【作者】 韩业珍；

【导师】 魏晓妹；

【作者基本信息】 西北农林科技大学 ， 水文学及水资源， 2010， 硕士

【摘要】 宝鸡峡灌区是全国十大灌区之一,自灌区成立至今已有三十多年。在灌区的发展过程中,由于外部环境因素变化的影响,地下水系统循环条件发生了很大变化,直接影响着灌区地下水资源的演变趋势和开发利用格局。因此,研究变化环境因素对地下水动态的影响,探讨地下水动态的时空变异规律,对灌区地下水资源的合理开发和水资源的可持续利用具有重要的理论意义和应用价值。本文在总结归纳前人研究工作的基础上,从宝鸡峡灌区地下水循环的角度,分析了灌区地下水系统及外部环境因素的变化趋势,结合灌区地下水动态的演变特征,分析了地下水动态的主要影响因子;建立了地下水位动态的多元线性回归模型和BP神经网络模型,并对灌区地下水位进行了预测;将地统计学与GIS相结合,研究了灌区地下水位的时空变异规律。获得了如下主要研究结论:(1)概化了宝鸡峡灌区地下水系统,明确了气候因素和人类活动是地下水系统的外部环境因素,分析了地下水动态变化特征,分析结果表明:黄土台塬区地下水位自1982年至2005年,大致经历了四个阶段:水位上升期、水位平稳期、水位下降期和水位平稳(回升)期;渭河阶地区地下水位总体上呈现水位下降趋势。灌区范围内地下水硬度自2002年以来,呈现上升趋势,且上升趋势比较明显;碱度近几年变化不明显,基本上呈平稳趋势;矿化度呈上升趋势,但趋势不十分明显。(2)运用Mann-Kendall法、Spearman检验法和回归检验法对灌区1982～2007年的降水量、蒸发量、地表水灌溉水量和地下水开采量进行趋势分析,结果表明:灌区范围内降水量总体上呈下降趋势,但下降趋势不显著,且丰枯交替出现;灌区范围内蒸发量总体呈上升趋势,但上升趋势不显著且上升与下降呈周期性交替出现;灌区地表水灌溉水量呈下降趋势,且变化趋势显著;灌区地下水开采量呈上升趋势,变化趋势不显著。(3)运用灰色关联度分析法对灌区地下水位动态变化的主要驱动因素进行了研究。研究结果表明:黄土台塬区地下水动态的影响因素由大到小排序为:蒸发、降水、地表水灌溉、地下水开采;渭河阶地区地下水动态影响因素由大到小的排序为:蒸发、降水、地下水开采、地表水灌溉。通过黄土台塬区和渭河阶地区的对比分析可以发现各影响因子对渭河阶地区地下水动态的影响作用要大于对黄土台塬区地下水动态的影响作用。(4)利用宝鸡峡灌区地下水位动态及外部环境因素资料,建立了灌区地下水位动态的多元线性回归模型和BP神经网络模型,两种模型的地下水位模拟及检验精度分析表明,BP神经网络模拟和预测地下水位动态精度比多元线性回归模型高。结合地下水动态影响因素的变化趋势分析,应用所建模型,对灌区的地下水位动态进行了预测,表明未来几年内灌区地下水位整体上将呈现下降趋势,其中黄土台塬区地下水位下降速度要大于渭河阶地区地下水位的下降速度。(5)利用ARCGIS的地统计模块对地下水位进行空间分析,分别建立地下水位空间变异的球状、指数和高斯模型,通过各拟合参数的比较和Cross—Validatiom交叉验证发现克里格方法中高斯模型精度最高。由高斯模型分析得出:灌区地下水位具有强的空间相关性且呈现增强趋势;灌区地下水位具有明显的各向异性,且灌区内地下水位具有较好的空间连续性。长轴所在角度在65°方向,与灌区南部的渭河走向基本一致。克里格插值结果显示:灌区地下水位由西北方向向东南方向递减,2002年至2007年6年间地下水位变幅较小。地下水位的标准误差预测图表明模型进行地下水位空间预测精度较高,灌区内地下水观测井的分布较为均匀、合理。更多还原

【Abstract】 Baojixia irrigation area is one of the top ten irrigation areas, it has been 30 years since the irrigation area was founded.With the development of the irrigation area, because of the impact of the external environmental factors, groundwater system circulation condition has changed a lot which directly influence the evolution trend and the utilization pattern of the groundwater. Therefore, the study of the impact of groundwater dynamic by the external environmental.factors and the spatial and temporal variability of the groundwater has a great theoretical significance and application value for the rational development of groundwater resources and the sustainable use of water resources.This paper embarking from the groundwater resources transformtion, analyzed the groundwater system and the trend of the external environmental factors, combining with the evolution of the groundwater dynamic, analyzed the main factors of groundwater dynamic; established multiple regression model and BP neural network model of the groundwater level and predict the groundwater level; combined geostatistics and GIS together to study the temporal and spatial variation of the groundwater level.The main conclusions obtained are as follows:(1) Generalized the groundwater system of Baojixia irrigation area and maked clear the climatic factors and human activities were the external environmental factors of groundwater systems, analyzed the dynamic characteristics of groundwater, the results showed that: the groundwater level of the loess tableland area has gone through four phases from 1982 to 2007 :the rising period,the steady period,the declining period and the steady period; the groundwater level of the Wei river terrace area showed a downward trend.The hardness of the groundwater has showed a rising trend since 2002 and the upward trend is obvious; alkalinity didn’t change significantly in recent years and shows a steady trend; mineralization is rising, but the trend is not very obvious.(2) Using Mann-Kendall method, Spearman test and regression test to analyze the trend of precipitation, evaporation, surface water irrigation and groundwater exploitation in the area from 1982 to 2007 .The results show that :the precipitation shows a downward trend, but not significant and the ample water and low water alternating; the evaporation show upward trend, but the rise was not significant and the rise and fall periodically alternating; surface water irrigation significantly decreased; groundwater exploitation was rising, but the trend was not significant.(3) Using the gray correlation analysis method to analyze the main driving factors of the groundwater level. The results show that: the order of factors of groundwater level in the loess tableland area was: evaporation, precipitation, surface water irrigation, groundwater exploitation; the order of factors of groundwater level in the Wei river terrace area was: evaporation, precipitation, groundwater exploitation, surface water irrigation. Through the comparision of the loess tableland area and the Wei river terrace area, it can be found that the influence caused by the factors in the Wei river terrace area is greater than the influence in the loess tableland area.(4) Using the groundwater level dynamic and the external environmental factors, established the multiple regression model and BP neural network model of the groundwater level, we can find that BP neural network has a higher accuracy than the multiple regression model by comparison. Combined the trend analysis of the impact factors of groundwater, using the model, predict the dynamics of the groundwater level which shows that the groundwater level will show a downward trend within the next few years and the rate of groundwater level decline in the loess tableland area is greater than the rate in Weihe rive terrace area.(5) Using the geostatistical analyst module of ARCGIS to anlyze the spatial variability of the groundwater level,established the spherical, exponential and Gaussian model ,we can find that Gaussian model has a highest accuracy through comparison and cross-validation. Derived from the Gaussian model: groundwater level has a strong spatial correlation and show increasing trend; groundwater level has obvious anisotropy and has good spatial continuity in the irrigation area.The angle of the major axis is 65°direction which is the same with the direction of the Weihe River.The results of Kriging show that: groundwater level decreasing from the northwest to southeast and had small amplitude from 2002 to 2007.Standard error map shows that the model predicted water table space high precision irrigation groundwater monitoring wells within the distribution is more uniform and rational.更多还原