基于地区线性矩法的陕西省极端降水时空特征

doi:10.13866/j.azr.2021.05.11

干旱区研究 ›› 2021, Vol. 38 ›› Issue (5): 1295-1305.doi: 10.13866/j.azr.2021.05.11

基于地区线性矩法的陕西省极端降水时空特征

罗志文^1,²(),王小军^3,⁴(),刘梦洋²,柯杭⁵,万婷⁶,尹义星²

1. 中国电建集团江西省电力设计院有限公司,江西南昌 330096
2. 南京信息工程大学,江苏南京 210044
3. 南京水利科学研究院水文水资源与水利工程科学国家重点实验室,江苏南京 210029
4. 水利部应对气候变化研究中心,江苏南京 210029
5. 福州市气象局,福建福州 350008
6. 江西省科技交流中心,江西南昌 330096

收稿日期:2021-03-04 修回日期:2021-06-24 出版日期:2021-09-15 发布日期:2021-09-24
通讯作者: 王小军
作者简介:罗志文（1994-）,男,硕士,助理工程师,主要从事水文气象、电力方面的研究. E-mail: luozhiwen567@163.com
基金资助:
国家自然科学基金(51911540477);国家自然科学基金(41961124006);中央财政水资源节约、管理与保护项目(126302001000160081);中央财政水资源节约、管理与保护项目(126302001000150005)

Spatiotemporal characteristics of extreme precipitation in Shaanxi Province based on the regional L-moments method

LUO Zhiwen^1,²(),WANG Xiaojun^3,⁴(),LIU Mengyang²,KE Hang⁵,WAN Ting⁶,YIN Yixing²

1. Power China Jiangxi Electric Power Engineering Co., LTD., Nanchang 330096, Jiangxi, China
2. Nanjing University of Information Science and Technology, Nanjing 210044, Jiangsu, China
3. Nanjing Hydraulic Research Institute, State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing 210029, Jiangsu, China
4. Research Center for Climate Change, Ministry of Water Resources, Nanjing 210029, Jiangsu, China
5. Fuzhou Meteorology Bureau, Fuzhou 350008, Fujian, China
6. Jiangxi Science and Technology Exchange Center, Nanchang 330096, Jiangxi, China

Received:2021-03-04 Revised:2021-06-24 Online:2021-09-15 Published:2021-09-24
Contact: Xiaojun WANG

摘要/Abstract

摘要：

基于1971—2015年陕西省无缺测情况、分布较均匀的58个气象站点的逐日降水数据,采用最大1日、3日、5日和7日的降水量代表极端降水,应用地区线性矩法研究区域极端降水的时空特征。结果表明：（1）陕西省可划分为6个水文气象一致区,其中广义极值分布（GEV）在各一致区的模拟效果最好,各一致区最优频率估计值与同频率的实测值较为吻合。（2）地区分析法计算的极端降水频率估计值较单站分析法具有更好的稳健性和准确性,尤其是在计算较长时段的极端降水情况下更显著。（3）重现期为2 a一遇时,陕南的地区增长因子大于陕北;重现期达到5 a一遇时则相反,且随着重现期的增长,地区增长因子及其在陕南、陕北的差异也在增大。（4）在100 a和50 a一遇重现期下,极端降水在陕西省南部较大,东部居中,中部的咸阳至商洛地区、西部的延安西北和榆林西部较小;极端降水的分布特征与陕西省独特的地理特征有关,尤其是东西向分布的秦岭阻挡了水汽向北输送,造成了极端降水的南北差异。

关键词: 陕西省, 极端降水, 时空特征, 地区线性矩法

Abstract:

