Search

To what extent plants thermoregulate their canopy temperature (Tc) in response to environmental variability is a fundamental question in ecology, and influences accurate projections of plants' metabolic response and resilience to climate change. However, debate remains, with opinions ranging from no to moderate plant thermoregulation capacities. Traditionally, it has been hypothesized that if plant thermoregulation occurs (i.e. ‘limited homeothermy’ hypothesis holds): 1) Tc will change more slowly than Ta over time, leading the Tcvs. Ta regression slope < 1; 2) Tc is cooler than Ta when Ta exceeds some threshold, typically during high net radiation conditions (e.g. at midday). Here, with global datasets of Tc, air temperature (Ta), and other environmental and biotic variables from FLUXNET and satellites, we tested the ‘limited homeothermy’ hypothesis across global extratropics, including temporal and spatial dimensions. Our results demonstrate that across daily to monthly timescales, over 80% of sites/ecosystems have Tcvs. Ta regression slopes≥1 or Tc>Ta around midday, which rejects the ‘limited homeothermy’ hypothesis. For those sites unsupporting the hypothesis, their Tc-Ta difference (ΔT) still exhibits considerable seasonality that is negatively, partially correlated with their canopy structure seasonality (as indicated by leaf area index), implying a certain degree of thermoregulation capability. Across global sites, both site-mean ΔT and slope indicator exhibit considerable spatial variability, with ΔT having greater variability than the slope indicator. Furthermore, this large spatial ΔT variation (0-6°C) can be mainly explained by environmental variables (38%) and, to a lesser extent, by biological factors (15%). Our results suggested that plant thermoregulation patterns are diverse across global extratropics, with most ecosystems rejecting the ‘limited homeothermy’ hypothesis, but their thermoregulation still occurs, implying that slope

Methane (CH4) emissions from natural landscapes constitute roughly half of global CH4 contributions to the atmosphere, yet large uncertainties remain in the absolute magnitude and the seasonality of emission quantities and drivers. Eddy covariance (EC) measurements of CH4 flux are ideal for constraining ecosystem-scale CH4 emissions due to quasi-continuous and high-temporal-resolution CH4 flux measurements, coincident carbon dioxide, water, and energy flux measurements, lack of ecosystem disturbance, and increased availability of datasets over the last decade. Here, we (1) describe the newly published dataset, FLUXNET-CH4 Version 1.0, the first open-source global dataset of CH4 EC measurements (available at https://fluxnet.org/data/fluxnet-ch4-community-product/, last access: 7 April 2021). FLUXNET-CH4 includes half-hourly and daily gap-filled and non-gap-filled aggregated CH4 fluxes and meteorological data from 79 sites globally: 42 freshwater wetlands, 6 brackish and saline wetlands, 7 formerly drained ecosystems, 7 rice paddy sites, 2 lakes, and 15 uplands. Then, we (2) evaluate FLUXNET-CH4 representativeness for freshwater wetland coverage globally because the majority of sites in FLUXNET-CH4 Version 1.0 are freshwater wetlands which are a substantial source of total atmospheric CH4 emissions; and (3) we provide the first global estimates of the seasonal variability and seasonality predictors of freshwater wetland CH4 fluxes. Our representativeness analysis suggests that the freshwater wetland sites in the dataset cover global wetland bioclimatic attributes (encompassing energy, moisture, and vegetation-related parameters) in arctic, boreal, and temperate regions but only sparsely cover humid tropical regions. Seasonality metrics of wetland CH4 emissions vary considerably across latitudinal bands. In freshwater wetlands (except those between 20∘ S to 20∘ N) the spring onset of elevated CH4 emissions starts 3 d earlier, and the CH4 emission season lasts 4 d longer, for each degree Celsius increase in mean annual air temperature. On average, the spring onset of increasing CH4 emissions lags behind soil warming by 1 month, with very few sites experiencing increased CH4 emissions prior to the onset of soil warming. In contrast, roughly half of these sites experience the spring onset of rising CH4 emissions prior to the spring increase in gross primary productivity (GPP). The timing of peak summer CH4 emissions does not correlate with the timing for either peak summer temperature or peak GPP. Our results provide seasonality parameters for CH4 modeling and highlight seasonality metrics that cannot be predicted by temperature or GPP (i.e., seasonality of CH4 peak). FLUXNET-CH4 is a powerful new resource for diagnosing and understanding the role of terrestrial ecosystems and climate drivers in the global CH4 cycle, and future additions of sites in tropical ecosystems and site years of data collection will provide added value to this database. All seasonality parameters are available at https://doi.org/10.5281/zenodo.4672601 (Delwiche et al., 2021). Additionally, raw FLUXNET-CH4 data used to extract seasonality parameters can be downloaded from https://fluxnet.org/data/fluxnet-ch4-community-product/ (last access: 7 April 2021), and a complete list of the 79 individual site data DOIs is provided in Table 2 of this paper.

