Jiajun Ren
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Jiajun Ren has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Junzhen Meng
College of Surveying and Geo-Informatics, North China University of Water Resources and Electric Power, Zhengzhou 450046, China
3 shared publications - Linnan Fan
College of Surveying and Geo-Informatics, North China University of Water Resources and Electric Power, Zhengzhou 450046, China
3 shared publications - Liya Xu
College of Surveying and Geo-Informatics, North China University of Water Resources and Electric Power, Zhengzhou 450046, China
3 shared publications - Yunfei Wang
College of Surveying and Geo-Informatics, North China University of Water Resources and Electric Power, Zhengzhou 450046, China
3 shared publications
Latest Publications
- Open Access
Hydrodynamic Mechanisms of Regulated Lake–Aquifer Exchange and Near-Shore Groundwater Salinization in an Arid Wetland
At Yuehai Lake, China, managed Yellow River diversion maintains open water while imposing a persistent hydraulic boundary on the shallow aquifer. We combined water balance, zonal Darcy estimation, a calibrated regulated-stage groundwater model, particle tracking, major ions, and stable isotopes. Yellow River diversion supplied 93.7% of quantified external inflows. Zonal Darcy and model leakage were 2.39 and 2.47 × 106 m3 a−1. Across the monitored stage range (1105.96–1106.42 m), modeled leakage was 2.41–2.53 × 106 m3 a−1; alternative upper-aquifer structures produced 1.91–3.02 × 106 m3 a−1 without reversing exchange or eliminating the mound. Particles from 441 lakebed cells moved a median 24.5 m over 1065 d, indicating slow near-shore advection. Selected-screen isotopes were consistent with evaporatively enriched lake-water influence, whereas major ions showed a distinct west–east shift from Ca–Mg–HCO3 toward Na–SO4 and Na–Cl facies. Yuehai Lake therefore functions as a regulated losing lake. Its salinity pattern likely reflects lake leakage, agricultural return flow, soil-salt mobilization, background saline groundwater, and restricted drainage. Management should integrate lake-stage, groundwater-flow, and salinity monitoring.
Open AccessHydrodynamic Mechanisms of Regulated Lake–Aquifer Exchange and Near-Shore Groundwater Salinization in an Arid Wetland
At Yuehai Lake, China, managed Yellow River diversion maintains open water while imposing a persistent hydraulic boundary on the shallow aquifer. We combined water balance, zonal Darcy estimation, a calibrated regulated-stage groundwater model, particle tracking, major ions, and stable isotopes. Yellow River diversion supplied 93.7% of quantified external inflows. Zonal Darcy and model leakage were 2.39 and 2.47 × 106 m3 a−1. Across the monitored stage range (1105.96–1106.42 m), modeled leakage was 2.41–2.53 × 106 m3 a−1; alternative upper-aquifer structures produced 1.91–3.02 × 106 m3 a−1 without reversing exchange or eliminating the mound. Particles from 441 lakebed cells moved a median 24.5 m over 1065 d, indicating slow near-shore advection. Selected-screen isotopes were consistent with evaporatively enriched lake-water influence, whereas major ions showed a distinct west–east shift from Ca–Mg–HCO3 toward Na–SO4 and Na–Cl facies. Yuehai Lake therefore functions as a regulated losing lake. Its salinity pattern likely reflects lake leakage, agricultural return flow, soil-salt mobilization, background saline groundwater, and restricted drainage. Management should integrate lake-stage, groundwater-flow, and salinity monitoring.
- Open Access
- 13 July 2026
- Published by MDPI AG in Sustainability
- Vol. 18 (14) , 7124
Quantifying Ecological Water Demand and Spatial Correspondence Under Landscape Pattern Dynamics in Yuehai Lake
Hydrological processes in dryland urban lakes are jointly shaped by landscape pattern dynamics and water resource scarcity, yet the spatial correspondence between landscape fragmentation and lake ecological water demand remains poorly understood. This study took Yuehai Lake, a typical dryland urban lake in Northwest China, as a case study. Landscape pattern analysis was integrated with a water balance model to quantify ecological water demand and its spatial correspondence with landscape metrics. The model coupled the Penman–Monteith equation, a depth-modified evaporation model, and a Darcy’s Law-based zonal seepage calculation. Results showed that: (1) the landscape structure remained highly stable over 2014–2022, with the Aggregation Index ranging from 95.07% to 95.28% and the Largest Patch Index from 90.20% to 90.70%; (2) the annual ecological water demand for maintaining ecosystem integrity was estimated at 2036.97 × 104 m3, comprising inherent lake water volume of 1138.02 × 104 m3 (55.9%), evapotranspiration of 659.72 × 104 m3 (32.4%), and lakebed seepage of 239.23 × 104 m3 (11.7%); and (3) evapotranspiration was concentrated between May and August, accounting for 80.5% of annual losses, with water surface evaporation dominating the flux at 91.5%. These findings suggest a spatial correspondence between landscape metrics and ecological water demand components, providing quantitative support for differentiated water supplementation strategies in dryland urban lakes.