Extreme precipitation can cause severe disasters in arid and semi-arid regions, such as in the Shaanxi Province of Northern China. To investigate the spatiotemporal characteristics of extreme precipitation in the Shaanxi Province, this study adopted the daily precipitation data of 58 meteorological stations from 1971 to 2015 with no missing observations and relatively uniform distribution and used the maximum precipitation on the 1st, 3rd, 5th, and 7th day to represent the extreme precipitation. The regional L-moments method was further applied to study the temporal and spatial characteristics of the extreme regional precipitation, which involves the screening and processing of precipitation data, identification of homogeneous regions, goodness-of-fit test, quantile estimation for each region, comparison between at-site and regional estimation, deriving regional growth factors, and mapping of the spatial patterns of extreme precipitation. The results and conclusions of the study were: (1) Shaanxi Province can be divided into six hydrometeorological homogeneous regions, among which GEV distribution in each homogeneous region has the best simulation effect, and the estimated optimal quantiles of each homogeneous region are in good agreement with the measured value of the same frequency. (2) The estimated extreme precipitation quantiles calculated by the regional analysis method have better robustness and accuracy compared with the single-station analysis method, especially more significant in calculating extreme precipitation over a long period. (3) When the return period is once every 2 years, the regional growth factor of southern Shaanxi is greater than that of northern Shaanxi; when the return period is once every 5 years, the opposite is true, and with the increase of the return period, the regional growth factor and the difference between southern Shaanxi and northern Shaanxi also increase. (4) In the 100-and 50-year return periods, extreme precipitation is large in the south, centered in the east, the Xianyang-Shangluo region in the middle, the northwest corner of Yan’an in the west, and the west of Yulin are small. The distribution characteristics of extreme precipitation are related to the unique geographical characteristics of Shaanxi Province, especially the east-west Qinling Mountains, which block the water vapor transmission to the north, causing differences in extreme precipitation between the north and south.

Key words: Shaanxi Province, extreme precipitation, spatiotemporal characteristics, regional L-moment method

罗志文,王小军,刘梦洋,柯杭,万婷,尹义星. 基于地区线性矩法的陕西省极端降水时空特征[J]. 干旱区研究, 2021, 38(5): 1295-1305.

LUO Zhiwen,WANG Xiaojun,LIU Mengyang,KE Hang,WAN Ting,YIN Yixing. Spatiotemporal characteristics of extreme precipitation in Shaanxi Province based on the regional L-moments method[J]. Arid Zone Research, 2021, 38(5): 1295-1305.

