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

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

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)

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

Transpiração pelo método da sonda de dissipação térmica em floresta de transição Amazônica-Cerrado

Com este trabalho objetivou-se analisar o comportamento do fluxo de seiva em espécies da floresta de transição Amazônia Cerrado e caracterizar a dependência do fluxo de seiva, em função do déficit de pressão de vapor da atmosfera (DPV). O fluxo de seiva foi medido utilizando-se sondas de dissipação térmica em 5 espécies diferentes. Os dados foram divididos em quatro estações. No período experimental as curvas de variação sazonal do fluxo de seiva evidenciam a ocorrência de picos no período úmido. Esta tendência sazonal do fluxo de seiva foi evidenciada pela relação entre valores diários de transpiração e do DPV. O valor limite do DPV nessas estações foi de 1 a 1,5 kPa. Com referência ao fluxo de seiva como representativo da taxa transpiratória das plantas, a redução do fluxo no período seco não confirma a hipótese da manutenção do processo de evapotranspiração. O fluxo de seiva nas espécies do estudo é influenciado mais pelas condições atmosféricas do que mesmo pela disponibilidade de água no solo, o que suscita um possível armazenamento da água no caule nos períodos mais secos quando a área foliar diminui mas a taxa transpiratória não é significativamente distinta daquela do período chuvoso