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Evapotranspiration and its partitioning during and following a mountain pine beetle infestation of a lodgepole pine stand in the interior of British Columbia, Canada 加拿大不列颠哥伦比亚省内陆地区一片山地松树林遭受山地松树甲虫侵袭期间和之后的蒸散量及其分配情况
Pub Date : 2024-02-14 DOI: 10.3389/ffgc.2024.1352853
Shaorong Hao, Xin Jia, Hongxian Zhao, Xinhao Li, Yanmei Mu, T. Zha, Peng Liu, C. Bourque
Massive tree mortality events in western Canada due to widespread infestation by mountain pine beetle (MPB) are expected to impact local-to-regional evapotranspiration (ET) dynamics during and after a disturbance. How ecosystem-level ET and its components may vary with canopy-tree mortality (treefall) and subsequent understory recovery remains unclear.We used 10 years of continuous eddy-covariance and remote-sensing data (2007–2016) and machine-learning models based on random forest and xgboost to determine forest- and climate-driven effects at temporal scales appropriate for a lodgepole pine-dominated stand following a major, five-year MPB disturbance initiated in the summer of 2006.Total annual ET over the 10 years ranged from 207.2 to 384.6 mm, with annual plant transpiration (T) contributing to 57 ± 5.4% (mean ± standard deviation) of annual ET. Annual ET initially declined (2007–2011) and then increased (2011–2016), with ET and T/ET increasing at statistically non-significant rates of approximately 3.2 and 1.2% per year from 2007 to 2016. Air temperature (Ta) and vapor pressure deficit (VPD) were the most important predictors of seasonal variation in ET and T/ET during the 10-year period, with high Ta, VPD, and photosynthetically active radiation (PAR) causing ET and T/ET to increase. Annual ET increased with both increasing spring Ta and decreasing VPD. Annual T/ET was shown to increase with increasing VPD and decrease with increasing volumetric soil water content at a 5-cm depth (VWC5). Enhanced vegetation index (EVI, an indicator of canopy greenness) lagged T and overstory tree mortality, whereas previous- and current-year values of EVI were shown to be poor predictors of annual ET and T/ET.These findings suggest that the promotion of climate factors on forest ecosystem-level water vapor fluxes may offset reductions promoted by MPB outbreaks. Climate processes affected water vapor fluxes more than biotic factors, like stand greenness, highlighting the need to include climate-regulatory mechanisms in predictive models of ET dynamics during and subsequent to stand disturbance. Climate and forest-greenness effects on water vapor fluxes need to be explored at even longer time scales, e.g., at decadal scales, to capture long-drawn-out trends associated with stand disturbance and its subsequent recovery.
由于山松甲虫(MPB)的广泛侵袭,加拿大西部发生了大规模的树木死亡事件,预计这将在干扰期间和干扰之后影响当地到区域的蒸散(ET)动态。我们使用了 10 年连续的涡度协方差和遥感数据(2007-2016 年)以及基于随机森林和 xgboost 的机器学习模型,以确定在 2006 年夏季开始的为期 5 年的重大 MPB 干扰之后,以落叶松为主的林分在适当的时间尺度上受森林和气候驱动的影响。10 年间的年蒸散发总量从 207.2 毫米到 384.6 毫米不等,植物年蒸腾作用(T)占年蒸散发总量的 57 ± 5.4%(平均值 ± 标准偏差)。年蒸散发最初下降(2007-2011 年),然后上升(2011-2016 年),从 2007 年到 2016 年,蒸散发和蒸腾/蒸腾的年增长率约为 3.2% 和 1.2%,在统计上并不显著。气温(Ta)和蒸气压差(VPD)是这 10 年间预测蒸散发和蒸腾/蒸腾(T/ET)季节变化的最重要因素,高气温(Ta)、蒸气压差和光合有效辐射(PAR)会导致蒸散发和蒸腾/蒸腾(T/ET)增加。年蒸散发随着春季 Ta 的增加和 VPD 的降低而增加。年蒸散发随 VPD 的增加而增加,随 5 厘米深土壤容积含水量(VWC5)的增加而减少。增强植被指数(EVI,树冠绿色度指标)滞后于年蒸散发和上层树木死亡率,而 EVI 的上年值和当年值则不能很好地预测年蒸散发和年蒸散发/年蒸散发。气候过程对水汽通量的影响大于生物因素(如林分绿度),这突出表明有必要将气候调节机制纳入林分干扰期间及之后的蒸散发动态预测模型中。气候和森林绿度对水汽通量的影响需要在更长的时间尺度(如十年尺度)上进行探索,以捕捉与林分干扰及其后续恢复相关的长期趋势。