图/表 11

图1

图2

表1

图3

表2

表3

图4

图5

图6

表4

图7

参考文献 26

[1]	任正果, 张明军, 王圣杰, 等. 1961—2011年中国南方地区极端降水事件变化[J]. 地理学报, 2014, 69(5): 640-649. doi: 10.11821/dlxb201405007
	[ Ren Zhengguo, Zhang Mingjun, Wang Shengjie, et al. Change in precipitation extremes in South China during 1961-2011[J]. Acta Geographica Sinica, 2014, 69(5): 640-649. ] doi: 10.11821/dlxb201405007
[2]	刘梦洋, 王小军, 柯杭, 等. 甘肃省陇东地区夏季极端降水及典型年环流特征分析[J]. 干旱区研究, 2021, 38(3): 775-784.
	[ Liu Mengyang, Wang Xiaojun, Ke Hang, et al. Analysis of extreme summer precipitation characteristics and typical years’circulation characteristics in Longdong Region[J]. Arid Zone Research, 2021, 38(3): 775-784. ]
[3]	Re M, Barros V R. Extreme rainfalls in SE South America[J]. Climatic Change, 2009, 96(1-2): 119-136. doi: 10.1007/s10584-009-9619-x
[4]	Hu Y, Maskey S, Uhlenbrook S. Trends in temperature and rainfall extremes in the Yellow River source region, China[J]. Climatic Change, 2012, 110(1): 403-429. doi: 10.1007/s10584-011-0056-2
[5]	翟盘茂, 王萃萃, 李威. 极端降水事件变化的观测研究[J]. 气候变化研究进展, 2007, 3(3): 144-148.
	[ Zhai Panmao, Wang Cuicui, Li Wei. A review on study of change in precipitation extremes[J]. Advances in Climate Change Research, 2007, 3(3): 144-148. ]
[6]	杨金虎, 江志红, 王鹏祥, 等. 中国年极端降水事件的时空分布特征[J]. 气候与环境研究, 2008, 13(1): 75-82.
	[ Yang Jinhu, Jiang Zhihong, Wang Pengxiang, et al. Temporal and spatial characteristic of extreme precipitation event in China[J]. Climatic and Environmental Research, 2008, 13(1): 75-82. ]
[7]	杨涵洧, 龚志强, 王晓娟, 等. 中国东部夏季极端降水年代际变化特征及成因分析[J]. 大气科学, 2020, 45(2): 1-14.
	[ Yang Hanwei, Gong Zhiqiang, Wang Xiaojuan, et al. Analysis of the characteristics and causes of interdecadal changes in the summer extreme precipitation over Eastern China[J]. Chinese Journal of Atmospheric Sciences, 2020, 45(2): 1-14. ]
[8]	龙妍妍, 范广洲, 李飞, 等. 高原夏季风对中国夏季极端降水的影响研究[J]. 高原气象, 2018, 37(1): 1-12.
	[ Long Yanyan, Fan Guangzhou, Li Fei, et al. Study on the influence of plateau summer extreme precipitation over China[J]. Plateau Meteorology, 2018, 37(1): 1-12. ]
[9]	郑小华, 娄盼星, 刘环, 等. 陕西极端降水时空变化特征研究[J]. 沙漠与绿洲气象, 2019, 13(4): 9-16.
	[ Zheng Xiaohua, Lou Panxin, Liu Huan, et al. Spatial and temporal variability of extreme precipitation in the Shaanxi Province during 1961-2011[J]. Desert and Oasis Meteorology, 2019, 13(4): 9-16. ]
[10]	王雯燕. 陕西50多年来汛期极端降水事件变化特征研究[D]. 成都: 成都信息工程大学, 2016.
	[ Wang Wenyan.Study on the Variation Characteristics of Extreme Precipitation Events in Shaanxi Over the Past 50 Years[D]. Chengdu: Chengdu University of Information Technology, 2016. ]
[11]	李双双, 汪成博, 延军平, 等. 面向事件过程的秦岭南北极端降水时空变化特征[J]. 地理学报, 2020, 75(5): 989-1007. doi: 10.11821/dlxb202005008
	[ Li Shuangshuang, Wang Chengbo, Yan Junping, et al. Variability of the event-based extreme precipitation in the South and North Qinling Mountains[J]. Acta Geographica Sinica, 2020, 75(5): 989-1007. ] doi: 10.11821/dlxb202005008
[12]	肖科丽, 王娜, 魏娜, 等. 2010年陕西盛夏极端多雨的气候特征及成因研究[J]. 气候与环境研究, 2014, 19(3): 311-320.
	[ Xiao Keli, Wang Na, Wei Na, et al. Climatic features and cause analysis of extreme rain events in Shaanxi during the midsummer of 2010[J]. Climatic and Environmental Research, 2014, 19(3): 311-320. ]
[13]	Yin Y X, Chen H S, Xu C Y, et al. Spatio-temporal characteristics of the extreme precipitation by L-moment-based index-flood method in the Yangtze River Delta Region, China[J]. Theoretical & Applied Climatology, 2016, 124(3-4): 1005-1022.
