基于游程理论和Copula函数的辽宁省农业气象干旱特征研究
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摘要
基于辽宁省近30年的气象站点逐月气候数据,计算SPEI-3指数作为农业气象干旱指标,利用游程理论对辽宁省的干旱特征变量进行识别,使用Copula函数同时从站点尺度和区域尺度对辽宁省干旱特征的联合分布和重现期进行了分析。结果表明:Frank-Copula函数对辽宁省农业气象干旱特征变量联合分布特征模拟效果最好;在相同干旱历时和干旱强度的条件下,各站点的联合重现期比单变量重现期小。干旱事件多集中在干旱历时小于4个月,干旱强度小于3的情况下发生;辽宁省不同干旱类型发生概率由高到低的排序为:季内中旱、跨季中旱、月内轻旱、半年以上重旱、月内中旱、季内轻旱。其中在季内中旱、月内轻旱和月内中旱的情况下,辽西地区发生干旱的概率最高;当干旱历时保持一致,随着干旱强度的增加,其最大最小联合重现期有明显增大的趋势;而当干旱强度保持一致,随着干旱历时的增加,不同干旱等级下联合重现期的变化规律不一致。
This paper takes Liaoning Province as the research area, uses the monthly climate data of the meteorological stations of nearly 30 years recorded in Liaoning Province to calculate SPEI-3 agricultural meteorological drought index, and identifies drought events in Liaoning Province based on the run theory. Finally, the Copula function was used to analyze the joint distribution and return period of drought events in Liaoning Province at both site scale and regional scale. The results show that: the characteristics of joint distribution of agricultural meteorological drought feature variables in Liaoning Province were analyzed by Frank-Copula function simulation performed best. Under the same drought duration and drought intensity conditions, the joint return period of each site was smaller than the univariate return period. The most frequent drought events were droughts with duration of less than 4 months and intensity of less than 3. The occurrence probability of drought in Liaoning Province was ranked in descending order as follows: mid-season drought, inter-season drought, light month drought, heavy drought more than six months, medium-term drought, and light season drought. With the drought in the season, light drought in the month, and moderate drought in the month, the probability of drought in the western Liaoning was the highest. When the drought duration was consistent, the joint return period increased significantly with the increase of drought intensity. When the drought intensity kept consistent, with the increase of drought duration, the variation rule of joint return period under different drought grades was inconsistent.
This paper takes Liaoning Province as the research area, uses the monthly climate data of the meteorological stations of nearly 30 years recorded in Liaoning Province to calculate SPEI-3 agricultural meteorological drought index, and identifies drought events in Liaoning Province based on the run theory. Finally, the Copula function was used to analyze the joint distribution and return period of drought events in Liaoning Province at both site scale and regional scale. The results show that: the characteristics of joint distribution of agricultural meteorological drought feature variables in Liaoning Province were analyzed by Frank-Copula function simulation performed best. Under the same drought duration and drought intensity conditions, the joint return period of each site was smaller than the univariate return period. The most frequent drought events were droughts with duration of less than 4 months and intensity of less than 3. The occurrence probability of drought in Liaoning Province was ranked in descending order as follows: mid-season drought, inter-season drought, light month drought, heavy drought more than six months, medium-term drought, and light season drought. With the drought in the season, light drought in the month, and moderate drought in the month, the probability of drought in the western Liaoning was the highest. When the drought duration was consistent, the joint return period increased significantly with the increase of drought intensity. When the drought intensity kept consistent, with the increase of drought duration, the variation rule of joint return period under different drought grades was inconsistent.
引文
[1] 赵勇. 干旱驱动机制与模拟评估[M]. 北京: 科学出版社, 2017.
[2] 于忱, 陈隽, 王红瑞, 等. 多变量Copula函数在干旱风险分析中的应用进展[J]. 南水北调与水利科技, 2018,16(1):14-21.
[3] 李颖, 冯玉, 唐伟男, 等. 基于Copula函数的辽西地区农业气象干旱灾害分析[J]. 干旱地区农业研究, 2018,36(2):279-287.
[4] 冯星, 孙东永, 胡维登, 等. Copula函数在渭河流域干旱分析中的应用研究[J]. 灌溉排水学报, 2017,36(12):110-117.
[5] 李阿龙. 河南省农业旱灾风险分析[D]. 保定:华北水利水电大学, 2016.
[6] 高贵生, 霍金虎, 金显玲. 降水距平百分率指标对西宁市农业气象干旱的适用性及干旱预测[J]. 青海草业, 2017,26(4):26-31.
[7] 叶建刚, 申双和, 吕厚荃. 修正帕默尔干旱指数在农业干旱监测中的应用[J]. 中国农业气象, 2009,30(2):257-261.
[8] Vicenteserrano S M, Beguería S, Lópezmoreno J I. A multiscalar drought index sensitive to global warming: the stan-dardized precipitation evapotranspiration index.[J]. Journal of Climate, 2010, 23(7):1696-1718.
