巴基斯坦干旱特征及其风险评估

doi:10.12118/j.issn.1000–6060.2021.04.18

干旱区地理 ›› 2021, Vol. 44 ›› Issue (4): 1058-1069.doi: 10.12118/j.issn.1000–6060.2021.04.18

巴基斯坦干旱特征及其风险评估

朱淑珍^1,^2,³(),黄法融^1,^2,^4,⁵(),李兰海^1,^2,^3,^4,⁵

1.中国科学院新疆生态与地理研究所荒漠与绿洲生态国家重点实验室,新疆乌鲁木齐 830011
2.中国科学院伊犁河流域生态系统研究站,新疆新源 835800
3.中国科学院大学,北京 100049
4.新疆干旱区水循环与水利用重点实验室,新疆乌鲁木齐 830011
5.中国科学院中亚生态与环境研究中心,新疆乌鲁木齐 830011

收稿日期:2020-05-30 修回日期:2020-10-26 出版日期:2021-07-25 发布日期:2021-08-02
通讯作者: 黄法融
作者简介:朱淑珍（1995-）,女,硕士研究生,主要从事积雪水文研究. E-mail: zhushuzhen19@mails.ucas.ac.cn
基金资助:
新疆维吾尔自治区自然科学青年基金(2017D01B52)

Drought characteristics and its risk assessment across Pakistan

ZHU Shuzhen^1,^2,³(),HUANG Farong^1,^2,^4,⁵(),LI Lanhai^1,^2,^3,^4,⁵

1. State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, Xinjiang, China
2. Ili Station for Watershed Ecosystem Research, Chinese Academy of Sciences, Xinyuan 835800, Xinjiang, China
3. University of Chinese Academy of Science, Beijing 100049, China
4. Key Laboratory of Water Cycle and Utilization in Arid Zone, Xinjiang Uygur Autonomous Region, Urumqi 830011, Xinjiang, China
5. Research Center for Ecology and Environment of Central Asia, Chinese Academy of Sciences, Urumqi 830011, Xinjiang, China

Received:2020-05-30 Revised:2020-10-26 Online:2021-07-25 Published:2021-08-02
Contact: Farong HUANG

摘要/Abstract

摘要：

干旱深刻影响着巴基斯坦的水资源、粮食产量和社会经济格局,分析当地的干旱特征及其风险对其社会发展具有重要意义,但当前鲜有学者从长期视角分析整个巴基斯坦的干旱特征或评估该地区的干旱灾害风险。通过采用1982—2013年第三代归一化植被指数（Global inventory modelling and mapping studies normalized difference vegetation index,GIMMS NDVI3g）数据集构建植被状态指数（Vegetation condition index,VCI）,分析了32 a巴基斯坦干旱范围和频率;采用由干旱频率、牲畜、土壤、作物、灌溉面积等资料表征的危险性、脆弱性、暴露度和防灾减灾能力对巴基斯坦干旱风险进行了评估。结果表明：（1）研究时段内,各月多年平均干旱范围存在差异,1月和9—12月干旱范围较大（面积比例大于40%）;5—8月干旱范围较小（面积比例小于30%）;5—6月受升温和降水减少影响,干旱面积有上升趋势。（2）研究区5—8月干旱频率较低,西部山区林地、草地9月干旱频率较高,东部平原1月和9—12月干旱频率较高。（3）巴基斯坦干旱风险空间格局主要由干旱频率、作物产量、大牲畜比例、土壤持水能力决定,其中干旱频率对区域干旱风险的影响最大。在牲畜比例和土壤持水性的分别作用下,巴基斯坦北部山区向平原过渡的地带和东南部自然植被覆盖地区干旱风险较高;受灌溉影响,地形平坦的河谷地区干旱风险较低;北部山区自然植被覆盖度高,干旱风险也较低。研究结果有益于巴基斯坦灾害风险管理。