[14]	李孟芮, 敖天其, 黎小东. 地区线性矩法在四川省暴雨频率分析中的应用[J]. 西南农业学报, 2019, 32(8): 1938-1943.
	[ Li Mengrui, Ao Tianqi, Li Xiaodong. Regional frequency analysis in hydrological frequency analysis of Sichuan Province[J]. Southwest China Journal of Agricultural Sciences, 2019, 32(8): 1938-1943. ]
[15]	吴俊梅, 林炳章, 邵月红. 地区线性矩法在太湖流域暴雨频率分析中的应用[J]. 水文, 2015, 35(5): 15-22.
	[ Wu Junmei, Lin Bingzhang, Shao Yuehong. Application of regional L-moments analysis method in precipitation frequency analysis for Taihu Lake Basin[J]. Journal of China Hydrology, 2015, 35(5): 15-22. ]
[16]	刘梦洋, 尹义星, 韩翠, 等. 基于地区线性矩法的江西省极端降水频率分析[J]. 水电能源科学, 2018, 36(9): 1-5.
	[ Liu Mengyang, Yin Yixing, Han Cui, et al. Frequency analysis of extreme precipitation in Jiangxi Province based on regional L-moments method[J]. Water Resources and Power, 2018, 36(9): 1-5. ]
[17]	陈希, 林炳章, 吴俊梅, 等. 水文气象分区线性矩法在广西暴雨频率分析中的应用[J]. 水电能源科学, 2014, 32(11): 5-9.
	[ Chen Xi, Lin Bingzhang, Wu Junmei, et al. Application of hydrometeorological regional L-moments method to storm frequency analysis in Guangxi[J]. Water Resources and Power, 2014, 32(11): 5-9. ]
[18]	牛晨. 关中地区极端降水时空变化规律及频率分布研究[D]. 杨凌: 西北农林科技大学, 2018.
	[ Niu Chen. Study on Spatial and Temporal Variation of Extreme Precipitation and Frequency Distribution in Guanzhong Area[D]. Yangling: Northwest A & F University, 2018. ]
[19]	李敏, 林炳章, 邵月红, 等. 地区线性矩法估算的暴雨频率设计值空间连续性问题探[J]. 水文, 2015, 35(4): 14-19.
	[ Li Min, Lin Bingzhang, Shao Yuehong, et al. Study on spatial continuity of precipitation quantile estimates based on regional L-moments analysis[J]. Journal of China Hydrology, 2015, 35(4): 14-19. ]
[20]	Kumar R, Chatterjee C. Regional flood frequency analysis using L-moments for North Brahmaputra Region of India[J]. Journal of Hydrologic Engineering, 2005, 10(1): 1-7. doi: 10.1061/(ASCE)1084-0699(2005)10:1(1)
[21]	李孟芮, 敖天其, 黎小东. 基于地区线性矩法对四川省水文频率分析的研究[J]. 水利水电技术, 2018, 49(11): 54-61.
	[ Li Mengrui, Ao Tianqi, Li Xiaodong. Regional L-moments analysis-based study on hydrological frequency analysis of Sichuan Province[J]. Water Resources and Hydropower Engineering, 2018, 49(11): 54-61. ]
[22]	Hosking J R M, Wallis. Regional Frequency Analysis: An Approach Based on L-moments[M]. Cambridge: Cambridge University Press, 1997.
[23]	Hosking J R M. L-moments: Analysis and estimation of distributions using linear combinations of order statistics[J]. Journal of the Royal Statal Society: Series B( Methodological), 1990, 52(1): 105-124.
[24]	邵月红, 吴俊梅, 李敏. 基于水文气象分区线性矩法的淮河流域极值降雨频率分析[J]. 水文, 2016, 36(6): 16-23.
	[ Shao Yuehong, Wu Junmei, Li Min. Frequency analysis of extreme precipitation in Huaihe River Basin based on hydrometeorological regional L-moments method[J]. Journal of China Hydrology, 2016, 36(6): 16-23. ]
[25]	邵月红, 刘玲, 吴俊梅, 等. 基于年最大抽样序列的暴雨频率估计值的修正研究[J]. 水文, 2019, 39(1): 9-16.
	[ Shao Yuehong, Liu Ling, Wu Junmei, et al. Revision of extreme precipitation frequency estimation based on annual maximum series[J]. Journal of China Hydrology, 2019, 39(1): 9-16. ]
[26]	殷田园, 殷淑燕, 李富民. 秦岭南北区域夏季极端降水与西太平洋副热带高压的关系[J]. 干旱区研究, 2019, 36(6): 1379-1390.
	[ Yin Tianyuan, Yin Shuyan, Li Fumin. Relationship between the summer extreme precipitation in the South and North of the Qinling Mountains and the Western Pacific Subtropical High[J]. Arid Zone Research, 2019, 36(6): 1379-1390. ]

