An analytical-numerical method for optimizing the extraction layout of riverside well field: a case study of the Luan River, China
Year: 2026
Type: article
Abstract: The sustainable exploitation of a riverside well field is critical for securing regional water supply. Optimizing its layout is a complex, multi-objective engineering challenge aimed at balancing yield, stability, and quality. This study adopted an analytical-numerical workflow to optimize the layout of the riverside well field of Luan River in Chengde City. The analysis first determined an optimal basic scenario with a river-well distance of 25 m and an inter-well spacing of 150 m, yielding a maximum allowable extraction capacity of 5.31 × 10 4 m 3 /d. Subsequently, three detailed engineering scenarios incorporating diverse intake structures and interventions were designed and compared. The results demonstrate that Scenario 3 represents the optimal layout, which includes 14 extraction wells, 2 infiltration galleries, 1 radial collector well, and riverbed modification. This scenario not only meets the target extraction rate of 8 × 10 4 m 3 /d, but also performs well under the particularly dry and extremely dry year/month conditions defined from the historical runoff record of the Luan River. Furthermore, backward particle-tracking analysis indicates that Scenario 3 reduces the risk of direct groundwater capture from the nearby land-based potential pollution-source areas. Consequently, Scenario 3 represents the optimal choice based on yield, reliability, and risk. In conclusion, this study demonstrates the effectiveness of the analytical-numerical workflow for optimizing riverside well field layouts. The findings provide decision support for the Luan River project and offer a practical reference for similar systems under comparable hydrogeological conditions.
Source: Environmental Earth Sciences
Authors: Xiaoyuan Li, Wenzhong Wang, Shenghua Song
Language: English
Subfield: Environmental Engineering
Field: Environmental Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $2,290
Awards: G103494221, G5755634662
OpenAlex ID: W7171566563