[9] Potopová V, ?těpánek P, Mo?ny M, et al. Performance of the standardised precipitation evapotranspi-ration index at v-arious lags for agricultural drought risk assessment in the Czech Republic[J]. Agricultural & Forest Meteorology, 2015, 202(202):26-38.
[10] Lloyd-Hughes B. A spatio-temporal structure-based approach to drought characterisation[J]. International Journal of Cli-matology, 2012,32(3):406-418.
[11] 李波. 中国气象灾害大典. 辽宁卷[M]. 北京:气象出版社, 2005.
[12] 吴琼, 赵春雨, 王大钧, 等. 1951-2014年辽宁省气象干旱时空特征分析[J]. 干旱区资源与环境, 2016,30(3):151-157.
[13] 何鑫, 吴吉东, 李颖, 等. 基于SPEI的辽西地区气象干旱时空分布特征[J]. 干旱区地理, 2017,40(2):340-347.
[14] 张运福, 房一禾, 龚强. 基于SPEI指数的辽宁省生长季干旱时空特征[J]. 生态学杂志, 2017,36(1):190-197.
[15] 王春乙, 蔡菁菁, 张继权. 基于自然灾害风险理论的东北地区玉米干旱、冷害风险评价[J]. 农业工程学报, 2015,31(6):238-245.
[16] 蔡福, 张淑杰, 纪瑞鹏, 等. 近30年辽宁玉米水分适宜度时空演变特征及农业干旱评估[J]. 应用生态学报, 2015,26(1):233-240.
[17] 王凯, 雷虹, 刘建华. 春季辽宁西北部主要绿化树种根叶抗旱生理性状评价[J]. 应用生态学报, 2016,27(6):1853-1860.
[18] 马一泓, 王术, 于佳禾, 等. 水稻生长对干旱胁迫的响应及抗旱性研究进展[J]. 种子, 2016,35(7):45-49.
[19] 高蓓, 姜彤, 苏布达, 等. 基于SPEI的1961-2012年东北地区干旱演变特征分析[J]. 中国农业气象, 2014,35(6):656-662.
[20] 徐一丹, 任传友, 马熙达, 等. 基于SPI/SPEI指数的东北地区多时间尺度干旱变化特征对比分析[J]. 干旱区研究, 2017,34(6):1250-1262.
[21] Thornthwaite C W. An Approach toward a Rational Classification of Climate[J]. Geographical Review, 1948,38(1):55-94.
[22] 赵静, 严登华, 杨志勇, 等. 标准化降水蒸发指数的改进与适用性评价[J]. 物理学报, 2015,64(4):382-390.
[23] Herbst P H, Bredenkamp D B, Barker H M G. A technique for the evaluation of drought from rai-nfall data[J]. Journ-al of Hydrology, 1966,4(66):264-272.
[24] 芦佳玉, 延军平, 李英杰. 基于SPEI及游程理论的云贵地区1960—2014年干旱时空变化特征[J]. 浙江大学学报(理学版), 2018,5(3):363-372.
[25] 宋松柏. Copulas函数及其在水文中的应用[M]. 北京:科学出版社, 2012.
[26] 侯芸芸. 基于Copula函数的多变量洪水频率计算研究[D]. 杨凌:西北农林科技大学, 2010.
[27] 左冬冬, 侯威, 颜鹏程, 等. 基于游程理论和两变量联合分布的中国西南地区干旱特征研究[J]. 物理学报, 2014,63(23):45-56.
[28] Dai A. Increasing drought under global warming in observations and models[J]. Nature Climate Change, 2013,3(1):52-58.
[29] 杨思遥, 孟丹, 李小娟, 等. 华北地区2001-2014年植被变化对SPEI气象干旱指数多尺度的响应[J]. 生态学报, 2018,38(3):1028-1039.
[30] 王晓峰, 张园, 冯晓明, 等. 基于游程理论和Copula函数的干旱特征分析及应用[J]. 农业工程学报, 2017,33(10):206-214.
[31] 张雨, 宋松柏. Copulas函数在多变量干旱联合分布中的应用[J]. 灌溉排水学报, 2010,29(3):64-68.
[32] 肖名忠, 张强, 陈永勤, 等. 基于三变量Copula函数的东江流域水文干旱频率分析[J]. 自然灾害学报, 2013,22(2):99-108.
[2] 于忱, 陈隽, 王红瑞, 等. 多变量Copula函数在干旱风险分析中的应用进展[J]. 南水北调与水利科技, 2018,16(1):14-21.
[3] 李颖, 冯玉, 唐伟男, 等. 基于Copula函数的辽西地区农业气象干旱灾害分析[J]. 干旱地区农业研究, 2018,36(2):279-287.
[4] 冯星, 孙东永, 胡维登, 等. Copula函数在渭河流域干旱分析中的应用研究[J]. 灌溉排水学报, 2017,36(12):110-117.
[5] 李阿龙. 河南省农业旱灾风险分析[D]. 保定:华北水利水电大学, 2016.
