Jianduo Li

1.6k total citations · 1 hit paper
42 papers, 1.1k citations indexed

About

Jianduo Li is a scholar working on Global and Planetary Change, Atmospheric Science and Water Science and Technology. According to data from OpenAlex, Jianduo Li has authored 42 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Global and Planetary Change, 23 papers in Atmospheric Science and 16 papers in Water Science and Technology. Recurrent topics in Jianduo Li's work include Climate variability and models (20 papers), Hydrology and Watershed Management Studies (16 papers) and Plant Water Relations and Carbon Dynamics (13 papers). Jianduo Li is often cited by papers focused on Climate variability and models (20 papers), Hydrology and Watershed Management Studies (16 papers) and Plant Water Relations and Carbon Dynamics (13 papers). Jianduo Li collaborates with scholars based in China, United States and Australia. Jianduo Li's co-authors include Qingyun Duan, Chiyuan Miao, Wei Gong, Zhenhua Di, Aizhong Ye, Yanjun Gan, Yongjiu Dai, Wei Shangguan, Fei Chen and Guo Zhang and has published in prestigious journals such as Water Resources Research, Geophysical Research Letters and Journal of Hydrology.

In The Last Decade

Jianduo Li

35 papers receiving 1.1k citations

Hit Papers

A 1 km daily soil moisture dataset over China using in si... 2022 2026 2023 2024 2022 40 80 120

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jianduo Li China 19 707 559 332 317 106 42 1.1k
Saeed Golian Iran 22 1.2k 1.7× 658 1.2× 587 1.8× 379 1.2× 91 0.9× 59 1.6k
Haijun Yu China 16 729 1.0× 364 0.7× 333 1.0× 232 0.7× 40 0.4× 38 1.0k
Yunqing Xuan United Kingdom 19 874 1.2× 477 0.9× 646 1.9× 440 1.4× 124 1.2× 52 1.4k
Lijun Chao China 13 474 0.7× 262 0.5× 325 1.0× 292 0.9× 58 0.5× 30 872
Xiaodong Ming United Kingdom 10 689 1.0× 422 0.8× 351 1.1× 166 0.5× 76 0.7× 17 878
L. Goncalves Brazil 16 815 1.2× 674 1.2× 435 1.3× 296 0.9× 73 0.7× 43 1.2k
Lori A. Schultz United States 9 634 0.9× 292 0.5× 161 0.5× 309 1.0× 76 0.7× 26 919
Alfonso Senatore Italy 22 996 1.4× 456 0.8× 700 2.1× 225 0.7× 70 0.7× 63 1.4k
A. Ünal Şorman Türkiye 18 627 0.9× 514 0.9× 541 1.6× 344 1.1× 44 0.4× 48 1.2k
Giuseppe Mendicino Italy 21 889 1.3× 351 0.6× 657 2.0× 216 0.7× 86 0.8× 63 1.3k

Countries citing papers authored by Jianduo Li

Since Specialization
Citations

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

Fields of papers citing papers by Jianduo Li

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jianduo Li

This figure shows the co-authorship network connecting the top 25 collaborators of Jianduo Li. A scholar is included among the top collaborators of Jianduo Li 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 Jianduo Li. Jianduo Li 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.
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).
2.
Shen, Hui, Jianduo Li, Guocan Wu, Aizhong Ye, & Yuna Mao. (2025). Can CMIP6 Models Accurately Reproduce Terrestrial Evapotranspiration Across China?. International Journal of Climatology. 45(6).
4.
Chen, Yueli, Ting Wei, Jianduo Li, Yufei Xin, & Minghu Ding. (2024). Future changes in global rainfall erosivity: Insights from the precipitation changes. Journal of Hydrology. 638. 131435–131435. 12 indexed citations
5.
Li, Jianduo, Yanjun Gan, Guo Zhang, et al.. (2024). Quantifying the Interactions of Noah‐MP Land Surface Processes on the Simulated Runoff Over the Tibetan Plateau. Journal of Geophysical Research Atmospheres. 129(7). 1 indexed citations
8.
Peng, Xindong, et al.. (2022). Extension and Evaluation of University of Washington Moist Turbulence Scheme to Gray‐Zone Scales. Journal of Advances in Modeling Earth Systems. 14(8).
9.
Shangguan, Wei, Ruqing Zhang, Lu Li, et al.. (2022). Assessment of Agricultural Drought Based on Reanalysis Soil Moisture in Southern China. Land. 11(4). 502–502. 18 indexed citations
10.
Li, Jianduo, Chiyuan Miao, Guo Zhang, et al.. (2022). Global Evaluation of the Noah‐MP Land Surface Model and Suggestions for Selecting Parameterization Schemes. Journal of Geophysical Research Atmospheres. 127(5). 39 indexed citations
11.
Zhang, Guo, Jianduo Li, Guangsheng Zhou, et al.. (2021). Effects of Mosaic Representation of Land Use/Land Cover on Skin Temperature and Energy Fluxes in Noah‐MP Land Surface Model Over China. Journal of Geophysical Research Atmospheres. 126(13). 8 indexed citations
12.
Li, Jianduo, Chiyuan Miao, Wei Wei, et al.. (2021). Evaluation of CMIP6 Global Climate Models for Simulating Land Surface Energy and Water Fluxes During 1979–2014. Journal of Advances in Modeling Earth Systems. 13(6). 62 indexed citations
13.
Li, Jianduo, Fei Chen, Xingjie Lu, et al.. (2020). Quantifying Contributions of Uncertainties in Physical Parameterization Schemes and Model Parameters to Overall Errors in Noah‐MP Dynamic Vegetation Modeling. Journal of Advances in Modeling Earth Systems. 12(7). 24 indexed citations
14.
Gan, Yanjun, Xin‐Zhong Liang, Qingyun Duan, et al.. (2019). Assessment and Reduction of the Physical Parameterization Uncertainty for Noah‐MP Land Surface Model. Water Resources Research. 55(7). 5518–5538. 38 indexed citations
15.
Li, Jianduo, Guo Zhang, Fei Chen, Xindong Peng, & Yanjun Gan. (2019). Evaluation of Land Surface Subprocesses and Their Impacts on Model Performance With Global Flux Data. Journal of Advances in Modeling Earth Systems. 11(5). 1329–1348. 18 indexed citations
16.
Li, Jianduo, Qingyun Duan, Ying‐Ping Wang, et al.. (2018). Parameter optimization for carbon and water fluxes in two global land surface models based on surrogate modelling. International Journal of Climatology. 38(S1). 29 indexed citations
17.
Gan, Yanjun, Qingyun Duan, Aizhong Ye, et al.. (2018). A systematic assessment and reduction of parametric uncertainties for a distributed hydrological model. Journal of Hydrology. 564. 697–711. 29 indexed citations
18.
Li, Jianduo, Fei Chen, Guo Zhang, et al.. (2018). Impacts of Land Cover and Soil Texture Uncertainty on Land Model Simulations Over the Central Tibetan Plateau. Journal of Advances in Modeling Earth Systems. 10(9). 2121–2146. 48 indexed citations
19.
Gong, Wei, Qingyun Duan, Jianduo Li, et al.. (2015). Multiobjective adaptive surrogate modeling‐based optimization for parameter estimation of large, complex geophysical models. Water Resources Research. 52(3). 1984–2008. 77 indexed citations
20.
Di, Zhenhua, Qingyun Duan, Wei Gong, et al.. (2014). Assessing WRF model parameter sensitivity: A case study with 5 day summer precipitation forecasting in the Greater Beijing Area. Geophysical Research Letters. 42(2). 579–587. 65 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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