Temporal and Spatial Characteristics of Multidimensional Extreme Precipitation Indicators: A Case Study in the Loess Plateau, China

• Main Authors: Chaoxing Sun, Guohe Huang, Yurui Fan
• Format: Article
• Language: English
• Published: MDPI AG 2020-04-01
• Series: Water
• Subjects: extreme precipitation; Gaussian mixture model; copula; multidimensional; spatial–temporal changes; Loess Plateau
• Online Access: https://www.mdpi.com/2073-4441/12/4/1217

Extreme precipitation can seriously affect the ecological environment, agriculture, human safety, and property resilience. A full-scale and scientific assessment in extreme precipitation characteristics is necessary for water resources management and providing decision-making support to mitigate the potential losses brought by extreme precipitation. In the present study, a multidimensional risk assessment framework is developed to investigate the spatial–temporal changes in different extreme precipitation indicators. The Gaussian mixture model (GMM) is applied to fit the distribution for each indicator and carry out single index risk assessment. The joint probabilistic features of multiple extreme indicators can be explored through coupling the GMM distributions into copulas. In addition, the moving window approach and the Mann–Kendall test are integrated to examine non-stationary risks (evaluated by “AND”, “OR”, and Kendall return periods) of multidimensional indicators along with their changing trends and significance. The proposed assessment framework is applied to the Loess Plateau, China. Four extreme precipitation indicators are characterized: the amount (P95), the number of days (D95), the intensity (I95), and the proportion (R95) of extreme precipitation. The spatial–temporal changes of these indicators and their multidimensional combinations (including six two-dimensional and three three-dimensional combinations) are fully identified and quantitatively evaluated.