Jong Ahn Chun

1.8k total citations
64 papers, 1.4k citations indexed

About

Jong Ahn Chun is a scholar working on Global and Planetary Change, Water Science and Technology and Environmental Engineering. According to data from OpenAlex, Jong Ahn Chun has authored 64 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 31 papers in Global and Planetary Change, 30 papers in Water Science and Technology and 20 papers in Environmental Engineering. Recurrent topics in Jong Ahn Chun's work include Hydrology and Watershed Management Studies (30 papers), Plant Water Relations and Carbon Dynamics (11 papers) and Flood Risk Assessment and Management (11 papers). Jong Ahn Chun is often cited by papers focused on Hydrology and Watershed Management Studies (30 papers), Plant Water Relations and Carbon Dynamics (11 papers) and Flood Risk Assessment and Management (11 papers). Jong Ahn Chun collaborates with scholars based in South Korea, United States and France. Jong Ahn Chun's co-authors include Sang‐Soo Baek, JongCheol Pyo, Daeha Kim, Richard A. Cooke, Moon‐Seong Kang, Kyung Hwa Cho, J. W. Eheart, Yakov Pachepsky, Mayzonee Ligaray and Woo‐Seop Lee and has published in prestigious journals such as Water Resources Research, Journal of Hydrology and Atmospheric Environment.

In The Last Decade

Jong Ahn Chun

62 papers receiving 1.3k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jong Ahn Chun South Korea 21 589 565 494 181 169 64 1.4k
Dongguo Shao China 25 992 1.7× 530 0.9× 421 0.9× 202 1.1× 328 1.9× 83 2.0k
Heng Lü China 19 440 0.7× 409 0.7× 262 0.5× 348 1.9× 155 0.9× 59 1.7k
Yunfeng Qiao China 20 359 0.6× 329 0.6× 244 0.5× 118 0.7× 111 0.7× 64 1.2k
Rui Zou China 21 611 1.0× 327 0.6× 517 1.0× 377 2.1× 236 1.4× 52 1.6k
Geophrey K. Anornu Ghana 21 699 1.2× 373 0.7× 622 1.3× 117 0.6× 189 1.1× 76 1.7k
Ayşegül Tanık Türkiye 18 327 0.6× 285 0.5× 541 1.1× 80 0.4× 82 0.5× 95 1.3k
J. Hoogeveen Italy 8 818 1.4× 473 0.8× 525 1.1× 43 0.2× 107 0.6× 12 1.7k
Chris M. Mannaerts Netherlands 21 584 1.0× 673 1.2× 277 0.6× 130 0.7× 57 0.3× 65 1.4k
Raj Cibin United States 22 1.0k 1.8× 685 1.2× 582 1.2× 323 1.8× 54 0.3× 76 1.7k
Akihiko Kondoh Japan 20 314 0.5× 586 1.0× 446 0.9× 43 0.2× 161 1.0× 117 1.3k

Countries citing papers authored by Jong Ahn Chun

Since Specialization
Citations

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

Fields of papers citing papers by Jong Ahn Chun

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jong Ahn Chun. 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 Jong Ahn Chun. The network helps show where Jong Ahn Chun may publish in the future.

Co-authorship network of co-authors of Jong Ahn Chun

This figure shows the co-authorship network connecting the top 25 collaborators of Jong Ahn Chun. A scholar is included among the top collaborators of Jong Ahn Chun 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 Jong Ahn Chun. Jong Ahn Chun 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
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Jang, Jiyi, Ather Abbas, Hye‐In Kim, et al.. (2023). Prediction and interpretation of pathogenic bacteria occurrence at a recreational beach using data-driven algorithms. Ecological Informatics. 78. 102370–102370. 7 indexed citations
3.
Kim, Daeha, et al.. (2023). Constraining the power-function complementary relationship with a steady-state Budyko equation for predicting terrestrial evapotranspiration globally. Agricultural and Forest Meteorology. 344. 109808–109808. 4 indexed citations
4.
Cha, Dong‐Hyun, Chang‐Keun Song, Myong‐In Lee, et al.. (2023). Role of Land–Atmosphere Interaction in the 2016 Northeast Asia Heat Wave: Impact of Soil Moisture Initialization. Journal of Geophysical Research Atmospheres. 128(2). 11 indexed citations
5.
Kim, Daeha, et al.. (2023). Divergent flash drought risks indicated by evaporative stress and soil moisture projections under warming scenarios. Environmental Research Letters. 18(9). 94023–94023. 4 indexed citations
6.
Abbas, Ather, Laurie Boithias, Yakov Pachepsky, et al.. (2022). AI4Water v1.0: an open-source python package for modeling hydrological time series using data-driven methods. Geoscientific model development. 15(7). 3021–3039. 22 indexed citations
7.
Chun, Jong Ahn, et al.. (2022). Climate risk management for the rainfed rice yield in Lao PDR using APCC MME seasonal forecasts. Agricultural Water Management. 274. 107976–107976. 3 indexed citations
8.
Kim, Daeha, Minha Choi, & Jong Ahn Chun. (2022). Linking the complementary evaporation relationship with the Budyko framework for ungauged areas in Australia. Hydrology and earth system sciences. 26(23). 5955–5969. 7 indexed citations
9.
Abbas, Ather, Laurie Boithias, Yakov Pachepsky, et al.. (2021). AI4Water v1.0: An open source python package for modeling hydrological time series using data-driven methods. 2 indexed citations
10.
Chun, Jong Ahn. (2020). Assessment of Noah land surface model-based soil moisture using GRACE-observed TWSA and TWSC. Journal of Korea Water Resources Association. 53(4). 285–291. 2 indexed citations
11.
Baek, Sang‐Soo, Mayzonee Ligaray, Yakov Pachepsky, et al.. (2020). Assessment of a green roof practice using the coupled SWMM and HYDRUS models. Journal of Environmental Management. 261. 109920–109920. 56 indexed citations
12.
Lee, Hyunju, Woo‐Seop Lee, Jong Ahn Chun, & Hwa Woon Lee. (2020). Probabilistic Heat Wave Forecast Based on a Large-Scale Circulation Pattern Using the TIGGE Data. Weather and Forecasting. 35(2). 367–377. 5 indexed citations
13.
Kim, Daeha, et al.. (2019). Incorporating the logistic regression into a decision-centric assessment of climate change impacts on a complex river system. Hydrology and earth system sciences. 23(2). 1145–1162. 13 indexed citations
14.
Timlin, Dennis, et al.. (2019). Evaluation of the agricultural policy environmental extender (APEX) for the Chesapeake Bay watershed. Agricultural Water Management. 221. 477–485. 12 indexed citations
15.
Kim, Daeha, et al.. (2018). Assessing water supply capacity in a complex river basin under climate change using the logistic eco-engineering decision scaling framework. Biogeosciences (European Geosciences Union). 1 indexed citations
16.
Kim, Daeha, Il‐Won Jung, & Jong Ahn Chun. (2017). A comparative assessment of rainfall–runoff modelling against regional flow duration curves for ungauged catchments. Hydrology and earth system sciences. 21(11). 5647–5661. 16 indexed citations
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18.
Ahmed, Mukhtar, Muhammad Asif, Muhammad Sajad, et al.. (2013). Could agricultural system be adapted to climate change?: A review. Australian Journal of Crop Science. 7(11). 1642–1653. 14 indexed citations
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20.
Chun, Jong Ahn, et al.. (2010). Runoff Losses of Suspended Sediment, Nitrogen, and Phosphorus from a Small Watershed in Korea. Journal of Environmental Quality. 39(3). 981–990. 19 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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