地下水自然补给评价:以北金奈含水层为例

Q2 Earth and Planetary Sciences Environmental Geosciences Pub Date : 2019-06-15 DOI:10.1306/EG.01091918005
T. Subramanian, M. Abraham
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引用次数: 3

摘要

地下水是印度城市和农村饮用水的主要来源。地下水自然补给量的估算是地下水可持续发展的关键。利用水位涨落法、水量平衡法、线性回归模型、非线性回归模型等多种方法估算自然补给量。将水量平衡法估算的补给量与水位涨落法估算的补给量进行了比较,两者吻合较好。利用水位波动法估算补给比较费力,考虑到数据的可用性和可靠性的困难,本研究采用水量平衡法作为建立回归方程的标准。建立了简单的线性和非线性回归模型,通过与水平衡模型的关联来估算研究区自然补给。这些模型用10年的数据进行校准,用5年的数据进行验证。统计分析表明,水量平衡法估算的补给量与自然补给量的线性回归模型和非线性回归模型估算的补给量没有显著差异。研究期间,水位波动法、水量平衡法、线性回归模型和非线性回归模型的平均回灌率分别为15.09%、14.92%、14.62%和14.57%。研究证明,回归方程可以有效地用于未计量流域的补给计算,并通过适当的校正,可以消除繁琐的数据密集型计算方法。
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Assessment of natural groundwater recharge: A case study of North Chennai Aquifer
Groundwater is the major source of drinking water in both urban and rural India. Estimation of natural groundwater recharge is essential for the sustainable development of groundwater. Natural recharge was estimated by various methods, such as the water level fluctuation method, water balance method, linear regression model, and nonlinear regression model. The recharge estimates by the water balance method was compared with the recharge obtained from the water level fluctuation method for the study area and found to be in good agreement. Estimation of recharge by the water level fluctuation method is laborious, and envisaging the difficulties in the availability and reliability of data, the water balance method is taken as the standard for developing regression equations in the present study. Simpler linear and nonlinear regression models were developed for the study area to estimate natural recharge by correlating with the water balance model. The models were calibrated with 10-yr data and validated with 5-yr data. The statistical analysis showed that no significant difference exists between the recharge estimate by the water balance method and the two estimates of natural recharges, such as linear regression and nonlinear regression models. The average recharge percentages from the water level fluctuation method, water balance method, linear regression model, and nonlinear regression model are 15.09%, 14.92%, 14.62%, and 14.57%, respectively, for the watershed during the study period. The study proves that regression equations can be efficiently used in recharge computation with proper calibration for ungauged basins, and laborious data-intensive computation methods can be eliminated.
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Environmental Geosciences
Environmental Geosciences Earth and Planetary Sciences-Earth and Planetary Sciences (all)
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