Effects of waterlogging on water and carbon dioxide fluxes and environmental variables in a Siberian larch forest, 1998–2011

Ohta, Takeshi; Kotani, Ayumi; Iijima, Yoshihiro; Maximov, Trofim C.; Ito, Syogo; Hanamura, Miho; Kononov, Alexander V.; Maximov, A.

doi:10.1016/j.agrformet.2013.12.012

Cited by 73 publications

(71 citation statements)

References 58 publications

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“…The model-simulated NEE was validated with the field observation data at this AsiaFlux site (Ohta et al, 2001(Ohta et al, , 2008(Ohta et al, , 2014. The data were quality controlled by the steady-state test as indicated by the quality flag 0.…”

Section: Experimental Settingsmentioning

confidence: 99%

Non-Gaussian data assimilation of satellite-based leaf area index observations with an individual-based dynamic global vegetation model

Arakida

Miyoshi

Ise

et al. 2017

Nonlin. Processes Geophys.

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Abstract. We developed a data assimilation system based on a particle filter approach with the spatially explicit individual-based dynamic global vegetation model (SEIB-DGVM). We first performed an idealized observing system simulation experiment to evaluate the impact of assimilating the leaf area index (LAI) data every 4 days, simulating the satellite-based LAI. Although we assimilated only LAI as a whole, the tree and grass LAIs were estimated separately with high accuracy. Uncertain model parameters and other state variables were also estimated accurately. Therefore, we extended the experiment to the real world using the real Moderate Resolution Imaging Spectroradiometer (MODIS) LAI data and obtained promising results.

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Section: Experimental Settingsmentioning

confidence: 99%

Non-Gaussian data assimilation of satellite-based leaf area index observations with an individual-based dynamic global vegetation model

Arakida

Miyoshi

Ise

et al. 2017

Nonlin. Processes Geophys.

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“…1): Fairbanks in Alaska (Nakai et al, 2013), Tiksi (Kodama et al, 2007;Yabuki et al, 2011;Watanabe et al, 2000) and Chokurdakh (Iwahana et al, 2014) in northeast Siberia, Yakutsk in central east Siberia (Ohta et al, 2001(Ohta et al, , 2008(Ohta et al, , 2014Kotani et al, 2013;Lopez et al, 2007;Yabuki et al, 2011), Tura in central Siberia , and Kevo in northern Europe (Sato et al, 2001; precipitation and air pressure in Kevo are provided by Finnish Meteorological Institute). The longitudes and latitudes of the sites are summarized in Table 1, as well as the observational periods for data collection.…”

Section: Study Sites and Observational Periodsmentioning

confidence: 99%

“…tic conditions at those sites. It is very difficult, however, to create a complete data set for driving a model solely from observations, which are prone to data gaps due to instrument problems, human mistakes, or other reasons (Nakai et al, 2013;Kodama et al, 2007;Watanabe et al, 2000;Iwahana et al, 2014;Ohta et al, 2001Ohta et al, , 2008Ohta et al, , 2014Kotani et al, 2013;Lopez et al, 2007;Nakai et al, 2008;Sato et al, 2001). Despite efforts to maintain and improve our observational network, data gaps are unavoidable, as the availability of instrumentation is often limited by logistical or funding issues.…”

Section: Introductionmentioning

confidence: 99%

The GRENE-TEA model intercomparison project (GTMIP) Stage 1 forcing data set

Sueyoshi

Saitô

Miyazaki

et al. 2016

Earth Syst. Sci. Data

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Abstract. Here, the authors describe the construction of a forcing data set for land surface models (including both physical and biogeochemical models; LSMs) with eight meteorological variables for the 35-year period from 1979 to 2013. The data set is intended for use in a model intercomparison study, called GTMIP, which is a part of the Japanese-funded Arctic Climate Change Research Project. In order to prepare a set of site-fitted forcing data for LSMs with realistic yet continuous entries (i.e. without missing data), four observational sites across the pan-Arctic region (Fairbanks, Tiksi, Yakutsk, and Kevo) were selected to construct a blended data set using both global reanalysis and observational data. Marked improvements were found in the diurnal cycles of surface air temperature and humidity, wind speed, and precipitation. The data sets and participation in GTMIP are open to the scientific community

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“…Many of these studies have concentrated on the mass and energy exchange between forest ecosystems and atmosphere, such as tropical forests [5,6], temperate forests [7][8][9], and boreal forests [10][11][12]. In addition, several studies were conducted in endemic forest ecosystems, for instance, bamboo forests [13], olive trees [14][15][16], riparian woodland [17,18], and mangrove forest [19].…”

Section: Introductionmentioning

confidence: 99%

Forest Evapotranspiration and Energy Flux Partitioning Based on Eddy Covariance Methods in an Arid Desert Region of Northwest China

Feng

et al. 2017

Advances in Meteorology

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In this study, the characteristics of energy flux partitioning and evapotranspiration of P. euphratica forests were examined in the extreme arid region of Northwest China. Energy balance closure of the ecosystem was approximately 72% ( + LE = 0.72 * ( -)+ 7.72, 2 = 0.79, = 12095), where is the net radiation, is the soil heat flux, is the sensible heat flux, and LE is the latent heat flux. LE was the main term of energy consumption at annual time scale because of higher value in the growing season. The ratios of the latent (LE) and sensible ( ) heat fluxes to net radiation were 0.47 and 0.28 throughout the year, respectively. Moreover, the yearly evapotranspiration of P. euphratica forests was 744 mm year −1 . And the mean daily ET was 5.09 mm⋅d −1 in the vibrant growing season. In particular, a small spike in the actual evapotranspiration distribution occurred during the soil ablation period due to the higher temperature and sufficient soil moisture associated with soil thawing. This period is accompanied by a series of physical processes, such as moisture transfer and heat exchange.

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Effects of waterlogging on water and carbon dioxide fluxes and environmental variables in a Siberian larch forest, 1998–2011

Cited by 73 publications

References 58 publications

Non-Gaussian data assimilation of satellite-based leaf area index observations with an individual-based dynamic global vegetation model

Non-Gaussian data assimilation of satellite-based leaf area index observations with an individual-based dynamic global vegetation model

The GRENE-TEA model intercomparison project (GTMIP) Stage 1 forcing data set

Forest Evapotranspiration and Energy Flux Partitioning Based on Eddy Covariance Methods in an Arid Desert Region of Northwest China

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