毛乌素沙地地下水干旱的多尺度分析——基于重力恢复及气候试验地下水储量变化指数
作者:
作者单位:

作者简介:

通讯作者:

中图分类号:

基金项目:

陕西省科技厅项目(2023JCYB449)


Multi-scale analysis of groundwater drought in the Maowusu Sandland based on GGSI
Author:
Affiliation:

Fund Project:

Shaanxi Provincial Science and Technology Department Project

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 文章评论
    摘要:

    地下水干旱是影响半干旱沙区植被建设的重要因素,毛乌素沙地位于中国北方干旱半干旱区,地下水资源管理是实现该地区长期可持续发展的重要保障。由于缺乏对地下水在空间和时间维度上的直接观测,地下水干旱的定量评估面临挑战。旨在探索地下水储量的变化规律,基于重力恢复及气候试验(Gravity Recovery and Climate Experiment,GRACE)卫星数据,并结合全球陆地数据同化系统(Global Land Data Assimilation System,GLDAS)观测数据,反演毛乌素沙地2002-2021年地下水储量动态变化。进而构建地下水水位估算指标,并重新定义为GRACE地下水储量变化指数(GRACE Groundwater Storage Index,GGSI),以量化分析该地区的地下水干旱状况。研究选择4种不同的分布函数,通过KS检验选取最优分布函数,其次在不同时间尺度上计算GGSI以定量分析地下水干旱。计算GGSI与降水之间的相关系数,揭示地下水干旱对降水的滞后效应,最后进行GGSI与SPI的时滞分析。结果表明:1)不同的拟合函数对数据拟合结果有不同的反应,该研究区域的最佳拟合函数为Pearson III函数,Pearson III函数能够更准确地反映该地区地下水储量的变化趋势;2)2002-2021年期间GGSI呈波动变化,随着时间尺度的增大,GGSI值变化趋势更为明显,呈先上升后下降整体变化比较稳定的趋势,不同时间尺度下,同一地区的干旱起始、结束及严重程度各异,但干旱期总体一致。毛乌素沙地的干旱年份为2007、2021年;3)干旱对降水的滞后时间集中在5个月和8个月,在这两段滞后期中都表现出了较高的相关性。

    Abstract:

    Groundwater drought is a critical factor affecting vegetation develoment in semi-arid sandy regions. Located in the arid and semi-arid zone of northern China, the Maowusu Sandland relies on groundwater resource management for its enduring sustainable development. However, the scarcity of direct groundwater observations across spatial and temporal dimensions presents challenges for the quantitative assessment of groundwater drought. Based on the observed data of the Global Land Data Assimilation System (GLDAS), this study aims to explore the changing patterns of groundwater storage by utilizing Gravity Recovery and Climate Experiment (GRACE) satellite data to invert the dynamic changes in groundwater storage in the Maowusu Sandland from 2002 to 2021. To quantitatively analyze the regional groundwater drought conditions, the study constructs a groundwater-level estimation index and refines it as the GRACE Groundwater Storage Index (GGSI). This paper first selects four different distribution functions and choose the optimal distribution function by the KS test, then calculate the GGSI in different time scales to quantitatively analyze groundwater drought. Subsequently, the correlation coefficients between GGSI and precipitation are calculated to reveal the lag effect of groundwater drought. Finally, the time lag analysis of GGSI and SPI is carried out. The key findings are as follows: 1) Different fitting functions have different responses to the data fitting results, and the best fitting function for this study area is the Pearson III function, which can more accurately reflect the trend of the groundwater storage in this area; 2) From 2002 to 2021, the GGSI exhibited fluctuating changes, with trends becoming more apparent as the time scale increases, showing an overall stable trend of initial increase followed by a decrease. The onset, conclusion, and severity of drought varied across different time scales, but the drought periods were generally consistent. The years 2007 and 2021 were identified as drought years in the Maowusu Sandland; 3) The consistent delay between drought and precipitation in this area spans from 5 to 8 months, with significant correlations observed within both of these lag periods.

    参考文献
    相似文献
    引证文献
引用本文

袁慧,张琪,朱永华,夏必胜,王文发.毛乌素沙地地下水干旱的多尺度分析——基于重力恢复及气候试验地下水储量变化指数.生态学报,2025,45(8):3978~3994

复制
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数: