Effect of Ecological Water Supplement on Groundwater Restoration in the Yongding River Based on Multi-Model Linkage
Abstract
:1. Introduction
2. Materials and Methods
2.1. Geological and Hydrogeological Conditions in the Study Area
2.2. Workflow and Data Collection
2.3. Models
2.3.1. Fuzzy Mathematics Model
2.3.2. Numerical Simulation Model
2.3.3. Machine Learning Model
2.3.4. Model Performance Evaluation Metrics
3. Results
3.1. Evaluation of the Infiltration Volume of the Yongding River Channel
3.2. Numerical Model Performance
3.3. Influence of the Ecological Water Supplement of the Yongding River on the Groundwater Flow Systems
3.3.1. Actual Groundwater Level Variation in the Study Area from 2017 to 2019
3.3.2. Groundwater Budget Variation before and after Ecological Water Supplement
3.3.3. The Effect of the Ecological Water Supplement of the Yongding River on groundwater Level Variation
3.3.4. The Impact Range of the Yongding River Ecological Water Supplement
4. Discussion
4.1. Contribution of the Ecological Water Supplement in the Yongding River Channel to Groundwater Restoration
4.2. Suggestions of Ecological Water Supplement and Groundwater Exploitation
- The ecological water supplement should increase by 40 × 106 m3/year in the downstream area. The potential artificial ecological water supply sources mainly include the water from the mid-route of the South-to-North Water Diversion Project, the water from the Guanting Reservoir, and the reclaimed water collected from the cities along the Yongding River. Then, the total ecological water supplement in the study area is 170 × 106 m3/year.
- The groundwater exploitation should be reduced by 35.77 × 106 m3/year in the downstream area, including the eastern part of Yongqing County, the southern part of Langfang City, the southern part of Wuqing District, and the western part of Beichen District.
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Escalante, E.F.; Dillon, P.; Stefan, C.; Sapiano, M.; Bonilla, J.; International Association of Hydrogeologists Commission on Managing Aquifer Recharge. Report of Activities Madrid, May, 2019. 2020. Available online: https://recharge.iah.org/ (accessed on 18 December 2022).
- Gale, I.; Dillon, P. Strategies for Managed Aquifer Recharge (MAR) in Semi-Arid Areas; UNESCO Paris: Paris, France, 2005. [Google Scholar]
- Yan, Z.; Zhou, Z.; Sang, X.; Wang, H. Water replenishment for ecological flow with an improved water resources allocation model. Sci. Total Environ. 2018, 643, 1152–1165. [Google Scholar] [CrossRef] [PubMed]
- Zheng, Y.; Vanderzalm, J.; Hartog, N.; Escalante, E.F.; Stefan, C. The 21st century water quality challenges for managed aquifer recharge: Towards a risk-based regulatory approach. Hydrogeol. J. 2022. [Google Scholar] [CrossRef] [PubMed]
- Hao, Q.; Shao, J.; Cui, Y.; Xie, Z. Applicability of artificial recharge of groundwater in the Yongding River alluvial fan in Beijing through numerical simulation. J. Earth Sci. 2014, 25, 575–586. [Google Scholar] [CrossRef]
- Gan, Y.; Wang, Y.; Duan, Y.; Deng, Y.; Guo, X.; Ding, X. Hydrogeochemistry and arsenic contamination of groundwater in the Jianghan Plain, central China. J. Geochem. Explor. 2014, 138, 81–93. [Google Scholar] [CrossRef]
- Hu, L.; Xu, Z.; Huang, W. Development of a river-groundwater interaction model and its application to a catchment in Northwestern China. J. Hydrol. 2016, 543, 483–500. [Google Scholar] [CrossRef]
