机构地区:[1]甘肃农业大学水利水电工程学院,甘肃兰州730070 [2]中国科学院西北生态环境资源研究院干旱区生态安全与可持续发展重点实验室/甘肃省祁连山生态环境研究中心,甘肃兰州730000 [3]中国科学院新疆生态与地理研究所荒漠与绿洲生态国家重点实验室/阜康荒漠生态系统研究站,新疆乌鲁木齐830011
出 处:《中国沙漠》2025年第2期1-16,共16页Journal of Desert Research
基 金:国家自然科学基金项目(32301671;42330603);博士后创新人才支持计划资助项目(BX20230409);青年导师扶持基金项目(GAU-QDFC-2023-13)。
摘 要:为研究灌溉农业发展背景下干旱绿洲区不同水力区域地下水埋深的动态变化特征,以新疆三工河流域中部绿洲区为研究区域,自南向北划分为冲洪积扇绿洲区(ADFO)、冲积平原上部绿洲区(APOU)和冲积平原下部绿洲区(APOL)3个水力单元,利用1995—2016年9口长期监测井的地下水埋深数据、灌溉农业发展数据、水文气象数据及区域社会经济等资料,采用集合经验模态分解、小波分析和灰色关联度等多种方法分析地下水埋深变化特征及其影响因素,构建BP神经网络模型预估未来该区域地下水埋深变化。结果表明:三工河流域绿洲区地下水埋深年际变化波动较大,22年间整体呈持续下降趋势,尤其ADFO区下降最显著,年均降幅1.03 m;流域灌溉方式转变的过渡期内(2006—2010年)地下水埋深发生突变,且节水灌溉时期(2012年之后)各水力区域地下水埋深均深于大水漫灌时期(2006年之前),增幅呈ADFO(12.25~15.59 m)>APOU(5.30~8.23 m)>APOL(1.03~1.71 m);流域地下水埋深变化主要影响因素为耕地面积、地下水开采量和山区径流量;耦合不同水力区域地下水埋深的BP神经网络模型具有较高的模拟精度,在退地减水背景下,预测2017—2036年3个水力区域地下水位分别回升6.74 m(ADFO)、2.55 m(APOU)和0.35 m(APOL)。该研究结果对干旱区合理调控地下水资源、指导绿洲灌溉农业管理及生态环境保护具有重要的指导和借鉴意义。To investigate the dynamic characteristics of groundwater table depth(GTD) in different hydrological regions of arid oasis areas under the development of irrigated agriculture, the oasis area in the central part of Sangong River Basin in Xinjiang was selected as the target study region, and this oasis area was divided into three hydraulic units from south to north, i.e., the alluvial fan oasis area(ADFO), upper alluvial plain oasis area(APOU) and lower alluvial plain oasis area(APOL). Using the GTD data of 9 long-term monitoring wells as well as irrigation agriculture development, hydrometeorological and regional socio-economic information from 1995 to 2016, the variation characteristics and influencing factors of GTD were analyzed based on a variety of powerful methods such as ensemble empirical mode decomposition, wavelet analysis and grey correlation degree, and a BP neural network model was developed to predict the change of GTD in the studied region under the future changing environment. We note that the interannual variations of GTD fluctuated greatly in the oasis area of Sangong River Basin, with a continuous downward trend during the past 22 years, especially in ADFO area with an average annual decline rate of 1.03 m. The change points of GTD for all wells were found to have occurred during 2006-2010, which represents the transition period of agricultural irrigation schemes from traditional flood irrigation to water-saving irrigation, and the GTD during the water-saving irrigation period(after 2012) was deeper than that the traditional flood irrigation period(before 2006) in each hydrological region, with an increase of ADFO(12.25-15.59 m) > APOU(5.30-8.23 m) > APOL(1.03-1.71 m). The main influencing factors of GTD change in the basin are the cultivated land area, groundwater pumping and mountain annual runoff.The simulation and validation results indicate that the BP neural network model coupled with groundwater table depth in different hydrological regions has good modelling accuracy, and under the impl
关 键 词:地下水埋深 不同水力区域 动态变化特征 BP神经网络 三工河流域
分 类 号:S273.4[农业科学—农业水土工程]
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