一致区	AMP1	AMP3	AMP5	AMP7
1区	0.88	0.19	-1.53	-0.97
2区	-1.07	-0.58	-1.34	-1.57
3区	-0.44	-2.32	-3.03	-2.84
4区	0.98	-1.48	-0.47	-0.96
5区	0.36	-0.39	0.28	0.28
6区	0.95	-0.41	0.49	0.11

序列	分区	最优分布	Z_min	可接受的分布
AMP1	1	GEV	0.29	GEV GNO PE3
	2	GEV	-0.67	GLO GEV GNO
	3	GLO	0.55	GLO GEV GNO
	4	GEV	-0.56	GLO GEV GNO PE3
	5	GNO	-0.09	GEV GNO PE3
	6	GEV	0.05	GEV GNO PE3
AMP7	1	GEV	-0.02	GEV GNO PE3
	2	GEV	-0.66	GLO GEV GNO
	3	GEV	-0.31	GLO GEV GNO
	4	GEV	-0.26	GLO GEV GNO PE3
	5	GPA	-0.54	GPA
	6	PE3	0.11	GEV GNO PE3

分区	站点	Obs(mean) /mm	Obs(max) /mm	Est-50y /mm	RE/%
1区	府谷	52.7	181.8	117.0	35.6
	榆林	51.9	105.7	115.3	9.1
	神木	56.8	141.1	126.2	10.6
	佳县	53.4	216.4	118.5	45.2
	横山	45.2	92.3	100.4	8.7
	子洲	61.3	150	136.0	9.3
	吴堡	57.4	112.5	127.4	13.3
	清涧	56.2	98.5	124.8	26.7
	延川	59.1	157.4	131.1	16.7
	延长	56.3	115.1	124.9	8.5
	宜川	56.4	113.2	125.1	10.5
	靖边	47.0	113.2	104.3	7.9
3区	陇县	58.7	214.6	124.1	42.2
	宝鸡	59.1	169.7	124.9	26.4
	千阳	63.9	175.1	135.0	22.9
	麟游	53.5	152.4	113.1	25.8
	岐山	59.3	116.1	125.3	7.9
	白水	65.3	174.2	137.9	20.8
	合阳	57.5	114.7	121.6	6.0
	乾县	53.0	117.3	111.9	4.6
	耀县	52.6	96.5	111.1	15.1
	兴平	52.5	146.7	111.0	24.3
	渭南	48.6	102.8	102.7	0.1
	秦都	46.5	158.5	98.3	38.0
6区	略阳	73.9	161.8	143.8	11.2
	勉县	68.5	147.5	133.3	9.6
	城固	63.1	103.8	122.8	18.3
	宁强	88.2	184.6	171.7	7.0
	南郑	73.2	151.8	142.5	6.1
	宁陕	74.5	304.5	145.0	52.4
	紫阳	93.5	210.8	182.1	13.6
	石泉	77.4	126	150.7	19.6
	汉阴	80.8	163.7	157.3	3.9
	镇巴	121.9	238.2	237.3	0.4
	平利	69.6	112.4	135.4	20.5
	镇坪	75.6	174.2	147.2	15.5

分区	2 a	5 a	10 a	20 a	50 a	100 a	200 a	500 a	1000 a
1	0.91	1.28	1.55	1.83	2.22	2.54	2.89	3.38	3.79
2	0.91	1.25	1.49	1.74	2.10	2.39	2.70	3.15	3.53
3	0.91	1.25	1.49	1.75	2.11	2.41	2.74	3.20	3.59
4	0.93	1.25	1.47	1.69	2.00	2.24	2.48	2.83	3.10
5	0.93	1.23	1.44	1.65	1.93	2.16	2.39	2.72	2.98
6	0.93	1.24	1.45	1.66	1.95	2.17	2.40	2.71	2.96