- Longcang, S.; Rong, G.; Jiawen, L.; Jianshi, G.; Xiaohu, T.; Guizhang, Z.; Yuezan, T. A integrated method to quantify flow exchanges between surface water and groundwater: Take Suzhou section of the Xinbian River as an example. Adv. Water Sci. 2022, 33, 57–67. [Google Scholar]
- Du, Y.; Ma, T.; Deng, Y.; Shen, S.; Lu, Z. Sources and fate of high levels of ammonium in surface water and shallow groundwater of the Jianghan Plain, Central China. Environ. Sci. Process. Impacts 2017, 19, 161–172. [Google Scholar] [CrossRef]
- He, J.; Yao, J.; Li, A.; Tan, Z.; Xie, G.; Shi, H.; Zhang, X.; Sun, W.; Du, P. Potential impact of water transfer policy implementation on lake eutrophication on the Shandong Peninsula: A difference-in-differences approach. Hydrol. Res. 2020, 51, 1063–1076. [Google Scholar] [CrossRef]
- Liang, Y.; Ma, R.; Wang, Y.; Wang, S.; Qu, L.; Wei, W.; Gan, Y. Hydrogeological controls on ammonium enrichment in shallow groundwater in the central Yangtze River Basin. Sci. Total Environ. 2020, 741, 140350. [Google Scholar] [CrossRef]
- Koch, J.C.; Runkel, R.L.; Striegl, R.; McKnight, D.M. Hydrologic controls on the transport and cycling of carbon and nitrogen in a boreal catchment underlain by continuous permafrost. J. Geophys. Res. Biogeosci. 2013, 118, 698–712. [Google Scholar] [CrossRef]
- Stoliker, D.L.; Repert, D.A.; Smith, R.L.; Song, B.; LeBlanc, D.R.; McCobb, T.D.; Conaway, C.H.; Hyun, S.P.; Koh, D.-C.; Moon, H.S. Hydrologic controls on nitrogen cycling processes and functional gene abundance in sediments of a groundwater flow-through lake. Environ. Sci. Technol. 2016, 50, 3649–3657. [Google Scholar] [CrossRef] [PubMed]
- Barthel, R.; Banzhaf, S. Groundwater and surface water interaction at the regional-scale–a review with focus on regional integrated models. Water Resour. Manag. 2016, 30, 1–32. [Google Scholar] [CrossRef] [Green Version]
- Kollet, S.J.; Maxwell, R.M. Integrated surface–groundwater flow modeling: A free-surface overland flow boundary condition in a parallel groundwater flow model. Adv. Water Resour. 2006, 29, 945–958. [Google Scholar] [CrossRef] [Green Version]
- Kolditz, O.; Bauer, S.; Bilke, L.; Böttcher, N.; Delfs, J.-O.; Fischer, T.; Görke, U.J.; Kalbacher, T.; Kosakowski, G.; McDermott, C. OpenGeoSys: An open-source initiative for numerical simulation of thermo-hydro-mechanical/chemical (THM/C) processes in porous media. Environ. Earth Sci. 2012, 67, 589–599. [Google Scholar] [CrossRef]
- Brunner, P.; Simmons, C.T. HydroGeoSphere: A Fully Integrated, Physically Based Hydrological Model. Groundwater 2012, 50, 170–176. [Google Scholar] [CrossRef] [Green Version]
- Xia, J.; Wang, Q.; Zhang, X.; Wang, R.; She, D. Assessing the influence of climate change and inter-basin water diversion on Haihe River basin, eastern China: A coupled model approach. Hydrogeol. J. 2018, 26, 1455–1473. [Google Scholar] [CrossRef]
- Wu, B.; Zheng, Y.; Tian, Y.; Wu, X.; Yao, Y.; Han, F.; Liu, J.; Zheng, C. Systematic assessment of the uncertainty in integrated surface water-groundwater modeling based on the probabilistic collocation method. Water Resour. Res. 2015, 50, 5848–5865. [Google Scholar] [CrossRef]
- Feng, D.; Yi, Z.; Mao, Y.; Zhang, A.; Xin, W. An integrated hydrological modeling approach for detection and attribution of climatic and human impacts on coastal water resources. J. Hydrol. 2017, 557, 305–320. [Google Scholar] [CrossRef]
- Xue, L.; Ye, S.Y.; Wei, A.H.; Zhou, P.P.; Wang, L.H. Modelling the response of shallow groundwater level to combined climate and water-diversion scenarios in Beijing-Tianjin-Hebei Plain, China. Hydrogeol. J. 2017, 25, 1733–1744. [Google Scholar]
