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AbstractThermal infrared sensing of evapotranspiration (E) through surface energy balance (SEB) models is challenging due to uncertainties in determining the aerodynamic conductance (gA) and due to inequalities between radiometric (TR) and aerodynamic temperatures (T0). We evaluated a novel analytical model, the Surface Temperature Initiated Closure (STIC1.2), that physically integrates TR observations into a combined Penman‐Monteith Shuttleworth‐Wallace (PM‐SW) framework for directly estimating E, and overcoming the uncertainties associated with T0 and gA determination. An evaluation of STIC1.2 against high temporal frequency SEB flux measurements across an aridity gradient in Australia revealed a systematic error of 10–52% in E from mesic to arid ecosystem, and low systematic error in sensible heat fluxes (H) (12–25%) in all ecosystems. Uncertainty in TR versus moisture availability relationship, stationarity assumption in surface emissivity, and SEB closure corrections in E were predominantly responsible for systematic E errors in arid and semi‐arid ecosystems. A discrete correlation (r) of the model errors with observed soil moisture variance (r = 0.33–0.43), evaporative index (r = 0.77–0.90), and climatological dryness (r = 0.60–0.77) explained a strong association between ecohydrological extremes and TR in determining the error structure of STIC1.2 predicted fluxes. Being independent of any leaf‐scale biophysical parameterization, the model might be an important value addition in working group (WG2) of the Australian Energy and Water Exchange (OzEWEX) research initiative which focuses on observations to evaluate and compare biophysical models of energy and water cycle components.

A comprehensive understanding of the effects of agricultural management on climate–crop interactions has yet to emerge. Using a novel wavelet–statistics conjunction approach, we analysed the synchronisation amongst fluxes (net ecosystem exchange NEE, evapotranspiration and sensible heat flux) and seven environmental factors (e.g., air temperature, soil water content) on 19 farm sites across Australia and New Zealand. Irrigation and fertilisation practices improved positive coupling between net ecosystem productivity (NEP = −NEE) and evapotranspiration, as hypothesised. Highly intense management tended to protect against heat stress, especially for irrigated crops in dry climates. By contrast, stress avoidance in the vegetation of tropical and hot desert climates was identified by reverse coupling between NEP and sensible heat flux (i.e., increases in NEP were synchronised with decreases in sensible heat flux). Some environmental factors were found to be under management control, whereas others were fixed as constraints at a given location. Irrigated crops in dry climates (e.g., maize, almonds) showed high predictability of fluxes given only knowledge of fluctuations in climate (R2 > 0.78), and fluxes were nearly as predictable across strongly energy- or water-limited environments (0.60

Summary For instantaneous latent heat flux ( λE ) estimates from thermal remote sensing data to be useful in the hydrologic sciences, they require integration over longer time frames (e.g., months to years). This is not trivial because thermal remote sensing data acquired under cloud-free daytime conditions require upscaling to a monthly energy amount that is both relevant over cloudy periods and considers daytime and nighttime. Previous work has compared upscaling approaches, but as yet there is no authoritative comparison that does so under conditions relevant for thermal remote sensing. In this paper we describe, under the conditions relevant for thermal remote sensing, a generic framework for comparing any upscaling approach that assumes self-preservation. Then we use eddy-flux data from two sites in contrasting climates to systematically evaluate the accuracy of different upscaling proposals within the framework. We assumed that the instantaneous estimate of the latent heat flux measured by the eddy-flux technique would have been measured by a satellite sensor. We then scaled this estimate to a monthly period using four approaches and compared the result with the observed monthly integral. This design enabled us to isolate the accuracy of each upscaling method. The four methods upscaled λE by: (i) observed solar irradiance ( S ); (ii) modelled solar irradiance from a sine function ( S SIN ); (iii) modelled top-of-atmosphere solar irradiance ( S TOA ); and (iv) observed available energy ( A E ). We showed that upscaling λE using observed data ( S , A E ) resulted in underestimation of monthly evaporative energy, while the use of modelled data ( S SIN , S TOA ) led to overestimation, primarily due to the relationship between error and both the season (day-of-year) and cloud fraction. Of the two observed fluxes, upscaling with S resulted in lower overall errors than when using A E ( S bias: −1.11 M J m −2 d −1 or −16%; A E bias: −2.15 M J m −2 d −1 or −34%). Of the two modelled fluxes, upscaling with S TOA had lower errors than the widely used S SIN method ( S SIN bias: 1.03 M J m −2 d −1 or 14%; S TOA bias: 0.91 M J m −2 d −1 or 13%). We subsequently developed a simple procedure to minimise bias from all four upscaling approaches, and concluded that modelled data ( S TOA ) can be used to upscale λE to longer timescales for thermal remote sensing applications. This study developed the theory to minimise upscaling bias at two sites with contrasting climates, further work is needed to extend the approach to all global terrestrial climates.

AbstractThe FLUXNET2015 dataset provides ecosystem-scale data on CO2, water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such as gap-filled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the first time in this paper. In addition, 206 of these sites are for the first time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible.

AbstractDespite the importance of high-latitude surface energy budgets (SEBs) for land-climate interactions in the rapidly changing Arctic, uncertainties in their prediction persist. Here, we harmonize SEB observations across a network of vegetated and glaciated sites at circumpolar scale (1994–2021). Our variance-partitioning analysis identifies vegetation type as an important predictor for SEB-components during Arctic summer (June-August), compared to other SEB-drivers including climate, latitude and permafrost characteristics. Differences among vegetation types can be of similar magnitude as between vegetation and glacier surfaces and are especially high for summer sensible and latent heat fluxes. The timing of SEB-flux summer-regimes (when daily mean values exceed 0 Wm−2) relative to snow-free and -onset dates varies substantially depending on vegetation type, implying vegetation controls on snow-cover and SEB-flux seasonality. Our results indicate complex shifts in surface energy fluxes with land-cover transitions and a lengthening summer season, and highlight the potential for improving future Earth system models via a refined representation of Arctic vegetation types.