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引用次数: 0
Evapotranspiration and its partitioning during and following a mountain pine beetle infestation of a lodgepole pine stand in the interior of British Columbia, Canada 加拿大不列颠哥伦比亚省内陆地区一片山地松树林遭受山地松树甲虫侵袭期间和之后的蒸散量及其分配情况
Pub Date : 2024-02-14 DOI: 10.3389/ffgc.2024.1352853
Shaorong Hao, Xin Jia, Hongxian Zhao, Xinhao Li, Yanmei Mu, T. Zha, Peng Liu, C. Bourque
Massive tree mortality events in western Canada due to widespread infestation by mountain pine beetle (MPB) are expected to impact local-to-regional evapotranspiration (ET) dynamics during and after a disturbance. How ecosystem-level ET and its components may vary with canopy-tree mortality (treefall) and subsequent understory recovery remains unclear.We used 10 years of continuous eddy-covariance and remote-sensing data (2007–2016) and machine-learning models based on random forest and xgboost to determine forest- and climate-driven effects at temporal scales appropriate for a lodgepole pine-dominated stand following a major, five-year MPB disturbance initiated in the summer of 2006.Total annual ET over the 10 years ranged from 207.2 to 384.6 mm, with annual plant transpiration (T) contributing to 57 ± 5.4% (mean ± standard deviation) of annual ET. Annual ET initially declined (2007–2011) and then increased (2011–2016), with ET and T/ET increasing at statistically non-significant rates of approximately 3.2 and 1.2% per year from 2007 to 2016. Air temperature (Ta) and vapor pressure deficit (VPD) were the most important predictors of seasonal variation in ET and T/ET during the 10-year period, with high Ta, VPD, and photosynthetically active radiation (PAR) causing ET and T/ET to increase. Annual ET increased with both increasing spring Ta and decreasing VPD. Annual T/ET was shown to increase with increasing VPD and decrease with increasing volumetric soil water content at a 5-cm depth (VWC5). Enhanced vegetation index (EVI, an indicator of canopy greenness) lagged T and overstory tree mortality, whereas previous- and current-year values of EVI were shown to be poor predictors of annual ET and T/ET.These findings suggest that the promotion of climate factors on forest ecosystem-level water vapor fluxes may offset reductions promoted by MPB outbreaks. Climate processes affected water vapor fluxes more than biotic factors, like stand greenness, highlighting the need to include climate-regulatory mechanisms in predictive models of ET dynamics during and subsequent to stand disturbance. Climate and forest-greenness effects on water vapor fluxes need to be explored at even longer time scales, e.g., at decadal scales, to capture long-drawn-out trends associated with stand disturbance and its subsequent recovery.