- Kitanidis, P.K. Persistent questions of heterogeneity, uncertainty, and scale in subsurface flow and transport. Water Resour. Res. 2015, 51, 5888–5904. [Google Scholar] [CrossRef]
- Lu, D.; Zhang, G.; Webster, C.; Barbier, C. An improved multilevel Monte Carlo method for estimating probability distribution functions in stochastic oil reservoir simulations. Water Resour. Res. 2016, 52, 9642–9660. [Google Scholar] [CrossRef]
- Liao, Q.; Zhang, D.; Tchelepi, H. A two-stage adaptive stochastic collocation method on nested sparse grids for multiphase flow in randomly heterogeneous porous media. J. Comput. Phys. 2017, 330, 828–845. [Google Scholar] [CrossRef]
- Razavi, S.; Tolson, B.A.; Burn, D.H. Review of surrogate modeling in water resources. Water Resour. Res. 2012, 48, W07401. [Google Scholar] [CrossRef]
- Asher, M.J.; Croke, B.F.; Jakeman, A.J.; Peeters, L.J. A review of surrogate models and their application to groundwater modeling. Water Resour. Res. 2015, 51, 5957–5973. [Google Scholar] [CrossRef] [Green Version]
- Zhang, J.; Lin, G.; Li, W.; Wu, L.; Zeng, L. An iterative local updating ensemble smoother for estimation and uncertainty assessment of hydrologic model parameters with multimodal distributions. Water Resour. Res. 2018, 54, 1716–1733. [Google Scholar] [CrossRef]
- Wang, X.; Liu, T.; Zheng, X.; Peng, H.; Xin, J.; Zhang, B. Short-term prediction of groundwater level using improved random forest regression with a combination of random features. Appl. Water Sci. 2018, 8, 125. [Google Scholar] [CrossRef] [Green Version]
- Chakraborty, M.; Sarkar, S.; Mukherjee, A.; Shamsudduha, M.; Ahmed, K.M.; Bhattacharya, A.; Mitra, A. Modeling regional-scale groundwater arsenic hazard in the transboundary Ganges River Delta, India and Bangladesh: Infusing physically-based model with machine learning. Sci. Total Environ. 2020, 748, 141107. [Google Scholar] [CrossRef]
- Fu, Y.; Cao, W.; Pan, D.; Ren, Y. Changes of groundwater arsenic risk in different seasons in Hetao Basin based on machine learning model. Sci. Total Environ. 2022, 817, 153058. [Google Scholar] [CrossRef]
- Peng, J.; Li, S.; Qi, L. Study on river regulation measures of dried-up rivers of Haihe River basin, China. Water Sci. Technol. 2013, 67, 1224–1229. [Google Scholar] [CrossRef]
- Jiang, B.; Wong, C.P.; Lu, F.; Ouyang, Z.; Wang, Y. Drivers of drying on the Yongding River in Beijing. J. Hydrol. 2014, 519, 69–79. [Google Scholar] [CrossRef]
- Hu, H.; Mao, X.; Yang, Q. Impacts of Yongding River ecological restoration on the groundwater environment: Scenario prediction. Vadose Zone J. 2018, 17, 1–15. [Google Scholar] [CrossRef]
- Luo, Z.; Zhao, S.; Wu, J.; Zhang, Y.; Liu, P.; Jia, R. The influence of ecological restoration projects on groundwater in Yongding River Basin in Beijing, China. Water Supply 2019, 19, 2391–2399. [Google Scholar] [CrossRef]
- Sun, K.; Hu, L.; Guo, J.; Yang, Z.; Zhai, Y.; Zhang, S. Enhancing the understanding of hydrological responses induced by ecological water replenishment using improved machine learning models: A case study in Yongding River. Sci. Total Environ. 2021, 768, 145489. [Google Scholar] [CrossRef] [PubMed]
- Han, Y.; Zhai, Y.; Guo, M.; Cao, X.; Lu, H.; Li, J.; Wang, S.; Yue, W. Hydrochemical and Isotopic Characterization of the Impact of Water Diversion on Water in Drainage Channels, Groundwater, and Lake Ulansuhai in China. Water 2021, 13, 3033. [Google Scholar] [CrossRef]
- Liu, C.; Pan, C.; Chang, Y.; Luo, M. An integrated autoregressive model for predicting water quality dynamics and its application in Yongding River. Ecol. Indic. 2021, 133, 108354. [Google Scholar] [CrossRef]
