基于浓度预测模型的岩溶碳汇估算方法。

IF 8.4 2区 环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES
Journal of Environmental Management Pub Date : 2025-01-01 Epub Date: 2024-12-27 DOI:10.1016/j.jenvman.2024.123845
Yan Zhen, Haodong Zheng, Qiong Xiao, Chunlai Zhang, Chengwu Wang
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引用次数: 0

摘要

岩溶作用可以降低大气/土壤中的CO2浓度。准确估算喀斯特碳汇对全球气候变化研究至关重要。本研究以丽江流域为研究区域。在连续14个月测定溶解无机碳(DIC)和溶解有机碳(DOC)浓度数据的基础上,建立了DIC和DOC与海拔、坡度、坡向、降雨量和温度的关系。在6种回归算法中,选择随机森林(RF)、增强回归树(BRT)和BP神经网络(BP)进行叠加积分,构建DIC和DOC浓度预测模型,准确率分别达到91%和83%。在此基础上,对2000 - 2022年丽江流域DIC和DOC浓度的时空分布进行了预测。预测结果表明,DIC和DOC浓度具有稳定的空间分布特征,与盆地岩性分布一致。采用溶质负荷法对丽江流域23 a岩溶碳汇进行了估算。23年碳汇总体呈增长趋势,但波动较大。在23年喀斯特碳汇估算结果的基础上,采用时间序列预测模型对丽江流域2023 - 2030年进行了预测。预测结果延续了历史数据的波动性和趋势。模型验证结果与相关研究结果对比表明,本文构建的浓度预测模型在流域尺度岩溶碳汇估算中具有较高的精度和较好的适用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Estimation method for karst carbon sinks on the basis of a concentration prediction model.

Karstification can reduce the CO2 concentration in the atmosphere/soil. Accurate estimation of karst carbon sinks is crucial for the study of global climate change. In this study, the Lijiang River Basin was taken as the research area. On the basis of the measured dissolved inorganic carbon (DIC) and dissolved organic carbon (DOC) concentration data from 14 consecutive months, the relationships of DIC and DOC to elevation, slope, aspect, rainfall, and temperature were established. Among six regression algorithms, the random forest (RF), boosted regression tree (BRT) and BP neural network (BP) were selected for stacking integration to construct DIC and DOC concentration prediction models, achieving accuracies of 91% and 83%, respectively. On the basis of these models, the spatial and temporal distributions of DIC and DOC concentrations in the Lijiang River Basin from 2000 to 2022 were predicted. The prediction results reveal that DIC and DOC concentrations have a stable spatial distribution, which is consistent with the lithology distribution in the basin. The solute load method was used to estimate the karst carbon sink in the Lijiang River Basin over 23 years. The carbon sink over 23 years showed an overall growth trend, although with significant fluctuations. On the basis of the estimation results of karst carbon sinks over 23 years, a time series prediction model is used to predict the Lijiang River Basin from 2023 to 2030. The prediction results continue the volatility and trend of the historical data. A comparison of the model verification results with related research findings revealed that the concentration prediction model constructed in this study has high accuracy and good applicability in the estimation of karst carbon sinks at the watershed scale.

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来源期刊
Journal of Environmental Management
Journal of Environmental Management 环境科学-环境科学
CiteScore
13.70
自引率
5.70%
发文量
2477
审稿时长
84 days
期刊介绍: The Journal of Environmental Management is a journal for the publication of peer reviewed, original research for all aspects of management and the managed use of the environment, both natural and man-made.Critical review articles are also welcome; submission of these is strongly encouraged.
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