scholarly journals Comparing machine learning-derived global estimates of soil respiration and its components with those from terrestrial ecosystem models

2021 ◽  
Author(s):  
Haibo Lu ◽  
Shihua Li ◽  
Minna Ma ◽  
Vladislav Bastrikov ◽  
Xiuzhi Chen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Soil Respiration ◽  
Terrestrial Ecosystem ◽  
Ecosystem Models ◽  
Global Estimates

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.

A coupled multi-proxy and process modelling approach for extraction of quantitative terrestrial ecosystem information from speleothems

2021 ◽  
Author(s):  
Franziska Lechleitner ◽  
Christopher C. Day ◽  
Oliver Kost ◽  
Micah Wilhelm ◽  
Negar Haghipour ◽  
...  
Keyword(s):  
Climate Change ◽  
Soil Respiration ◽  
Western Europe ◽  
Global Climate ◽  
Terrestrial Ecosystem ◽  
Terrestrial Ecosystems ◽  
Clear Correlation ◽  
Northern Spain ◽  
Regional Temperature ◽  
Global Carbon

<p>Terrestrial ecosystems are intimately linked with the global climate system, but their response to ongoing and future anthropogenic climate change remains poorly understood. Reconstructing the response of terrestrial ecosystem processes over past periods of rapid and substantial climate change can serve as a tool to better constrain the sensitivity in the ecosystem-climate response.</p><p>In this talk, we will present a new reconstruction of soil respiration in the temperate region of Western Europe based on speleothem carbon isotopes (δ<sup>13</sup>C). Soil respiration remains poorly constrained over past climatic transitions, but is critical for understanding the global carbon cycle and its response to ongoing anthropogenic warming. Our study builds upon two decades of speleothem research in Western Europe, which has shown clear correlation between δ<sup>13</sup>C and regional temperature reconstructions during the last glacial and the deglaciation, with exceptional regional coherency in timing, amplitude, and absolute δ<sup>13</sup>C variation. By combining innovative multi-proxy geochemical analysis (δ<sup>13</sup>C, Ca isotopes, and radiocarbon) on three speleothems from Northern Spain, and quantitative forward modelling of processes in soil, karst, and cave, we show how deglacial variability in speleothem δ<sup>13</sup>C is best explained by increasing soil respiration. Our study is the first to quantify and remove the effects of prior calcite precipitation (PCP, using Ca isotopes) and bedrock dissolution (open vs closed system, using the radiocarbon reservoir effect) from the speleothem δ<sup>13</sup>C signal to derive changes in respired δ<sup>13</sup>C over time. Our approach allows us to estimate the temperature sensitivity of soil respiration (Q<sub>10</sub>), which is higher than current measurements, suggesting that part of the speleothem signal may be related to a change in the composition of the soil respired δ<sup>13</sup>C. This is likely related to changing substrate through increasing contribution from vegetation biomass with the onset of the Holocene.</p><p>These results highlight the exciting possibilities speleothems offer as a coupled archive for quantitative proxy-based reconstructions of climate and ecosystem conditions.</p>

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.

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>

scholarly journals Modeling the Carbon Cycle of a Subtropical Chinese Fir Plantation Using a Multi-Source Data Fusion Approach

Forests ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 369
Author(s):  
Longwei Hu ◽  
Honglin He ◽  
Yan Shen ◽  
Xiaoli Ren ◽  
Shao-kui Yan ◽  
...  
Keyword(s):  
Data Fusion ◽  
Terrestrial Ecosystem ◽  
Chinese Fir ◽  
Model Parameters ◽  
Ecosystem Models ◽  
Flux Data ◽  
Source Data ◽  
Simulation Results ◽  
Chinese Fir Plantation

Process-based terrestrial ecosystem models are increasingly being used to predict carbon (C) cycling in forest ecosystems. Given the complexity of ecosystems, these models inevitably have certain deficiencies, and thus the model parameters and simulations can be highly uncertain. Through long-term direct observation of ecosystems, numerous different types of data have accumulated, providing valuable opportunities to determine which sources of data can most effectively reduce the uncertainty of simulation results, and thereby improve simulation accuracy. In this study, based on a long-term series of observations (biometric and flux data) of a subtropical Chinese fir plantation ecosystem, we use a model–data fusion framework to evaluate the effects of different constrained data on the parameter estimation and uncertainty of related variables, and systematically evaluate the uncertainty of parameters. We found that plant C pool observational data contributed to significant reductions in the uncertainty of parameter estimates and simulation, as these data provide information on C pool size. However, none of the data effectively constrained the foliage C pool, indicating that this pool should be a target for future observational activities. The assimilation of soil organic C observations was found to be important for reducing the uncertainty or bias in soil C pools. The key findings of this study are that the assimilation of multiple time scales and types of data stream are critical for model constraint and that the most accurate simulation results are obtained when all available biometric and flux data are used as constraints. Accordingly, our results highlight the importance of using multi-source data when seeking to constrain process-based terrestrial ecosystem models.

Diagnosing and assessing uncertainties of terrestrial ecosystem models in a multimodel ensemble experiment: 1. Primary production

2010 ◽  
Vol 17 (3) ◽  
pp. 1350-1366 ◽  
Author(s):  
WEILE WANG ◽  
JENNIFER DUNGAN ◽  
HIROFUMI HASHIMOTO ◽  
ANDREW R. MICHAELIS ◽  
CRISTINA MILESI ◽  
...  
Keyword(s):  
Primary Production ◽  
Terrestrial Ecosystem ◽  
Ecosystem Models ◽  
Multimodel Ensemble
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