干旱区研究

干旱区研究 ›› 2024, Vol. 41 ›› Issue (9): 1456-1467.doi: 10.13866/j.azr.2024.09.03

• 第三次新疆综合科学考察 • 上一篇    下一篇

天山北坡经济带水库群时空变化特征及驱动机制

王婷1(), 沈赣华2, 刘兵1,3(), 孙莹琳1, 汪再光4   

  1. 1.石河子大学水利建筑工程学院，新疆 石河子 832000
    2.新疆德润经济建设发展有限公司，新疆 乌鲁木齐 830000
    3.石河子大学，寒旱区生态水利工程兵团重点实验室，新疆 石河子 832000
    4.石河子天兴水利勘测设计院，新疆 石河子 832000
  • 收稿日期:2024-04-19 修回日期:2024-06-03 出版日期:2024-09-15 发布日期:2024-09-25
  • 通讯作者: 刘兵. E-mail： 515441180@qq.com
  • 作者简介:王婷（2001-），女，硕士研究生，主要从事水资源高效利用研究. E-mail： 2496878250@qq.com
  • 基金资助:
    第三次新疆综合科学考察项目(2022xjkk090207);第三次新疆综合科学考察项目(2021xjkk0804);兵团科技攻关计划项目(2021AB021);石河子大学高层次人才科研启动项目(RCZK202026)

Evolution characteristics of spatial and temporal distribution pattern and driving force analysis of reservoirs in the economic zone on the north slope of Tianshan Mountains

WANG Ting1(), SHEN Ganhua2, LIU Bing1,3(), SUN Yinglin1, WANG Zaiguang4   

  1. 1. Colleye of Water Conservancy & Architectural Engineerring, Shihezi University, Shihezi 832000, Xinjiang, China
    2. Xinjiang Derun Economic Construction and Development Co. Ltd., Urumqi 830000, Xinjiang, China
    3. Key Laboratory of Cold and Arid Regions Eco-Hydraulic Engineering of Xinjiang Production & Construction Corps, Shihezi University, Shihezi 832000, Xinjiang, China
    4. Shihezi Tianxing Water Conservancy Survey and Design Institute, Shihezi 832000, Xinjiang, China
  • Received:2024-04-19 Revised:2024-06-03 Published:2024-09-15 Online:2024-09-25

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摘要：

为验证天山北坡经济带水库群建设在时间上的延续性及空间上的均衡性，利用1990—2020年遥感数据提取各水库水域面积，结合统计资料分析水库数量和库容变化过程，采用莫兰指数量化水库空间分布的聚集程度，建立时空地理加权回归模型分析水库驱动因素。结果表明：近30 a来，研究区水库水域面积呈先增后减的变化趋势，其中，1990—2015年增加了46.25%，2015—2020年减少了1.63%。水库数量和库容呈增长趋势，分别增长了64.94%、71.06%。1990—2020年各代表年份（1990年、1995年、2000年、2005年、2010年、2015年、2020年）水库莫兰指数分别为0.81、0.83、0.79、0.91、0.66、0.73、0.78，水库空间分布存在正相关性。水库分布高值区主要在奎屯河流域的农业灌溉区，低值区主要在以乌鲁木齐市为代表的工业区。影响水库库容变化的主要因素有蒸发量、降雨量、高程、人口和生产总值等。蒸发量对库容有负向影响，降雨量的影响在空间上具有不确定性，高程和人口对西段水库建设有正向影响，对东段水库建设有负向影响，生产总值对东段及西段部分区域水库建设有正向影响，对中段水库建设有负向影响。研究结果可为同类地区水库规划建设及运行管理提供参考依据。

关键词: 水库, 时序变化, 空间分布特征, 驱动力, 时空地理加权回归

Abstract:

The temporal continuity and spatial equilibrium of reservoir group construction in the north slope of the Tianshan Mountain Economic Belt was verified, by extracting the water area of each reservoir from the 1990-2020 remote sensing data. The variation process of reservoir quantity and storage capacity was also analyzed by combining statistical data. The Moran index was used to quantify the aggregation degree of reservoir spatial distribution, and geographically and temporally weighted regression was established to analyze the driving factors of the reservoir. The results showed that in the past 30 years, the reservoir water area in the study region first increased by 46.25% from 1990 to 2015 and then decreased by 1.63% from 2015 to 2020. The number of reservoirs and storage capacity enhanced by 64.94% and 71.06% respectively. From 1990 to 2020, the Moreland index of the reservoirs in each representative year was 0.81, 0.83, 0.79, 0.91, 0.66, 0.73, and 0.78, respectively, along with a positive correlation between the spatial distribution of the reservoirs. The high-value area of reservoir distribution was mainly concentrated in the agricultural irrigation region of the Kuitun River Basin, and the low-value area in the industrial region was represented by Urumqi City. The main factors affecting the variation in reservoir capacity were evaporation, rainfall, elevation, population, and GDP. Evaporation adversely affected storage capacity, and the effect of rainfall was not uniform in space. Altitude and population had a positive impact on the construction of the reservoir in the west section and a negative influence in the east section. The GDP had a positive association with reservoir construction in the eastern section and a part of the western section, but a negative correlation in the middle section. These results can provide a reference for reservoir planning, construction, and operation management in similar areas.

Key words: reservoir, temporal variation, spatial distribution characteristics, driving forces, geographically and temporally weighted regression