- Xie, Y.; Jinjin, G.; Guo, Y.; Peng, W.; Wang, L. Temporal and Spatial Variation of water quality in the Yongding River Basin. Proc. IOP Conf. Ser. Earth Environ. Sci. 2021, 831, 012052. [Google Scholar] [CrossRef]
- Hou, L.; Peng, W.; Qu, X.; Chen, Q.; Fu, Y.; Dong, F.; Zhang, H. Runoff changes based on dual factors in the upstream area of Yongding river basin. Pol. J. Environ. Stud. 2019, 28, 143–152. [Google Scholar] [CrossRef] [PubMed]
- Litang, H.; Jianli, G.; Shouquan, Z.; Kangning, S.; Zhengqiu, Y. Response of groundwater regime to ecological water replenishment of the Yongding River. Hydrogeol. Eng. Geol. 2020, 47, 5–11. [Google Scholar]
- Mingzhu, L.; Seyf-Laye, A.-S.M.; Ibrahim, T.; Gbandi, D.-B.; Honghan, C. Tracking sources of groundwater nitrate contamination using nitrogen and oxygen stable isotopes at Beijing area, China. Environ. Earth Sci. 2014, 72, 707–715. [Google Scholar] [CrossRef]
- Yu, Y.; Ma, M.; Zheng, F.; Liu, L.; Zhao, N.; Li, X.; Yang, Y.; Guo, J. Spatio-temporal variation and controlling factors of water quality in Yongding River replenished by reclaimed water in Beijing, North China. Water 2017, 9, 453. [Google Scholar] [CrossRef] [Green Version]
- Wang, L.; Wang, Z.; Koike, T.; Yin, H.; Yang, D.; He, S. The assessment of surface water resources for the semi-arid Yongding River Basin from 1956 to 2000 and the impact of land use change. Hydrol. Process. Int. J. 2010, 24, 1123–1132. [Google Scholar] [CrossRef]
- Qiang, Y.; Yu, Z.; Cuiping, Y.; Dong, H. Review and suggestions on river ecological management in urban section of Yongding River in Beijing. China Flood Drought Manag. 2018, 28, 28–32. [Google Scholar]
- Guo, H.; Chen, X.; Liu, G. Study on the practices of water rights trading in South-to-North Water Diversion Middle Route Project. South North Water Transf. Water Sci. Technol. 2018, 16, 175–182. [Google Scholar]
- Huan, H.; Wang, J.-S.; Zhai, Y.-Z.; Zheng, J.-Q. Chemical Characteristics and Evolution of Groundwater in the Yongding River Alluvial Fan of Beijing Plain. Acta Geosci. Sin. 2011, 32, 357–366. [Google Scholar]
- Li, D.; Xiao, W.; Zhao, M.; Zhang, J.; Liu, S.; Liu, J. Flood dispatching numerical simulation for detention basins of yongding river. J. Tianjin Univ. 2015, 48, 76–86. [Google Scholar]
- Hu, H.; Mao, X.; Yang, Q. Development of a groundwater flow and reactive solute transport model in the Yongding River alluvial fan, China. Front. Earth Sci. 2019, 13, 371–384. [Google Scholar] [CrossRef]
- Qi-chen, H.; Jing-li, S.; Yu, L.; Zhenhua, X.; Yali, C. Optimization of artificial recharge of groundwater system based on parallel genetic algorithm—A case study in the alluvial fan of Yongding River in Beijing. South North Water Transf. Water Sci. Technol. 2015, 13, 67–71. [Google Scholar]
- Xiaofang, Y.; Mingyu, W.; Liya, W.; Jianhui, Z. Investigation of key controlling factors and numerical simulation uncertainty of the groundwater level companying with Yongding river ecological restoration. J. Univ. Chin. Acad. Sci. 2015, 32, 192–199. [Google Scholar]
- Zhang, K.; Gann, D.; Ross, M.; Robertson, Q.; Sarmiento, J.; Santana, S.; Rhome, J.; Fritz, C. Accuracy assessment of aster, srtm, alos, and tdx dems for hispaniola and implications for mapping vulnerability to coastal flooding. Remote Sens. Environ. 2019, 225, 290–306. [Google Scholar] [CrossRef]
- Beijing Water Authority. Beijing Water Resources Bulletin; Beijing Water Authority: Beijing, China, 2018. (In Chinese) [Google Scholar]
