地球科学进展

地球科学进展 ›› 2014, Vol. 29 ›› Issue (8): 956 -967. doi: 10.11867/j.issn.1001-8166.2014.08.0956

所属专题： 极端天气；

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全球变暖背景下珠江流域极端气温与降水事件时空变化的区域研究
黄强( ), 陈子燊( )   
  1. 中山大学水资源与环境系, 广东 广州 510275
  • 出版日期:2014-09-16
  • 基金资助:
    国家自然科学基金项目“变化环境下广东旱涝时空变化规律与风险概率研究”(编号：41371498)资助

Regional Study on the Trends of Extreme Temperature and Precipitation Events in the Pearl River Basin

Qiang Huang( ), Zishen Chen( )   

  1. Department of Water Resource and Enviroment, Sun Yat-sen University, Guangzhou 510275, China
  • Online:2014-09-16 Published:2014-09-17
全文 ( 31 ) 下载PDF ( 1557 )

利用中国气象局最新编制的0.5°×0.5°逐日地面气温、降水网格数据, 统计了16种极端气温与降水指数来定义极端气候事件, 通过改进的MannKendall趋势检验方法对珠江流域极端气温与降水事件的时空变化特征进行了研究, 并从区域的视角检验了变化趋势的显著性和一致性, 最后通过偏MannKendall检验探讨了极端气温和降水事件变化与自然界大尺度气候振荡的潜在联系。研究发现：①在过去半个多世纪里, 珠江流域总体上呈现出极端高温事件增多, 极端低温事件减少, 短时间极端降水增多, 长时间极端降水减少的趋势, 珠江流域面临着高温干旱和暴雨洪涝的威胁;②极端气温事件的变化趋势具有区域尺度上的显著性和一致性, 而极端降水事件在区域层面上的趋势则不明显, 并且区域差异大;③反映了大尺度气候振荡的多变量ENSO指数年际变化对珠江流域极端气温与降水事件的变化趋势没有显著的影响, 在一定程度上说明了极端气温与降水事件的变化趋势并不是自然界大尺度气候振荡导致的必然结果, 而可能是与人类活动共同作用的结果。

关键词: 气候变化, 极端气温与降水指数, 多变量ENSO指数, 趋势检验, 区域显著性与一致性

With the risk of global warming, exploring the changing pattern of extreme climate events in different places is explored for disaster prevention and mitigation. The 0.5°×0.5° grid dataset of daily temperature and precipitation from China Meteorological Administration was used to defined extreme climate events based on the 16 kinds of extreme temperature and precipitation indices. Spatio-temperal variations of the extreme temperature and precipitation events were analyzed through the modified MannKendall trend detecting method across the Pearl River basin, and the significance and consistency of the observed trends were also assessed in a regional perspective. Additionally, whether the observed trends are significantly linked to the largescale climate fluctuation system was investigated. The results indicate that a trend of more extreme high temperature events and less extreme low temperature events, more short time precipitation events and less long time precipitation events has been found in the Pearl River basin over the past half century, which could, consequently, increase the drought and flood risks. It is worthwhile to note that the trends of extreme temperature events are field significant and regional consistent, while the trends of extreme precipitation events are not. Since no significant covariability has been found between the observed trends and the large-scale climate fluctuation system characterized by the multivariate ENSO index, these trends can not be seen as the inevitable outcome of largescale climate fluctuation. Instead, that may be attributed to the common effects of natural and anthropogenic climate change.

Key words: Climate change, Extreme temperature and precipitation indices, Multivariate ENSO index, Trend analysis, Field significance and regional consistency.

中图分类号: 

