伟德国际1946源于英国 管理入口
首页  团队队伍*

姓名：钮杰

职称：特聘教授

研究领域：

        机器学习/深度学习在环境及水文学的应用

        水文数值模拟

        流域水文及溶质运移模拟

Email：niujay@gmail.com; jay_niu@hotmail.com

招生专业：地质资源与地质工程（水文地质方向）、资源与环境（地质工程方向）

教育背景

美国密西根州立大学       环境工程博士                                                                 2007 - 2013

南开大学                           环境科学硕士                                                                 2004 - 2007

南开大学                           环境科学本科                                                                 2000 - 2004

工作经历

特聘教授             bevitor伟德水文与地下水工程系               2022 - 

副教授                 暨南老员工命科学与技术学院                                                   2021 - 2022

副教授                 暨南大学地下水与地球科学研究院                                           2017 - 2021

博士后                 美国劳伦斯伯克利国家实验室                                                   2014 - 2017

博士后                 美国加州大学圣芭芭拉分校地球研究所（工作地点同上）   2014 - 2016

助研                     美国密西根州立大学                                                                   2007 - 2014

学术服务

  •   SCI期刊 Water 编委，SCI期刊 Frontiers in Water 编委

  •   多个 SCI 一、二区期刊审稿

  •   国家自然科学基金面上、青年项目评审专家

  •   广东省自然科学基金面上项目评审专家

  •   广东省科技厅专家库评审专家

  •   广东省海岛海域资源专家库评审专家

员工奖励

  •   第二届广东省智慧水利创新大赛——广汇源杯三等奖（2022）4人

  •   研究生国家奖学金（2020）1人

  •   暨南大学研究生学术创新论坛（2019）学术创新成果奖 1人

  •   第一届全国老员工水环境模型大赛（2018）三等奖 1人

个人奖励

  •   第二届广东省智慧水利创新大赛——广汇源杯优秀指导教师（2022）

  •   暨南大学“双百英才计划”暨南杰青第一层次（2021）

  •   中科院第一届“先导杯”并行计算大赛（2020）优胜奖

科研项目

  •   2020 - 2023    国家自然科学基金面上项目，水文过程模型与深度学习模型，主持，65万元

  •   2022 - 2023    暨南大学团队建设优秀青年骨干项目，物理信息深度学习模型，主持，5万元

  •   2018 - 2021    广东省自然科学基金自由申请项目，水文过程模型，主持，10万元

  •   2018 - 2021    暨南大学科研培育与创新基金项目，水文过程模型，主持，15万元

媒体报道

  • 2020年6月    WIRES：分数阶对流扩散模型模拟溶质运移非菲克现象的优势和挑战。ASN China Advanced Science News.

  • 2020年6月    Predicting solute transport in surface and subsurface water. Advanced Science News.

  • 2018年7月    ‘Nowcasting’ beach water quality. ACS News Service Weekly PressPac.

  • 2014年8月    Quantifying Changes in Great Lakes Watersheds. 美国劳伦兹伯克利国家实验室网站.

发表文章

37.  Jie Niu(#), Yanqun Lu(#), Mengyu Xie*, Linjian Ou, Lei Cui, Han Qiu, Songhui Lu*. Prediction of Aureococcus anophageffens using machine learning and deep learning. Marine Pollutin Bulletin, 2024, 200: 116148. Doi:10.1016/j.marpolbul.2024.116148.

36.  Jie Niu(#), Wei Xu(#), Shan Li*, Feifei Dong, Han Qiu*. 1-D Coupled Surface Flow and Transport Equations revisited via the Physics-Informed Neural Network Approach. Journal of Hydrology, 2023, 130048. Doi:10.1016/j.hydrol.2023.130048. 一区

35.  Jie Niu(#), Shan Liu(#), Wei Xu*, Feifei Dong, Fen Huang, Han Qiu. An efficient LSTM network for predicting the tailing and multi-peaked breakthrough curves. Journal of Hydrology, 2023, 129914. Doi:10.1016/j.hydrol.2023.129914. 一区

34.  Jie Niu(#), Ziyang Feng(#), Mingxia He*, Mengyu Xie, Yanqun Lv, Juan Zhang, Liwei Sun, Qi Liu, Bill X. Hu. Incorporating marine particulate carbon into machine learning for accurate estimation of coastal chlorophyll-a. Marine Pollution Bulletin, 2023, 115089. Doi:10.1016/j.marpolbul.2023.115089.

