous ET estimates from METRIC and TSEB had the smallest (RMSE = 0.14 mm h-1) and largest (RMSE = 0.27 mm h-1) errors, respectively. At the daily scale, SEBAL was the best performing model (RMSE = 1.0 mm d-1), followed by TVT (RMSE = 1.1 mm d-1), METRIC (RMSE = 1.2 mm d-1), SEBS (RMSE = 1.3 mm d-1), and TSEB (RMSE = 1.5 mm d-1). Overall, the CB models provided smaller errors than the PB models. Larger errors in daily ET estimation were observed during low vegetation and drier conditions, especially for the PB models. Keywords: Flux tower, Landsat, Southern Great Plains, Water use.

Surface Energy Flux Estimation in Two Boreal Settings in Alaska Using a Thermal-Based Remote Sensing Model

Recent Arctic warming has led to changes in the hydrological cycle. Circum-Arctic and circumboreal ecosystems are showing evidence of “greening” and “browning” due to temperature warming leading to shrub encroachment, tree mortality and deciduousness. Increases in latent heat flux from increased evapotranspiration rates associated with deciduous-dominated ecosystems may be significant, because deciduous vegetation has extremely high-water use and water storage capacity compared to coniferous and herbaceous plant species. Thus, the impact of vegetation change in boreal ecosystems on regional surface energy balance is a significant knowledge gap that must be addressed to better understand observed trends in water use/availability and tree mortality. To this end, output from a two-source energy balance model (TSEB) with modifications for high latitude boreal ecosystems was evaluated using flux tower measurements and Terra/Aqua MODIS remote sensing data collected over the two largest boreal forest types in Alaska (birch and black spruce). Data under clear and overcast days and from leaf-out to senescence from 2012 to 2016 were used for validation. Using flux tower observations and local model inputs, modifications to the model formulation for soil heat flux, net radiation partitioning, and canopy transpiration were required for the boreal forest. These improvements resulted in a mean absolute percent difference of around 23% for turbulent daytime fluxes when surface temperature from the flux towers was used, similar to errors reported in other studies conducted in warmer climates. Results when surface temperature from Terra/Aqua MODIS estimates were used as model input suggested that these model improvements are pertinent for regional scale applications. Vegetation indices and LAI time-series from the Terra/Aqua MODIS products were confirmed to be appropriate for energy flux estimation in the boreal forest to describe vegetation properties (LAI and green fraction) when field observations are not available. Model improvements for boreal settings identified in this study will be implemented operationally over North America to map surface energy fluxes at regional scales using long time series of remote sensing estimates as part of NOAA’s GOES Evapotranspiration and Drought Information System.

Interoperability of ECOSTRESS and Landsat for mapping evapotranspiration time series at sub-field scales

Land-surface temperature retrieved from thermal infrared (TIR) remote sensing has proven to be a valuable constraint in surface energy balance models for estimating evapotranspiration (ET). For optimal utility in agricultural water management applications, frequent thermal imaging (<4-day revisit) at sub-field (100 m or less) spatial resolution is desired. While, the current suite of Landsat satellites (7 and 8) provides the required spatial resolution, the 8-day combined revisit can be inadequate to capture rapid changes in surface moisture status or crop phenology, particularly in areas of persistent cloud cover. The new ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission, with an average 4-day revisit interval and nominal 70-m resolution, provides a valuable research platform for augmenting Landsat TIR sampling and for investigating TIR-based ET mapping mission requirements more broadly. This study investigates the interoperability of Landsat and ECOSTRESS imaging for developing ET image timeseries with high spatial (30-m) and temporal (daily) resolution. A data fusion algorithm is used to fuse Landsat and ECOSTRESS ET retrievals at 30 m with daily 500-m retrievals using TIR data from the Moderate Resolution Imaging Spectroradiometer (MODIS) over target agricultural sites spanning the United States.The added value of the combined multi-source dataset is quantified in comparison with daily flux tower observations collected within these target domains. In addition, we investigate ET model performance as a function of ECOSTRESS view angle, overpass time, and time separation between TIR and Landsat visible to shortwave infrared (VSWIR) data acquisitions used to generate land-surface temperature, leaf area index, and albedo inputs to the surface energy balance model. The results demonstrate the value of the higher temporal sampling provided by ECOSTRESS, especially in areas that are frequently impacted by cloud cover. Limiting usage to ECOSTRESS scenes collected between 9:00 a.m. to 5:00 p.m. and nadir viewing angles <20° yielded daily (24-h) ET retrievals of comparable quality to the well-tested Landsat baseline. We also discuss challenges in using land-surface temperature from a thermal free-flyer system for ET retrieval, which may have ramifications for future TIR water-use mapping missions.