- 13 July 2026
- Published by MDPI AG in Sustainability
- Vol. 18 (14) , 7124
Open AccessQuantifying Ecological Water Demand and Spatial Correspondence Under Landscape Pattern Dynamics in Yuehai Lake
Hydrological processes in dryland urban lakes are jointly shaped by landscape pattern dynamics and water resource scarcity, yet the spatial correspondence between landscape fragmentation and lake ecological water demand remains poorly understood. This study took Yuehai Lake, a typical dryland urban lake in Northwest China, as a case study. Landscape pattern analysis was integrated with a water balance model to quantify ecological water demand and its spatial correspondence with landscape metrics. The model coupled the Penman–Monteith equation, a depth-modified evaporation model, and a Darcy’s Law-based zonal seepage calculation. Results showed that: (1) the landscape structure remained highly stable over 2014–2022, with the Aggregation Index ranging from 95.07% to 95.28% and the Largest Patch Index from 90.20% to 90.70%; (2) the annual ecological water demand for maintaining ecosystem integrity was estimated at 2036.97 × 104 m3, comprising inherent lake water volume of 1138.02 × 104 m3 (55.9%), evapotranspiration of 659.72 × 104 m3 (32.4%), and lakebed seepage of 239.23 × 104 m3 (11.7%); and (3) evapotranspiration was concentrated between May and August, accounting for 80.5% of annual losses, with water surface evaporation dominating the flux at 91.5%. These findings suggest a spatial correspondence between landscape metrics and ecological water demand components, providing quantitative support for differentiated water supplementation strategies in dryland urban lakes.
- Open Access
A Synergistic Remote Sensing Inversion Study of Water Depth in Inland Lakes Integrating Chlorophyll-a Concentration and Optical Indices
Accurate bathymetric information for inland lakes is essential for water resource management, ecological monitoring, and environmental research. However, the accuracy and robustness of remote sensing-based bathymetric retrieval are often constrained by the complex optical properties of inland waters and the limited representation of conventional inversion features. To address these challenges, this study systematically compared the performance of a multiband logarithmic ratio model and three machine learning models, including Random Forest (RF), XGBoost, and AdaBoost, for inland lake bathymetric retrieval. Furthermore, a synergistic retrieval framework integrating chlorophyll-a concentration (Chla) and a Water Optical Index (WOI) was proposed. The results show that: (1) The overall accuracy of the Random Forest, XGBoost, and AdaBoost models constructed with the integration of chlorophyll-a concentration and WOI (, , and ; MAE m, m, and m; RMSE m, m, and m) outperforms that of models using only multispectral band information (, , and ; MAE m, m, and m; RMSE m, m, and m). Moreover, all these machine learning models significantly outperform the traditional numerical model (; MAE m; RMSE m), with the Random Forest model achieving the best overall performance. This indicates that the proposed method offers higher applicability and retrieval accuracy in complex inland lake environments. (2) The optimal Random Forest model integrating chlorophyll-a concentration and WOI achieved high-precision bathymetric inversion for inland lakes (, MAE m, RMSE m). Based on the three-dimensional bathymetry derived from this model, the estimated lake storage capacity was m3, compared with a measured volume of m3, yielding a relative error of . This result provides reliable and highly accurate data to support water resource management.Open AccessA Synergistic Remote Sensing Inversion Study of Water Depth in Inland Lakes Integrating Chlorophyll-a Concentration and Optical Indices
Accurate bathymetric information for inland lakes is essential for water resource management, ecological monitoring, and environmental research. However, the accuracy and robustness of remote sensing-based bathymetric retrieval are often constrained by the complex optical properties of inland waters and the limited representation of conventional inversion features. To address these challenges, this study systematically compared the performance of a multiband logarithmic ratio model and three machine learning models, including Random Forest (RF), XGBoost, and AdaBoost, for inland lake bathymetric retrieval. Furthermore, a synergistic retrieval framework integrating chlorophyll-a concentration (Chla) and a Water Optical Index (WOI) was proposed. The results show that: (1) The overall accuracy of the Random Forest, XGBoost, and AdaBoost models constructed with the integration of chlorophyll-a concentration and WOI (, , and ; MAE m, m, and m; RMSE m, m, and m) outperforms that of models using only multispectral band information (, , and ; MAE m, m, and m; RMSE m, m, and m). Moreover, all these machine learning models significantly outperform the traditional numerical model (; MAE m; RMSE m), with the Random Forest model achieving the best overall performance. This indicates that the proposed method offers higher applicability and retrieval accuracy in complex inland lake environments. (2) The optimal Random Forest model integrating chlorophyll-a concentration and WOI achieved high-precision bathymetric inversion for inland lakes (, MAE m, RMSE m). Based on the three-dimensional bathymetry derived from this model, the estimated lake storage capacity was m3, compared with a measured volume of m3, yielding a relative error of . This result provides reliable and highly accurate data to support water resource management.