Jiangtao Liu

909 total citations · 1 hit paper
22 papers, 513 citations indexed

About

Jiangtao Liu is a scholar working on Environmental Engineering, Water Science and Technology and Global and Planetary Change. According to data from OpenAlex, Jiangtao Liu has authored 22 papers receiving a total of 513 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Environmental Engineering, 10 papers in Water Science and Technology and 8 papers in Global and Planetary Change. Recurrent topics in Jiangtao Liu's work include Hydrology and Watershed Management Studies (10 papers), Hydrological Forecasting Using AI (5 papers) and Precipitation Measurement and Analysis (5 papers). Jiangtao Liu is often cited by papers focused on Hydrology and Watershed Management Studies (10 papers), Hydrological Forecasting Using AI (5 papers) and Precipitation Measurement and Analysis (5 papers). Jiangtao Liu collaborates with scholars based in United States, China and Saudi Arabia. Jiangtao Liu's co-authors include Chaopeng Shen, Kathryn Lawson, Dapeng Feng, Farshid Rahmani, Wei Zhi, Li Li, Yuchen Bian, Zongxue Xu, Bo Pang and Tadd Bindas and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nature Communications and SHILAP Revista de lepidopterología.

In The Last Decade

Jiangtao Liu

20 papers receiving 501 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Differentiable, Learnable, Regionalized Process‐Based Models With Multiphysical Outputs can Approach State‐Of‐The‐Art Hydrologic Prediction Accuracy

2022 142 citations Dapeng Feng, Jiangtao Liu et al. Water Resources Research profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Jiangtao Liu United States	12	289	267	236	118	37	22	513
Sabahattin Işık United States	8	364 1.3×	261 1.0×	239 1.0×	51 0.4×	67 1.8×	15	506
Reza Ghazavi Iran	12	282 1.0×	399 1.5×	298 1.3×	43 0.4×	33 0.9×	34	649
D. Nalley Canada	8	292 1.0×	320 1.2×	523 2.2×	206 1.7×	52 1.4×	13	760
Aris Psilovikos Greece	14	205 0.7×	176 0.7×	179 0.8×	42 0.4×	67 1.8×	35	545
Antonio‐Juan Collados‐Lara Spain	17	335 1.2×	177 0.7×	341 1.4×	214 1.8×	25 0.7×	40	677
Cristina Prieto Spain	10	656 2.3×	494 1.9×	550 2.3×	108 0.9×	55 1.5×	23	819
C. Bryan Young United States	13	227 0.8×	304 1.1×	387 1.6×	354 3.0×	87 2.4×	37	763
Cristiano das Neves Almeida Brazil	11	187 0.6×	169 0.6×	259 1.1×	200 1.7×	39 1.1×	35	482
Joseph A. Hevesi United States	8	226 0.8×	280 1.0×	243 1.0×	273 2.3×	43 1.2×	18	591
Jason Davison United States	7	504 1.7×	291 1.1×	339 1.4×	119 1.0×	41 1.1×	9	638

Countries citing papers authored by Jiangtao Liu

Since Specialization

Citations

This map shows the geographic impact of Jiangtao Liu's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Jiangtao Liu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jiangtao Liu more than expected).

Fields of papers citing papers by Jiangtao Liu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jiangtao Liu. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Jiangtao Liu. The network helps show where Jiangtao Liu may publish in the future.

Co-authorship network of co-authors of Jiangtao Liu

This figure shows the co-authorship network connecting the top 25 collaborators of Jiangtao Liu. A scholar is included among the top collaborators of Jiangtao Liu based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Jiangtao Liu. Jiangtao Liu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Liu, Jiangtao, Chaopeng Shen, Fearghal O’Donncha, et al.. (2025). From RNNs to Transformers: benchmarking deep learning architectures for hydrologic prediction. Hydrology and earth system sciences. 29(23). 6811–6828.

Song, Yalan, Tadd Bindas, Chaopeng Shen, et al.. (2025). Distinct hydrologic response patterns and trends worldwide revealed by physics-embedded learning. Nature Communications. 16(1). 9169–9169.