The increasing frequency and amplitude of extreme climatic events may decline ecosystem productivity and disturb the global carbon cycle. Recent and upcoming advances in remote sensing technology, such as hyperspectral reflectance and chlorophyll sun-induced fluorescence (SIF) missions, are boosting research on the monitoring of vegetation responses to heat and drought stress. To understand the impacts of stress on vegetation and the corresponding optical signals that can be sensed from space, it is essential to monitor the continuous dynamics of ecosystem carbon and water fluxes and optical signal responses to environmental changes on the ground.We collected a unique dataset of synergistic observations of remote sensing and carbon-water flux measurements from multiple field sites of different vegetation types. This dataset elucidated variations of physiology, fluxes, and optical signals, including SIF and spectral vegetation indices. For example, in light-sensitive beech forests in Germany, we found that photoprotection is generally active. Gross primary productivity (GPP) and surface conductance (Gs) clearly decreased when heatwaves occurred. On the contrary, chlorophyll content changed only marginally, which was reflected by minimal changes in the chlorophyll index at red edge (CIred). The photochemical reflectance index (PRI), related to non-photochemical quenching (NPQ) via xanthophyll´s cycle, was sensitive to flash heat stress and related to vapor pressure deficit (VPD). But for longer and lower intensity of stress in another event, PRI only changed marginally. SIF was more sensitive to incident radiation (PPFD), but did not decrease with increasing air temperature (Ta) and VPD. However, SIF yield (the ratio of SIF and absorbed photosynthetically active radiation) decreased significantly during the heatwave. In contrast, in the light and heat-tolerant rice paddy in China, we observed that vegetation did not show negative effects at the early growing stage (nutritive growth) during an extreme heatwave (Ta>35 ̊C). Due to the high relative humidity (from evaporated water), VPD remained low despite the high temperatures. GPP increased slightly accompanied by a small decrease of Gs as VPD slightly increased. SIF, SIF yield, and PRI noticeably increased with increasing CIred, indicating that heat might have accelerated the physiology rather than stressed plants in the rice paddy, which could be due to an overall higher temperature optimum compared to the European beach forest.Our results demonstrate that water supply shortage combined with heat waves can cause immediate down-regulation of photosynthesis and that the new remote sensing missions could detect this vegetation response. However, if the water supply is abundant during the heatwave, responses of both physiological and remote sensing parameters may not be sensitive to heat stress. Due to species and ecosystem differences in terms of heat resistance, the global response of vegetation remains hard to predict indicating the need to remotely monitor these responses in order to improve process-based models. The outcomes of this work will possibly provide new insights on the utilization of novel optical remote sensing information for vegetation monitoring during extreme events.

Carbon (C) and greenhouse gas (GHG) research has traditionally required data collection and analysis using advanced and often expensive instruments, complex and proprietary software, and highly specialized research technicians. Partly as a result, relatively little C and GHG research has been conducted in resource-constrained developing countries. At the same time, these are often the same countries and regions in which climate change impacts will likely be strongest and in which major science uncertainties are centered, given the importance of dryland and tropical systems to the global C cycle. Increasingly, scientific communities have adopted appropriate technology and approach (AT&A) for C and GHG research, which focuses on low-cost and low-technology instruments, open-source software and data, and participatory and networking-based research approaches. Adopting AT&A can mean acquiring data with fewer technical constraints and lower economic burden and is thus a strategy for enhancing C and GHG research in developing countries. However, AT&A can have higher uncertainties; these can often be mitigated by carefully designing experiments, providing clear protocols for data collection, and monitoring and validating the quality of obtained data. For implementing this approach in developing countries, it is first necessary to recognize the scientific and moral importance of AT&A. At the same time, new AT&A techniques should be identified and further developed. All these processes should be promoted in collaboration with local researchers and through training local staff and encouraged for wide use and further innovation in developing countries.