基于地区线性矩法的陕西省极端降水时空特征

Spatiotemporal characteristics of extreme precipitation in Shaanxi Province based on the regional L-moments method

RichHTML

PDF (PC)

赞

可视化

摘要/Abstract

引用本文

使用本文

图/表 11

参考文献 26

相关文章 15

Metrics

本文评价

推荐阅读 0

[1]	胡亚男, 裴浩, 姜艳丰, 苗百岭, 贾成朕. 1991—2021年内蒙古降水酸碱度时空变化特征分析[J]. 干旱区研究, 2023, 40(4): 552-562.
[2]	王娟, 王钊, 郭斌, 何慧娟, 董金芳. 陕西黄河流域植被碳利用率时空特征及对气候的敏感性研究[J]. 干旱区研究, 2023, 40(12): 1959-1968.
[3]	邬晓丹,罗敏,孟凡浩,萨楚拉,尹超华,包玉海. 气候暖湿化背景下新疆极端气候事件时空演变特征分析[J]. 干旱区研究, 2022, 39(6): 1695-1705.
[4]	张昊琛,萨楚拉,孟凡浩,罗敏,王牧兰,高红豆. 内蒙古地表冻融指数动态变化与驱动因素分析[J]. 干旱区研究, 2022, 39(6): 1996-2008.
[5]	王琦琨,武玮,杨雪琪,桑国庆. 陕西省生境质量时空演变及驱动机制分析[J]. 干旱区研究, 2022, 39(5): 1684-1694.
[6]	王姣妍. 基于MSWEP降水产品的新疆干旱时空特征分析[J]. 干旱区研究, 2022, 39(5): 1398-1409.
[7]	吴秀兰,段春锋,玛依拉·买买提艾力,张婧莉,张太西. 基于MCI的新疆近60 a干旱时空特征分析[J]. 干旱区研究, 2022, 39(1): 75-83.
[8]	丁莹莹,邱德勋,吴常雪,穆兴民,高鹏. 关中平原极端降水时空变化及其与大气环流的关系[J]. 干旱区研究, 2022, 39(1): 104-112.
[9]	卢宝宝,孙慧兰,姜泉泉,曹丽君,兰小丽,张乐乐,刘天弋. 近53 a新疆水分盈亏量时空变化特征[J]. 干旱区研究, 2021, 38(6): 1579-1589.
[10]	肖薇薇,安彬,史佳琪. 气候变化背景下1958—2017年陕西省0 cm地温时空变化特征[J]. 干旱区研究, 2021, 38(5): 1244-1253.
[11]	赵水霞,王文君,吴英杰,全强,王思楠,陈晓俊,刘铁军. 近59 a锡林郭勒草原旱灾驱动气候因子分析[J]. 干旱区研究, 2021, 38(3): 785-793.
[12]	刘梦洋,王小军,柯杭,罗志文,尹义星. 甘肃省陇东地区夏季极端降水及典型年环流特征分析[J]. 干旱区研究, 2021, 38(3): 775-784.
[13]	邹磊,余江游,王飞宇,张彦. 渭河流域极端降水时空演变规律及其对大气环流因子的响应[J]. 干旱区研究, 2021, 38(3): 764-774.
[14]	张行, 陈海, 史琴琴, 张敏, 刘迪. 陕西省景观生态脆弱性时空演变及其影响因素[J]. 干旱区研究, 2020, 37(2): 496-505.
[15]	马爱华, 岳大鹏, 赵景波, 胡倩. 近60 a来内蒙古极端降水的时空变化及其影响[J]. 干旱区研究, 2020, 37(1): 74-85.