由于山松甲虫(MPB)的广泛侵袭,加拿大西部发生了大规模的树木死亡事件,预计这将在干扰期间和干扰之后影响当地到区域的蒸散(ET)动态。我们使用了 10 年连续的涡度协方差和遥感数据(2007-2016 年)以及基于随机森林和 xgboost 的机器学习模型,以确定在 2006 年夏季开始的为期 5 年的重大 MPB 干扰之后,以落叶松为主的林分在适当的时间尺度上受森林和气候驱动的影响。10 年间的年蒸散发总量从 207.2 毫米到 384.6 毫米不等,植物年蒸腾作用(T)占年蒸散发总量的 57 ± 5.4%(平均值 ± 标准偏差)。年蒸散发最初下降(2007-2011 年),然后上升(2011-2016 年),从 2007 年到 2016 年,蒸散发和蒸腾/蒸腾的年增长率约为 3.2% 和 1.2%,在统计上并不显著。气温(Ta)和蒸气压差(VPD)是这 10 年间预测蒸散发和蒸腾/蒸腾(T/ET)季节变化的最重要因素,高气温(Ta)、蒸气压差和光合有效辐射(PAR)会导致蒸散发和蒸腾/蒸腾(T/ET)增加。年蒸散发随着春季 Ta 的增加和 VPD 的降低而增加。年蒸散发随 VPD 的增加而增加,随 5 厘米深土壤容积含水量(VWC5)的增加而减少。增强植被指数(EVI,树冠绿色度指标)滞后于年蒸散发和上层树木死亡率,而 EVI 的上年值和当年值则不能很好地预测年蒸散发和年蒸散发/年蒸散发。气候过程对水汽通量的影响大于生物因素(如林分绿度),这突出表明有必要将气候调节机制纳入林分干扰期间及之后的蒸散发动态预测模型中。气候和森林绿度对水汽通量的影响需要在更长的时间尺度(如十年尺度)上进行探索,以捕捉与林分干扰及其后续恢复相关的长期趋势。
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引用次数: 0
Editorial: Impacts of global change on protective forests in mountain areas 社论:全球变化对山区防护林的影响
Pub Date : 2024-02-13 DOI: 10.3389/ffgc.2024.1375285
Alessandra Bottero, Christine Moos, Ana Stritih, Michaela Teich
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引用次数: 0
Editorial: Impacts of global change on protective forests in mountain areas 社论:全球变化对山区防护林的影响
Pub Date : 2024-02-13 DOI: 10.3389/ffgc.2024.1375285
Alessandra Bottero, Christine Moos, Ana Stritih, Michaela Teich
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引用次数: 0
Salient features and ecosystem services of tree species in mountainous indigenous agroforestry systems of North-Eastern Tanzania 坦桑尼亚东北部山区本土农林系统中树种的显著特征和生态系统服务
Pub Date : 2024-02-13 DOI: 10.3389/ffgc.2023.1082864
Oforo Didas Kimaro, Ellen Desie, D. Kimaro, K. Vancampenhout, K. Feger
Indigenous agroforestry systems in tropical mountainous environments provide crucial ecosystem services, but these ecosystems are also facing some challenges. A loss of diversity and native tree species in the overstory layer has been a growing concern in agroforestry worldwide, yet the drivers behind it remain inadequately understood. We hypothesize that the choice of overstory tree species is closely linked to the ecosystem services required by farmers, their livelihood strategy, and the salient features of each system. We, therefore, investigated four different farming systems in the mountains of northeastern Tanzania, i.e., the Kihamba on Mt. Kilimanjaro, Ginger agroforestry in the South Pare mountains, and Miraba and Mixed spices agroforestry in the West and East Usambara. In 82 farms, we collected data on the structure, tree species composition (both native and non-native), diversity, and associated provisioning ecosystem services as identified by smallholder farmers. Our results indicate that although all studied systems are multi-layered with three or four vertical layers, they have notable differences in their salient features concerning structure, composition, and diversity. The unique climate, landscape setting, soil, historical background, and economic opportunities that exist in each region contribute to those differences. Our findings indicate that the Kihamba system had the highest number of native tree species, and the largest diversity in species used for provisioning services, followed by Ginger agroforestry. No native species were used in Miraba or Mixed spices agroforestry, where a limited number of non-native tree species are planted mainly for fuel and timber or as a crop, respectively. Our findings regarding reported provisioning ES corroborate our hypothesis and imply that policies to increase resilience and restore the native tree species cover of the agroforestry systems of Tanzania can only be successful if knowledge of the ES potential of native species is increased, and interventions are tailored to each system’s ES needs for conservation as well as livelihood.
