# Biomass and carbon stock models with climatic factors for individual Quercus mongolica trees and their allocation patterns

## TL;DR

This study develops accurate models to predict the biomass and carbon storage of Mongolian oak trees in Northeast China, incorporating climate factors to improve prediction accuracy.

## Contribution

The study introduces climate-inclusive compatibility models that significantly improve the accuracy of biomass and carbon stock predictions for Quercus mongolica.

## Key findings

- Aboveground biomass models achieved adjusted R²adj values between 0.7048 and 0.9618 with high accuracy.
- Incorporating climate factors improved leaf and branch model accuracy in compatibility models.
- Underground biomass models showed adjusted R²adj values between 0.7702 and 0.7801 with acceptable prediction errors.

## Abstract

As the environmental problems caused by the greenhouse effect become more and more serious, and the forest as the largest carbon pool can effectively slow down the greenhouse effect, it is particularly important to accurately predict the carbon storage of the forest. In order to accurately estimate the biomass and carbon storage of Quercus mongolica in Northeast China, the biomass allocation pattern of Q. mongolica was analyzed. In this study, data of 175 Q. mongolica trees in Heilongjiang, Jilin, Liaoning and eastern Inner Mongolia were collected, including aboveground organ biomass, DBH, tree height, age and climatic factors, as well as published carbon content data of different organs. In this study, the biomass allocation pattern of individual Q. mongolica was analyzed. An additively compatible aboveground biomass and carbon storage model and an algebraically controlled aggregation model were established using nonlinear simultaneous equations. After selecting the aggregate biomass compatibility model, climate factors were added to establish a compatibility model containing climate factors. In addition, the root-stem ratio model was used to construct the underground compatible biomass and carbon storage model. The adjusted R2adj values of the final established aboveground components and aboveground total biomass and carbon storage models were between 0.7048 and 0.9618, the total relative error ( TRE ) was within ± 1%, and the average prediction error ( MPE ) was below 10%, which met the modeling accuracy standard. The belowground biomass models showed adjusted R²adj values between 0.7702 and 0.7801, TRE ≤ 1%, and MPE < 15%. This study elucidated the biomass allocation pattern of individual Q. mongolica. All the developed models meet the accuracy requirements and can be applied to predict the biomass and carbon storage of Q. mongolica in Northeast China. In the compatibility model with climate factors, the accuracy of leaf and branch models has been greatly improved, indicating that the addition of climate factors in the independent model can greatly improve the accuracy of each component model, which can provide a theoretical basis for the establishment of each component model in the compatibility model of other tree species.