Jian Sha

673 total citations
26 papers, 527 citations indexed

About

Jian Sha is a scholar working on Water Science and Technology, Global and Planetary Change and Environmental Engineering. According to data from OpenAlex, Jian Sha has authored 26 papers receiving a total of 527 indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Water Science and Technology, 13 papers in Global and Planetary Change and 10 papers in Environmental Engineering. Recurrent topics in Jian Sha's work include Hydrology and Watershed Management Studies (17 papers), Hydrological Forecasting Using AI (9 papers) and Climate variability and models (9 papers). Jian Sha is often cited by papers focused on Hydrology and Watershed Management Studies (17 papers), Hydrological Forecasting Using AI (9 papers) and Climate variability and models (9 papers). Jian Sha collaborates with scholars based in China, United States and Norway. Jian Sha's co-authors include Zhong-Liang Wang, Zhong‐Liang Wang, Xue Li, Xue Li, Xue Li, Yuqiu Wang, Man Zhang, Xue Li, Yue Zhao and Dennis P. Swaney and has published in prestigious journals such as Journal of Hydrology, Journal of Environmental Management and International Journal of Environmental Research and Public Health.

In The Last Decade

Jian Sha

26 papers receiving 517 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jian Sha China 13 348 238 209 78 77 26 527
Frederico Fábio Mauad Brazil 13 318 0.9× 139 0.6× 168 0.8× 52 0.7× 72 0.9× 68 542
Murat Ay Türkiye 9 365 1.0× 285 1.2× 358 1.7× 20 0.3× 62 0.8× 21 701
Mary Love M. Tagert United States 10 242 0.7× 214 0.9× 120 0.6× 68 0.9× 27 0.4× 30 438
Patricia Jimeno‐Sáez Spain 16 532 1.5× 319 1.3× 437 2.1× 28 0.4× 22 0.3× 27 698
Shaoyuan Feng China 14 293 0.8× 226 0.9× 311 1.5× 15 0.2× 32 0.4× 21 759
Hongwei Guo China 11 318 0.9× 169 0.7× 93 0.4× 39 0.5× 188 2.4× 16 515
Mehran Mahdian Iran 7 183 0.5× 122 0.5× 104 0.5× 21 0.3× 25 0.3× 11 363
Randall Etheridge United States 11 213 0.6× 84 0.4× 115 0.6× 126 1.6× 72 0.9× 32 503
Lien Rodríguez‐López Chile 11 179 0.5× 71 0.3× 98 0.5× 32 0.4× 61 0.8× 46 362
Hamid Raeisi Vanani Iran 11 135 0.4× 108 0.5× 165 0.8× 31 0.4× 19 0.2× 12 460

