Modeling terrestrial ecosystem productivity of an estuarine ecosystem in the Sundarban Biosphere Region, India using seven ecosystem models

2017 ◽  
Vol 356 ◽  
pp. 73-90 ◽  
Author(s):  
Srikanta Sannigrahi
Keyword(s):  
Terrestrial Ecosystem ◽  
Ecosystem Productivity ◽  
Ecosystem Models ◽  
Estuarine Ecosystem

scholarly journals Global radiation, photosynthetically active radiation, and the diffuse component dataset of China, 1981–2010

2018 ◽  
Vol 10 (3) ◽  
pp. 1217-1226 ◽  
Author(s):  
Xiaoli Ren ◽  
Honglin He ◽  
Li Zhang ◽  
Guirui Yu
Keyword(s):  
Solar Radiation ◽  
Photosynthetically Active Radiation ◽  
Global Radiation ◽  
Terrestrial Ecosystem ◽  
Diffuse Radiation ◽  
Research Network ◽  
Diffuse Component ◽  
Ecosystem Productivity ◽  
Ecosystem Models ◽  
Active Radiation

Abstract. Solar radiation, especially photosynthetically active radiation (PAR), is the main energy source of plant photosynthesis, and the diffuse component can enhance canopy light use efficiency, thus increasing ecosystem productivity. In order to predict the terrestrial ecosystem productivity precisely, we not only need global radiation and PAR as driving variables, but also need to treat diffuse radiation and diffuse PAR explicitly in ecosystem models. Therefore, we generated a series of radiation datasets, including global radiation, diffuse radiation, PAR, and diffuse PAR of China from 1981 to 2010, based on the observations of the China Meteorology Administration (CMA) and the Chinese Ecosystem Research Network (CERN). The dataset should be useful for the analysis of the spatiotemporal variations of solar radiation in China and the impact of diffuse radiation on terrestrial ecosystem productivity based on ecosystem models. The dataset is freely available from Zenodo on the following website: https://zenodo.org/record/1198894#.Wx6–C_MwWo (https://doi.org/10.11922/sciencedb.555, Ren et al., 2018).

scholarly journals Global radiation, photosynthetically active radiation, and the diffuse components dataset of China, 1981–2010

2018 ◽  
Author(s):  
Xiaoli Ren ◽  
Honglin He ◽  
Li Zhang ◽  
Guirui Yu
Keyword(s):  
Solar Radiation ◽  
Photosynthetically Active Radiation ◽  
Global Radiation ◽  
Terrestrial Ecosystem ◽  
Diffuse Radiation ◽  
Research Network ◽  
Ecosystem Productivity ◽  
Ecosystem Models ◽  
Active Radiation ◽  
The Impact

Abstract. Solar radiation, especially photosynthetically active radiation (PAR), is the main energy source of plant photosynthesis; and the diffuse component can enhance canopy light use efficiency, thus increasing ecosystem productivity. In order to predict the terrestrial ecosystem productivity precisely, we not only need global radiation and PAR as driving variables, but also need to treat diffuse radiation and diffuse PAR explicitly in ecosystem models. Therefore, we generated a series of radiation datasets, including global radiation, diffuse radiation, PAR, and diffuse PAR of China from 1981 to 2010, based on the observations of China Meteorology Administration (CMA) and Chinese Ecosystem Research Network (CERN). The dataset should be useful for the analysis of the spatio-temporal variations of solar radiation in China and the impact of diffuse radiation on terrestrial ecosystem productivity based on ecosystem models. The dataset is freely available from the Zenodo at the website of https://zenodo.org/record/1198894 (DOI: 10.11922/sciencedb.555).

scholarly journals Estimation of Incident Photosynthetically Active Radiation from GOES Visible Imagery

2008 ◽  
Vol 47 (3) ◽  
pp. 853-868 ◽  
Author(s):  
Tao Zheng ◽  
Shunlin Liang ◽  
Kaicun Wang
Keyword(s):  
Photosynthetically Active Radiation ◽  
Terrestrial Ecosystem ◽  
New Method ◽  
High Temporal Resolution ◽  
Atmospheric Conditions ◽  
Ecosystem Models ◽  
Surface Conditions ◽  
Active Radiation ◽  
Ground Measurement ◽  
Visible Imagery

Abstract Incident photosynthetically active radiation (PAR) is an important parameter for terrestrial ecosystem models. Because of its high temporal resolution, the Geostationary Operational Environmental Satellite (GOES) observations are very suited to catch the diurnal variation of PAR. In this paper, a new method is developed to derive PAR using GOES data. What makes this new method distinct from the existing method is that it does not need external knowledge of atmospheric conditions. The new method retrieves both atmospheric and surface conditions using only at-sensor radiance through interpolation of time series of observations. Validations against ground measurement are carried out at four “FLUXNET” sites. The values of RMSE of estimated and ground-measured instantaneous PAR at the four sites are 130.71, 131.44, 141.16, and 190.22 μmol m−2 s−1, respectively. At the four validation sites, the RMSE as the percentage of estimated mean PAR value are 9.52%, 13.01%, 13.92%, and 24.09%, respectively; the biases are −101.54, 16.56, 11.09, and 53.64 μmol m−2 s−1, respectively. The independence of external atmospheric information enables this method to be applicable to many situations in which external atmospheric information is not available. In addition, topographic impacts on surface PAR are examined at the 1-km resolution at which PAR is retrieved using the GOES visible band data.

