Changes in extreme precipitation in the Yangtze River basin and its association with global mean temperature and ENSO

• Published in: International journal of climatology 2018-03, Vol.38 (4), p.1989-2005
• Main Authors: Lü, Mingquan, Wu, Sheng‐Jun, Chen, Jilong, Chen, Chundi, Wen, Zhaofei, Huang, Yuanyang
• Format: Article
• Language: English
• Subjects: Annual precipitation; Atmospheric precipitations; Change detection; Construction planning; Daily precipitation; Dam construction; Disasters; El Nino; El Nino phenomena; El Nino-Southern Oscillation event; El Niño–Southern Oscillation; Erosion; extreme precipitation; Extreme values; Extreme weather; Flood management; Flooding; Floods; generalized extreme value (GEV) distribution; global temperature; Global temperatures; Heavy rainfall; Identification; Iterative methods; Landslides; Mathematical models; Mean temperatures; Precipitation; Rain; Rainfall; River basins; Rivers; Seasons; Sensitivity; Southern Oscillation; Stations; Surface temperature; Temperature effects; Trends; Yangtze River basin
• ISSN: 0899-8418; 1097-0088
• DOI: 10.1002/joc.5311

ABSTRACT Extreme precipitation events may lead to secondary disasters such as floods, landslides and erosion. The Yangtze River basin (YRB), which is home to a large population, is susceptible to flooding associated with heavy rainfall. Many dams have been constructed or planned for in the YRB. Understanding the changing behaviour of extreme precipitation and its relationship with other climatic factors will increase planners' ability to plan for, manage and respond to related flood events. This study investigated the trends in annual and seasonal maximum daily precipitation (Mx1day) using the iterative Mann–Kendall test. The non‐stationary Mx1day values were modelled to detect whether such changes were associated with rising local temperature (LTEM), global surface temperature (GTEM) or the El Niño–Southern Oscillation (ENSO). Location and scale parameters were considered individually in the non‐stationary generalized extreme value model during this study. Rising trends in annual Mx1day were identified at 73% of the stations studied. The June–July–August seasonal Mx1day exhibited the most pronounced increase among the four seasons. When location and scale were considered, 78 stations and 31 stations exhibited non‐stationary Mx1day, respectively. The impacts of both LTEM and ENSO on observed annual Mx1day in the YRB did not have field significance, while the impacts of GTEM did. At the YRB scale, the median sensitivity of the annual Mx1day to GTEM was 7.05% °C−1, while the median sensitivity of the annual Mx1day to LTEM was only 0.8% °C−1. The relationship between annual Mx1day and GTEM was more suitable for the Clausius–Clapeyron equation. The relationships between ENSO and observed seasonal Mx1day in March–April–May, June–July–August (JJA) and September–October–November (SON) were not field significant. However, a significant negative correlation between December–January–February Mx1day and ENSO was identified. The opposite effects of ENSO on extreme precipitation in JJA and SON may weaken the influence of ENSO on annual Mx1day. At the Yangtze River basin scale, the median sensitivity of the annual Mx1day to GTEM was 7.05% °C−1. The relationships between ENSO and observed seasonal Mx1day in March–April–May, June–July–August and September–October–November were not field significant. However, a significant negative correlation between DJF Mx1day and ENSO was identified. The opposite effects of ENSO on extreme precipitation in JJA and SON may weaken the inf