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标准化地下水指数法分析黑河中游地下水时空演变规律
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  • 英文篇名:Constructing standardized groundwater index to analyze temporal-spatial evolution of groundwater in middle reaches of Heihe river
  • 作者:艾启阳 ; 粟晓玲 ; 张更喜 ; 牛纪苹
  • 英文作者:Ai Qiyang;Su Xiaoling;Zhang Gengxi;Niu Jiping;College of Water Resources and Architectural Engineering, Northwest A & F University;Key Laboratory for Agricultural Soil and Water Engineering in Arid Area of Ministry of Education, Northwest A & F University;
  • 关键词:地下水 ; 干旱 ; 标准化地下水指数 ; 非参数法 ; 黑河中游
  • 英文关键词:groundwater;;drought;;standardized groundwater index;;non-parametric method;;Heihe river middle reaches
  • 中文刊名:NYGU
  • 英文刊名:Transactions of the Chinese Society of Agricultural Engineering
  • 机构:西北农林科技大学水利与建筑工程学院;西北农林科技大学旱区农业水土工程教育部重点实验室;
  • 出版日期:2019-05-23
  • 出版单位:农业工程学报
  • 年:2019
  • 期:v.35;No.362
  • 基金:国家自然科学基金项目(51879222)
  • 语种:中文;
  • 页:NYGU201910009
  • 页数:6
  • CN:10
  • ISSN:11-2047/S
  • 分类号:77-82
摘要
地下水干旱威胁区域社会经济发展和生态环境健康。基于河西走廊黑河流域中游地区1985—2010年23眼井的月地下水位数据,选择4种不同的分布函数构建标准化地下水指数(standardized groundwater index,SGI),采用K-S检验(kolmogorov-smirnov test)和AIC准则(Akaike information criterion)进行分布函数的优选,对K-S检验未通过的井使用非参数法构建SGI,分析地下水时空演变规律。结果表明:1)不同拟合函数对参数化SGI拟合结果有强烈影响。2)研究区的最佳拟合函数为Beta分布(18眼井),其次为广义极值分布(3眼井),另有2眼井未通过K-S检验,使用非参数法构建SGI。3)张掖区SGI指数变化趋势总体上呈先下降后回升趋势(13眼井),临泽和高台区呈持续下降趋势(10眼井)。地下水指数的构建可为地下水资源的监测、管理及预报提供依据。
        Groundwater drought severely impacts social economic development and ecological health. In order to manage groundwater resources scientifically, it is essential to establish a groundwater drought index to provide information for monitoring, management, and drought prediction of groundwater resources. In this study, a groundwater drought index was built by using the standardized groundwater index(SGI) method based on 4 kinds of distribution functions which were Gamma distribution, Beta distribution, lognormal(logN) distribution and generalized extreme value(GEV) distribution. The SGI was derivative of standardized precipitation index(SPI). The kolmogorov-smirnov test(K-S test) was used to determine whether the monthly water level data conformed to the theoretical distribution. If all the functions passed the K-S test, the best fitting distribution was selected by Akaike information criterion(AIC). If the only one passed the K-S test, the function was considered the best. Otherwise, non-parametric method had to be used for SGI fitting. The middle reaches of Heihe river basin was selected as a study case and the data was monthly groundwater level data of 23 wells from 1985 to 2010. The spatial and temporal evolutions of groundwater drought were analyzed in study area. The results showed that the SGI calculated by Gamma distribution and by logN distribution was generally inclined to negative values, indicating that the SGI were skewed towards no drought condition; and the SGI calculated by GEV distribution and by Beta distribution was positively skew, which means that the calculated result was skewed towards drought conditions. Therefore, it was necessary to select the optimal fitting function to calculate SGI for evaluation of groundwater drought by using a series of historical data. In the middle reaches of Heihe river basin, the optimal fitting functions were Beta function and GEV function, which accounted for 78% and 13% of the wells, respectively. The non-parametric method was adopted for groundwater data series of the other 2 wells that did not pass the K-S test. On the whole, the SGI calculated based on the optimal function in different part of study area showed 2 different trends. For example, the SGI showed a trend from decline to rising in Zhangye area while a trend of continuous decline in Linze and Gaotai area, which means that the groundwater drought had been alleviated in Linze and Gaotai. As a derivative of SPI index, SGI index has advantages and disadvantages similar to SPI index. However, due to the inherent periodicity of groundwater level sequences, it is necessary to choose optimal fitting functions to calculate SGI. The non-parametric method has been successfully used in the field of hydrology, and it has an advantage to analyze non-stationary sequences in spite of its limitations of overfitting. Further research should focus on the applicability of SGI in other regions considering the local hydrogeological conditions to find out whether there is a correlation between the optimal fitting function and hydrogeological conditions.
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