关键词: 干旱, 风险评估, 归一化植被指数（NDVI）, 植被状态指数（VCI）, 巴基斯坦

Abstract:

Pakistan is in South Asia (60°-80°N, 23°-37°E) with a total area of 88×10⁴ km². The southeastern part of Pakistan is mountainous and hilly with a complex topography and different climate types. In summer, this area is controlled by subtropical high pressure, which results in hot and dry weather with a small amount of precipitation. The average annual rainfall is less than 250 mm, and a quarter of the area receives less than 120 mm. Because of the high summer temperatures, Pakistan has been greatly affected by long-term drought, which has severely restricted its social and economic development. In this study, we used the Global Inventory Modeling and Mapping Studies Normalized Difference Vegetation Index 3^rd Generation (GIMMS NDVI3g) dataset from 1982 to 2013 to construct the vegetation condition index (VCI). We then used the VCI to analyze the range and frequency of drought in Pakistan for the past 32 years. The drought frequency was used to characterize the risk of drought based on sources from the Pakistan Statistics Bureau and reports in the literature. Information on livestock, soil, crops, and irrigation were used to evaluate the drought vulnerability, exposure, and disaster prevention and mitigation capabilities of Pakistan. Additionally, ERA-Interim data that had been verified by 10 meteorological stations in Pakistan were used for temperature and precipitation reanalysis. The influencing factors of the drought range were determined using partial correlation coefficients, and the partial least squares method was used to identify the dominant influencing factors for the spatial patterns of regional drought risk. The following results were obtained. (1) During the study period, the multiyear average drought range differed for each month. The drought range was larger from January to September than from September to December (area ratio was greater than 40%). The drought range was smaller from May to August (area ratio was less than 30%). The increase in temperature and decrease in precipitation from May to June caused the arid area to increase. (2) In the study area, the drought frequency was low from May to August. The frequency was higher in September for the woodlands and grasslands of the western mountainous areas. The frequency was higher in January and September than in December for the eastern plains. (3) The spatial pattern of drought risk was mainly determined by the drought frequency, crop yield, proportion of large livestock, and water storage capacity of the soil. Among these, the drought frequency had the highest impact on the regional drought risk. The livestock ratio and the water storage capacity of the soil indicated a higher drought risk in the transition zone from mountains to plains in the northern area and the southeastern area covered by natural vegetation. Irrigation reduced the drought risk in the valley area with flat terrain. Additionally, the natural vegetation in the northern mountainous area reduced the drought risk as well. In this study, we analyzed the long-term drought characteristics in Pakistan and evaluated the drought risk due to natural and human factors. This research can provide useful information for drought and disaster risk management in Pakistan.

Key words: drought, risk assessment, normalized difference vegetation index(NDVI), vegetation condition index(VCI), Pakistan

朱淑珍,黄法融,李兰海. 巴基斯坦干旱特征及其风险评估[J]. 干旱区地理, 2021, 44(4): 1058-1069.

ZHU Shuzhen,HUANG Farong,LI Lanhai. Drought characteristics and its risk assessment across Pakistan[J]. Arid Land Geography, 2021, 44(4): 1058-1069.