热带山区环境中的本土农林系统提供了重要的生态系统服务,但这些生态系统也面临着一些挑战。顶层树种多样性和本地树种的丧失已成为全球农林业日益关注的问题,但人们对其背后的驱动因素仍缺乏足够的了解。我们假设,上层树种的选择与农民所需的生态系统服务、他们的生计策略以及每个系统的显著特征密切相关。因此,我们在坦桑尼亚东北部山区调查了四种不同的农耕系统,即乞力马扎罗山上的基汉巴、南帕雷山区的生姜农林业以及西乌桑巴拉和东乌桑巴拉的米拉巴和混合香料农林业。在 82 个农场中,我们收集了有关结构、树种组成(包括本地树种和非本地树种)、多样性以及由小农确定的相关生态系统服务供应的数据。我们的研究结果表明,尽管所有研究的系统都是多层次的,有三层或四层垂直层,但它们在结构、组成和多样性方面的显著特征却有明显差异。每个地区独特的气候、地貌环境、土壤、历史背景和经济机遇造成了这些差异。我们的研究结果表明,基汉巴系统中本地树种的数量最多,用于提供服务的树种的多样性最大,其次是金格农林业。米拉巴农林业和混合香料农林业没有使用本地树种,它们种植了数量有限的非本地树种,主要用作燃料和木材或作物。我们关于提供 ES 的研究结果证实了我们的假设,并意味着只有增加对本地树种 ES 潜力的了解,并根据每个系统对保护和生计 ES 的需求采取干预措施,提高坦桑尼亚农林系统的复原力和恢复本地树种覆盖率的政策才能取得成功。
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引用次数: 0
Salient features and ecosystem services of tree species in mountainous indigenous agroforestry systems of North-Eastern Tanzania 坦桑尼亚东北部山区本土农林系统中树种的显著特征和生态系统服务
Pub Date : 2024-02-13 DOI: 10.3389/ffgc.2023.1082864
Oforo Didas Kimaro, Ellen Desie, D. Kimaro, K. Vancampenhout, K. Feger
Indigenous agroforestry systems in tropical mountainous environments provide crucial ecosystem services, but these ecosystems are also facing some challenges. A loss of diversity and native tree species in the overstory layer has been a growing concern in agroforestry worldwide, yet the drivers behind it remain inadequately understood. We hypothesize that the choice of overstory tree species is closely linked to the ecosystem services required by farmers, their livelihood strategy, and the salient features of each system. We, therefore, investigated four different farming systems in the mountains of northeastern Tanzania, i.e., the Kihamba on Mt. Kilimanjaro, Ginger agroforestry in the South Pare mountains, and Miraba and Mixed spices agroforestry in the West and East Usambara. In 82 farms, we collected data on the structure, tree species composition (both native and non-native), diversity, and associated provisioning ecosystem services as identified by smallholder farmers. Our results indicate that although all studied systems are multi-layered with three or four vertical layers, they have notable differences in their salient features concerning structure, composition, and diversity. The unique climate, landscape setting, soil, historical background, and economic opportunities that exist in each region contribute to those differences. Our findings indicate that the Kihamba system had the highest number of native tree species, and the largest diversity in species used for provisioning services, followed by Ginger agroforestry. No native species were used in Miraba or Mixed spices agroforestry, where a limited number of non-native tree species are planted mainly for fuel and timber or as a crop, respectively. Our findings regarding reported provisioning ES corroborate our hypothesis and imply that policies to increase resilience and restore the native tree species cover of the agroforestry systems of Tanzania can only be successful if knowledge of the ES potential of native species is increased, and interventions are tailored to each system’s ES needs for conservation as well as livelihood.
