Shaowei Ning

1.3k total citations
64 papers, 939 citations indexed

About

Shaowei Ning is a scholar working on Global and Planetary Change, Water Science and Technology and Ecology, Evolution, Behavior and Systematics. According to data from OpenAlex, Shaowei Ning has authored 64 papers receiving a total of 939 indexed citations (citations by other indexed papers that have themselves been cited), including 36 papers in Global and Planetary Change, 23 papers in Water Science and Technology and 12 papers in Ecology, Evolution, Behavior and Systematics. Recurrent topics in Shaowei Ning's work include Hydrology and Drought Analysis (23 papers), Hydrology and Watershed Management Studies (13 papers) and Climate variability and models (12 papers). Shaowei Ning is often cited by papers focused on Hydrology and Drought Analysis (23 papers), Hydrology and Watershed Management Studies (13 papers) and Climate variability and models (12 papers). Shaowei Ning collaborates with scholars based in China, Japan and Nepal. Shaowei Ning's co-authors include Juliang Jin, Hiroshi Ishidaira, Yi Cui, Chengguo Wu, Shangming Jiang, Jie Wang, Yuliang Zhou, Parmeshwar Udmale, Li‐Bing Zhang and Li Liu and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and The Science of The Total Environment.

In The Last Decade

Shaowei Ning

61 papers receiving 912 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Shaowei Ning China 17 413 288 208 181 126 64 939
Devesh Sharma India 18 618 1.5× 363 1.3× 252 1.2× 60 0.3× 140 1.1× 66 1.0k
Aondover Tarhule United States 19 629 1.5× 304 1.1× 199 1.0× 76 0.4× 273 2.2× 38 1.1k
Long Phi Hoang Netherlands 18 558 1.4× 399 1.4× 131 0.6× 70 0.4× 201 1.6× 21 1.0k
Jeeban Panthi United States 12 415 1.0× 269 0.9× 201 1.0× 36 0.2× 217 1.7× 30 821
Yutaka ICHIKAWA Japan 15 593 1.4× 430 1.5× 147 0.7× 40 0.2× 219 1.7× 90 986
Komlavi Akpoti Ghana 17 474 1.1× 288 1.0× 124 0.6× 122 0.7× 160 1.3× 41 967
Juana P. Moiwo China 21 426 1.0× 458 1.6× 146 0.7× 261 1.4× 191 1.5× 43 1.3k
Ronghao Chu China 16 466 1.1× 449 1.6× 96 0.5× 74 0.4× 58 0.5× 31 946
Mohsin Hafeez Australia 18 514 1.2× 522 1.8× 141 0.7× 97 0.5× 63 0.5× 48 1.1k
Dominic Mazvimavi South Africa 20 636 1.5× 608 2.1× 99 0.5× 70 0.4× 53 0.4× 72 1.2k

Countries citing papers authored by Shaowei Ning

Since Specialization
Citations

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

Fields of papers citing papers by Shaowei Ning

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Shaowei Ning

This figure shows the co-authorship network connecting the top 25 collaborators of Shaowei Ning. A scholar is included among the top collaborators of Shaowei Ning 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 Shaowei Ning. Shaowei Ning 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.
Ning, Shaowei, Cheng Yang, Yuliang Zhou, et al.. (2025). Bayesian Model Averaging for Satellite Precipitation Data Fusion: From Accuracy Estimation to Runoff Simulation. Remote Sensing. 17(7). 1154–1154. 1 indexed citations
3.
Ning, Shaowei, Xiaoyan Xu, Shenghan Wang, et al.. (2024). Comparative analysis of machine learning models and explainable AI for agriculture drought prediction: A case study of the Ta-pieh mountains. Agricultural Water Management. 306. 109176–109176. 5 indexed citations
4.
Yuan, Hongwei, Jiwei Yang, Jia Liu, et al.. (2024). Effects of Alternative Stress of Drought–Flood on Summer Maize Growth and Yield. Water. 16(19). 2742–2742. 2 indexed citations
5.
Li, Huimin, Shaowei Ning, Yuliang Zhou, et al.. (2023). Spatial and temporal analysis of drought resistance of different vegetation in the Ta-pieh Mountains based on multi-source data. Journal of Water and Climate Change. 14(11). 4198–4219. 2 indexed citations
6.
7.
Wu, Chengguo, Shaowei Ning, Juliang Jin, et al.. (2021). Construction and application of comprehensive drought index based on uncertainty cloud reasoning algorithm. The Science of The Total Environment. 779. 146533–146533. 9 indexed citations
8.
Udmale, Parmeshwar, Yutaka ICHIKAWA, Shaowei Ning, Sangam Shrestha, & Indrajit Pal. (2020). A statistical approach towards defining national-scale meteorological droughts in India using crop data. Environmental Research Letters. 15(9). 94090–94090. 11 indexed citations
9.
Chen, Menglu, Juliang Jin, Shaowei Ning, Yuliang Zhou, & Parmeshwar Udmale. (2020). Early Warning Method for Regional Water Resources Carrying Capacity Based on the Logical Curve and Aggregate Warning Index. International Journal of Environmental Research and Public Health. 17(7). 2206–2206. 9 indexed citations
10.
Jiang, Shangming, et al.. (2019). Optimal Water Resources Regulation for the Pond Irrigation System Based on Simulation—A Case Study in Jiang-Huai Hilly Regions, China. International Journal of Environmental Research and Public Health. 16(15). 2717–2717. 7 indexed citations
11.
Li, Yuanyuan, et al.. (2019). The evaluation of latest GPM-Era precipitation data in Yellow River Basin. Guotu ziyuan yaogan. 31(1). 164–170. 1 indexed citations
12.
Cui, Yi, et al.. (2019). Estimation of maize evapotraspiration under drought stress - A case study of Huaibei Plain, China. PLoS ONE. 14(11). e0223756–e0223756. 6 indexed citations
13.
Wu, Chengguo, et al.. (2019). Regional water resource carrying capacity evaluation based on multi-dimensional precondition cloud and risk matrix coupling model. The Science of The Total Environment. 710. 136324–136324. 71 indexed citations
14.
15.
Wang, Jie, Hao Wang, Shaowei Ning, & Hiroshi Ishidaira. (2018). Predicting future land cover change and its impact on streamflow and sediment load in a trans-boundary river basin. SHILAP Revista de lepidopterología. 379. 217–222. 3 indexed citations
16.
Ning, Shaowei, Fan Song, Parmeshwar Udmale, et al.. (2017). Error Analysis and Evaluation of the Latest GSMap and IMERG Precipitation Products over Eastern China. Advances in Meteorology. 2017. 1–16. 75 indexed citations
17.
18.
Ning, Shaowei, Jie Wang, Juliang Jin, & Hiroshi Ishidaira. (2016). Assessment of the Latest GPM-Era High-Resolution Satellite Precipitation Products by Comparison with Observation Gauge Data over the Chinese Mainland. Water. 8(11). 481–481. 65 indexed citations
19.
Ning, Shaowei, Hiroshi Ishidaira, Parmeshwar Udmale, & Yutaka ICHIKAWA. (2015). Remote Sensing Based Analysis of Recent Variations in Water Resources and Vegetation of a Semi-Arid Region. Water. 7(11). 6039–6055. 9 indexed citations
20.
Wang, Jie, Hiroshi Ishidaira, Wenchao Sun, & Shaowei Ning. (2013). Development and Interpretation of New Sediment Rating Curve Considering the Effect of Vegetation Cover for Asian Basins. The Scientific World JOURNAL. 2013(1). 154375–154375. 16 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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