图/表 9

图1

图2

图3

图4

图5

图6

图7

图8

图9

参考文献 43

[1]	陈丙寅, 杨辽, 陈曦, 等. 基于改进型TVDI在干旱区旱情监测中的应用研究[J]. 干旱区地理, 2019, 42(4):902-913.
	[ Chen Bingyin, Yang Liao, Chen Xi, et al. Application of modified TVDI in drought monitoring in arid areas[J]. Arid Land Geography, 2019, 42(4):902-913. ]
[2]	张强, 张良, 崔显成, 等. 干旱监测与评价技术的发展及其科学挑战[J]. 地球科学进展, 2011, 26(7):763-778.
	[ Zhang Qiang, Zhang Liang, Cui Xiancheng, et al. Progresses and challenges in drought assessment and monitoring[J]. Advances in Earth Science, 2011, 26(7):763-778. ]
[3]	沙莎, 郭铌, 李耀辉, 等. 植被状态指数VCI与几种气象干旱指数的对比——以河南省为例[J]. 冰川冻土, 2013, 35(4):990-998.
	[ Sha Sha, Guo Ni, Li Yaohui, et al. Comparison of the vegetation condition index with meteorological drought indices: A case study in Henan Province[J]. Journal of Glaciology and Geocryology, 2013, 35(4):990-998. ]
[4]	Adnan S, Uliah K, Gao S, et al. Shifting of agro-climatic zones, their drought vulnerability, and precipitation and temperature trends in Pakistan[J]. International Journal of Climatology, 2017, 37(Suppl. 1):529-543. doi: 10.1002/joc.2017.37.issue-S1
[5]	冯冬蕾, 程志刚, 赵雷, 等. 4种干旱判别指数在东北地区适用性分析[J]. 干旱区地理, 2020, 43(2):371-379.
	[ Feng Donglei, Cheng Zhigang, Zhao Lei, et al. Applicability analysis of four drought indexes in northeast China[J]. Arid Land Geography, 2020, 43(2):371-379. ]
[6]	邹磊, 余江游, 夏军, 等. 基于SPEI的渭河流域干旱时空变化特征分析[J]. 干旱区地理, 2020, 43(2):329-338.
	[ Zou Lei, Yu Jiangyou, Xia Jun, et al. Temporal-spatial variation characteristics of drought in the Weihe River Basin based on SPEI[J]. Arid Land Geography, 2020, 43(2):329-338. ]
[7]	Lu J, Carbong G J, Gao P. Mapping the agricultural drought based on the long-term AVHRR NDVI and north American regional reanalysis(NARR) in the United States, 1981—2013[J]. Applied Geography, 2019, 104:10-20. doi: 10.1016/j.apgeog.2019.01.005
[8]	Zhang X, Obringer R, Wei C, et al. Droughts in India from 1981 to 2013 and implications to wheat production[J]. Scientific Reports, 2017, 7:44552, doi: 10.1038/srep44552. doi: 10.1038/srep44552
[9]	Shah S U, Iqbal J. Spatial-temporal variations of vegetation and drought severity across Tharparkar, Pakistan, using remote sensing-derived indices[J]. Journal of Applied Remote Sensing, 2016, 10(3):036005, doi: 10.1117/1.JRS.10.036005. doi: 10.1117/1.JRS.10.036005
[10]	Adnan S, Uliah K, Gao S. Characterization of drought and its assessment over Sindh, Pakistan during 1951—2010[J]. Journal of Meteorological Research, 2015, 29(5):837-857. doi: 10.1007/s13351-015-4113-z
[11]	Khan F K. Oxford school atlas for Pakistan[M]. Oxford: Oxford University Press, 2008.
[12]	Kern A, Marjanovic H, Barcza Z. Evaluation of the quality of NDVI3g dataset against collection 6 MODIS NDVI in central Europe between 2000 and 2013[J]. Remote Sensing, 2016, 8(11):955, doi: 10.3390/rs8110955. doi: 10.3390/rs8110955
[13]	蔡斌, 陆文杰, 郑新江. 气象卫星条件植被指数监测土壤状况[J]. 国土资源遥感, 1995(4):45-50.
	[ Cai Bin, Lu Wenji, Zheng Xinjiang. Using meteorological satellite’s VCI to monitor soil state[J]. Remote Sensing for Land and Resources, 1995(4):45-50. ]
[14]	Unganai L S, Kogan F N. Drought monitoring and corn yield estimation in southern Africa from AVHRR data[J]. Remote Sensing of Environment, 1998, 63(3):219-232. doi: 10.1016/S0034-4257(97)00132-6
