地理学报 ›› 2023, Vol. 78 ›› Issue (7): 1744-1763.doi: 10.11821/dlxb202307014

• 水文地理 • 上一篇    下一篇

1990—2020年天山北坡地下水储量估算及其时空演变规律

王宗侠1,2(), 刘苏峡1,2,3()   

  1. 1.中国科学院地理科学与资源研究所 中国科学院陆地水循环及地表过程重点实验室,北京 100101
    2.中国科学院大学资源与环境学院,北京 100049
    3.中国科学院大学中丹学院,北京 100049
  • 收稿日期:2023-04-10 修回日期:2023-06-30 出版日期:2023-07-25 发布日期:2023-08-01
  • 通讯作者: 刘苏峡(1965-), 女, 湖北黄陂人, 研究员, 博士生导师, 主要从事水文水资源研究。E-mail: liusx@igsnrr.ac.cn
  • 作者简介:王宗侠(1998-), 男, 广东湛江人, 博士生, 主要从事水文水资源研究。E-mail: wangzongxia731x@igsnrr.ac.cn
  • 基金资助:
    第三次新疆综合科学考察项目(2021xjkk0803)

Estimation and spatiotemporal evolution of groundwater storage on the northern slope of the Tianshan Mountains over the past three decades

WANG Zongxia1,2(), LIU Suxia1,2,3()   

  1. 1. Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
    2. College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
    3. Sino-Danish College, University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2023-04-10 Revised:2023-06-30 Published:2023-07-25 Online:2023-08-01
  • Supported by:
    The Third Xinjiang Scientific Expedition Program(2021xjkk0803)

摘要:

天山北坡位于中国新疆,地处亚欧桥的国门,社会经济发展迅速,其绿洲经济和灌溉农业对地下水资源的依赖程度高。揭示1990—2020年地下水储量的演变规律对维持区域可持续发展具有重要意义。但由于长时序和较高分辨率的区域地下水观测数据匮乏,使得该任务又极具挑战性。本文基于重建的陆地水储量(TWS)变化数据、ERA5-Land再分析数据和其他相关的土壤质地、海拔高程、植被指数和冰川分布等辅助数据,建立了随机森林降尺度模型,据此估算了天山北坡1990—2020年的8 km分辨率地下水储量(GWS)变化,分析了天山北坡GWS的时空演变规律。估算的GWS变化与实测地下水位序列的时间变化具有较高的一致性,二者的相关系数最高达到0.68。天山北坡GWS具有明显的年内变化和年际变化。GWS在夏季和春季较高,在秋季和冬季较低,峰值出现在6月,最低值出现在10月。1990—2020年天山北坡有85%以上的地区GWS发生了显著下降,全区平均的变化趋势为-0.38 cm/a。其中,艾比湖水系和中段诸河区GWS下降最剧烈,额敏河流域下降速率最小;耕地下降速率最大,草地次之,裸地下降速率最小。日益增强的农业耗水活动是1990—2020年天山北坡GWS持续下降的主要驱动因素。相比于季节变化和亚季节变化,长期趋势性变化是天山北坡GWS时间变化的主导模式。但对于额敏河流域,GWS变化由三者共同主导。特别值得指出的是,在冰川覆盖区联合重力卫星与水储量组分方程开展GWS估算工作面临的挑战之一是缺乏长序列、高分辨率的冰川水储量数据,以往大部分研究忽视了冰川覆盖的影响。本文研究表明,若忽视冰川的影响,将导致天山北坡的平均GWS变化速率至少高估27.56%,应引起重视。

关键词: 地下水储量, 陆地水储量, GRACE, 降尺度

Abstract:

Located in Xinjiang Uygur Autonomous Region, at the national gateway to the Asia-Europe Bridge, the northern slope of the Tianshan Mountains (NSTM) has been experiencing rapid economic development. The oasis economy and irrigated agriculture of NSTM are highly dependent on groundwater. Therefore, it is of great significance to reveal the spatiotemporal evolution of groundwater storage for regional sustainable development. However, long-sequence and high-resolution groundwater observations are relatively scarce on regional scale, which makes the analysis extremely challenging. Based on reconstructed terrestrial water storage anomaly data, ERA5-Land reanalysis data and other auxiliary data such as soil texture, elevation, vegetation index and glaciers, a random forest downscaling model was developed to retrieve high-resolution (8 km) groundwater storage anomaly (GWSA) of NSTM from 1990 to 2020, with which the spatiotemporal evolution of groundwater storage was analyzed. The temporal variation of our GRACE (Gravity Recovery and Climate Experiment)-based GWSA was in high agreement with that of in-situ groundwater level data. The correlation coefficient between GWSA and in-situ data reached a maximum of 0.68. Groundwater storage in NSTM exhibited significant intra- and inter-annual variability, which was higher in spring and summer and lower in autumn and winter, with the peak occurring in June and the minimum in October. Groundwater storage in more than 85% of the NSTM declined significantly during 1990-2020, with a rate of -0.38 cm/a. Groundwater storage in the Ebinur Lake and Central Rivers declined most dramatically, while that in the Emin River was almost constant. The declining rate was the greatest in cropland, followed by grassland, and least in bare land. Increasing demand for water in agriculture was the main driver of groundwater depletion over the past three decades. Compared with seasonal and sub-seasonal variability, long-term variability was the dominant pattern of temporal variability of groundwater storage in NSTM. As for the Emin River, it was jointly dominated by long-term, seasonal and sub-seasonal variability. In particular, it is worth noting that one of the challenges in estimating GWSA in glacier-covered areas is the lack of long-sequence and high-resolution glacier mass data, and most previous studies have ignored the influence of glaciers. This paper showed that ignoring the influence of glaciers would lead to an overestimation of the average groundwater storage change rate in NSTM by at least 27.56%, which needs to be taken seriously.

Key words: groundwater storage, terrestrial water storage, GRACE, downscaling