Lele Shu

651 total citations
29 papers, 270 citations indexed

About

Lele Shu is a scholar working on Water Science and Technology, Global and Planetary Change and Atmospheric Science. According to data from OpenAlex, Lele Shu has authored 29 papers receiving a total of 270 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Water Science and Technology, 14 papers in Global and Planetary Change and 12 papers in Atmospheric Science. Recurrent topics in Lele Shu's work include Hydrology and Watershed Management Studies (18 papers), Climate variability and models (7 papers) and Cryospheric studies and observations (6 papers). Lele Shu is often cited by papers focused on Hydrology and Watershed Management Studies (18 papers), Climate variability and models (7 papers) and Cryospheric studies and observations (6 papers). Lele Shu collaborates with scholars based in China, United States and France. Lele Shu's co-authors include Paul Ullrich, Christopher Duffy, Yu Zhang, Paul C. Hanson, Hilary A. Dugan, Kelly M. Cobourn, Cayelan C. Carey, Robert Ladwig, Zhaoguo Li and Hao Chen and has published in prestigious journals such as Nature, SHILAP Revista de lepidopterología and Ecology.

In The Last Decade

Lele Shu

26 papers receiving 265 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Lele Shu China 11 145 101 91 66 44 29 270
Kyongho Son United States 10 253 1.7× 112 1.1× 191 2.1× 78 1.2× 57 1.3× 20 381
Nathan C. Healey United States 8 174 1.2× 60 0.6× 171 1.9× 79 1.2× 58 1.3× 14 342
Cristóbal Puelma Chile 3 196 1.4× 70 0.7× 207 2.3× 110 1.7× 18 0.4× 3 314
André St‐Hilaire Canada 12 161 1.1× 75 0.7× 111 1.2× 39 0.6× 31 0.7× 30 310
Jean Nepomuscene Namugize United Kingdom 7 153 1.1× 45 0.4× 160 1.8× 34 0.5× 45 1.0× 8 324
Kangsheng Wu United States 7 275 1.9× 76 0.8× 176 1.9× 51 0.8× 118 2.7× 8 355
Diana Šarauskienė Lithuania 13 189 1.3× 39 0.4× 176 1.9× 55 0.8× 34 0.8× 36 365
Borbála Széles Austria 9 257 1.8× 95 0.9× 247 2.7× 129 2.0× 22 0.5× 19 373
Darius Jakimavičius Lithuania 12 137 0.9× 31 0.3× 136 1.5× 41 0.6× 41 0.9× 29 311
Sarmistha Singh United States 10 103 0.7× 49 0.5× 122 1.3× 47 0.7× 25 0.6× 23 231

Countries citing papers authored by Lele Shu

Since Specialization
Citations

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

Fields of papers citing papers by Lele Shu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Lele Shu

This figure shows the co-authorship network connecting the top 25 collaborators of Lele Shu. A scholar is included among the top collaborators of Lele Shu 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 Lele Shu. Lele Shu 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.
Chen, Yaling, Xianhong Meng, Lele Shu, et al.. (2025). The Sensitivity of Land‐Atmosphere Coupling to Soil Moisture Over the Tibetan Plateau Based on the Improved Noah‐MP Model. Journal of Geophysical Research Atmospheres. 130(8).
2.
Meng, Xianhong, Yu Zhang, Lunyu Shang, et al.. (2025). A 10-Year Dataset of Land Surface Observations for the Semi-Humid Alpine Grassland in the Source Region of the Yellow River. Advances in Atmospheric Sciences. 42(6). 1261–1272.
3.
4.
Meng, Xianhong, Siqiong Luo, Lin Zhao, et al.. (2025). How does the continuous changes in spring soil moisture over the Tibetan Plateau affect the summer precipitation in East China?. Climate Dynamics. 63(4). 2 indexed citations
5.
Shu, Lele, Xiaodong Li, Yan Chang, et al.. (2024). Advancing understanding of lake–watershed hydrology: a fully coupled numerical model illustrated by Qinghai Lake. Hydrology and earth system sciences. 28(7). 1477–1491. 10 indexed citations
6.
Shu, Lele, Paul Ullrich, Xianhong Meng, et al.. (2024). rSHUD v2.0: advancing the Simulator for Hydrologic Unstructured Domains and unstructured hydrological modeling in the R environment. Geoscientific model development. 17(2). 497–527. 4 indexed citations
7.
Adhikari, Tirtha Raj, Lele Shu, Suraj Shrestha, et al.. (2024). Evaluation of distributed and semi-distributed hydrological models in complex River Basin system, Nepal. SHILAP Revista de lepidopterología. 8. 49–57. 1 indexed citations
8.
Meng, Xianhong, Lele Shu, Hao Chen, et al.. (2024). An Evaluation of Evapotranspiration Products over the Tibetan Plateau. Journal of Hydrometeorology. 25(11). 1665–1677. 1 indexed citations
9.
Wen, Lijuan, et al.. (2023). Study on Characteristics of Water Level Variations and Water Balance of the Largest Lake in the Qinghai-Tibet Plateau. Water. 15(20). 3614–3614. 6 indexed citations
10.
Meng, Xianhong, Shihua Lyu, Zhaoguo Li, et al.. (2023). Dataset of Comparative Observations for Land Surface Processes over the Semi-Arid Alpine Grassland against Alpine Lakes in the Source Region of the Yellow River. Advances in Atmospheric Sciences. 40(6). 1142–1157. 11 indexed citations
11.
Li, Zhaoguo, Shaobo Zhang, Xianhong Meng, et al.. (2022). Effect of snow cover on water and heat transfer in alpine meadows in the source region of Yellow River. The Science of The Total Environment. 859(Pt 1). 160205–160205. 11 indexed citations
12.
Liu, Yumeng, Xianhong Meng, Lin Zhao, et al.. (2022). Associated Summer Rainfall Changes over the Three Rivers Source Region in China with the East Asian Westerly Jet from 1979 to 2015. Journal of Applied Meteorology and Climatology. 61(10). 1385–1397. 5 indexed citations
13.
14.
Ladwig, Robert, Paul C. Hanson, Hilary A. Dugan, et al.. (2021). Lake thermal structure drives interannual variability in summer anoxia dynamics in a eutrophic lake over 37 years. Hydrology and earth system sciences. 25(2). 1009–1032. 65 indexed citations
15.
16.
Shu, Lele, Paul Ullrich, & Christopher Duffy. (2020). Simulator for Hydrologic Unstructured Domains (SHUD v1.0): numerical modeling of watershed hydrology with the finite volume method. Geoscientific model development. 13(6). 2743–2762. 13 indexed citations
17.
Ullrich, Paul, et al.. (2020). Using Convolutional Neural Networks for Streamflow Projection in California. Frontiers in Water. 2. 43 indexed citations
18.
Shu, Lele, Paul Ullrich, & Christopher Duffy. (2019). A fast/automated watershed modeling workflow with the Penn State Integrated Hydrologic Model (PIHM): Essential data, simulation, applications and visualization. AGU Fall Meeting Abstracts. 2019.
19.
Ward, Nicole K., Lele Shu, Jemma Stachelek, et al.. (2018). Integrating fast and slow processes is essential for simulating human–freshwater interactions. AMBIO. 48(10). 1169–1182. 12 indexed citations
20.
Navaratna, Dimuth, Lele Shu, & Veeriah Jegatheesan. (2010). Existence, impacts, transport and treatments of herbicides in Great Barrier Reef catchments in Australia. Deakin Research Online (Deakin University). 425–457. 12 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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