[15]	Fensholt R, Horion S, Tagesson T, et al. Global-scale mapping of changes in ecosystem functioning from earth observation-based trends in total and recurrent vegetation[J]. Global Ecology and Biogeography, 2015, 24:1003-1017. doi: 10.1111/geb.2015.24.issue-9
[16]	Xu C, Liu H, Wiliams A P, et al. Trends toward an earlier peak of the growing season in northern Hemisphere mid-latitudes[J]. Global Change Biology, 2016, 22:2852-2860. doi: 10.1111/gcb.2016.22.issue-8
[17]	Zeng F W, Collatz G J, Pinzon J E, et al. Evaluating and quantifying the climate-driven interannual variability in global inventory modeling and mapping studies(GIMMS) normalized difference vegetation index(NDVI3g) at global scales[J]. Remote Sensing, 2016(5):3918-3950.
[18]	Piao S, Mohammat A, Fang J, et al. NDVI-based increase in growth of temperate grasslands and its responses to climate changes in China[J]. Global Environmental Change, 2006, 16(4):340-348. doi: 10.1016/j.gloenvcha.2006.02.002
[19]	Weedon G P, Gomes S, Viterbo P. The WATCH forcing data 1958—2001: A meteorological forcing dataset for land surface and hydrological models[R]. Watch Technical Report, 2010.
[20]	Rigden A J, Salvucci G D, Dara E, et al. Partitioning evapotranspiration over the continental United States using weather station data[J]. Geophysical Research Letters, 2018, 45:9605-9613. doi: 10.1029/2018GL079121
[21]	Kogan F, Sullivan J. Development of global drought watch system using NOAA/AVHRR data[J]. Advances in Space Research, 1993, 13(5):219-222. doi: 10.1016/0273-1177(93)90548-P
[22]	Kogan F. Droughts of the late 1980s in the United States as derived from NOAA polar-orbiting satellite data[J]. Bulletin of the American Meteorological Society, 1995, 76(5):655-668. doi: 10.1175/1520-0477(1995)076<0655:DOTLIT>2.0.CO;2
[23]	Bajgiran P R, Daevishsefat A A, Khalili A, et al. Using AVHRR-based vegetation indices for drought monitoring in the northwest of Iran[J]. Journal of Arid Environments, 2008, 72(6):1086-1096. doi: 10.1016/j.jaridenv.2007.12.004
[24]	Kuri F, Murwira A, Murwirs K S, et al. Predicting maize yield in Zimbabwe using dry dekads derived from remotely sensed vegetation condition index[J]. International Journal of Applied Earth Observation and Geoinformation, 2014, 33(12):39-46. doi: 10.1016/j.jag.2014.04.021
[25]	李维娇, 王云鹏. 基于VCI的2003—2017年广东省干旱时空变化特征分析[J]. 华南师范大学学报(自然科学版), 2020, 52(3):85-91.
	[ Li Weijiao, Wang Yunpeng. An analysis of the spatial-temporal characteristics of drought in Guangdong based on vegetation condition index from 2003 to 2017[J]. Journal of South China Normal University (Natural Science Edition), 2020, 52(3):85-91. ]
[26]	Kogan F. Droughts of the late 1980s in the United States as derived from NOAA polar-orbiting satellite data[J]. Bulletin of the American Meteorological Society, 1995, 76(5):655-668. doi: 10.1175/1520-0477(1995)076<0655:DOTLIT>2.0.CO;2
[27]	张继权, 李宁. 主要气象灾害风险评价与管理的数量化方法及其应用[M]. 北京: 北京师范大学出版社, 2007.
	[ Zhang Jiquan, Li Ning. Quantitative methods and applications of risk assessment and management on main meteorological risks[M]. Beijing: Beijing Normal University Publishing House, 2007. ]
[28]	葛全胜, 邹铭, 郑景云, 等. 中国自然灾害风险综合评估初步研究[M]. 北京: 科学出版社, 2008.
	[ Ge Quansheng, Zhou Ming, Zheng Jingyun, et al. Integrated assessment of natural disaster risk in China[M]. Beijing: Science Press, 2008. ]
