Haksu Lee

1.3k citations
36 papers · 826 · h-index 14

Impact in

Papers in

Haksu Lee

32 papers receiving 798 citations

Peers

Haksu Lee
Comparison fields: 5 of 57
  • Water Science and Technology 544
  • Global and Planetary Change 584
  • Atmospheric Science 461
  • Environmental Engineering 248
  • Computer Science Applications 16
Replace Davide Muraro with:
Davide Muraro Italy
Lu Su United States
Qi Tang Germany
Simon Etter Switzerland
Linda Speight United Kingdom
Julie Demargne United States
Priyank J. Sharma India
Louise Arnal United Kingdom
Jan Verkade Netherlands
Teresita Canchala Colombia
Haksu Lee relative to Davide Muraro Italy Davide Muraro's profile →
Citations per field
00.5×2×2.5×
Davide Muraro · 1×
Citations per year

Countries citing papers authored by Haksu Lee

Since Specialization
Citations

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

Fields of papers citing papers by Haksu Lee

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Haksu Lee, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Haksu Lee Line = papers co-authored together Haksu Lee links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 36 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2013243
2 2011114
3 201570
4 201444
5 201444
6 201236
7 201435
8 201828
9 200424
10 201722
11 201919
12 201918
13 201716
14 201414
15 201213
16 201411
17 201511
18 20169
19
Representative Elementary Watershed (REW) approach: A new blueprint for distributed hydrological modelling at the catchment scale
20058
20 20218

About Haksu Lee

Haksu Lee is a scholar working on Water Science and Technology, Atmospheric Science, Global and Planetary Change, Environmental Engineering and Organic Chemistry, having authored 36 papers that have together received 826 indexed citations. Recurring topics across this work include Hydrology and Watershed Management Studies (24 papers), Meteorological Phenomena and Simulations (20 papers), Flood Risk Assessment and Management (11 papers), Hydrological Forecasting Using AI (7 papers), Precipitation Measurement and Analysis (6 papers), Climate variability and models (4 papers), Soil Moisture and Remote Sensing (3 papers) and Supramolecular Self-Assembly in Materials (3 papers). The work is most often cited by research in Water Science and Technology (544 citations), Global and Planetary Change (584 citations), Atmospheric Science (461 citations), Environmental Engineering (248 citations) and Computer Science Applications (16 citations). Haksu Lee has collaborated with scholars based in United States, South Korea and Netherlands. Frequent co-authors include Dong‐Jun Seo, Dong-Jun Seo, Victor Koren, Limin Wu, James Dean Brown, Minxue He, Satish Kumar Regonda, John C. Schaake, Robert Hartman and H. Herr. Their work appears in journals such as Journal of Hydrology, Journal of Hydrometeorology, Stochastic Environmental Research and Risk Assessment, Advances in Water Resources and ACS Applied Materials & Interfaces.

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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