Kashyap Rahul, Kuttippurath Jayanarayanan, Kumar Pankaj
CORAL, Indian Institute of Technology Kharagpur, Kharagpur, 721302, India.
CORAL, Indian Institute of Technology Kharagpur, Kharagpur, 721302, India.
J Environ Manage. 2023 Jun 15;336:117655. doi: 10.1016/j.jenvman.2023.117655. Epub 2023 Mar 8.
Accurate estimation of carbon cycle is a challenging task owing to the complexity and heterogeneity of ecosystems. Carbon Use Efficiency (CUE) is a metric to define the ability of vegetation to sequester carbon from the atmosphere. It is key to understand the carbon sink and source pathways of ecosystems. Here, we quantify CUE using remote sensing measurements to examine its variability, drivers and underlying mechanisms in India for the period 2000-2019, by applying the principal component analyses (PCA), multiple linear regression (MLR) and causal discovery. Our analysis shows that the forests in the hilly regions (HR) and northeast (NE), and croplands in the western areas of South India (SI) exhibit high (>0.6) CUE. The northwest (NW), Indo-Gangetic plain (IGP) and some areas in Central India (CI) show low (<0.3) CUE. In general, the water availability as soil moisture (SM) and precipitation (P) promote higher CUE, but higher temperature (T) and air organic carbon content (AOCC) reduce CUE. It is found that SM has the strongest relative influence (33%) on CUE, followed by P. Also, SM has a direct causal link with all drivers and CUE; reiterating its importance in driving vegetation carbon dynamics (VCD) for the cropland dominated India. The long-term analysis reveals that the low CUE regions in NW (moisture induced greening) and IGP (irrigation induced agricultural boom) have an increasing trend in productivity (greening). However, the high CUE regions in NE (deforestation and extreme events) and SI (warming induced moisture stress) exhibit a decreasing trend in productivity (browning), which is a great concern. Our study, therefore, provides new insights on the rate of carbon allocation and the need of proper planning for maintaining balance in the terrestrial carbon cycle. This is particularly important in the context of drafting policy decisions for the mitigation of climate change, food security and sustainability.
由于生态系统的复杂性和异质性，准确估算碳循环是一项具有挑战性的任务。碳利用效率（CUE）是定义植被从大气中固碳能力的一个指标。它是理解生态系统碳汇和碳源途径的关键。在此，我们利用遥感测量来量化CUE，通过应用主成分分析（PCA）、多元线性回归（MLR）和因果发现，研究2000 - 2019年期间印度CUE的变异性、驱动因素及潜在机制。我们的分析表明，丘陵地区（HR）和东北部（NE）的森林以及印度南部（SI）西部地区的农田呈现出较高（>0.6）的CUE。西北部（NW）、印度 - 恒河平原（IGP）和印度中部（CI）的一些地区CUE较低（<0.3）。总体而言，作为土壤湿度（SM）和降水量（P）的水分可利用性促进了更高的CUE，但较高的温度（T）和空气有机碳含量（AOCC）会降低CUE。研究发现，SM对CUE的相对影响最强（33%），其次是P。此外，SM与所有驱动因素和CUE都存在直接因果联系；这再次强调了其在驱动以农田为主的印度植被碳动态（VCD）中的重要性。长期分析表明，NW（水分诱导绿化）和IGP（灌溉诱导农业繁荣）的低CUE地区生产力（绿化）呈上升趋势。然而，NE（森林砍伐和极端事件）和SI（变暖诱导水分胁迫）的高CUE地区生产力（褐变）呈下降趋势，这令人十分担忧。因此，我们的研究为碳分配速率以及为维持陆地碳循环平衡进行合理规划的必要性提供了新的见解。在起草缓解气候变化、粮食安全和可持续性的政策决策背景下，这尤为重要。
