Changes in evapotranspiration, transpiration and evaporation across natural and managed landscapes in the Amazon, Cerrado and Pantanal biomes

Land-use and land-cover change (LULCC) can dramatically affect the magnitude, seasonality and main drivers of evaporation (E) and transpiration (T), together as evapotranspiration (ET), with effects on overall ecosystem function, as well as both the hydrological cycle and climate system at multiple scales. Our understanding of tropical ecosystem responses to LULCC and global change processes is still limited, mainly due to a lack of ground-based observations that cover a variety of ecosystems, land-uses and land-covers. In this study, we used a network of nine eddy covariance flux towers installed in natural (forest, savanna, wetland) and managed systems (rainfed and irrigated cropland, pastureland) to explore how LULCC affects ET and its components in the Amazon, Cerrado and Pantanal biomes. At each site, tower-based ET measurements were partitioned into T and E to investigate how these fluxes varied between different land-uses and seasons. We found that ET, T and E decreased significantly during the dry season, except in Amazon forest ecosystems where T rates were maintained throughout the year. In contrast to Amazon forests, Cerrado and Pantanal ecosystems showed stronger stomatal control during the dry season. Cropland and pasture sites had lower ET and T compared to native vegetation in all biomes, but E was greater in Pantanal pasture when compared to Pantanal forest. The T fraction of ET was correlated with LAI and EVI, but relationships were weaker in Amazon forests. Our results highlight the importance of understanding the effects of LULCC on water fluxes in tropical ecosystems, and the implications for climate change mitigation policies and land management

Surface Energy Budgets of Arctic Tundra During Growing Season

This study analyzed summer observations of diurnal and seasonal surface energy budgets across several monitoring sites within the Arctic tundra underlain by permafrost. In these areas, latent and sensible heat fluxes have comparable magnitudes, and ground heat flux enters the subsurface during short summer intervals of the growing period, leading to seasonal thaw. The maximum entropy production (MEP) model was tested as an input and parameter parsimonious model of surface heat fluxes for the simulation of energy budgets of these permafrost‐underlain environments. Using net radiation, surface temperature, and a single parameter characterizing the thermal inertia of the heat exchanging surface, the MEP model estimates latent, sensible, and ground heat fluxes that agree closely with observations at five sites for which detailed flux data are available. The MEP potential evapotranspiration model reproduces estimates of the Penman‐Monteith potential evapotranspiration model that requires at least five input meteorological variables (net radiation, ground heat flux, air temperature, air humidity, and wind speed) and empirical parameters of surface resistance. The potential and challenges of MEP model application in sparsely monitored areas of the Arctic are discussed, highlighting the need for accurate measurements and constraints of ground heat flux.Plain Language SummaryGrowing season latent and sensible heat fluxes are nearly equal over the Arctic permafrost tundra regions. Persistent ground heat flux into the subsurface layer leads to seasonal thaw of the top permafrost layer. The maximum energy production model accurately estimates the latent, sensible, and ground heat flux of the surface energy budget of the Arctic permafrost regions.Key PointThe MEP model is parsimonious and well suited to modeling surface energy budget in data‐sparse permafrost environmentsPeer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/150560/1/jgrd55584.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/150560/2/jgrd55584_am.pd

Gap-filling eddy covariance methane fluxes:Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands

Time series of wetland methane fluxes measured by eddy covariance require gap-filling to estimate daily, seasonal, and annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for wetland methane fluxes, with regards both to the best model algorithms and predictors. This study synthesizes results of different gap-filling methods systematically applied at 17 wetland sites spanning boreal to tropical regions and including all major wetland classes and two rice paddies. Procedures are proposed for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting half-hourly methane fluxes and annual emissions with realistic uncertainty estimates. Performance is compared between a conventional method (marginal distribution sampling) and four machine learning algorithms. The conventional method achieved similar median performance as the machine learning models but was worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on wetland soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. The python code for model development, evaluation, and uncertainty estimation is publicly available. This study outlines a modular and robust machine learning workflow and makes recommendations for, and evaluates an improved baseline of, methane gap-filling models that can be implemented in multi-site syntheses or standardized products from regional and global flux networks (e.g., FLUXNET)

Measurements of CO2 exchange over a woodland savanna (Cerrado Sensu stricto) in southeast Brasil