图1 珠江流域地理位置、主要支流及网格点
Fig. 1 Location of the gridded Pearl River Basin with main streams and major branches
表1 16种极端气温和降水指数
Table 1 The 16 kinds of extreme temperature and precipitation indices
极端气温指数 极端降水指数
指数名称 英文缩写 含义 指数名称 英文缩写 含义
日最高气温 TXX 每年的日最高气温 年降水量 PRCPTOT ≥1mm降水日的年累积量
日最低气温 TNN 每年的日最低气温 降水强度 SDII 年降水量与降水量≥1mm
日数的比值
暖昼日数 TX90p 最高气温>90%阈值的日数 日最大降水量 RX1d 日最大降水量
暖夜日数 TN90p 最低气温>90%阈值的日数 5日最大降水量 RX5d 连续5日最大降水量
冷昼日数 TX10p 最高气温<10%阈值的日数 非常湿天降水量 R95p 日降水量>95%分位值的总降水量
冷夜日数 TN10p 最低气温<10%阈值的日数 极端湿天降水量 R99p 日降水量>99%分位值的总降水量
暖期持续日数 WSDI 连续至少6天最高气温>90%
阈值的日数		 连续无雨日数 CDD 最长连续降水量<1mm日数
冷期持续日数 CSDI 连续至少6天最低气温<10%
阈值的日数		 连续有雨日数 CWD 最长连续降水量≥1mm日数
表2
Table 2 The results of MMK test for the Pearl River basin: percentage of grid points exhibiting significant upward (downward) trend at 0.05 significant level on the extreme temperature and precipitation indices
极端气温指数 TXX TNN TX90p TN90p TX10p TN10p WSDI CSDI
MMK检验 16% (0%) 93% (0%) 47% (0%) 98% (0%) 0% (6%) 0% (64%) 32% (0%) 0% (41%)
极端降水指数 PRCPTOT SDII RX1d RX5d R95p R99p CDD CWD
MMK检验 0% (11%) 9% (0%) 7% (2%) 0% (2%) 4% (3%) 8% (3%) 0% (0%) 1% (18%)
图2 极端气温指数变化趋势的空间分布
Fig. 2 Spatial distribution of trends on the extreme temperature indices
图3 极端降水指数变化趋势的空间分布
Fig. 3 Spatial distribution of trends on the extreme precipitation indices
图4 珠江流域的区域划分
Fig. 4 Delineation of the boundary for different subregions from the Pearl River basin
表3
Table 3 The results of MMK test for all subregions: percentage of grid points exhibiting significant upward (downward) trend at 0.05 significant level on the extreme temperature indices
极端气温指数 TXX TNN TX90p TN90p TX10p TN10p WSDI CSDI
南盘江、北盘江流域 20% (0%) 91% (0%) 56% (0%) 100% (0%) 0% (22%) 0% (96%) 29% (0%) 0% (80%)
郁江、左江、右江流域 36% (0%) 100% (0%) 67% (0%) 100% (0%) 0% (0%) 0% (46%) 36% (0%) 0% (26%)
红水河、柳江流域 0% (0%) 98% (0%) 18% (0%) 100% (0%) 0% (4%) 0% (51%) 16% (0%) 0% (2%)
桂江、贺江及西江干流地区 7% (0%) 100% (0%) 48% (0%) 100% (0%) 0% (0%) 0% (48%) 37% (0%) 0% (37%)
北江流域 0% (0%) 86% (0%) 27% (0%) 95% (0%) 0% (0%) 0% (59%) 18% (0%) 0% (27%)
东江流域 19% (0%) 63% (0%) 56% (0%) 81% (0%) 0% (6%) 0% (69%) 50% (0%) 0% (88%)
珠江三角洲 55% (0%) 100% (0%) 100% (0%) 100% (0%) 0% (0%) 0% (100%) 100% (0%) 0% (91%)
表4
Table 4 The results of MMK test for all subregions: percentage of grid points exhibiting significant upward (downward) trend at 0.05 significant level on the extreme precipitation indices
极端降水指数 PRCPTOT SDII RX1d RX5d R95p R99p CDD CWD
南盘江、北盘江流域 0% (38%) 0% (2%) 2% (7%) 0% (7%) 0% (13%) 0% (7%) 0% (0%) 0% (36%)
郁江、左江、右江流域 0% (5%) 10% (0%) 0% (3%) 3% (3%) 5% (0%) 5% (0%) 0% (0%) 3% (8%)
红水河、柳江流域 0% (10%) 14% (0%) 20% (0%) 0% (0%) 2% (0%) 22% (0%) 2% (0%) 0% (14%)