[29]	姚玉璧, 张强, 李耀辉, 等. 干旱灾害风险评估技术及其科学问题与展望[J]. 资源科学, 2013, 35(9):1884-1897.
	[ Yao Yubi, Zhang Qiang, Li Yaohui, et al. Drought risk assessment technological progresses and problems[J]. Resources Science, 2013, 35(9):1884-1897. ]
[30]	Mumtaz R, Baig S, Fatima I. Analysis of meteorological variations on wheat yield and its estimation using remotely sensed data: A case study of selected districts of Punjab Province, Pakistan (2001—2014)[J]. Italian Journal of Agronomy, 2017, 12(3):254-270.
[31]	Nyvall J. Soil water storage capacity and available soil moisture[J]. British Columbia, Ministry of Agriculture, Food and Fisheries, 2002, 4(619):2-5.
[32]	Parc. National agro-ecological resources database (Vol. Version 1.1): Natural resources division[R]. Islamabad: Pakistan Agricultural Research Council, 2009.
[33]	Pds. Punjab development statistics 2011[R]. Lahore: Bureau of Statistics, Government of the Punjab, 2011.
[34]	Mowp. Handbook on water statistics of Pakistan[M]. Islamabad: Ministry of Water and Power, Pakistan, 2012.
[35]	Siddiqi A, Wescoat J L. Energy use in large-scale irrigated agriculture in the Punjab Province of Pakistan[J]. Water International, 2013, 38(5):571-586. doi: 10.1080/02508060.2013.828671
[36]	Tsakiris G D, Adnan S, Khan A H. Effective rainfall for irrigated agriculture plains of Pakistan[J]. Pakistan Journal of Meteorology, 2009(6):61-72.
[37]	Huang Y, Chen L, Fu B, et al. The wheat yields and water-use efficiency in the Loess Plateau: Straw mulch and irrigation effects[J]. Agricultural Water Management, 2005, 72(3):209-222. doi: 10.1016/j.agwat.2004.09.012
[38]	Hao B Z, Xue Q W, Marek T H, et al. Soil water extraction, water use, and grain yield by drought-tolerant maize on the Texas High Plains[J]. Agricultural Water Management, 2015, 155:11-21. doi: 10.1016/j.agwat.2015.03.007
[39]	Li S, Li Y, Li X, et al. Effect of straw management on carbon sequestration and grain production in a maize-wheat cropping system in Anthrosol of the Guanzhong Plain[J]. Soil and Tillage Research, 2016, 157:43-51. doi: 10.1016/j.still.2015.11.002
[40]	Wang G, Liang Y, Zhang Q, et al. Mitigated CH₄ and N₂O emissions and improved irrigation water use efficiency in winter wheat field with surface drip irrigation in the North China Plain[J]. Agricultural Water Management, 2016, 163:403-407. doi: 10.1016/j.agwat.2015.10.012
[41]	Mekonnen D K, Channa H, Ringler C. The impact of water users’ associations on the productivity of irrigated agriculture in Pakistani Punjab[J]. Water International, 2015, 40(5-6):733-747. doi: 10.1080/02508060.2015.1094617
[42]	王春雨, 王军邦, 孙晓芳, 等. 孟印缅地区农田生产力脆弱性变化及气候影响机制——基于1982—2015年GIMMS3g植被指数[J]. 生态学报, 2019, 39(21):7793-7804.
	[ Wang Chunyu, Wang Junbang, Sun Xiaofang, et al. Vulnerability of farmland productivity and climatic impact in Bangladesh, India, and Myanmar, based on GIMMS3g NDVI in 1982—2015[J]. Acta Ecologica Sinica, 2019, 39(21):7793-7804. ]
[43]	赵健赟, 张波, 杨静. 基于GIMMS NDVI3g的青海高原植被分布特征研究[J]. 测绘工程, 2019, 28(5):8-13.
	[ Zhao Jianyun, Zhang Bo, Yang Jing. Engineering go surveying and mapping vegetationd is tribution characteristics of Qinghai Plateau based on GIMMS NDVI3g[J]. Engineering of Surveying and Mapping, 2019, 28(5):8-13. ]

