Arid Zone Research ›› 2024, Vol. 41 ›› Issue (12): 1981-1991.doi: 10.13866/j.azr.2024.12.01

• Weather and Climate • Previous Articles     Next Articles

Spatial and temporal drivers of drought analysis using the geodetector in Ordos

WANG Sinan1(), WU Yingjie1(), WANG Hongzhou2, LI Mingyang3, WANG Fei4, ZHANG Wenying4, MA Xiaoming5, YU Xiangqian1   

  1. 1. Institute of Water Resources of Pastoral Area Ministry of Water Resources, Hohhot 010020, Inner Mongolia, China
    2. School of National Safety and Emergency Management, Beijing Normal University, Zhuhai 519087, Guangdong, China
    3. Water Resources Research Institute of Shandong Province, Jinan 250014, Shandong, China
    4. Inner Mongolia Water Conservancy Development Center, Hohhot 010020, Inner Mongolia, China
    5. Water Resources Research Institute of Inner Mongolia Autonomous Region, Hohhot 010010, Inner Mongolia, China
  • Received:2024-05-21 Revised:2024-09-13 Online:2024-12-15 Published:2024-12-20
  • Contact: WU Yingjie E-mail:nmgnydx2016@163.com;508188330@qq.com

Abstract:

Drought is a significant natural disaster in Ordos, exacerbating desertification and degrading grassland vegetation. Therefore, studying drought in this region is crucial for effective drought prevention, desertification control, and ecological restoration. In this study, we explored the spatiotemporal dynamics and trends of drought and analyzed the driving factors behind the spatial differentiation of DSI using a geographic detector model. The results show that evapotranspiration (ET) and the normalized difference vegetation index (NDVI) in the Ordos exhibit a significant increasing trend (P<0.05), with rates of 4.291 mm·a-1 for ET and 0.004 a-1 for NDVI. Additionally, the interannual variation of DSI also showed a significant increase, with a trend change rate of 0.089. ET and NDVI showed a spatial pattern, with lower values in the southwest and higher values in the northeast. Conversely, PET showed a spatial pattern of higher values in the southwest and lower values in the northeast. The DSI showed a dry spatial pattern in the west and a wet pattern in the east. The spatial differentiation of the DSI was primarily affected by five factors, such as air temperature, precipitation, land use type, soil type, and the digital elevation model (DEM), with q value exceeding 0.15, indicating these are the main driving factors of drought in the Ordos. Multiple factors interact to drive drought in Ordos, with four key combinations—temperature and DEM, precipitation and DEM, sunshine duration and DEM, and relative humidity and DEM. Among these, the combination of precipitation (0.156) and DEM (0.248) exerted the strongest influence on drought occurrence, with a q value of 0.389. This study can provide a scientific basis for ecological protection and drought management measures in the region.

Key words: Ordos, DSI drought index, geographical detector, spatial temporal change, driving factor