Chaopeng Shen

8.6k total citations · 6 hit papers
105 papers, 4.6k citations indexed

About

Chaopeng Shen is a scholar working on Water Science and Technology, Environmental Engineering and Global and Planetary Change. According to data from OpenAlex, Chaopeng Shen has authored 105 papers receiving a total of 4.6k indexed citations (citations by other indexed papers that have themselves been cited), including 73 papers in Water Science and Technology, 60 papers in Environmental Engineering and 54 papers in Global and Planetary Change. Recurrent topics in Chaopeng Shen's work include Hydrology and Watershed Management Studies (72 papers), Hydrological Forecasting Using AI (39 papers) and Flood Risk Assessment and Management (37 papers). Chaopeng Shen is often cited by papers focused on Hydrology and Watershed Management Studies (72 papers), Hydrological Forecasting Using AI (39 papers) and Flood Risk Assessment and Management (37 papers). Chaopeng Shen collaborates with scholars based in United States, China and Canada. Chaopeng Shen's co-authors include Kathryn Lawson, Mantha S. Phanikumar, Kuai Fang, Dapeng Feng, Sergi Molins, Carl I. Steefel, David Trebotich, Jie Niu, Jiangtao Liu and Li Li 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

Chaopeng Shen

99 papers receiving 4.5k citations

Hit Papers

An overview of current applications, challenges, and futu... 2015 2026 2018 2022 2016 2015 2021 2022 2023 100 200 300 400
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.

  1. An overview of current applications, challenges, and future trends in distributed process-based models in hydrology
    2016 456 citations Benjamin B. Mirus, Chaopeng Shen et al. Journal of Hydrology profile →
  2. Improving the representation of hydrologic processes in Earth System Models
    2015 403 citations Martyn Clark, Mukesh Kumar et al. Water Resources Research profile →
  3. From Hydrometeorology to River Water Quality: Can a Deep Learning Model Predict Dissolved Oxygen at the Continental Scale?
    2021 204 citations Wei Zhi, Dapeng Feng et al. profile →
  4. 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 →
  5. Hybrid forecasting: blending climate predictions with AI models
    2023 93 citations Louise Slater, Louise Arnal et al. Hydrology and earth system sciences profile →
  6. Temperature outweighs light and flow as the predominant driver of dissolved oxygen in US rivers
    2023 88 citations Wei Zhi, Chaopeng Shen et al. profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Chaopeng Shen United States 34 2.9k 2.7k 2.0k 702 479 105 4.6k
Philip Brunner Switzerland 35 2.6k 0.9× 2.4k 0.9× 1.0k 0.5× 431 0.6× 522 1.1× 119 4.1k
Claudio Paniconi Canada 36 2.7k 0.9× 2.2k 0.8× 1.3k 0.7× 636 0.9× 1.2k 2.4× 97 4.1k
Grey Nearing United States 32 2.5k 0.9× 2.3k 0.9× 2.4k 1.2× 888 1.3× 254 0.5× 72 3.9k
Harald Kling Austria 15 4.2k 1.5× 1.9k 0.7× 3.8k 1.9× 1.6k 2.4× 287 0.6× 23 5.8k
Stefan Kollet Germany 35 3.6k 1.2× 2.7k 1.0× 3.5k 1.8× 2.2k 3.1× 982 2.1× 116 6.4k
Karsten Schulz Austria 32 1.9k 0.6× 1.6k 0.6× 2.0k 1.0× 1.1k 1.5× 227 0.5× 120 4.0k
Valentijn Pauwels Australia 42 2.8k 1.0× 2.7k 1.0× 2.5k 1.3× 2.1k 2.9× 663 1.4× 144 5.2k
Mario Putti Italy 31 1.5k 0.5× 1.8k 0.6× 925 0.5× 524 0.7× 942 2.0× 133 4.1k
François Anctil Canada 43 4.3k 1.5× 3.3k 1.2× 4.0k 2.0× 1.9k 2.8× 507 1.1× 212 7.4k
Bill X. Hu China 35 1.0k 0.3× 1.9k 0.7× 888 0.5× 448 0.6× 692 1.4× 231 4.4k

Countries citing papers authored by Chaopeng Shen

Since Specialization
Citations

This map shows the geographic impact of Chaopeng Shen'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 Chaopeng Shen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chaopeng Shen more than expected).