scholarly journals Bayesian calibration of terrestrial ecosystem models: a study of advanced Markov chain Monte Carlo methods

2017 ◽  
Vol 14 (18) ◽  
pp. 4295-4314 ◽  
Author(s):  
Dan Lu ◽  
Daniel Ricciuto ◽  
Anthony Walker ◽  
Cosmin Safta ◽  
William Munger
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Likelihood Function ◽  
Terrestrial Ecosystem ◽  
Error Model ◽  
Model Parameters ◽  
Posterior Distributions ◽  
Ecosystem Models ◽  
Bayesian Calibration

Abstract. Calibration of terrestrial ecosystem models is important but challenging. Bayesian inference implemented by Markov chain Monte Carlo (MCMC) sampling provides a comprehensive framework to estimate model parameters and associated uncertainties using their posterior distributions. The effectiveness and efficiency of the method strongly depend on the MCMC algorithm used. In this work, a differential evolution adaptive Metropolis (DREAM) algorithm is used to estimate posterior distributions of 21 parameters for the data assimilation linked ecosystem carbon (DALEC) model using 14 years of daily net ecosystem exchange data collected at the Harvard Forest Environmental Measurement Site eddy-flux tower. The calibration of DREAM results in a better model fit and predictive performance compared to the popular adaptive Metropolis (AM) scheme. Moreover, DREAM indicates that two parameters controlling autumn phenology have multiple modes in their posterior distributions while AM only identifies one mode. The application suggests that DREAM is very suitable to calibrate complex terrestrial ecosystem models, where the uncertain parameter size is usually large and existence of local optima is always a concern. In addition, this effort justifies the assumptions of the error model used in Bayesian calibration according to the residual analysis. The result indicates that a heteroscedastic, correlated, Gaussian error model is appropriate for the problem, and the consequent constructed likelihood function can alleviate the underestimation of parameter uncertainty that is usually caused by using uncorrelated error models.

scholarly journals EFFECTS OF OZONE POLLUTION ON TERRESTRIAL ECOSYSTEM PRODUCTIVITY

2007 ◽  
Vol 31 (2) ◽  
pp. 219-230 ◽  
Author(s):  
REN Wei ◽  
◽  
◽  
◽  
TIAN Han-Qin
Keyword(s):  
Terrestrial Ecosystem ◽  
Ozone Pollution ◽  
Ecosystem Productivity

Improved snow cover model in terrestrial ecosystem models over the Qinghai–Tibetan Plateau

2016 ◽  
Vol 218-219 ◽  
pp. 161-170 ◽  
Author(s):  
Wenping Yuan ◽  
Wenfang Xu ◽  
Minna Ma ◽  
Shengyun Chen ◽  
Wenjie Liu ◽  
...  
Keyword(s):  
Tibetan Plateau ◽  
Snow Cover ◽  
Terrestrial Ecosystem ◽  
Ecosystem Models

Changes in root zone storage capacity and their effects on river discharge and gross primary production

2020 ◽  
Author(s):  
Rodolfo Nóbrega ◽  
David Sandoval ◽  
Colin Prentice
Keyword(s):  
Land Cover ◽  
Primary Production ◽  
Mass Balance ◽  
Land Cover Change ◽  
Storage Capacity ◽  
Root Zone ◽  
Gross Primary Production ◽  
Terrestrial Ecosystem ◽  
Ecosystem Models ◽  
Stress Functions

<p>Root zone storage capacity (R<sub>z</sub>) is a parameter widely used in terrestrial ecosystem models that estimate the amount of soil moisture available for transpiration. However, R<sub>z</sub> is subject to large uncertainty, due to the lack of data on the distribution of soil properties and the depth of plant roots that actively take up water. Our study makes use of a mass-balance approach to investigate R<sub>z</sub> in different ecosystems, and changes in water fluxes caused by land-cover change. The method needs no land-cover or soil information, and uses precipitation (P) and evapotranspiration (ET) time series to estimate the seasonal water deficit. To account for some of the uncertainty in ET, we use different methods for ET estimation, including methods based on satellite estimates, and modelling approaches that back-calculate ET from other ecosystem fluxes. We show that reduced ET due to land-cover change reduces R<sub>z</sub>, which in turn increases baseflow in regions with a strong rainfall seasonality. This finding allows us to analyse the trade-off between gross primary production and hydrological fluxes at river basin scales. We also consider some ideas on how to use mass-balance R<sub>z</sub> in water-stress functions as incorporated in existing terrestrial ecosystem models.</p>

Sign in / Sign up

Export Citation Format

Share Document