基于CWSI的安徽省干旱时空特征及影响因素分析
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摘要
安徽省是我国的农业大省和重要商品粮食基地,因受自然地理位置等因素的影响,安徽省旱灾频发。论文基于MOD16产品,利用作物缺水指数CWSI,结合气象数据和MOD13数据,分析2000—2014年安徽省干旱时空分布特征和影响因素,结论如下:1)基于MOD16产品计算的作物缺水指数可用于安徽省干旱的监测。2)2000—2014年间安徽省CWSI多年均值为0.524,最大值在2011年(0.569),最小值在2003年(0.458),整体上呈现弱增加趋势;具有较强的空间分异性,表现为南部湿润、北部干旱。各市多年CWSI年均值相差不大,但变化趋势各异,淮北平原地区和江淮之间地区呈现显著增加趋势,而皖南地区呈显著下降趋势。3)安徽省CWSI年内月变化整体上呈先减少后增加的单谷型趋势,1—6月和10—12月CWSI值较高,最大值在3月,为0.66,7—9月CWSI值较低,最小值在8月,为0.27,具有明显的季节性差异,淮北平原易出现春旱、秋旱和冬旱,江淮丘陵易出现春旱及秋旱,沿江地区易出现春旱。4)不同土地利用类型的CWSI年内变化特征差异明显,林地和草地整体上呈现先减少后增加的单谷趋势,其月均最大值在3月,城镇和耕地则表现为多峰趋势,最大月均值在6月。5)CWSI与降雨、温度以及植被的关系密切,在降雨较多的地区,温度较低,植被指数较高,CWSI值较小,干旱程度较轻,说明降雨对CWSI变化的影响较大。
Anhui is one of the agricultural provinces and an important commodity grain base in China. Due to factors such as natural geographical location, drought occurs frequently in Anhui Province. Based on the MOD16 product, and the method of crop water stress index(CWSI)combined with meteorological data and MOD13 data, the spatial and temporal distribution characteristics and influencing factors of drought in Anhui Province from 2000 to 2014 were analyzed. The results showed that: 1) Crop water stress index based on MOD16 product can be used to monitor drought in Anhui Province. 2) During 2000-2014, the average annual CWSI for Anhui Province was 0.524, the maximum value being 0.569 in 2011 and the minimum value being 0.458 in 2003, which showed a weak increasing trend and had a strong spatial heterogeneity. In addition, the average CWSI varied among cities. The area between Huaibei plain and Jianghuai region showed a significant increasing trend, while the mountain areas in south Anhui showed a significant downward trend. 3) The monthly CWSI during one year in Anhui Province showed the single-valley trend, reaching its maximum of 0.66 in March and the minimum of 0.27 in August. Besides, there were obvious seasonal differences. Huaibei plain was prone to spring, autumn and winter drought, Jianghuai hills was prone to spring and autumn drought, and area along the Yangtze River was prone to spring drought. 4) The CWSI varied for different land covers in Anhui Province. Forest land and grassland showed the singlevalley pattern with the monthly average maximum in March, while the towns and cultivated land showed the multimodal pattern with the maximum monthly mean in June. 5) Rainfall,temperature and NDVI were closely related to CWSI. In areas with more rainfall and lower temperature, the NDVI was higher, moreover, the CWSI was smaller and the drought degree was lighter, which indicated that rainfall played the most important role in the change of CWSI.
Anhui is one of the agricultural provinces and an important commodity grain base in China. Due to factors such as natural geographical location, drought occurs frequently in Anhui Province. Based on the MOD16 product, and the method of crop water stress index(CWSI)combined with meteorological data and MOD13 data, the spatial and temporal distribution characteristics and influencing factors of drought in Anhui Province from 2000 to 2014 were analyzed. The results showed that: 1) Crop water stress index based on MOD16 product can be used to monitor drought in Anhui Province. 2) During 2000-2014, the average annual CWSI for Anhui Province was 0.524, the maximum value being 0.569 in 2011 and the minimum value being 0.458 in 2003, which showed a weak increasing trend and had a strong spatial heterogeneity. In addition, the average CWSI varied among cities. The area between Huaibei plain and Jianghuai region showed a significant increasing trend, while the mountain areas in south Anhui showed a significant downward trend. 3) The monthly CWSI during one year in Anhui Province showed the single-valley trend, reaching its maximum of 0.66 in March and the minimum of 0.27 in August. Besides, there were obvious seasonal differences. Huaibei plain was prone to spring, autumn and winter drought, Jianghuai hills was prone to spring and autumn drought, and area along the Yangtze River was prone to spring drought. 4) The CWSI varied for different land covers in Anhui Province. Forest land and grassland showed the singlevalley pattern with the monthly average maximum in March, while the towns and cultivated land showed the multimodal pattern with the maximum monthly mean in June. 5) Rainfall,temperature and NDVI were closely related to CWSI. In areas with more rainfall and lower temperature, the NDVI was higher, moreover, the CWSI was smaller and the drought degree was lighter, which indicated that rainfall played the most important role in the change of CWSI.
引文
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[3]包云轩,孟翠丽,申双和,等.基于CI指数的江苏省近50年干旱的时空分布规律[J].地理学报,2011,66(5):599-608.[BAO Y X,MENG C L,SHEN S H,et al.Temporal and spatial patterns of droughts for recent 50 years in Jiangsu based on meteorological drought composite index.Acta Geographica Sinica,2011,66(5):599-608.]
[4]WILHITE D A,GLANTZl M H.Understanding the drought phenomenon:The role of definitions[J].Water International,1985,10(3):111-120.
[5]黄健熙,张洁,刘峻明,等.基于遥感DSI指数的干旱与冬小麦产量相关性分析[J].农业机械学报,2015,46(3):166-173.[HUANG J X,ZHANG J,LIU J M,et al.Correlation analysis between drought and winter wheat yields based on remotely sensed drought severity index.Transactions of the CSAM,2015,46(3):166-173.]
[6]陈斌,张学霞,华开,等.温度植被干旱指数(TVDI)在草原干旱监测中的应用研究[J].干旱区地理,2013,36(5):930-937.[CHEN B,ZHANG X X,HUA K,et al.Application study of temperature vegetation drought index(TVDI)in grassland drought monitoring.Arid Land Geography,2013,36(5):930-937.]
[7]李华朋,张树清,高自强,等.MODIS植被指数监测农业干旱的适宜性评价[J].光谱学与光谱分析,2013,33(3):756-761.[LI H P,ZHANG S Q,GAO Z Q,et al.Evaluating the utility of MODIS vegetation index for monitoring agricultural drought.Spectroscopy and Spectral Analysis,2013,33(3):756-761.]
[8]李柏贞,周广胜.干旱指标研究进展[J].生态学报,2014,34(5):1043-1052.[LI B Z,ZHOU G S,Advance in the study on drought index.Acta Ecologica Sinica,2014,34(5):1043-1052.]
[9]陈修治,苏泳娴,李勇,等.基于被动微波遥感的中国干旱动态监测[J].农业工程学报,2013,29(16):151-158.[CHEN X Z,SU Y X,LI Y,et al.Monitoring drought dynamics of China using passive microwave remote sensing technology.Transactions of the CSAE,2013,29(16):151-158.]
[10]王永前,施建成,刘志红,等.微波植被指数在干旱监测中的应用[J].遥感学报,2014,18(4):843-867.[WANG Y Q,SHI J C,LIU Z H,et al.Application of microwave vegetation index in drought monitoring.Journal of Remote Sensing,2014,18(4):843-867.]
[11]黄友昕,刘修国,沈永林,等.农业干旱遥感监测指标及其适应性评价方法研究进展[J].农业工程学报,2015,31(16):186-195.[HUANG Y X,LIU X G,SHEN Y L,et al.Advances in remote sensing derived agricultural drought monitoring indices and adaptability evaluation methods.Transactions of the CSAE,2015,31(16):186-195.]
[12]唐侥,孙睿.基于气象和遥感数据的河南省干旱特征分析[J].自然资源学报,2013,28(4):646-655.[TANG Y,SUNR.Drought characteristics in Henan Province with meteorological and remote sensing data.Journal of Natural Resources,2013,28(4):646-655.]
[13]ALDERFASI A A,NIELSEN D C.Use of crop water stress index for monitoring water status and scheduling irrigation in wheat[J].Agricultural Water Management,2001,47(1):69-75.
[14]CHEN J Z,LIN L R,LüG A.An index of soil drought intensity and degree:An application on corn and a comparison with CWSI[J].Agricultural Water Management,2010,97(6):865-871.
