[1]LIANG Rengang,ZHOU Xu,LI Song,et al.Analysis of Temporal and Spatial Variation Characteristics of Drought in Guizhou Province and Its Influencing Factors Based on CWSI[J].Research of Soil and Water Conservation,2022,29(03):284-291.
Copy
Analysis of Temporal and Spatial Variation Characteristics of Drought in Guizhou Province and Its Influencing Factors Based on CWSI
Research of Soil and Water Conservation[ISSN 1005-3409/CN 61-1272/P] Volume:
29
Number of periods:
2022 03
Page number:
284-291
Column:
Public date:
2022-04-20
- Title:
- Analysis of Temporal and Spatial Variation Characteristics of Drought in Guizhou Province and Its Influencing Factors Based on CWSI
- Keywords:
- drought; CWSI; karst landscapes; vegetation types
- CLC:
- S423
- DOI:
- -
- Abstract:
- To promote the sustainable development of agriculture and ecological environment in Guizhou Province, based on the crop water stress index(CWSI), meteorological and vegetation index data, we used Theil-Sen Median trend analysis, Mann-Kendall test, and coefficient of variation and correlation analysis to analyze the spatial and temporal variation characteristics of drought in Guizhou Province from 2000 to 2019. The analysis was conducted to provide a scientific basis for drought research and water resources management in Guizhou Province by analyzing the characteristics, trends and influencing factors of drought from 2000 to 2019. The results show that:(1)the multi-year average value of CWSI in Guizhou Province was 0.43, with an overall light drought level, and the spatial distribution was wet in the southeast and dry in the northwest, and the multi-year drought changes were in a relieving tendency;(2)in terms of landscape types, the multi-year average value of CWSI in non-karst landscapes was 0.37, with an overall drought-free level, while the average value of karst landscapes was 0.47, with a light drought status;(3)from the perspective of vegetation types, except for coniferous forests, all other vegetation types were in light drought status, and the coefficient of variation values of coniferous forests were higher than those of other woodlands, indicating their high sensitivity to climatic factors and strong drought resistance;(4)CWSI in Guizhou Province was negatively correlated with NDVI and precipitation, and the areas of negative correlation was 93% and 95%, respectively; it was both positively and negatively correlated with temperature, and the area of negative correlation was 54%. The comprehensive analysis concluded that the southeastern part of Guizhou Province was wet and the northwestern part was dry, and the drought in the province was influenced by the karst landscape, precipitation and vegetation.
- References:
-
[1] Ji L, Peters A J. Assessing vegetation response to drought in the northern Great Plains using vegetation and drought indices[J]. Remote Sensing of Environment, 2003,87(1):85-98.
[2] Van Loon A F, Laaha G. Hydrological drought severity explained by climate and catchment characteristics[J]. Journal of Hydrology, 2015,526:3-14.
[3] 黄梦杰,贺新光,卢希安,等.长江流域的非平稳SPI干旱时空特征分析[J].长江流域资源与环境,2020,29(7):1597-1611.
[4] 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.
[5] West H, Quinn N, Horswell M. Remote sensing for drought monitoring & impact assessment:Progress, past challenges and future opportunities[J]. Remote Sensing of Environment, 2019,232:111291, https://doi.org/10,1016/j.rse.2019.111291.
[6] 杨涛,宫辉力,李小娟,等.土壤水分遥感监测研究进展[J].生态学报,2010,30(22):6264-6277.
[7] 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):123-128.
[8] 王玉娟,王树东,曾红娟,等.基于作物缺水指数法的渭河流域干旱特征[J].干旱区研究,2014,31(1):118-124.
[9] 王小平,郭铌.遥感监测干旱的方法及研究进展[J].干旱气象,2003,21(4):76-81.
[10] Mu Q, Heinsch F A, Zhao M, et al. Development of a global evapotranspiration algorithm based on MODIS and global meteorology data[J]. Remote Sensing of Environment, 2007,111(4):519-536.