Fields of papers citing papers by Chaopeng Shen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Chaopeng Shen. 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 Chaopeng Shen. The network helps show where Chaopeng Shen may publish in the future.

Co-authorship network of co-authors of Chaopeng Shen

This figure shows the co-authorship network connecting the top 25 collaborators of Chaopeng Shen. A scholar is included among the top collaborators of Chaopeng Shen 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 Chaopeng Shen. Chaopeng Shen is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
2.
Song, Yalan, Ming Pan, Hylke E. Beck, et al.. (2025). Improving differentiable hydrologic modeling with interpretable forcing fusion. Journal of Hydrology. 659. 133320–133320. 1 indexed citations
3.
Yang, Luyao, Jianduo Li, Yongjiu Dai, et al.. (2025). Calibration of the High‐Resolution Common Land Model in Simulating the Soil Moisture Over the Northeastern China Using an Adaptive Parameter Learning Method. Journal of Geophysical Research Atmospheres. 130(8).
4.
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.
5.
Knoben, Wouter, Mukesh Kumar, Alain Pietroniro, et al.. (2025). Technical note: How many models do we need to simulate hydrologic processes across large geographical domains?. Hydrology and earth system sciences. 29(11). 2361–2375. 1 indexed citations
6.
Ma, Kai, Chaopeng Shen, Ziyue Xu, & Daming He. (2024). Transfer learning framework for streamflow prediction in large-scale transboundary catchments: Sensitivity analysis and applicability in data-scarce basins. Journal of Geographical Sciences. 34(5). 963–984. 7 indexed citations
7.
Shen, Chaopeng, Sagy Cohen, K. J. Van Meter, et al.. (2024). The Geometry of Flow: Advancing Predictions of River Geometry With Multi‐Model Machine Learning. Water Resources Research. 60(10). 6 indexed citations
8.
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
9.
Wang, Dagang, et al.. (2024). Development of objective function-based ensemble model for streamflow forecasts. Journal of Hydrology. 632. 130861–130861. 8 indexed citations
10.
Song, Yalan, Farshid Rahmani, Wei Zhi, et al.. (2024). Deep learning insights into suspended sediment concentrations across the conterminous United States: Strengths and limitations. Journal of Hydrology. 639. 131573–131573. 9 indexed citations
11.
Reichert, Peter, et al.. (2024). Metamorphic testing of machine learning and conceptual hydrologic models. Hydrology and earth system sciences. 28(11). 2505–2529. 7 indexed citations
12.
13.
Song, Yalan, Wouter Knoben, Martyn Clark, et al.. (2024). When ancient numerical demons meet physics-informed machine learning: adjoint-based gradients for implicit differentiable modeling. Hydrology and earth system sciences. 28(13). 3051–3077. 12 indexed citations
14.
Liu, Xiaofeng, Yalan Song, & Chaopeng Shen. (2024). Bathymetry Inversion Using a Deep‐Learning‐Based Surrogate for Shallow Water Equations Solvers. Water Resources Research. 60(3). 4 indexed citations
15.
Rahmani, Farshid, et al.. (2023). A deep learning-based novel approach to generate continuous daily stream nitrate concentration for nitrate data-sparse watersheds. The Science of The Total Environment. 878. 162930–162930. 33 indexed citations
16.
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
17.
Song, Yalan, Wen‐Ping Tsai, Alan M. Rhoades, et al.. (2023). LSTM-Based Data Integration to Improve Snow Water Equivalent Prediction and Diagnose Error Sources. Journal of Hydrometeorology. 25(1). 223–237. 13 indexed citations
18.
Slater, Louise, Louise Arnal, Marie‐Amélie Boucher, et al.. (2023). Hybrid forecasting: blending climate predictions with AI models. Hydrology and earth system sciences. 27(9). 1865–1889. 93 indexed citations breakdown →
19.
Nagendra, S., Daniel Kifer, Benjamin B. Mirus, et al.. (2022). Constructing a Large-Scale Landslide Database Across Heterogeneous Environments Using Task-Specific Model Updates. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 15. 4349–4370. 15 indexed citations
20.

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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