[15]SEZEN S M,YAZAR A,DASGAN Y,et al.Evaluation of crop water stress index(CWSI)for red pepper with drip and furrow irrigation under varying irrigation regimes[J].Agricultural Water Management,2014,143:59-70.
[16]KULLBERG E G,DEJONGE K C,CHAVEZ J L.Evaluation of thermal remote sensing indices to estimate crop evapotranspiration coefficients[J].Agricultural Water Management,2017,179(S1):64-73.
[17]IDSO S B,JACKSON R D,PINTER P J,et al.Normalizing the stress degree day for environmental variability[J].Agricultural Meteorology,1981,24:45-55.
[18]JACKSON R D,IDSO S B,REGINATO R J,et al.Canopy temperature as a crop water stress indicator[J].Water Resources Research,1981,17(4):1133-1138.
[19]JACKSON R D,KUSTAS W P,CHOUDHURY B J.A reexamination of the crop water stress index[J].Irrigation Science,1988,9:309-317.
[20]申广荣,田国良.作物缺水指数监测旱情方法研究[J].干旱地区农业研究,1998,16(1):126-131.[SHEN G R,TIANG L.Drought monitoring with crop water stress index.Agricultural Research in the Arid Areas,1998,16(1):126-131.]
[21]刘安麟,李星敏,何延波,等.作物缺水指数法的简化及在干旱遥感监测中的应用[J].应用生态学报,2004,15(2):210-214.[LIU A L,LI X M,HE Y B,et al.Simplification of crop shortage water index and its application in drought remote sensing monitoring.Chinese Journal of Applied Ecology,2004,15(2):210-214.]
[22]宋小宁,赵英时.改进的区域缺水遥感监测方法[J].中国科学D辑(地球科学),2006,36(2):188-194.[SONG X N,ZHAO Y S.Improved remote sensing monitoring method for regional water shortage.Science in China Series D(Earth Sciences),2006,36(2):188-194.]
[23]王玲玲,张友静,佘远见,等.遥感旱情监测方法的比较与分析[J].遥感信息,2010,25(5):49-53.[WANG L L,ZHANG Y J,SHE Y J,et al.Analysis and comparison of drought monitoring methods by remote sensing.Remote Sensing Information,2010,25(5):49-53.]
[24]田国珍,武永利,梁亚春,等.基于蒸散发的干旱监测及时效性分析[J].干旱区地理,2016,39(4):721-729.[TIAN GZ,WU Y L,LIANG Y C,et al.Drought monitoring and timeliness based on evapotranspiration model.Arid Land Geography,2016,39(4):721-729.]
[25]MU Q Z,HEINSCH F A,ZHAO M S,et al.Development of a global evapotranspiration algorithm based on MODISand global meteorology data[J].Remote Sensing of Environment,2007,111(4):519-536.
[26]MU Q Z,ZHAO M S,RUNNING S W.Improvements to a MODIS global terrestrial evapotranspiration algorithm[J].Remote Sensing of Environment,2011,115(8):1781-1800.
[27]BAI J J,YU Y,DI L P.Comparison between TVDI and CWSI for drought monitoring in the Guanzhong Plain,China[J].Journal of Integrative Agriculture,2017,16(2):389-397.
[28]何慧娟,卓静,李红梅,等.基于MOD16产品的陕西关中地区干旱时空分布特征[J].干旱地区农业研究,2016,34(1):236-241.[HE H J,ZHUO J,LI H M,et al.Spatial-temporal distribution characteristics of drought in Guanzhong region of Shaanxi Province based on MOD16 products.Agricultural Research in the Arid Areas,2016,34(1):236-241.]
[29]喻元.基于CWSI与TVDI的关中地区干旱监测对比与干旱时空特征研究[D].西安:陕西师范大学,2015.[YU Y.Study on Drought Monitoring and Drought Spatial and Temporal Characteristics in Guanzhong Area Based on CWSIand TVDI.Xi’an:Shaanxi Normal University,2015.]
[30]刘惠敏,马小群,孙秀帮.安徽省MODIS干旱监测技术研究[J].气象,2010,36(4):111-115.[LIU H M,MA X Q,SUN X B.Drought monitoring composite MODIS satellite remote sensing technique in Anhui Province.Meteorological Monthly,2010,36(4):111-115.]
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