- Beijing Water Authority. Beijing Water Resources Bulletin; Beijing Water Authority: Beijing, China, 2019. (In Chinese) [Google Scholar]
- Tianjin Water Authority. Tianjin Water Resources Bulletin; Beijing Water Authority: Beijing, China, 2018. (In Chinese) [Google Scholar]
- Tianjin Water Authority. Tianjin Water Resources Bulletin; Beijing Water Authority: Beijing, China, 2019. (In Chinese) [Google Scholar]
- Hebei Water Authority. Hebei Water Resources Bulletin; Beijing Water Authority: Beijing, China, 2018. (In Chinese) [Google Scholar]
- Hebei Water Authority. Hebei Water Resources Bulletin; Beijing Water Authority: Beijing, China, 2019. (In Chinese) [Google Scholar]
- Haihe River Water Conservancy Commission. Haihe River Basin Water Resources Bulletin; Haihe River Water Conservancy Commission: Tianjin, China, 2018. (In Chinese) [Google Scholar]
- Haihe River Water Conservancy Commission. Haihe River Basin Water Resources Bulletin; Haihe River Water Conservancy Commission: Tianjin, China, 2019. (In Chinese) [Google Scholar]
- Institute of Hydrogeology and Environmental Geology, Chinese Academy of Geological Sciences. 1:50000 Environmental Geological Survey in Non-Capital Functional Relief Area; Institute of Hydrogeology and Environmental Geology, Chinese Academy of Geological Sciences: Shijiazhuang, China, 2019. (In Chinese) [Google Scholar]
- Institute of Hydrogeology and Environmental Geology, Chinese Academy of Geological Sciences. Investigation Report on Water Resources in Haihe River Basin; Institute of Hydrogeology and Environmental Geology, Chinese Academy of Geological Sciences: Shijiazhuang, China, 2022. (In Chinese) [Google Scholar]
- Zadeh, L.A. Fuzzy sets. Inf. Control 1965, 8, 338–353. [Google Scholar] [CrossRef] [Green Version]
- Coronel, E.G.; Alesso, C.A.; Bollero, G.A.; Armstrong, K.L.; Martin, N.F. Field-specific yield response to variable seeding depth of corn in theMidwestField-water supply response to variable seeding depth of corn in the Midwest. Agrosystems Geosci. Environ. 2020, 3, e20034. [Google Scholar] [CrossRef]
- Mcdonald, M.G. A Modular Three-Dimensional Finite-Difference Ground-water Flow Model. Tech. Water Resour. Investig. 1988, 83-875, 83–875. [Google Scholar] [CrossRef] [Green Version]
- Gong, C.; Zhang, Z.; Wang, W.; Duan, L.; Wang, Z. An assessment of different methods to determine water supply for estimating groundwater recharge using lysimeters. Sci. Total Environ. 2021, 788, 147799. [Google Scholar] [CrossRef] [PubMed]
- Jingli, S.; Ling, L.; Yali, C.; Zhaoji, Z. Groundwater flow simulation and its application in groundwater resource evaluation in the North China Plain, China. Acta Geol. Sin. Engl. Ed. 2013, 87, 243–253. [Google Scholar] [CrossRef]
- Verikas, A.; Gelzinis, A.; Bacauskiene, M. Mining data with random forests: A survey and results of new tests. Pattern Recognit. J. Pattern Recognit. Soc. 2011, 44, 330–349. [Google Scholar] [CrossRef]
- Xi, C.; Ishwaran, H. Random forests for genomic data analysis. Genomics 2012, 99, 323–329. [Google Scholar]
- Molnar, C. Interpretable Machine Learning; Lulu Press: Morrisville, NC, USA, 2020. [Google Scholar]
- Zijian, J.; Yali, C.; Shouquan, Z.; Wan, C.; Jingli, S. Evaluation of the Impact of EcologicalWater Supplement on Groundwater Restoration Based on Numerical Simulation: A Case Study in the Section of Yongding River, Beijing Plain. Water 2021, 13, 3059. [Google Scholar] [CrossRef]
- Karian, W.; Huawei, C.; Shidong, F.; Fulin, L.; Zhen, W.; Dandan, X. Analysis of exploitation control in typical groundwater over-exploited area in North China Plain. Hydrol. Sci. J. 2021, 66, 851–861. [Google Scholar]