Using high-spatiotemporal thermal satellite ET retrievals to monitor water use over California vineyards of different climate, vine variety and trellis design

Mapping the spatial variability of actual evapotranspiration (ETa) across vineyards is useful for optimizing irrigation scheduling and efficiency, leading to conservation of water resources and more sustainable wine grape production. To support efficient irrigation strategies, we investigate the utility of thermal infrared-based ETa maps over a range of vineyards located throughout California, each representing a unique local climate, trellis design, grape variety, row orientation and management practice. ETa maps are derived by combining the Disaggregated Atmosphere Land Exchange Inverse (ALEXI/DisALEXI) surface energy balance model and the Spatial Temporal Adaptive Reflectance Fusion Model (STARFM) to generate ETa estimates at high spatial (30 m) and temporal (daily) resolution. Model output is evaluated for years 2017 and 2018 over vineyard sites located in Sonoma, Sacramento, and Madera counties in California that are being monitored as part of the Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX). Overall, modeled daily ET estimates compare well with flux tower observations, with average root mean square error (RMSE), mean absolute error (MAE) and mean bias error (MBE) of 0.88 mm day−1, 0.70 mm day−1, and 0.17 mm day−1 respectively, over all four individual vineyard locations, aligning with past GRAPEX studies. Despite general agreement, record wildfires in northern California during 2018 likely resulted in positive model bias, while misrepresentation of leaf area index within a double-trellis designed canopy at the southern-most vineyard resulted in negative model bias. Spatial analysis of monthly total ET highlights the advantages of utilizing a satellite-based approach to characterize the variability in water use within and surrounding the targeted vineyards. A reliable spatial ET product at scale has the potential to improve water allocation and conservation efforts by identifying areas of uneven water use due to variations in soil texture and composition and other environmental or anthropogenic factors.

Evaluating integrated surface/subsurface permafrost thermal hydrology models in ATS (v0.88) against observations from a polygonal tundra site

Abstract. Numerical simulations are essential tools for understanding the complex hydrologic response of Arctic regions to a warming climate. However, strong coupling among thermal and hydrological processes on the surface and in the subsurface and the significant role that subtle variations in surface topography have in regulating flow direction and surface storage lead to significant uncertainties. Careful model evaluation against field observations is thus important to build confidence. We evaluate the integrated surface/subsurface permafrost thermal hydrology models in the Advanced Terrestrial Simulator (ATS) against field observations from polygonal tundra at the Barrow Environmental Observatory. ATS couples a multiphase, 3D representation of subsurface thermal hydrology with representations of overland nonisothermal flows, snow processes, and surface energy balance. We simulated thermal hydrology of a 3D ice-wedge polygon with geometry that is abstracted but broadly consistent with the surface microtopography at our study site. The simulations were forced by meteorological data and observed water table elevations in ice-wedge polygon troughs. With limited calibration of parameters appearing in the soil evaporation model, the 3-year simulations agreed reasonably well with snow depth, summer water table elevations in the polygon center, and high-frequency soil temperature measurements at several depths in the trough, rim, and center of the polygon. Upscaled evaporation is in good agreement with flux tower observations. The simulations were found to be sensitive to parameters in the bare soil evaporation model, snowpack, and the lateral saturated hydraulic conductivity. Timing of fall freeze-up was found to be sensitive to initial snow density, illustrating the importance of including snow aging effects. The study provides new support for an emerging class of integrated surface/subsurface permafrost simulators.

Satellite observations reveal seasonal redistribution of northern ecosystem productivity in response to interannual climate variability

Interannual variability (IAV) in ecosystem productivity may reveal vulnerabilities of vegetation to climate stressors. We analyzed IAV of northern hemisphere ecosystems using several remote sensing datasets, including longstanding observations of the normalized difference vegetation index (NDVI) and more novel metrics for productivity including solar-induced chlorophyll fluorescence (SIF) and the near-infrared reflectance of vegetation (NIRv). Although previous studies have suggested SIF better tracks variations in ecosystem productivity at seasonal timescales, we found that satellite datasets (including SIF) and eddy covariance flux tower observations were subject to significant uncertainty when assessing IAV at fine spatial scales. Even when observations were aggregated regionally, IAV in productivity estimated by the various satellite products were not always well correlated. In response to these inconsistencies, we applied a statistical method on regionally aggregated productivity data in four selected North American ecoregions and identified two dominant modes of IAV—seasonal redistribution and amplification—that were consistent across satellite datasets. The seasonal redistribution mode, which played a stronger role at lower latitudes, associated high (low) spring productivity with warm (cold) spring and summer temperatures, but also with lower (higher) productivity and water availability in summer and fall, indicating that enhanced growth in spring may contribute to an earlier depletion of water resources. The amplification mode associated an increase (decrease) in productivity across the growing season with above-average (below-average) summer moisture conditions. Even though our statistical analysis at large spatial scales revealed meaningful links between terrestrial productivity and climate drivers, our analysis does suggest that IAV and long-term trends in presently available novel and more established satellite observations must be interpreted cautiously, with careful attention to the spatial scales at which a robust signal emerges.

Evaluation of the Soil, Vegetation, and Snow (SVS) Land Surface Model for the Simulation of Surface Energy Fluxes and Soil Moisture under Snow-Free Conditions

The recently developed Soil, Vegetation, and Snow (SVS) land surface model is being progressively implemented at Environment and Climate Change Canada (ECCC) for operational numerical weather and hydrological predictions. The objective of this study is to evaluate the ability of SVS, in offline point-scale mode and under snow-free conditions, to simulate the surface heat fluxes and soil moisture when compared to flux tower observations and simulations from the Canadian Land Surface Scheme (CLASS), used here as a benchmark model. To do this, we performed point-scale simulations of between 4 and 12 years of data records at six selected sites of the FLUXNET network under arid, Mediterranean and tropical climates. At all sites, SVS shows realistic simulations of latent heat flux, sensible heat flux and net radiation. Soil heat flux is reasonably well simulated for the arid sites and one Mediterranean site and poorly simulated for the tropical sites. On the other hand, surface soil moisture was reasonably well simulated at the arid and Mediterranean sites and poorly simulated at the tropical sites. SVS performance was comparable to CLASS not only for energy fluxes and soil moisture, but also for more specific processes such as evapotranspiration and water balance.