[11] Mu Q, Zhao M, Running S W. Improvements to a MODIS global terrestrial evapotranspiration algorithm[J]. Remote Sensing of Environment, 2011,115(8):1781-1800.
[12] 温媛媛,赵军,王炎强,等.基于MOD16的山西省地表蒸散发时空变化特征分析[J].地理科学进展,2020,39(2):255-264.
[13] 汪左,王芳,张运.基于CWSI的安徽省干旱时空特征及影响因素分析[J].自然资源学报,2018,33(5):853-866.
[14] 曾晓燕,许顺国,牟瑞芳.岩溶生态脆弱性的成因[J].地质灾害与环境保护,2006,17(1):5-8.
[15] 陈国富.岩溶石山区地表蒸散发及水文过程定量研究[D].重庆:西南大学,2013.
[16] 黄荣辉,刘永,王林,等.2009年秋至2010年春我国西南地区严重干旱的成因分析[J].大气科学,2012,36(3):443-457.
[17] 冯禹,崔宁博,徐燕梅,等.贵州省干旱时空分布特征研究[J].干旱区资源与环境,2015,29(8):82-86.
[18] 陈学凯,徐建新,雷宏军,等.贵州省干旱时空变化特征及其对气候变化的响应[J].灌溉排水学报,2015,34(8):72-81.
[19] 慎东方,商崇菊,方小宇,等.贵州省干旱历时和干旱烈度的时空特征分析[J].干旱区资源与环境,2016,30(7):138-143.
[20] 王盈盈,王志良,张泽中,等.基于SPEI的贵州省分区干旱时空演变特征[J].灌溉排水学报,2019,38(6):119-128.
[21] 王世杰,张信宝,白晓永.南方喀斯特石漠化分区的名称商榷与环境特点[J].山地学报,2013,31(1):18-24.
[22] Jackson R D, Kustas W P, Choudhury B J. A reexamination of the crop water stress index[J]. Irrigation Science, 1988,9(4):309-317.
[23] 刘海,黄跃飞,郑粮,等.长时序丹江口水源区NDVI数据集构建及其时空动态变化分析[J].长江流域资源与环境,2020,29(8):1780-1789.
[24] 袁丽华,蒋卫国,申文明,等.2000—2010年黄河流域植被覆盖的时空变化[J].生态学报,2013,33(24):7798-7806.
[25] 张强,邹旭凯,肖风劲,等. GB/T20481-2006.气象干旱等级[S].北京:中国标准出版社,2006.
[26] 王纯枝,毛留喜,吕厚荃,等.基于作物缺水指数的区域旱情遥感监测[A].中国气象学会.中国气象学会2007年年会生态气象业务建设与农业气象灾害预警分会场论文集[C].中国气象学会:中国气象学会,2007:10.
[27] 牛宁,李建平.2004年中国长江以南地区严重秋旱特征及其同期大气环流异常[J].大气科学,2007(2):254-264.
[28] 张继,周旭,蒋啸,等.贵州高原不同地貌区和植被类型水分利用效率的时空分异特征[J].山地学报,2019,37(2):173-185.
[29] Liaqat U W, Choi M. Accuracy comparison of remotely sensed evapotranspiration products and their associated water stress footprints under different land cover types in Korean peninsula[J]. Journal of Cleaner Production, 2016,155(1):93-104.
[30] Zhang T, Peng J, Liang W, et al. Spatial-temporal patterns of water use efficiency and climate controls in China's Loess Plateau during 2000—2010[J]. Science of the Total Environment, 2016,565:105-122.
[31] Zolotokrylin A N, Titkova T B, Brito-Castillo L. Wet and dry patterns associated with ENSO events in the Sonoran Desert from, 2000—2015[J]. Journal of Arid Environments, 2016,134:21-32.
- Similar References:
Memo
-
Last Update:
2022-04-20