热带山区环境中的本土农林系统提供了重要的生态系统服务,但这些生态系统也面临着一些挑战。顶层树种多样性和本地树种的丧失已成为全球农林业日益关注的问题,但人们对其背后的驱动因素仍缺乏足够的了解。我们假设,上层树种的选择与农民所需的生态系统服务、他们的生计策略以及每个系统的显著特征密切相关。因此,我们在坦桑尼亚东北部山区调查了四种不同的农耕系统,即乞力马扎罗山上的基汉巴、南帕雷山区的生姜农林业以及西乌桑巴拉和东乌桑巴拉的米拉巴和混合香料农林业。在 82 个农场中,我们收集了有关结构、树种组成(包括本地树种和非本地树种)、多样性以及由小农确定的相关生态系统服务供应的数据。我们的研究结果表明,尽管所有研究的系统都是多层次的,有三层或四层垂直层,但它们在结构、组成和多样性方面的显著特征却有明显差异。每个地区独特的气候、地貌环境、土壤、历史背景和经济机遇造成了这些差异。我们的研究结果表明,基汉巴系统中本地树种的数量最多,用于提供服务的树种的多样性最大,其次是金格农林业。米拉巴农林业和混合香料农林业没有使用本地树种,它们种植了数量有限的非本地树种,主要用作燃料和木材或作物。我们关于提供 ES 的研究结果证实了我们的假设,并意味着只有增加对本地树种 ES 潜力的了解,并根据每个系统对保护和生计 ES 的需求采取干预措施,提高坦桑尼亚农林系统的复原力和恢复本地树种覆盖率的政策才能取得成功。
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引用次数: 0
Enhancing data-driven soil moisture modeling with physically-guided LSTM networks 利用物理引导的 LSTM 网络加强数据驱动的土壤湿度建模
Pub Date : 2024-02-08 DOI: 10.3389/ffgc.2024.1353011
Qingtian Geng, Sen Yan, Qingliang Li, Cheng Zhang
In recent years, deep learning methods have shown significant potential in soil moisture modeling. However, a prominent limitation of deep learning approaches has been the absence of physical mechanisms. To address this challenge, this study introduces two novel loss functions designed around physical mechanisms to guide deep learning models in capturing physical information within the data. These two loss functions are crafted to leverage the monotonic relationships between surface water variables and shallow soil moisture as well as deep soil water. Based on these physically-guided loss functions, two physically-guided Long Short-Term Memory (LSTM) networks, denoted as PHY-LSTM and PHYs-LSTM, are proposed. These networks are trained on the global ERA5-Land dataset, and the results indicate a notable performance improvement over traditional LSTM models. When used for global soil moisture forecasting for the upcoming day, PHY-LSTM and PHYs-LSTM models exhibit closely comparable results. In comparison to conventional data-driven LSTM models, both models display a substantial enhancement in various evaluation metrics. Specifically, PHYs-LSTM exhibits improvements in several key performance indicators: an increase of 13.6% in Kling-Gupta Efficiency (KGE), a 20.7% increase in Coefficient of Determination (R2), an 8.2% reduction in Root Mean Square Error (RMSE), and a 4.4% increase in correlation coefficient (R). PHY-LSTM also demonstrates improvements, with a 14.8% increase in KGE, a 19.6% increase in R2, an 8.2% reduction in RMSE, and a 4.4% increase in R. Additionally, both models exhibit enhanced physical consistency over a wide geographical area. Experimental results strongly emphasize that the incorporation of physical mechanisms can significantly bolster the predictive capabilities of data-driven soil moisture models.
近年来,深度学习方法在土壤水分建模方面显示出巨大潜力。然而,深度学习方法的一个突出局限是缺乏物理机制。为了应对这一挑战,本研究引入了两个围绕物理机制设计的新型损失函数,以指导深度学习模型捕捉数据中的物理信息。这两个损失函数的设计充分利用了地表水变量与浅层土壤水分以及深层土壤水分之间的单调关系。基于这些物理引导的损失函数,提出了两个物理引导的长短期记忆(LSTM)网络,分别称为 PHY-LSTM 和 PHYs-LSTM。这些网络在全球ERA5-Land数据集上进行了训练,结果表明其性能明显优于传统的LSTM模型。当用于未来一天的全球土壤湿度预测时,PHY-LSTM 和 PHYs-LSTM 模型的结果非常接近。与传统的数据驱动 LSTM 模型相比,这两种模型在各种评估指标上都有大幅提升。具体来说,PHYs-LSTM 在几个关键性能指标上都有所提高:Kling-Gupta 效率 (KGE) 提高了 13.6%,决定系数 (R2) 提高了 20.7%,均方根误差 (RMSE) 降低了 8.2%,相关系数 (R) 提高了 4.4%。PHY-LSTM 也有所改进,KGE 增加了 14.8%,R2 增加了 19.6%,RMSE 减少了 8.2%,R 增加了 4.4%。实验结果有力地证明,将物理机制纳入土壤水分模型可以显著提高数据驱动型土壤水分模型的预测能力。
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引用次数: 0
Enhancing data-driven soil moisture modeling with physically-guided LSTM networks 利用物理引导的 LSTM 网络加强数据驱动的土壤湿度建模
Pub Date : 2024-02-08 DOI: 10.3389/ffgc.2024.1353011
Qingtian Geng, Sen Yan, Qingliang Li, Cheng Zhang
