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姓名：鈕杰

職稱：特聘教授

研究領(lǐng)域：

機(jī)器學(xué)習(xí)/深度學(xué)習(xí)在環(huán)境及水文學(xué)的應(yīng)用

水文數(shù)值模擬

流域水文及溶質(zhì)運(yùn)移模擬

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

招生專業(yè)：地質(zhì)資源與地質(zhì)工程（水文地質(zhì)方向）、資源與環(huán)境（地質(zhì)工程方向）

教育背景

美國密西根州立大學(xué) 環(huán)境工程博士 2007 - 2013

南開大學(xué) 環(huán)境科學(xué)碩士 2004 - 2007

南開大學(xué) 環(huán)境科學(xué)本科 2000 - 2004

工作經(jīng)歷

特聘教授貴州大學(xué)資源與環(huán)境工程學(xué)院水文與地下水工程系 2022 -

副教授暨南大學(xué)生命科學(xué)與技術(shù)學(xué)院 2021 - 2022

副教授暨南大學(xué)地下水與地球科學(xué)研究院 2017 - 2021

博士后美國勞倫斯伯克利國家實(shí)驗(yàn)室 2014 - 2017

博士后美國加州大學(xué)圣芭芭拉分校地球研究所（工作地點(diǎn)同上） 2014 - 2016

助研美國密西根州立大學(xué) 2007 - 2014

學(xué)術(shù)服務(wù)

SCI 期刊 Journal of Hydrology 編委 (Associate Editor)
SCI 期刊 Water 編委 (Topic Editor)
SCI 期刊 Frontiers in Water 編委 (Associate Editor)
多個(gè) SCI 一、二區(qū)期刊審稿
國家自然科學(xué)基金面上、青年項(xiàng)目評(píng)審專家
廣東省自然科學(xué)基金面上項(xiàng)目評(píng)審專家
廣東省科技廳專家?guī)煸u(píng)審專家
廣東省海島海域資源專家?guī)煸u(píng)審專家

學(xué)生獎(jiǎng)勵(lì)

第二屆廣東省智慧水利創(chuàng)新大賽——廣匯源杯三等獎(jiǎng)（2022）4人
研究生國家獎(jiǎng)學(xué)金（2020）1人
暨南大學(xué)研究生學(xué)術(shù)創(chuàng)新論壇（2019）學(xué)術(shù)創(chuàng)新成果獎(jiǎng) 1人
第一屆全國大學(xué)生水環(huán)境模型大賽（2018）三等獎(jiǎng) 1人

個(gè)人獎(jiǎng)勵(lì)

第二屆廣東省智慧水利創(chuàng)新大賽——廣匯源杯優(yōu)秀指導(dǎo)教師（2022）
暨南大學(xué)“雙百英才計(jì)劃”暨南杰青第一層次（2021）
中科院第一屆“先導(dǎo)杯”并行計(jì)算大賽（2020）優(yōu)勝獎(jiǎng)

科研項(xiàng)目

2025 - 2029 國家自然科學(xué)基金重點(diǎn)項(xiàng)目，巖溶流域水-碳循環(huán)耦合機(jī)制，參與，30 / 230萬元
2024 - 2027 貴州省科技支撐計(jì)劃項(xiàng)目，喀斯特地區(qū)中小河流預(yù)報(bào)，項(xiàng)目負(fù)責(zé)人，50萬元
2020 - 2023 國家自然科學(xué)基金面上項(xiàng)目，水文過程模型與深度學(xué)習(xí)模型，主持，65萬元
2022 - 2023 暨南大學(xué)學(xué)科建設(shè)優(yōu)秀青年骨干項(xiàng)目，物理信息深度學(xué)習(xí)模型，主持，5萬元
2018 - 2021 廣東省自然科學(xué)基金自由申請(qǐng)項(xiàng)目，水文過程模型，主持，10萬元
2018 - 2021 暨南大學(xué)科研培育與創(chuàng)新基金項(xiàng)目，水文過程模型，主持，15萬元

媒體報(bào)道

2020年6月 WIRES：分?jǐn)?shù)階對(duì)流擴(kuò)散模型模擬溶質(zhì)運(yùn)移非菲克現(xiàn)象的優(yōu)勢(shì)和挑戰(zhàn)。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. 美國勞倫茲伯克利國家實(shí)驗(yàn)室網(wǎng)站.

發(fā)表文章

43. T. Yao, C. Wu*, P. J.-F. Yeh, B. X. Hu, Y. Jiao, Q. Li, J. Niu. Widespread global enhancement of vegetation resistance to compound dry-hot events due to anthropogenic climate change. Ecological Indicators, 2025 178(113880). Doi:10.1016/j.ecolind.2025.113880.

42. M. He, J. Niu*, D. Liu, Y. Fu, Q. Wu, S. Zhou, P. Wu, C. Wu, B.X. Hu. Revealing distribution patterns of river obstructions in China via deep-learning and satellite imagery. Journal of Hydrology, 2025, 660. Doi:10.1016/j.jhydrol.2025.133299. 一區(qū)

41. C. Wu*, P.J.F. Yeh, T. Yao, Z. Gong, J. Niu, S. Yuan. Controls of climate seasonality and vegetation dynamics on the seasonal variability of terrestrial water storage under diverse climate regimes. Water Resources Research, 2025, 61 (2). Doi:10.1029/2024WR038065.

40. Jie Niu(#), Xufeng Wang(#), Jiangling Chen, Yingcan Zhao*, Xiaohui Chen, Baoling Yang, Na Liu, Pan Wu*. Machine learning based predictive analysis of DNA cleavage induced by diverse nanomaterials. Scientific reports, 2024, 14:21966. Doi:10.1038/s41598-024-73140-1.

39. Yang Ya, Liu Dongdong*, She Dongli, Niu Jie, Fei Yuanhang, Yang Shimei. Integrating calibrated PTFs and modified OpenKarHydro framework to map the responses of ecohydrological processes to climate change across the Loess Plateau. Catena, 2024, 246. Doi:10.1016/j.catena.2024.108391.

38. Junjie Tang, Dongdong Liu*, Chongju Shang, Jie Niu. Impacts of land use change on surface infiltration capacity and urban flood risk in a representative karst mountain city over the last two decades. Journal of Cleaner Production, 2024, 454. Doi:10.1016/j.clepro.2024.142196.

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 Pollution 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. 一區(qū)

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. 一區(qū)

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. 一區(qū)

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. 一區(qū)

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. 一區(qū)

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. 一區(qū)

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. 一區(qū)

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. 一區(qū)

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. 一區(qū)

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. 一區(qū)

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 一區(qū)

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 一區(qū)

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 一區(qū)

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

會(huì)議

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)

2025年7月更新