33.  Han Qiu, Jie Niu(*), Dean G. Baas, Mantha S. Phanikumar(*). An integrated watershed-scale framework to model nitrogen transport and transformations. Science of The Total Environment, 2023, 163348. Doi:10.1016/j.scitotenv.2023.163348. 一区

32.  Rui-Ze Liang, Yang-Guang Gu(*), Hia-Song Li, Yan-Jie Han, Jie Niu, Hong Su, Richard W. Jordan, Xiang-Tian Man, Shi-Jun Jiang. Multi-index assessment of heavy metal contamination in surface sediments of the Pearl River estuary intertidal zone. Marine Pollution Bulletin, 2023, 186: 114445. Doi:10.1016/j.marpolbul.2022.114445.

31.  Yong Liu, Feifei Dong, Jie Niu. A Process-guided Hybrid Bayesian Belief Network to Bridge Watershed Modeling and BMP Planning. Journal of Hydrology, 2022, 614(8): 128620. Doi:10.1016/j.jhydrol.2022.128620.

30.  Feifei Dong, Jincheng Li, Chao Dai, Jie Niu, Yan Chen, Jiacong Huang, Yong Liu. Understanding robustness in multiscale nutrient abatement: Probabilistic simulation-optimization using Bayesian network emulators. Journal of Cleaner Production, 2022, 378(12): 134394. Doi:10.1016/j.jclepro.2022.134394.

29.  Juan Zhang, Ziyang Feng, Jie Niu(*), John M. Melack, Jin Zhang, Han Qiu, Bill X. Hu, and William J. Riley. Spatial and temporal variations of evapotranspiration, groundwater and precipitation in Amazonia. Journal of Geophysical Research – Atmospheres, 2022, 127(10). DOI: 10.1029/2021JD034959. Nature Index

28.  Qi liu(#), Jie Niu(#), Ping Lu(*), Feifei Dong, Fujun Zhou, Xianglian Meng, Wei Xu, Shan Li, and Bill X. Hu. Interannual and seasonal variations of permafrost thaw depth on the Qinghai-Tibetan plateau: A comparative study using long short-term memory, convolutional neural networks, and random forest. Science of The Total Environment, 2022, 838(9): 155886. Doi:10.1016/j.scitotenv.2022.155886. 一区

27.  Qi Liu, Dongwei Gui⁎, Lei Zhang, Jie Niu(*), Heng Dai, Guanghui Wei, Bill X. Hu. Simulation of regional groundwater levels in arid regions using interpretable machine learning models. Science of The Total Environment, 2022, 831(1): 154902. Doi:10.1016/j.scitotenv.2022.154902. 一区

26.  Qi Liu, Yi Liu, Jie Niu(*), Dongwei Gui, Xiaonong (Bill X.) Hu. Prediction of the Irrigation Area Carrying Capacity in the Tarim River Basin under Climate Change. Agriculture, 2022, 12(5): 657. Doi:10.3390/agriculture12050657.

25.  XU Wei, NIU Jie(*), GAN Wenyu, GOU Siyu, ZHANG Shuai, QIU Han, JIANG Tianjiu*. Identification of paralytic shellfish toxins producing microalgae using machine learning and deep learning methods. Journal of Oceanology and Limnology, 2022. Doi: 10.1007/s00343-022-1312-1.

24.  L. Sun, J. Niu(*), F. Huang, J. Feng, C. Wu, Q. Han, B.X. Hu. An Efficient Fractional-in-Time Transient Storage Model for Simulating the Multi-peaked Breakthrough Curves. Journal of Hydrology, 2021, 600, 126570. 一区

23.  Chuanhao Wu, Pat J-F Yeh, Jiali Ju, Yi-Ying Chen, Kai Xu, Heng Dai, Jie Niu, Bill X Hu, Guoru Huang. Assessing the spatio-temporal uncertainties in future meteorological droughts from CMIP5 models, emission scenarios and bias corrections. Journal of Climate, 2020, 1 (aop), 1-58.

22.  Haifan Liu, Heng Dai(*), Jie Niu(*), Bill X Hu, Dongwei Gui, Han Qiu, Ming Ye, Xingyuan Chen, Chuanhao Wu, Jin Zhang, William Riley. Hierarchical sensitivity analysis for a large-scale process-based hydrological model applied to an Amazonian watershed. Hydrology and Earth System Sciences, 2020, 24 (10), 4971-4996. 一区

21.  P. Li, P. Hua, D. Gui, J. Niu, P. Pei, J. Zhang, P. Krebs. A comparative analysis of artificial neural networks and wavelet hybrid approaches to long-term toxic heavy metal prediction. Scientific reports, 2020, 10(1), 1-15.