Bindas, Tadd, Wen‐Ping Tsai, Jiangtao Liu, et al.. (2024). Improving River Routing Using a Differentiable Muskingum‐Cunge Model and Physics‐Informed Machine Learning. Water Resources Research. 60(1). 33 indexed citations

Beck, Hylke E., Jens de Bruijn, Reetik Kumar Sahu, et al.. (2024). Deep dive into hydrologic simulations at global scale: harnessing the power of deep learning and physics-informed differentiable models ( δ HBV-globe1.0-hydroDL). Geoscientific model development. 17(18). 7181–7198. 21 indexed citations

Liu, Jiangtao, Yuchen Bian, Kathryn Lawson, & Chaopeng Shen. (2024). Probing the limit of hydrologic predictability with the Transformer network. Journal of Hydrology. 637. 131389–131389. 33 indexed citations

Li, Zhihong, et al.. (2024). Identification of shallow groundwater recharge and circulation in an intensive agricultural area of the Sanjiang Plain, China, based on hydrochemistry, isotopes and hydrogeological conditions. Hydrogeology Journal. 32(7). 1887–1908. 3 indexed citations

Li, Zhihong, et al.. (2024). Hydrochemical processes and inorganic nitrogen sources of shallow groundwater in the Sanjiang Plain, northeast China. Water Environment Research. 96(9). e11121–e11121. 3 indexed citations

Zhi, Wei, Hubert Baniecki, Jiangtao Liu, et al.. (2024). Increasing phosphorus loss despite widespread concentration decline in US rivers. Proceedings of the National Academy of Sciences. 121(48). e2402028121–e2402028121. 8 indexed citations

Xu, Chonggang, Forrest M. Hoffman, Jiangtao Liu, et al.. (2023). A differentiable, physics-informed ecosystem modeling and learning framework for large-scale inverse problems: demonstration with photosynthesis simulations. Biogeosciences. 20(13). 2671–2692. 25 indexed citations

10.

Zhi, Wei, et al.. (2023). Widespread deoxygenation in warming rivers. Nature Climate Change. 13(10). 1105–1113. 75 indexed citations

11.

Liu, Jiangtao, David Hughes, Farshid Rahmani, Kathryn Lawson, & Chaopeng Shen. (2023). Evaluating a global soil moisture dataset from a multitask model (GSM3 v1.0) with potential applications for crop threats. Geoscientific model development. 16(5). 1553–1567. 16 indexed citations

12.

Xu, Chonggang, et al.. (2023). A differentiable, physics-informed ecosystem modeling and learning framework for large-scale inverse problems: Demonstration with photosynthesis simulations. Zenodo (CERN European Organization for Nuclear Research). 2 indexed citations

13.

Liu, Jiangtao, Farshid Rahmani, Kathryn Lawson, & Chaopeng Shen. (2022). A Multiscale Deep Learning Model for Soil Moisture Integrating Satellite and In Situ Data. Geophysical Research Letters. 49(7). 59 indexed citations

14.

Feng, Dapeng, Jiangtao Liu, Kathryn Lawson, & Chaopeng Shen. (2022). Differentiable, Learnable, Regionalized Process‐Based Models With Multiphysical Outputs can Approach State‐Of‐The‐Art Hydrologic Prediction Accuracy. Water Resources Research. 58(10). 142 indexed citations breakdown →

15.

Xu, Zongxue, et al.. (2020). Spatiotemporal Variability of Precipitation in Beijing, China during the Wet Seasons. Water. 12(3). 716–716. 11 indexed citations

16.

Li, Jianzhang, et al.. (2019). Research on the accuracy of atmospheric precipitable water vapor with BDS. Bulletin of Surveying and Mapping. 35. 1 indexed citations

17.

Liu, Jiangtao, et al.. (2018). Assessment and Correction of the PERSIANN-CDR Product in the Yarlung Zangbo River Basin, China. Remote Sensing. 10(12). 2031–2031. 17 indexed citations

18.

Xu, Zongxue, et al.. (2018). Assessment of Satellite-Derived Precipitation Products for the Beijing Region. Remote Sensing. 10(12). 1914–1914. 28 indexed citations

19.

Liu, Jiangtao, et al.. (2018). [Hydrochemical Characteristics of Groundwater and the Origin in Alluvial-proluvial Fan of Qinhe River].. PubMed. 39(12). 5428–5439. 10 indexed citations

20.

Zhang, Wanyi, et al.. (2014). Characteristics and genesis of mineral deposits in East Ujimqin Banner, western segment of the Great Xing’an Mountains, NE China. Journal of Asian Earth Sciences. 97. 459–471. 24 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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