Empirical vs. light-use efficiency modelling for estimating carbon fluxes in a mid-succession ecosystem developed on abandoned karst grassland

Abstract

Karst systems represent an important carbon sink worldwide. However, several phenomena such as the CO 2 degassing and the exchange of cave air return a considerable amount of CO 2 to the atmosphere. It is therefore of paramount importance to understand the contribution of the ecosystem to the carbon budget of karst areas. In this study conducted in a midsuccession ecosystem developed on abandoned karst grassland, two types of model were assessed, estimating the gross primary production (GPP) or the net ecosystem exchange (NEE) based on seven years of eddy covariance data (2013-2019): (1) a quadratic vegetation index-based empirical model with five alternative vegetation indices as proxies of GPP and NEE, and (2) the vegetation photosynthesis model (VPM) which is a light use efficiency model to estimate only GPP. The Enhanced Vegetation Index (EVI) was the best proxy for NEE whereas SAVI performed very similarly to EVI in the case of GPP in the empirical model setting. The empirical model performed better than the VPM model which tended to underestimate GPP. Therefore, for this ecosystem, we suggest the use of the empirical model provided that the quadratic relationship observed persists. However, the VPM model would be a good alternative under a changing climate, as it is rooted in the understanding of the photosynthesis process, if the scalars it involves could be improved to better estimate GPP.

Effect of Climate Change on CO2 Flux in Temperate Grassland, Subtropical Artificial Coniferous Forest and Tropical Rain Forest Ecosystems

“…After comparing the effects of temperature, moisture, and photosynthetically active radiation on RE and GEE, we found that temperature was the main influencing factor, which is consistent with many studies [ 49 , 50 ]. However, in some plant photosynthesis and respiration models [ 51 , 52 , 53 , 54 ], GEE is mainly calculated based on light and RE is mainly calculated based on temperature, which is different from the results of this paper. This shows that when calculating RE and GEE in models, indicators at different locations may require different weights.…”

Terrestrial carbon cycle: tipping edge of climate change between the atmosphere and biosphere ecosystems

“…145 The RS environment signicantly boosts the accuracy of the model, especially for production efficiency models such as the LUE models, to analyze the regional carbon ux. 15,47,137,146 Also, these models are reliable sources of regional GPP and NPP in RS carbon ux estimation, given that they provide knowledge on the biotic strata, growth, conditions, etc. These models are dynamically related to RS with the biotic absorbed photosynthetically active radiation (APAR) 15 and fraction of photosynthetically active radiation (fPAR) 48 in estimating the key uxes.…”

“…Few models optimize the RS data as parameters in the estimation to reduce the uncertainty in the simulated carbon uxes. 146 Limitations in the RS-based estimation exist in the dataset in terms of atmospheric contamination (cloud cover and reectivity), sensor errors, retrieval algorithm errors, etc., which signicantly increase the uncertainty. 15 These data errors pertain to the data sources, such as the errors in data acquisition, processing, and validation.…”

“…RS environment significantly boosts the accuracy of the models, especially for the production efficiency models like LUE models, to analyze the regional carbon flux 15,47,137,146 . And these models are one of the reliable sources of regional GPP and NPP in RS carbon flux estimation, as they provide knowledge on the biotic strata, growth, conditions etc.…”

“…They quantify capricious surface terrestrial flux to understand the variability of an ecosystem in a realistic approach with response to the enviro-climatic conditions. Few models optimize RS data as parameters in the estimation to reduce the uncertainty in the simulated carbon fluxes 146 .…”