20.  L. Liu, L. Sun, J. Niu(*), W.J. Riley. Modeling Green Roof Potential to Mitigate Urban Flooding in a Chinese City. Water, 2020, 12(8), 2082.

19.  Sun, L., Qiu, H., Wu, C., Niu, J.(*), Hu, B.X.. A review of applications of fractional advection-dispersion equations for anomalous solute transport in surface and subsurface water. WIREs Water, 2020. 10.1002/wat2.1448.

18.  Sun, L., Qiu, H., Niu, J.(*), Hu, B.X., Kelly, J.F., Bolster, D., Phanikumar, M.S.(*). Comparison of negative skewed space fractional models with time nonlocal approaches for stream solute transport modeling. Journal of Hydrology, 2020, 582, 124504. 一区

17.  Sun, L., Niu, J.(*), Hu, B.X.(*), Wu, C., Dai, H.. An efficient approximation of non-Fickian transport using a time-fractional transient storage model. Advances in Water Resources, 2020, 135, 103486.

16.  Zhang, J., Zhang, X. Y., Niu, J.(*), Hu, B. X., Soltanian, M. R., Qiu, H., Yang, L.. Prediction of groundwater level in seashore reclaimed land using wavelet and artificial neural network-based hybrid model. Journal of Hydrology, 2019, 577, 123948. 一区

15.  Qiu Han(#), Jie Niu(*), B.X. Hu(*). Quantifying the integrated water and carbon cycle in a data-limited karst basin using a process-based hydrologic model. Environmental Earth Sciences, 2019, 78(11), 328.

14.  Qiu Han(#), Jie Niu(*), Mantha S. Phanikumar(*). Quantifying the space – Time variability of water balance components in an agricultural basin using a process-based hydrologic model and the Budyko framework. Science of The Total Environment. 2019, 676, 176-189. 一区

13.  Kai Xu(#); Guangxiong Qin; Jie Niu; Chuanhao Wu(*); Bill X Hu; Guoru Huang; Peng Wang. Comparative analysis of meteorological and hydrological drought over the Pearl River basin in southern China. Hydrology Research, 2018.5.24, 50(1): 301~318.

12.  Dai, Heng(#)(*); Ye, Ming; Niedoroda, Alan W.; Zhang, Xiaoying; Chen, Xingyuan; Song, Xuehang; Niu, Jie. Hierarchical sensitivity analysis for simulating barrier island geomorphologic responses to future storms and sea-level rise. Theoretical and Applied Climatology, 2018.11.9, NA(NA). SCIE, SSCI

11.  Zhang, Juan(#); Qiu, Han; Li, Xiaoyu; Niu, Jie(*); Neyers, Meredith B; Hu, Xiaonong; Phanikumar, Mantha S.(*). Real-Time Nowcasting of Microbiological Water Quality at Recreational Beaches: A Wavelet and Artificial Neural Network-Based Hybrid Modeling Approach. Environmental Science & Technology, 2018.8.7, 52(15): 8446~8455. SCIE Nature Index 一区

10.  Jie Niu(#)(*); Chaopeng Shen; JEFFREY Q. CHAMBERS; JOHN M. MELACK; WILLIAM J. RILEY. Interannual Variation in Hydrologic Budgets in an Amazonian Watershed with a Coupled Subsurface–Land Surface Process Model. Journal of Hydrometeorology, 2017.9, 18(9): 2597~2617. SCIE, SSCI

9.  Kuai Fang(#); Chaopeng Shen(*); JB Fisher; Jie Niu. Improving Budyko curve-based estimates of long-term water partitioning using hydrologic signatures from GRACE. Water Resources Research, 2016.6.2, 52(7): 5537~5554. SCIE, SSCI

8.  J Tang(#)(*); WJ Riley; Jie Niu. Incorporating root hydraulic redistribution in CLM4.5: Effects on predicted site and global evapotranspiration, soil moisture, and water storage. Journal of Advances in Modeling Earth Systems, 2015.11.12, 7(4): 1828~1848. SCIE, SSCI

7.  Jie Niu(#); MS Phanikumar(*). Modeling watershed-scale solute transport using an integrated, process-based hydrologic model with applications to bacterial fate and transport. Journal of Hydrology, 2015.7.11, 529(2015): 35~48. SCIE, SSCI 一区

6.  Jie Niu(#); C Shen; SG Li; MS Phanikumar(*). Quantifying storage changes in regional Great Lakes watersheds using a coupled subsurface - land surface process model and GRACE, MODIS products. Water Resources Research, 2014.9.19, 50(9): 7359~7377. SCIE, SSCI 一区