In recent years, deep learning methods have shown significant potential in soil moisture modeling. However, a prominent limitation of deep learning approaches has been the absence of physical mechanisms. To address this challenge, this study introduces two novel loss functions designed around physical mechanisms to guide deep learning models in capturing physical information within the data. These two loss functions are crafted to leverage the monotonic relationships between surface water variables and shallow soil moisture as well as deep soil water. Based on these physically-guided loss functions, two physically-guided Long Short-Term Memory (LSTM) networks, denoted as PHY-LSTM and PHYs-LSTM, are proposed. These networks are trained on the global ERA5-Land dataset, and the results indicate a notable performance improvement over traditional LSTM models. When used for global soil moisture forecasting for the upcoming day, PHY-LSTM and PHYs-LSTM models exhibit closely comparable results. In comparison to conventional data-driven LSTM models, both models display a substantial enhancement in various evaluation metrics. Specifically, PHYs-LSTM exhibits improvements in several key performance indicators: an increase of 13.6% in Kling-Gupta Efficiency (KGE), a 20.7% increase in Coefficient of Determination (R2), an 8.2% reduction in Root Mean Square Error (RMSE), and a 4.4% increase in correlation coefficient (R). PHY-LSTM also demonstrates improvements, with a 14.8% increase in KGE, a 19.6% increase in R2, an 8.2% reduction in RMSE, and a 4.4% increase in R. Additionally, both models exhibit enhanced physical consistency over a wide geographical area. Experimental results strongly emphasize that the incorporation of physical mechanisms can significantly bolster the predictive capabilities of data-driven soil moisture models.
近年来,深度学习方法在土壤水分建模方面显示出巨大潜力。然而,深度学习方法的一个突出局限是缺乏物理机制。为了应对这一挑战,本研究引入了两个围绕物理机制设计的新型损失函数,以指导深度学习模型捕捉数据中的物理信息。这两个损失函数的设计充分利用了地表水变量与浅层土壤水分以及深层土壤水分之间的单调关系。基于这些物理引导的损失函数,提出了两个物理引导的长短期记忆(LSTM)网络,分别称为 PHY-LSTM 和 PHYs-LSTM。这些网络在全球ERA5-Land数据集上进行了训练,结果表明其性能明显优于传统的LSTM模型。当用于未来一天的全球土壤湿度预测时,PHY-LSTM 和 PHYs-LSTM 模型的结果非常接近。与传统的数据驱动 LSTM 模型相比,这两种模型在各种评估指标上都有大幅提升。具体来说,PHYs-LSTM 在几个关键性能指标上都有所提高:Kling-Gupta 效率 (KGE) 提高了 13.6%,决定系数 (R2) 提高了 20.7%,均方根误差 (RMSE) 降低了 8.2%,相关系数 (R) 提高了 4.4%。PHY-LSTM 也有所改进,KGE 增加了 14.8%,R2 增加了 19.6%,RMSE 减少了 8.2%,R 增加了 4.4%。实验结果有力地证明,将物理机制纳入土壤水分模型可以显著提高数据驱动型土壤水分模型的预测能力。
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引用次数: 0
Enhancement effects of mangrove restoration on blue carbon storage in Qinzhou Bay 钦州湾红树林恢复对蓝碳储存的增效作用
Pub Date : 2024-02-07 DOI: 10.3389/ffgc.2024.1328783
Wentao Song, Yukun Hou, Wenjuan Zhu, Yaocheng Fan, Haoyu Xu, Chengyu Cai, Guanghao Li, Lin Huang
Mangroves are the main carbon sinks in tropical regions and have high capabilities for carbon sequestration. Protection and restoration of mangroves are necessary to reduce carbon emissions and fight climate change. While the Qinzhou Bay as the main area of national mangrove restoration plan in the future, studies on its carbon pools, especially assessment of the carbon sink enhancement effect of restored mangroves along forest chronosequence, are lacking.This study aimed to quantify the changes in restored mangrove soil carbon stock, vegetation and root carbon stocks along the forest age sequence in Qinzhou Bay through field survey.The results revealed that the carbon stocks of vegetation and roots significantly increased with the developing forest age. Only in the soil layer above 30 cm, the soil carbon storage apparently increased with the developing forest age in non-cofferdam area, and then decreased slowly after reaching the peak (at 6 ~ 8 years). Moreover, the soil carbon storage of mangroves was greater in the cofferdam area than in the non-cofferdam area.This implied that the cofferdam restoration efforts may be more effective in enhancing blue carbon storage, during the initial stages of the restoration process. The results of this study suggested that mangrove restoration has substantial potential capacity in carbon storage and nutrient cycling, providing a reference for the protection and restoration efforts concerning mangroves.
