Comments to "Short-term forecasting of regional biospheric CO2 fluxes in Europe using a light-use-efficiency model"

Abstract. Forecasting atmospheric CO2 concentrations on synoptic timescales (∼ days) can benefit the planning of field campaigns by better predicting the location of important gradients. One aspect of this, accurately predicting the day-to-day variation in biospheric fluxes, poses a major challenge. This study aims to investigate the feasibility of using a diagnostic light-use-efficiency model, the Vegetation Photosynthesis Respiration Model (VPRM), to forecast biospheric CO2 fluxes on the timescale of a few days. As input, the VPRM model requires downward shortwave radiation, 2 m temperature, and enhanced vegetation index (EVI) and land surface water index (LSWI), both of which are calculated from MODIS reflectance measurements. Flux forecasts were performed by extrapolating the model input into the future, i.e., using downward shortwave radiation and temperature from a numerical weather prediction (NWP) model, as well as extrapolating the MODIS indices to calculate future biospheric CO2 fluxes with VPRM. A hindcast for biospheric CO2 fluxes in Europe in 2014 has been done and compared to eddy covariance flux measurements to assess the uncertainty from different aspects of the forecasting system. In total the range-normalized mean absolute error (normalized) of the 5 d flux forecast at daily timescales is 7.1 %, while the error for the model itself is 15.9 %. The largest forecast error source comes from the meteorological data, in which error from shortwave radiation contributes slightly more than the error from air temperature. The error contribution from all error sources is similar at each flux observation site and is not significantly dependent on vegetation type.

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Short-term forecasting of regional biospheric CO2 fluxes in Europe using a light-use-efficiency model

10.5194/gmd-2019-173 ◽

2019 ◽

Author(s):

Jinxuan Chen ◽

Christoph Gerbig ◽

Julia Marshall ◽

Kai Uwe Totsche

Keyword(s):

Land Surface ◽

Vegetation Index ◽

Vegetation Type ◽

Weather Prediction ◽

Shortwave Radiation ◽

Light Use Efficiency ◽

Co2 Fluxes ◽

Downward Shortwave Radiation ◽

Use Efficiency ◽

Light Use Efficiency Model

Abstract. Forecasting atmospheric CO2 concentrations on synoptic time scales (~ days) can benefit the planning of field campaigns by better predicting the location of important gradients. One aspect of this, accurately predicting the day-to-day variation in biospheric fluxes poses a major challenge. This research aims to investigate the feasibility of using a diagnostic light-use-efficiency model, the Vegetation Photosynthesis Respiration Model (VPRM), to forecast biospheric CO2 fluxes on the time scale of a few days. As input the VPRM model requires downward shortwave radiation, 2 m temperature, and Enhanced Vegetation Index (EVI) and Land Surface Water Index (LSWI), both of which are calculated from MODIS reflectance measurements. Flux forecasts were performed by extrapolating the model input into the future, i.e. using downward shortwave radiation and temperature from a numerical weather prediction (NWP) model, as well as extrapolating the MODIS indices to calculate future biospheric CO2 fluxes with VPRM. A hindcast for biospheric CO2 fluxes in Europe in 2014 has been done and compared to eddy covariance flux measurements to assess the uncertainty from different aspects of the forecasting system. In total the range-normalized mean absolute error (normalized) of the 5 day flux forecast at daily timescales is 7.1 %, while the error for the model itself is 15.9 %. The largest forecast error source comes from the meteorological data, which fail to accurately predict cloud cover, leading to overestimated shortwave radiation in the model. The error contribution from all error sources is similar at each flux observation site, and is not significantly dependent on vegetation type.