With increasing crop water demands and drought threats, mapping and monitoring of cropland evapotranspiration (ET) at high spatial and temporal resolutions becomes increasingly critical for water management and sustainability. However, estimating ET from satellite for precise water resources management is still challenging due to the limitations in both existing ET models and satellite input data. Specifically, the process of ET is complex and difficult to model, and existing satellite remote sensing data could not fulfill high resolutions in both space and time. To address the above two issues, this study presented a new high spatiotemporal resolution ET mapping framework, i.e., BESS-STAIR, which integrates a satellite-driven water-carbon-energy coupled biophysical model BESS (Breathing Earth System Simulator) with a generic and fully-automated fusion algorithm STAIR (SaTallite dAta IntegRation). In this framework, STAIR provides daily 30-meter multispectral surface reflectance by fusing Landsat and MODIS satellite data to derive fine-resolution leaf area index and visible/near-infrared albedo, all of which, along with coarse-resolution meteorological and CO2 data, are used to drive BESS to estimate gap-free 30-m resolution daily ET. We applied BESS-STAIR from 2000 through 2017 in six areas across the U.S. Corn Belt, and validated BESS-STAIR ET estimations using flux tower measurements over 12 sites (85 site-years). Results showed that BESS-STAIR daily ET achieved an overall R2 = 0.75, with RMSE = 0.93 mm d−1 and relative error = 27.9 % when benchmarked with the flux measurements. In addition, BESS-STAIR ET estimations well captured the spatial patterns, seasonal cycles, and interannual dynamics in different sub-regions. The high performance of the BESS-STAIR framework is primarily resulted from: (1) the implementation of coupled constraints on water, carbon, and energy in BESS, (2) high-quality daily 30-m data from STAIR fusion algorithm, and (3) BESS’s applicability under all-sky conditions. BESS-STAIR is calibration-free and has great potentials to be a reliable tool for water resources management and precision agriculture applications for the U.S. Corn Belt, and even for worldwide given the global coverage of its input data.

The savanna complex is a highly diverse global biome that occurs within the seasonally dry tropical to sub-tropical equatorial latitudes. Savannas are open-canopy environments that encompass a broad demographic continuum, often characterised by a dynamically changing dominance between C3-tree and C4-grass vegetation, where frequent environmental disturbances such as fire modulates the balance between ephemeral and perennial life forms. Climate change is projected to result in significant changes to the savanna floristic structure, with increases to woody biomass expected through CO2 fertilisation in mesic savannas and increased tree mortality expected through increased rainfall interannual variability in xeric savannas. The complex interaction between vegetation and climate that occurs in savannas has traditionally challenged current-generation terrestrial biosphere models (TBMs), which aim to simulate the interaction between the atmosphere and the land-surface to predict responses of vegetation to changing in environmental forcing. In this review, we examine whether TBMs are able to adequately represent savanna dynamics and what implications potential deficiencies may have for climate change projection scenarios that rely on these models. We start by highlighting the defining characteristic traits and behaviours of savanna, how these differ across continents, and how this information is (or is not) represented in the structural framework of many TBMs. We highlight three dynamic processes that we believe directly affect the water-use and productivity of the savanna system, namely: phenology; root-water access; and fire dynamics. Following this, we discuss how these processes are represented in many current generation TBMs and whether they are suitable for simulating savanna dynamics. Finally, we give an overview of how eddy-covariance observations in combination with other data sources, can be used in model benchmarking and inter-comparison frameworks to diagnose the performance of TBMs in this environment and formulate roadmaps for future development. Our investigation reveals that many TBMs systematically misrepresent phenology, effects of fire and root-water access (if they are considered at all) and that these should be critical areas for future development. Furthermore, such processes must not be static (i.e. prescribed behaviour), but be capable of responding to the changing environmental conditions in order to emulate the dynamic behaviour of savannas. Without such developments, however, TBMs will have limited predictive capability in making the critical projections needed to understand how savannas will respond to future global change.