巴基斯坦干旱特征及其风险评估

Drought characteristics and its risk assessment across Pakistan

RichHTML

PDF (PC)

赞

可视化

摘要/Abstract

引用本文

使用本文

图/表 9

参考文献 43

相关文章 15

Metrics

本文评价

推荐阅读 0

[1]	史维良, 车璐阳, 李涛. 陕西省汛期极端降水概率分布及综合危险性评估[J]. 干旱区地理, 2023, 46(9): 1407-1417.
[2]	卢冬燕, 朱秀芳, 刘婷婷, 张世喆. 2 ℃温升情景下中国气象干旱特征变化[J]. 干旱区地理, 2023, 46(8): 1227-1237.
[3]	成硕, 李艳忠, 星寅聪, 于志国, 王渊刚, 黄曼捷. 遥感降水产品对黄河源区水文干旱特征的模拟性能分析[J]. 干旱区地理, 2023, 46(7): 1063-1072.
[4]	史继清, 甘臣龙, 周刊社, 袁雷, 张东东. 西藏青稞主要种植区干旱时空分布及致灾危险性评估[J]. 干旱区地理, 2023, 46(7): 1098-1110.
[5]	姚岚博, 冶建明, 王芸, 朱现伟. 干旱区人居环境系统耦合协调的时空演变及作用机制研究——以新疆为例[J]. 干旱区地理, 2023, 46(6): 1013-1023.
[6]	王嘉年, 李向义, 李成道, 张爱林, 林丽莎. 自然光照和荫蔽条件下两种荒漠植物叶片凋落物分解特征研究[J]. 干旱区地理, 2023, 46(6): 949-957.
[7]	李乐乐, 钞锦龙, 赵德一, 李浩杰, 吴林栋, 李佳骏. 1957—2019年山西省暴雨时空分布特征与暴雨灾害风险评估[J]. 干旱区地理, 2023, 46(5): 689-699.
[8]	罗镕基, 王宏涛, 王成. 基于改进遥感生态指数的甘肃省古浪县生态质量评价[J]. 干旱区地理, 2023, 46(4): 539-549.
[9]	许昕彤,朱丽,吕潇雨,郭浩. MSWEP降水产品在黄河流域气象干旱监测中的适用性评价[J]. 干旱区地理, 2023, 46(3): 371-384.
[10]	吴盈盈,王振亭. 河套平原土壤风蚀风险评估[J]. 干旱区地理, 2023, 46(3): 418-427.
[11]	孙南沙,陈琼,刘峰贵,周强,郭媛媛. 2000—2020年河湟谷地农业干旱研究[J]. 干旱区地理, 2023, 46(3): 437-447.
[12]	肖东升,王宁,刘志成. 干旱地区“代表性人口格网数据集”精度研究——以甘宁青地区为例[J]. 干旱区地理, 2023, 46(3): 505-514.
[13]	曹玉娟, 司文洋, 杜自强, 梁寒雪, 雷添杰, 孙斌, 武志涛. 1982—2017年典型干旱年的中国GPP变化[J]. 干旱区地理, 2023, 46(10): 1577-1590.
[14]	张娟, 姚晓军, 李净, 王晓燕. 基于多源遥感数据的甘肃省农业干旱研究[J]. 干旱区地理, 2023, 46(1): 11-22.
[15]	韩大勇, 牛忠泽, 伍永明, 高健. 水热条件共同驱动新疆湿地植物丰富度空间分布格局[J]. 干旱区地理, 2023, 46(1): 86-93.