5.  C Shen(#)(*); Jie Niu; K Fang. Quantifying the effects of data integration algorithms on the outcomes of a subsurface - land surface processes model. Environmental Modeling & Software, 2014.9.1, 59: 146~161. SCIE, SSCI

4.  RM Maxwell(#)(*); Mario Putti; Steven Meyerhoff; Jens-Olaf Delfs; Ian M. Ferguson; Valeriy Ivanov; Jongho Kim; Olaf Kolditz; Stefan J. Kollet; Mukesh Kumar; Sonya Lopez; Jie Niu; Claudio Paniconi; Yong-Jin Park; Mantha S. Phanikumar; Chaopeng Shen; Edward A. Sudicky; Mauro Sulis. Surface - subsurface model inter comparison: A first set of benchmark results to diagnose integrated hydrology and feedbacks. Water Resources Research, 2014.2.22, 50(2): 1531~1549. SCIE, SSCI

3.  Itza Mendoza-Sanchez, Mantha S Phanikumar, Jie Niu, Jason R Masoner, Isabelle M Cozzarelli, Jennifer T McGuire. Quantifying wetland–aquifer interactions in a humid subtropical climate region: an integrated approach. Journal of Hydrology, 2013, 498, 237-253; SCIE, SSCI

2.  Chaopeng Shen, Jie Niu, Mantha S Phanikumar. Evaluating controls on coupled hydrologic and vegetation dynamics in a humid continental climate watershed using a subsurface‐land surface processes model. Water Resources Research, 2013, 49(5), 2552-2572; SCIE, SSCI

1.  Chaopeng Shen, Jie Niu, Eric J Anderson, Mantha S Phanikumar. Estimating longitudinal dispersion in rivers using Acoustic Doppler Current Profilers. Advances in Water Resources, 2010, 33(6), 615-623; SCIE, SSCI

会议

1.   Jie Niu, W.J. Riley, J. Melack and C. Shen, Temporal and spatial relationships between hydrologic and carbon budgets in an Amazonian watershed: Application of a coupled Subsurface – Land Surface Process Model, AGU annual Fall Meeting, San Francisco, CA. Poster: B41A-0410 (Session: Advances in Understanding the Sacling of Fine-Scale Spatial Hydrological and Biogeochemical Heterogeneity, Their Interactions, and Implications for Earth-System Dynamics) (Dec. 2015)

2.   K. Fang, C. Shen, J.B. Fisher and Jie Niu, GRACE-assisted Budyko Hypothesis for Improved Estimates of Long-term Water Partitioning, AGU annual Fall Meeting, San Francisco, CA. Poster: H41F-1387 (Session: Remote Sensing and Modeling of Water Resources) (Dec. 2015)

3.   Jie Niu, C. Shen, W.J. Riley, J. Melack and G. Bisht, Quantifying Water Budgets in Amazonian Watershed Using a coupled Subsurface – Land Surface Process Model, AGU annual Fall Meeting, San Francisco, CA. Poster: H13E-1161 (Session: Advances in Representation, Integration, and Coupling of Novel Processes in Hydrologic and Transdisciplinary Models) (Dec. 2014)

4.   Jie Niu, W. J. Riley, J. Melack and C. Shen, Temporal and spatial relationships between hydrologic and carbon budgets in an Amazonian watershed: Application of a coupled subsurface – land surface process model, AGU annual Fall Meeting, San Francisco, CA. Poster: B41A-0410 (Session: Advances in Understanding the Scaling of Fine-Scale Spatial Hydrological and Biogeochemical Heterogeneity, Their interactions, and Implications for Earth – System Dynamics Posters) (Dec. 2015)

5.    Jie Niu, C. Shen and M.S. Phanikumar, Quantifying Storage in Regional Great Lakes Watersheds Using GRACE, MODIS Products and Coupled Subsurface - Land Surface Process Models, AGU annual Fall Meeting, San Francisco, CA. poster: #H21B-1034 (Dec. 2013)

6.    Jie Niu and M.S. Phanikumar, Modeling Bacterial Fate and Transport in a Great Lakes Watershed using a Process-based, Integrated Hydrologic Model, AGU 45th annual Fall Meeting, San Francisco, CA. Poster: H51I-1468 (Session: Recent Advances in Understanding the Hydrology of the Great Lakes Region) (Dec. 2012)

7.    Jie Niu, C. Shen and M.S. Phanikumar, Quantifying Water Budgets in Regional Great Lakes Watersheds Using a Process-Based, Distributed Hydrologic Model, International Association for Great Lakes Research Annual Conference, Duluth, MN (May 30 - June 3, 2011) PA-14 (Session: Assessing Dynamics of the Great Lakes Water Budget)



2024年2月更新