Countries citing papers authored by Jian Sha

Since Specialization
Citations

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

Fields of papers citing papers by Jian Sha

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jian Sha

This figure shows the co-authorship network connecting the top 25 collaborators of Jian Sha. A scholar is included among the top collaborators of Jian Sha 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 Jian Sha. Jian Sha 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.
Li, Xue, Qiliang Sun, Yanfei Zhang, Jian Sha, & Man Zhang. (2024). Enhancing hydrological extremes prediction accuracy: Integrating diverse loss functions in Transformer models. Environmental Modelling & Software. 177. 106042–106042. 10 indexed citations
2.
Li, Xue, et al.. (2023). Using a two-step downscaling method to assess the impact of climate change on total nitrogen load in a small basin. Journal of Hydrology. 628. 130510–130510. 5 indexed citations
3.
Sha, Jian, et al.. (2023). A spatial weather generator based on conditional deep convolution generative adversarial nets (cDCGAN). Climate Dynamics. 62(2). 1275–1290. 3 indexed citations
4.
Chen, Xinyu, et al.. (2022). The impacts of climate changes on watershed streamflow and total dissolved nitrogen in Danjiang Watershed, China. Journal of Water and Climate Change. 14(1). 104–122. 3 indexed citations
5.
Sha, Jian, et al.. (2021). Influence of the Three Gorges Reservoir on climate drought in the Yangtze River Basin. Environmental Science and Pollution Research. 28(23). 29755–29772. 28 indexed citations
6.
Sha, Jian, et al.. (2021). Estimation of Watershed Hydrochemical Responses to Future Climate Changes Based on CMIP6 Scenarios in the Tianhe River (China). Sustainability. 13(18). 10102–10102. 8 indexed citations
7.
Tong, Yindong, Ziwei Chen, Qi Miao, et al.. (2021). Exploring dynamics of riverine phosphorus exports under future climate change using a process-based catchment model. Journal of Hydrology. 605. 127344–127344. 7 indexed citations
8.
Sha, Jian, Xue Li, Man Zhang, & Zhong‐Liang Wang. (2021). Comparison of Forecasting Models for Real-Time Monitoring of Water Quality Parameters Based on Hybrid Deep Learning Neural Networks. Water. 13(11). 1547–1547. 44 indexed citations
9.
Li, Xue, Jian Sha, & Zhong-Liang Wang. (2019). Comparison of drought indices in the analysis of spatial and temporal changes of climatic drought events in a basin. Environmental Science and Pollution Research. 26(11). 10695–10707. 39 indexed citations
10.
Li, Xue, Jian Sha, & Zhong‐Liang Wang. (2019). Comparison of daily streamflow forecasts using extreme learning machines and the random forest method. Hydrological Sciences Journal. 64(15). 1857–1866. 60 indexed citations
11.
Li, Xue, Jian Sha, & Zhong-Liang Wang. (2018). Application of feature selection and regression models for chlorophyll-a prediction in a shallow lake. Environmental Science and Pollution Research. 25(20). 19488–19498. 44 indexed citations
12.
13.
Li, Xue, Jian Sha, & Zhong-Liang Wang. (2017). Chlorophyll-A Prediction of Lakes with Different Water Quality Patterns in China Based on Hybrid Neural Networks. Water. 9(7). 524–524. 54 indexed citations
14.
Li, Xue, Jian Sha, & Zhong‐Liang Wang. (2016). A comparative study of multiple linear regression, artificial neural network and support vector machine for the prediction of dissolved oxygen. Hydrology research. 48(5). 1214–1225. 60 indexed citations
15.
Liu, Yan, et al.. (2015). Estimation of contribution ratios of pollutant sources to a specific section based on an enhanced water quality model. Environmental Science and Pollution Research. 22(10). 7569–7581. 9 indexed citations
16.
Li, Zeli, et al.. (2014). Application of Regional Nutrient Management Model in Tunxi Catchment: In Support of the Trans‐boundary Eco‐compensation in Eastern China. CLEAN - Soil Air Water. 42(12). 1729–1739. 14 indexed citations
17.
Sha, Jian, Zeli Li, Dennis P. Swaney, et al.. (2014). Application of a Bayesian Watershed Model Linking Multivariate Statistical Analysis to Support Watershed-Scale Nitrogen Management in China. Water Resources Management. 28(11). 3681–3695. 13 indexed citations
18.
Sha, Jian, Dennis P. Swaney, Bongghi Hong, et al.. (2014). Estimation of watershed hydrologic processes in arid conditions with a modified watershed model. Journal of Hydrology. 519. 3550–3556. 12 indexed citations
19.
Wang, Yuqiu, et al.. (2014). Spatio-temporal variations of water quality in Yuqiao Reservoir Basin, North China. Frontiers of Environmental Science & Engineering. 9(4). 649–664. 18 indexed citations
20.
Sha, Jian, Min Liu, Dong Wang, Dennis P. Swaney, & Yuqiu Wang. (2013). Application of the ReNuMa model in the Sha He river watershed: Tools for watershed environmental management. Journal of Environmental Management. 124. 40–50. 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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