[6] 高贵生, 霍金虎, 金显玲. 降水距平百分率指标对西宁市农业气象干旱的适用性及干旱预测[J]. 青海草业, 2017,26(4):26-31.
[7] 叶建刚, 申双和, 吕厚荃. 修正帕默尔干旱指数在农业干旱监测中的应用[J]. 中国农业气象, 2009,30(2):257-261.
[8] Vicenteserrano S M, Beguería S, Lópezmoreno J I. A multiscalar drought index sensitive to global warming: the stan-dardized precipitation evapotranspiration index.[J]. Journal of Climate, 2010, 23(7):1696-1718.
[9] Potopová V, ?těpánek P, Mo?ny M, et al. Performance of the standardised precipitation evapotranspi-ration index at v-arious lags for agricultural drought risk assessment in the Czech Republic[J]. Agricultural & Forest Meteorology, 2015, 202(202):26-38.
[10] Lloyd-Hughes B. A spatio-temporal structure-based approach to drought characterisation[J]. International Journal of Cli-matology, 2012,32(3):406-418.
[11] 李波. 中国气象灾害大典. 辽宁卷[M]. 北京:气象出版社, 2005.
[12] 吴琼, 赵春雨, 王大钧, 等. 1951-2014年辽宁省气象干旱时空特征分析[J]. 干旱区资源与环境, 2016,30(3):151-157.
[13] 何鑫, 吴吉东, 李颖, 等. 基于SPEI的辽西地区气象干旱时空分布特征[J]. 干旱区地理, 2017,40(2):340-347.
[14] 张运福, 房一禾, 龚强. 基于SPEI指数的辽宁省生长季干旱时空特征[J]. 生态学杂志, 2017,36(1):190-197.
[15] 王春乙, 蔡菁菁, 张继权. 基于自然灾害风险理论的东北地区玉米干旱、冷害风险评价[J]. 农业工程学报, 2015,31(6):238-245.
[16] 蔡福, 张淑杰, 纪瑞鹏, 等. 近30年辽宁玉米水分适宜度时空演变特征及农业干旱评估[J]. 应用生态学报, 2015,26(1):233-240.
[17] 王凯, 雷虹, 刘建华. 春季辽宁西北部主要绿化树种根叶抗旱生理性状评价[J]. 应用生态学报, 2016,27(6):1853-1860.
[18] 马一泓, 王术, 于佳禾, 等. 水稻生长对干旱胁迫的响应及抗旱性研究进展[J]. 种子, 2016,35(7):45-49.
[19] 高蓓, 姜彤, 苏布达, 等. 基于SPEI的1961-2012年东北地区干旱演变特征分析[J]. 中国农业气象, 2014,35(6):656-662.
[20] 徐一丹, 任传友, 马熙达, 等. 基于SPI/SPEI指数的东北地区多时间尺度干旱变化特征对比分析[J]. 干旱区研究, 2017,34(6):1250-1262.
[21] Thornthwaite C W. An Approach toward a Rational Classification of Climate[J]. Geographical Review, 1948,38(1):55-94.
[22] 赵静, 严登华, 杨志勇, 等. 标准化降水蒸发指数的改进与适用性评价[J]. 物理学报, 2015,64(4):382-390.
[23] Herbst P H, Bredenkamp D B, Barker H M G. A technique for the evaluation of drought from rai-nfall data[J]. Journ-al of Hydrology, 1966,4(66):264-272.
[24] 芦佳玉, 延军平, 李英杰. 基于SPEI及游程理论的云贵地区1960—2014年干旱时空变化特征[J]. 浙江大学学报(理学版), 2018,5(3):363-372.
[25] 宋松柏. Copulas函数及其在水文中的应用[M]. 北京:科学出版社, 2012.
[26] 侯芸芸. 基于Copula函数的多变量洪水频率计算研究[D]. 杨凌:西北农林科技大学, 2010.
[27] 左冬冬, 侯威, 颜鹏程, 等. 基于游程理论和两变量联合分布的中国西南地区干旱特征研究[J]. 物理学报, 2014,63(23):45-56.
[28] Dai A. Increasing drought under global warming in observations and models[J]. Nature Climate Change, 2013,3(1):52-58.
[29] 杨思遥, 孟丹, 李小娟, 等. 华北地区2001-2014年植被变化对SPEI气象干旱指数多尺度的响应[J]. 生态学报, 2018,38(3):1028-1039.
[30] 王晓峰, 张园, 冯晓明, 等. 基于游程理论和Copula函数的干旱特征分析及应用[J]. 农业工程学报, 2017,33(10):206-214.
[31] 张雨, 宋松柏. Copulas函数在多变量干旱联合分布中的应用[J]. 灌溉排水学报, 2010,29(3):64-68.
[32] 肖名忠, 张强, 陈永勤, 等. 基于三变量Copula函数的东江流域水文干旱频率分析[J]. 自然灾害学报, 2013,22(2):99-108.