Ye Qianjun, Li Zhenwei, Duan Liangxia, Xu Xianli
Huanjiang Observation and Research Station for Karst Ecosystem, Key Laboratory for Agro-Ecological Processes in Subtropical Region, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha 410125, China; College of Resources & Environment, Hunan Agricultural University, Changsha 410128, China.
Huanjiang Observation and Research Station for Karst Ecosystem, Key Laboratory for Agro-Ecological Processes in Subtropical Region, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha 410125, China; Guangxi Industrial Technology Research Institute for Karst Rocky Desertification Control, Nanning 530201, China; Guangxi Key Laboratory of Karst Ecological Processes and Services, Huanjiang 547100, China.
Sci Total Environ. 2022 Jun 10;824:153874. doi: 10.1016/j.scitotenv.2022.153874. Epub 2022 Feb 14.
Karst landscapes cover 7-12% of Earth's continental area, and approximately 25% of the world's population partially or completely relies on drinking water from karst aquifers. Water shortages are a challenge worldwide in karst mountainous landscapes. Knowledge of intra-annual variability in runoff and the potential drivers of variability is important for optimizing regional water resources use. The objectives of this study were to investigate temporal variations in the distribution of intra-annual runoff during 2003-2017 in six karst watersheds in southwest China and to identify the key drivers of these variations. The Gini coefficient and Lorentz asymmetry coefficient were used to represent intra-annual variability in runoff. Partial least squares-structural equation modeling (PLS-SEM) was used to decouple the effects of climate variables and vegetation dynamics on the distribution of intra-annual runoff. In all six watersheds, the Gini coefficient ranged from 0.15 to 0.59, with a mean value of greater than 1 for the Lorentz asymmetry coefficient. The heterogeneity of intra-annual runoff distribution showed a decreasing trend from 2003 to 2017. Climate variables and vegetation dynamics strongly influenced intra-annual variability in runoff and accounted for 19-63% and 17-67% of the variation in the Gini coefficient and Lorentz asymmetry coefficient, respectively. Vegetation was negatively correlated with the Gini coefficient, and the direct effect of climate on the Gini coefficient was greater than its indirect effect on the Gini coefficient through vegetation. As compared with traditional multivariate statistical methods, PLS-SEM provides additional valuable information, including information on the direct and indirect impacts of climate and vegetation on the distribution of intra-annual runoff. PLS-SEM is recommended as an effective approach for disentangling the coupled relationships between predictors and hydrological characteristics under different circumstances.
喀斯特地貌覆盖了地球大陆面积的7%-12%,全球约25%的人口部分或完全依赖喀斯特含水层的饮用水。水资源短缺是喀斯特山区面临的全球性挑战。了解径流的年内变化及其潜在驱动因素对于优化区域水资源利用至关重要。本研究的目的是调查2003-2017年中国西南六个喀斯特流域年内径流分布的时间变化,并确定这些变化的关键驱动因素。采用基尼系数和洛伦兹不对称系数来表示径流的年内变化。运用偏最小二乘结构方程模型(PLS-SEM)来解析气候变量和植被动态对年内径流分布的影响。在所有六个流域中,基尼系数在0.15至0.59之间,洛伦兹不对称系数的平均值大于1。2003年至2017年,年内径流分布的不均匀性呈下降趋势。气候变量和植被动态对径流的年内变化有强烈影响,分别占基尼系数和洛伦兹不对称系数变化的19%-63%和17%-67%。植被与基尼系数呈负相关,气候对基尼系数的直接影响大于其通过植被对基尼系数的间接影响。与传统多元统计方法相比,PLS-SEM提供了额外有价值的信息,包括气候和植被对年内径流分布的直接和间接影响信息。建议将PLS-SEM作为一种有效方法,用于解析不同情况下预测变量与水文特征之间的耦合关系。