Muthuvel Dineshkumar, Sivakumar Bellie
Department of Civil Engineering, Indian Institute of Technology Bombay, Powai, Mumbai, Maharashtra, 400076, India.
Department of Civil Engineering, Indian Institute of Technology Bombay, Powai, Mumbai, Maharashtra, 400076, India.
J Environ Manage. 2024 Nov;370:122511. doi: 10.1016/j.jenvman.2024.122511. Epub 2024 Sep 21.
Meteorological droughts often propagate to agricultural (and other) droughts, both spatially and temporally. The present study proposes a novel complex networks-based cascading spatial drought network to examine the spatial propagation of meteorological droughts in a region to agricultural droughts in other regions. This is done through: (1) establishing stable homogeneous drought communities; (2) investigating inter-community drought propagation; (3) locating drought sources; and (4) evaluating drought connections within major crop belts. The approach is implemented to study droughts in the Indian-subcontinent during the period 1948-2022. Monthly precipitation and root-zone soil moisture data from GLDAS (Global Land Data Assimilation System) are used to compute the standardized precipitation index (SPI) for meteorological droughts and standardized soil moisture index (SSI) for agricultural droughts. Primarily, the drought network is demarcated into several subsets of network communities within which clusters of localized propagation take place. Multi-community subgraphs combining different communities are also formed to understand the long-distance inter-community drought linkages. Using network centrality measures, such as degree, closeness, and clustering coefficient, network properties of scale-freeness, small-worldness, and presence of rich-clubs are checked. Although the overall network does not exhibit any of these properties, certain subgraphs have significant small-worldness, rich-clubs, and partial scale-freeness. Some of the crucial nodes that support these network properties lie in the monsoon pathways (in the Western Ghats), and others have a strong association with El Niño Southern Oscillation (ENSO) teleconnections, thus validating the ability of the drought network to capture seasonal and climatic features. Additionally, subgraphs of nodes with high productivity of different food crops are created to study drought propagation within crop belts. Barring potential shortcomings related to data dependencies, the cascading spatial drought network helps identify an impending agricultural drought that could strengthen our ability to forecast droughts.
气象干旱常常在空间和时间上蔓延至农业(及其他)干旱。本研究提出了一种基于复杂网络的新型级联空间干旱网络,以检验一个地区的气象干旱在空间上向其他地区农业干旱的蔓延情况。这通过以下方式实现:(1)建立稳定的同质干旱群落;(2)研究群落间干旱蔓延;(3)确定干旱源;(4)评估主要作物带内的干旱联系。该方法应用于研究1948 - 2022年期间印度次大陆的干旱情况。利用全球陆地数据同化系统(GLDAS)的月降水量和根区土壤湿度数据,计算气象干旱的标准化降水指数(SPI)和农业干旱的标准化土壤湿度指数(SSI)。首先,将干旱网络划分为几个网络群落子集,在这些子集内发生局部蔓延集群。还形成了结合不同群落的多群落子图,以了解远距离群落间干旱联系。使用度、接近度和聚类系数等网络中心性度量,检验网络的无标度性、小世界特性和富俱乐部的存在等网络属性。尽管整个网络未表现出这些属性中的任何一种,但某些子图具有显著的小世界特性、富俱乐部和部分无标度性。支持这些网络属性的一些关键节点位于季风路径(西高止山脉),其他节点与厄尔尼诺南方涛动(ENSO)遥相关有很强的关联,从而验证了干旱网络捕捉季节和气候特征的能力。此外,创建了不同粮食作物高生产力节点的子图,以研究作物带内的干旱蔓延。除了与数据依赖性相关的潜在缺点外,级联空间干旱网络有助于识别即将到来的农业干旱,从而增强我们预测干旱的能力。