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Modeling light use efficiency in a subtropical mangrove forest equipped with CO2 eddy covariance

Biogeosciences ◽

10.5194/bg-10-2145-2013 ◽

2013 ◽

Vol 10 (3) ◽

pp. 2145-2158 ◽

Cited By ~ 42

Author(s):

J. G. Barr ◽

V. Engel ◽

J. D. Fuentes ◽

D. O. Fuller ◽

H. Kwon

Keyword(s):

Eddy Covariance ◽

Environmental Conditions ◽

Mangrove Forest ◽

Environmental Drivers ◽

Model Parameters ◽

Co2 Uptake ◽

Light Use Efficiency ◽

Co2 Fluxes ◽

Use Efficiency ◽

Reflectance Data

Abstract. Despite the importance of mangrove ecosystems in the global carbon budget, the relationships between environmental drivers and carbon dynamics in these forests remain poorly understood. This limited understanding is partly a result of the challenges associated with in situ flux studies. Tower-based CO2 eddy covariance (EC) systems are installed in only a few mangrove forests worldwide, and the longest EC record from the Florida Everglades contains less than 9 years of observations. A primary goal of the present study was to develop a methodology to estimate canopy-scale photosynthetic light use efficiency in this forest. These tower-based observations represent a basis for associating CO2 fluxes with canopy light use properties, and thus provide the means for utilizing satellite-based reflectance data for larger scale investigations. We present a model for mangrove canopy light use efficiency utilizing the enhanced green vegetation index (EVI) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) that is capable of predicting changes in mangrove forest CO2 fluxes caused by a hurricane disturbance and changes in regional environmental conditions, including temperature and salinity. Model parameters are solved for in a Bayesian framework. The model structure requires estimates of ecosystem respiration (RE), and we present the first ever tower-based estimates of mangrove forest RE derived from nighttime CO2 fluxes. Our investigation is also the first to show the effects of salinity on mangrove forest CO2 uptake, which declines 5% per each 10 parts per thousand (ppt) increase in salinity. Light use efficiency in this forest declines with increasing daily photosynthetic active radiation, which is an important departure from the assumption of constant light use efficiency typically applied in satellite-driven models. The model developed here provides a framework for estimating CO2 uptake by these forests from reflectance data and information about environmental conditions.

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Estimating crop yield using a satellite-based light use efficiency model

Ecological Indicators ◽

10.1016/j.ecolind.2015.08.013 ◽

2016 ◽

Vol 60 ◽

pp. 702-709 ◽

Cited By ~ 21

Author(s):

Wenping Yuan ◽

Yang Chen ◽

Jiangzhou Xia ◽

Wenjie Dong ◽

Vincenzo Magliulo ◽

...

Keyword(s):

Crop Yield ◽

Light Use Efficiency ◽

Use Efficiency ◽

Light Use Efficiency Model

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Identification of a general light use efficiency model for gross primary production

Biogeosciences ◽

10.5194/bg-8-999-2011 ◽

2011 ◽

Vol 8 (4) ◽

pp. 999-1021 ◽

Cited By ~ 23

Author(s):

J. E. Horn ◽

K. Schulz

Keyword(s):

Primary Production ◽

Large Scale ◽

Gross Primary Production ◽

Daily Basis ◽

Climatic Conditions ◽

Environmental Drivers ◽

Light Use Efficiency ◽

Meteorological Forcing ◽

Use Efficiency ◽

Light Use Efficiency Model

Abstract. Non-stationary and non-linear dynamic time series analysis tools are applied to multi-annual eddy covariance and micrometeorological data from 44 FLUXNET sites to derive a light use efficiency model for gross primary production on a daily basis. The extracted typical behaviour of the canopies in response to meteorological forcing leads to a model formulation allowing for a variable influence of the environmental drivers temperature and moisture availability modulating the light use efficiency. Thereby, the model is applicable to a broad range of vegetation types and climatic conditions. The proposed model explains large proportions of the variation of the gross carbon uptake at the study sites while the optimized set of six parameters is well defined. With the parameters showing explainable and meaningful relations to site-specific environmental conditions, the model has the potential to serve as basis for general regionalization strategies for large scale carbon flux predictions.

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