Probabilistic Modeling of the Rainfall Severity and Height for Locating the Surface Artificial Recharge Structure

Junjie Wang (), Xiaocui Wang, Yuru Wang and Donghui Yang
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Junjie Wang: Shaanxi Provincial Land Engineering Construction Group Co., Ltd.
Xiaocui Wang: Shaanxi Provincial Land Engineering Construction Group Co., Ltd.
Yuru Wang: Shaanxi Provincial Land Engineering Construction Group Co., Ltd.
Donghui Yang: Shaanxi Provincial Land Engineering Construction Group Co., Ltd.

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2023, vol. 37, issue 2, No 19, 955-974

Abstract: Abstract Overuse of groundwater resources, drought, and population growth have drastically reduced the volume of water stored in aquifers. The main objective of the developed framework in the present study was the bivariate hydrological frequency analysis therefore was to establish a relationship between rainfall and groundwater fluctuations. Annual maximum sampling method and peaks-over-threshold sampling method were two kinds of data series that was used for bivariate hydrological frequency analysis. This multivariate system consisting of probabilistic modeling of rainfall characteristics has been developed with the aim of determining the severity and height of rainfall during the T-year return period and its results will be used to apply stress on the aquifer for surface artificial recharge. The results showed that the rainfall with a return period of 50-year increased the groundwater level by an average of about 170 cm at the recharge site. Furthermore, the effective radius values have been obtained more than 5 km in the flow path. Moreover, 6 effective parameters including permeability, hydraulic conductivity, saturated layer thickness, rainfall, slope and salinity were considered for the evaluation of artificial recharge locations using analytic hierarchy process (AHP). The results showed that about 9% of the study area is very suitable for constructing the structure.

Keywords: Artificial recharge; Groundwater resources; Hydraulic conductivity; Probabilistic uncertainty (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s11269-022-03415-7

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