- Yixuan, W.; Yanjun, S.; Ya, G.; Hang, L. Research progress on the changes of environmental and water resources in the upper Yongding River Basin. South North Water Transf. Water Sci. Technol. 2021, 4, 656–668. [Google Scholar]
- Kai, L.; Xueke, L.; Xin, L. Trends in groundwater changes driven by precipitation and anthropogenic activities on the southeast side of the Hu Line. Environ. Res. Lett. 2021, 16, 094032. [Google Scholar] [CrossRef]
- Alemu, Y.; Fenta, N.; Ashebir, B.; Minyahl, D.; Marc, C.; Kristine, W. Groundwater recharge and water table response to changing conditions for aquifers at different physiography: The case of a semi-humid river catchment, northwestern highlands of Ethiopia. Sci. Total Environ. 2020, 748, 142243. [Google Scholar] [CrossRef]
- Maréchal, J.C.; Dewandel, B.; Ahmed, S.; Galeazzi, L.; Zaidi, F.K. Combined estimation of specific yield and natural recharge in a semi-arid groundwater basin with irrigated agriculture. J. Hydrol. 2006, 329, 281–293. [Google Scholar] [CrossRef]
Section | Seepage Rate | Ecological Water Supplement (×106 m3) | Infiltration Volume (×106 m3) |
---|---|---|---|
S1 | 0.72 | 4.90 | 3.54 |
S2 | 0.72 | 44.07 | 31.82 |
S3 | 0.76 | 3.66 | 2.78 |
S4 | 0.62 | 40.52 | 25.02 |
S5 | 0.43 | 10.52 | 4.50 |
S6 | 0.38 | 22.83 | 8.68 |
S7 | 0.48 | 12.05 | 5.72 |
S8 | 0.48 | 0 | 0 |
S9 | 0.22 | 0 | 0 |
S10 | 0.19 | 0 | 0 |
Total | 0.59 | 138.55 | 82.06 |
Before Ecological Water Recharge | After Ecological Water Recharge | ||||
---|---|---|---|---|---|
Budget Tems | Volume (×106 m3/year) | Percentage | Volume (×106 m3/year) | Percentage | |
Recharge | Precipitation infiltration | 578 | 68.65% | 507 | 58.55% |
Mountain front recharge | 141 | 16.75% | 98 | 11.32% | |
Lateral inflow | 14 | 1.66% | 100 | 11.55% | |
Ecological water supplement infiltration | 0 | 0.00% | 82 | 9.47% | |
Regression quantity of well irrigation | 109 | 12.95% | 79 | 9.12% | |
Total recharge | 842 | 100% | 866 | 100.00% | |
Discharge | Evaporation | 18 | 1.80% | 3 | 0.46% |
Lateral outflow | 263 | 26.10% | 40 | 6.19% | |
Exploitation | 725 | 72.10% | 603 | 93.34% | |
Total discharge | 1006 | 100.00% | 646 | 100.00% | |
Recharge and discharge difference | −163 | 220 |
Feature Variables | |||
---|---|---|---|
Type | Indicator | Source | Form |
Meteorological factors | Precipitation | Statistical data | Accumulate |
Evaporation | Statistical data | Accumulate | |
Topographical factors | Landform | Remote sensing data | Distributed |
Lateral inflow | Numetical model | Distributed | |
Human factors | Ecological water supplement | Statistics caculating | Accumulate |
Artificial mining | Statistical data | Accumulate | |
Hydraulic feature | Hydraulic conductivity | Numerical model calibration | Distributed |
Specific yield | Numerical model calibration | Distributed | |
Labeled data | |||
Groundwater level rises or not | Yes (1) | ||
No (0) |
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Nan, T.; Cao, W. Effect of Ecological Water Supplement on Groundwater Restoration in the Yongding River Based on Multi-Model Linkage. Water 2023, 15, 374. https://doi.org/10.3390/w15020374
Nan T, Cao W. Effect of Ecological Water Supplement on Groundwater Restoration in the Yongding River Based on Multi-Model Linkage. Water. 2023; 15(2):374. https://doi.org/10.3390/w15020374
Chicago/Turabian StyleNan, Tian, and Wengeng Cao. 2023. "Effect of Ecological Water Supplement on Groundwater Restoration in the Yongding River Based on Multi-Model Linkage" Water 15, no. 2: 374. https://doi.org/10.3390/w15020374