The technique of eddy correlation was used to measure the net ecosystem exchange over a woodland savanna (Cerrado Sensu stricto) site (Gleba Pé de Gigante) in southeast Brazil. The data set included measurements of climatological variables and soil respiration using static soil chambers. Data were collected during the period from 10 October 2000 to 30 March 2002. Measured soil respiration showed average values of 4.8 molCO2 m-2s-1 year round. Its seasonal differences varied from 2 to 8 molCO2 m-2s-1 (Q10 = 4.9) during the dry (April to August) and wet season, respectively, and was concurrent with soil temperature and moisture variability. The net ecosystem CO2 flux (NEE) variability is controlled by solar radiation, temperature and air humidity on diel course. Seasonally, soil moisture plays a strong role by inducing litterfall, reducing canopy photosynthetic activity and soil respiration. The net sign of NEE is negative (sink) in the wet season and early dry season, with rates around -25 kgC ha-1day-1, and values as low as 40 kgC ha-1day-1. NEE was positive (source) during most of the dry season, and changed into negative at the onset of rainy season. At critical times of soil moisture stress during the late dry season, the ecosystem experienced photosynthesis during daytime, although the net sign is positive (emission). Concurrent with dry season, the values appeared progressively positive from 5 to as much as 50 kgC ha-1day-1. The annual NEE sum appeared to be nearly in balance, or more exactly a small sink, equal to 0.1 0.3 tC ha-1yr-1, which we regard possibly as a realistic one, giving the constraining conditions imposed to the turbulent flux calculation, and favourable hypothesis of succession stages, climatic variability and CO2 fertilization

Aircraft Regional-Scale Flux Measurements over Complex Landscapes of Mangroves, Desert, and Marine Ecosystems of Magdalena Bay, Mexico

Natural ecosystems are rarely structurally simple or functionally homogeneous. This is true for the complex coastal region of Magdalena Bay, Baja California Sur, Mexico, where the spatial variability in ecosystem fluxes from the Pacific coastal ocean, eutrophic lagoon, mangroves, and desert were studied. The Sky Arrow 650TCN environmental research aircraft proved to be an effective tool in characterizing land–atmosphere fluxes of energy, CO2, and water vapor across a heterogeneous landscape at the scale of 1 km. The aircraft was capable of discriminating fluxes from all ecosystem types, as well as between nearshore and coastal areas a few kilometers distant. Aircraft-derived average midday CO2 fluxes from the desert showed a slight uptake of −1.32 μmol CO2 m−2 s−1, the coastal ocean also showed an uptake of −3.48 μmol CO2 m−2 s−1, and the lagoon mangroves showed the highest uptake of −8.11 μmol CO2 m−2 s−1. Additional simultaneous measurements of the normalized difference vegetation index (NDVI) allowed simple linear modeling of CO2 flux as a function of NDVI for the mangroves of the Magdalena Bay region. Aircraft approaches can, therefore, be instrumental in determining regional CO2 fluxes and can be pivotal in calculating and verifying ecosystem carbon sequestration regionally when coupled with satellite-derived products and ecosystem models

Gap-filling eddy covariance methane fluxes : Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands

Time series of wetland methane fluxes measured by eddy covariance require gap-filling to estimate daily, seasonal, and annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for wetland methane fluxes, with regards both to the best model algorithms and predictors. This study synthesizes results of different gap-filling methods systematically applied at 17 wetland sites spanning boreal to tropical regions and including all major wetland classes and two rice paddies. Procedures are proposed for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting halfhourly methane fluxes and annual emissions with realistic uncertainty estimates. Performance is compared between a conventional method (marginal distribution sampling) and four machine learning algorithms. The conventional method achieved similar median performance as the machine learning models but was worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on wetland soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. The python code for model development, evaluation, and uncertainty estimation is publicly available. This study outlines a modular and robust machine learning workflow and makes recommendations for, and evaluates an improved baseline of, methane gap-filling models that can be implemented in multi-site syntheses or standardized products from regional and global flux networks (e.g., FLUXNET).Peer reviewe

Interpreting seasonal changes in the carbon balance of southern Amazonia using measurements of XCO2 and chlorophyll fluorescence from GOSAT

Amazon forests exert a major influence on the global carbon cycle, but quantifying the impact is complicated by diverse landscapes and sparse data. Here we examine seasonal carbon balance in southern Amazonia using new measurements of column-averaged dry air mole fraction of CO2 (XCO2) and solar induced chlorophyll fluorescence (SIF) from the Greenhouse Gases Observing Satellite (GOSAT) from July 2009 to December 2010. SIF, which reflects gross primary production (GPP), is used to disentangle the photosynthetic component of land-atmosphere carbon exchange. We find that tropical transitional forests in southern Amazonia exhibit a pattern of low XCO2 during the wet season and high XCO2 in the dry season that is robust to retrieval methodology and with seasonal amplitude double that of cerrado ecosystems to the east (4 ppm versus 2 ppm), including enhanced dilution of 2.5 ppm in the wet season. Concomitant measurements of SIF, which are inversely correlated with XCO2 in southern Amazonia (r =0.53, p<0.001), indicate that the enhanced variability is driven by seasonal changes in GPP due to coupling of strong vertical mixing with seasonal changes in underlying carbon exchange. This finding is supported by forward simulations of the Goddard Chemistry Transport Model (GEOS-Chem) which show that local carbon uptake in the wet season and loss in the dry season due to emissions by ecosystem respiration and biomass burning produces best agreement with observed XCO2. We conclude that GOSAT provides critical measurements of carbon exchange in southern Amazonia, but more samples are needed to examine moist Amazon forests farther north. Citation: Parazoo, N. C., et al. (2013), Interpreting seasonal changes in the carbon balance of southern Amazonia using measurements of XCO2 and chlorophyll fluorescence from GOSAT