| 吉林省西部潜水资源与生态环境风险分析 |
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| 引用本文： | 查恩爽,肖霄.吉林省西部潜水资源与生态环境风险分析[J].水文地质工程地质,2021,48(1):36-43. |
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| 作者姓名： | 查恩爽 肖霄 |
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| 作者单位： | 1.吉林大学建设工程学院，吉林 长春 130026 |
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| 基金项目： | 青海省共和县恰卜恰地区地下热水资源预可行性勘查;国家自然科学基金项目(41807155)。 |
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| 摘 要： | 吉林省西部是我国主要粮食产区，但区内农业水利规划管理同时面临潜水资源与生态环境双重风险。近20年来，区内曾尝试多种水资源利用模式，但缺少不同模式应用效果的定量化对比。文章建立了不同水资源利用模式，对比分析各模式的水资源与次生盐碱化风险。以洮儿河流域为例，采用循环神经网络预测2019—2023年该地区大气降水和地表水对地下水补给量；通过随机数值模拟预测现状开采、连续干旱、无序开采、地下水库建设、节水灌溉、旱田改水田6种情形下，区内潜水水位空间分布特征。以防止次生盐碱化为目标，定义水位埋深上限为1 m；以含水介质厚度为参考，定义水位埋深下限为12 m。遴选适合吉林省西部地区地下水资源可持续利用模式。结果显示:无序开采是导致区内水资源枯竭的主要诱因；地下水库建设和旱改水工程有助于潜水资源维护，但长期运行可加剧生态环境风险。节水灌溉（净采强度为2.0×108～3.0×108 m3/a）是降低区内水资源风险和生态环境风险的最佳方式。文章采用的神经网络—随机模拟分析方法成功预测了地下水位变化驱动因子和地下水位中长期变化趋势，为我国干旱半干旱地区潜水资源利用方案制定提供了新方法。
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| 关 键 词： | 地下水资源管理 风险评价 盐碱化 随机模拟 循环神经网络 数值模拟 |
| 收稿时间： | 2019-06-25 |
Assessment of resources and ecological risks induced by groundwater utilization in the unconfined aquifer in the western Jilin Province:A case study in the Taoer River catchment |
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| ZHA Enshuang,XIAO Xiao.Assessment of resources and ecological risks induced by groundwater utilization in the unconfined aquifer in the western Jilin Province:A case study in the Taoer River catchment[J].Hydrogeology and Engineering Geology,2021,48(1):36-43. |
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| Authors: | ZHA Enshuang XIAO Xiao |
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| Affiliation: | 1.College of Construction Engineering, Jilin University, Changchun, Jilin 130026, China2.College of Water Resources and Environment, Hebei GEO University, Shijiazhuang, Hebei 050031, China |
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| Abstract: | The western part of Jilin Province is one of the major agriculture zones in China.It is located in a semiarid zone with limited and unreliable water resources,and also with serious soil salinization.It is of critical importance to manage the groundwater levels in the unconfined aquifer,considering both the risks of water resources and the ecological problems related to the shallow buried depth of groundwater levels.However,there is still lack of quantitative risk analyses on the current strategies of water use in this area.In this study the recurrent neural network is used to predict the rainfall and river flux from 2019 to 2023,which are then used as the input variables in the stochastic groundwater flow models to predict the spatial distribution of groundwater levels in the unconfined aquifer.The groundwater levels under six scenarios,including the present-day recharge and discharge,drought,chaos extraction,managed aquifer recharge,drip irrigation and paddy farming,are calculated.Following a risk assessment, the drip irrigation with a net extraction rate ranging from 2.0×108 to 3.0×108 m3/a is consideredas the best strategy for groundwater resources utilization, which can effectively prevent the water resources in theunconfined aquifer from being over-exploited (with the buried depth of greater than 12 m), and also maintain thedepth of groundwater table of greater than 1m to reduce the risk of soil salinization. Meanwhile, chaos extractioninducing the water resources depletion is the major factor, and both the managed aquifer recharge and paddyfarming are helpful in water resources conservation, but may worse the soil salinization. The methodologyemployed in this study can be widely used in other arid and semi-arid areas for groundwater resourcesmanagement. |
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| Keywords: | groundwater resource management risk assessment soil salinization stochastic modeling recurrent neural networks numerical simulation |
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