红树林是热带地区的主要碳汇,具有很强的固碳能力。保护和恢复红树林对减少碳排放、应对气候变化十分必要。本研究旨在通过野外调查,量化钦州湾红树林恢复后土壤碳储量、植被和根系碳储量随林龄序列的变化。结果表明,植被和根系的碳储量随森林年龄的增长而明显增加,只有非围堰区 30 cm 以上土层的土壤碳储量随森林年龄的增长而明显增加,达到峰值(6~8 年)后缓慢减少。此外,围堰区红树林的土壤碳储量大于非围堰区,这意味着在红树林恢复的初期阶段,围堰恢复工作可能会更有效地提高蓝碳储量。该研究结果表明,红树林恢复在碳储存和营养循环方面具有巨大的潜在能力,为红树林的保护和恢复工作提供了参考。
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引用次数: 0
Enhancement effects of mangrove restoration on blue carbon storage in Qinzhou Bay 钦州湾红树林恢复对蓝碳储存的增效作用
Pub Date : 2024-02-07 DOI: 10.3389/ffgc.2024.1328783
Wentao Song, Yukun Hou, Wenjuan Zhu, Yaocheng Fan, Haoyu Xu, Chengyu Cai, Guanghao Li, Lin Huang
Mangroves are the main carbon sinks in tropical regions and have high capabilities for carbon sequestration. Protection and restoration of mangroves are necessary to reduce carbon emissions and fight climate change. While the Qinzhou Bay as the main area of national mangrove restoration plan in the future, studies on its carbon pools, especially assessment of the carbon sink enhancement effect of restored mangroves along forest chronosequence, are lacking.This study aimed to quantify the changes in restored mangrove soil carbon stock, vegetation and root carbon stocks along the forest age sequence in Qinzhou Bay through field survey.The results revealed that the carbon stocks of vegetation and roots significantly increased with the developing forest age. Only in the soil layer above 30 cm, the soil carbon storage apparently increased with the developing forest age in non-cofferdam area, and then decreased slowly after reaching the peak (at 6 ~ 8 years). Moreover, the soil carbon storage of mangroves was greater in the cofferdam area than in the non-cofferdam area.This implied that the cofferdam restoration efforts may be more effective in enhancing blue carbon storage, during the initial stages of the restoration process. The results of this study suggested that mangrove restoration has substantial potential capacity in carbon storage and nutrient cycling, providing a reference for the protection and restoration efforts concerning mangroves.
红树林是热带地区的主要碳汇,具有很强的固碳能力。保护和恢复红树林对减少碳排放、应对气候变化十分必要。本研究旨在通过野外调查,量化钦州湾红树林恢复后土壤碳储量、植被和根系碳储量随林龄序列的变化。结果表明,植被和根系的碳储量随森林年龄的增长而明显增加,只有非围堰区 30 cm 以上土层的土壤碳储量随森林年龄的增长而明显增加,达到峰值(6~8 年)后缓慢减少。此外,围堰区红树林的土壤碳储量大于非围堰区,这意味着在红树林恢复的初期阶段,围堰恢复工作可能会更有效地提高蓝碳储量。该研究结果表明,红树林恢复在碳储存和营养循环方面具有巨大的潜在能力,为红树林的保护和恢复工作提供了参考。
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Frontiers in Forests and Global Change
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