桂江、贺江及西江干流地区 0% (0%) 7% (0%) 11% (0%) 0% (4%) 15% (4%) 7% (7%) 0% (0%) 4% (11%)
北江流域 0% (0%) 5% (0%) 11% (0%) 0% (0%) 15% (0%) 7% (0%) 0% (0%) 4% (27%)
东江流域 0% (0%) 25% (0%) 0% (0%) 0% (0%) 6% (0%) 6% (6%) 0% (0%) 0% (19%)
珠江三角洲 0% (0%) 0% (0%) 0% (0%) 0% (0%) 0% (0%) 0% (0%) 0% (0%) 0% (0%)
表5
Table 5 Field significance and regional consistency of the trends on extreme temperature events for the whole Pearl River basin and subregions
区域显著性/一致性 TXX TNN TX90p TN90p TX10p TN10p WSDI CSDI
珠江流域 0/+(0.06) 1/+(0.00) 1/+(0.02) 1/+(0.00) 0/-(0.26) 1/-(0.01) 1/+(0.03) 1/-(0.03)
南盘江、北盘江流域 0/+(0.06) 1/+(0.00) 1/+(0.02) 1/+(0.00) 0/-(0.05) 1/-(0.00) 1/+(0.05) 1/-(0.00)
郁江、左江、右江流域 1/+(0.08) 1/+(0.00) 1/+(0.02) 1/+(0.00) 0/-(0.39) 1/-(0.04) 1/+(0.05) 0/-(0.11)
红水河、柳江流域 0/+(0.22) 1/+(0.00) 0/+(0.13) 1/+(0.00) 0/-(0.40) 1/-(0.05) 0/+(0.18) 0/-(0.18)
桂江、贺江及西江干流地区 0/+(0.22) 1/+(0.00) 1/+(0.05) 1/+(0.00) 0/-(0.44) 1/-(0.04) 1/+(0.05) 1/-(0.04)
北江流域 0/+(0.19) 1/+(0.00) 0/+(0.16) 1/+(0.00) 0/-(0.55) 0/-(0.04) 0/+(0.14) 0/-(0.11)
东江流域 0/+(0.07) 1/+(0.02) 1/+(0.02) 1/+(0.01) 0/-(0.35) 1/-(0.03) 1/+(0.03) 1/-(0.02)
珠江三角洲 1/+(0.03) 1/+(0.00) 1/+(0.00) 1/+(0.00) 0/-(0.27) 1/-(0.01) 1/+(0.00) 1/-(0.01)
表6
Table 6 Field significance and regional consistency of the trends on extreme precipitation events for the whole Pearl River basin and subregions
区域显著性/一致性 PRCPTOT SDII RX1d RX5d R95p R99p CDD CWD
珠江流域 0/-(0.18) 0/+(0.15) 0/+(0.22) 0/-(0.88) 0/+(0.42) 0/+(0.24) 0/+(0.15) 1/-(0.04)
南盘江、北盘江流域 1/-(0.03) 0/-(0.99) 0/+(0.58) 0/-(0.43) 0/-(0.55) 0/+(0.93) 0/+(0.62) 1/-(0.00)
郁江、左江、右江流域 0/-(0.31) 0/+(0.29) 0/+(0.25) 0/-(0.90) 0/+(0.40) 0/+(0.23) 0/+(0.15) 0/-(0.65)
红水河、柳江流域 0/-(0.13) 0/+(0.11) 1/+(0.05) 0/-(0.91) 0/+(0.30) 1/+(0.03) 0/+(0.18) 0/-(0.09)
桂江、贺江及西江干流地区 0/-(0.47) 0/+(0.23) 0/+(1.00) 0/-(0.64) 0/+(0.70) 0/-(0.79) 0/+(0.24) 0/-(0.65)
北江流域 0/-(0.96) 0/+(0.15) 0/+(0.37) 0/+(0.27) 0/+(0.30) 0/+(0.51) 0/+(0.34) 1/-(0.05)
东江流域 0/+(0.80) 0/+(0.18) 0/-(0.61) 0/-(0.80) 0/+(0.31) 0/+(0.88) 0/+(0.25) 0/-(0.05)
珠江三角洲 0/+(0.96) 0/+(0.24) 0/-(0.87) 0/+(0.60) 0/+(0.88) 0/-(0.81) 0/+(0.25) 0/-(0.26)
表7
Table 7 The results of PMK (MMK) test for the Pearl River basin: percentage of grid points exhibiting significant trend at 0.05 significant level on the extreme temperature and precipitation indices
极端气温指数 TXX TNN TX90p TN90p TX10p TN10p WSDI CSDI
PMK (原MMK) 检验 15% (16%) 93% (93%) 47% (47%) 98% (98%) 5% (6%) 58% (64%) 32% (32%) 39% (41%)
极端降水指数 PRCPTOT SDII RX1d RX5d R95p R99p CDD CWD
PMK (原MMK) 检验 11% (11%) 7% (9%) 9% (9%) 2% (2%) 5% (7%) 11% (11%) 0% (0%) 18% (19%)
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