Daehyok Shin

433 total citations
10 papers, 260 citations indexed

About

Daehyok Shin is a scholar working on Water Science and Technology, Global and Planetary Change and Environmental Engineering. According to data from OpenAlex, Daehyok Shin has authored 10 papers receiving a total of 260 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Water Science and Technology, 7 papers in Global and Planetary Change and 5 papers in Environmental Engineering. Recurrent topics in Daehyok Shin's work include Hydrology and Watershed Management Studies (9 papers), Flood Risk Assessment and Management (5 papers) and Hydrological Forecasting Using AI (4 papers). Daehyok Shin is often cited by papers focused on Hydrology and Watershed Management Studies (9 papers), Flood Risk Assessment and Management (5 papers) and Hydrological Forecasting Using AI (4 papers). Daehyok Shin collaborates with scholars based in Australia and United States. Daehyok Shin's co-authors include Guillermo A. Baigorria, Ashok Mishra, J. J. O’Brien, James W. Jones, Narendra Tuteja, David M. Kent, MA Bari, Dmitri Kavetski, David McInerney and Fitsum Woldemeskel and has published in prestigious journals such as Hydrology and earth system sciences, JAWRA Journal of the American Water Resources Association and Climate Research.

In The Last Decade

Daehyok Shin

10 papers receiving 257 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Daehyok Shin Australia	7	215	155	52	50	40	10	260
Qingxia Lin China	11	349 1.6×	197 1.3×	64 1.2×	97 1.9×	42 1.1×	16	411
Huating Xu China	7	279 1.3×	118 0.8×	46 0.9×	78 1.6×	49 1.2×	10	332
Dagmawi Asfaw United Kingdom	7	205 1.0×	73 0.5×	36 0.7×	70 1.4×	50 1.3×	11	260
Jaefar Nikbakht Iran	7	291 1.4×	196 1.3×	70 1.3×	28 0.6×	40 1.0×	16	389
Cuiping Yang China	5	296 1.4×	96 0.6×	26 0.5×	47 0.9×	69 1.7×	11	338
Shijie Li China	10	285 1.3×	150 1.0×	50 1.0×	91 1.8×	19 0.5×	17	347
Veber Afonso Figueiredo Costa Brazil	10	210 1.0×	140 0.9×	51 1.0×	58 1.2×	17 0.4×	30	288
Sabab Ali Shah South Korea	10	201 0.9×	172 1.1×	61 1.2×	21 0.4×	27 0.7×	17	325
Zezhong Zhang China	8	249 1.2×	112 0.7×	17 0.3×	31 0.6×	46 1.1×	22	293
Shuai Zhou China	10	254 1.2×	217 1.4×	45 0.9×	44 0.9×	29 0.7×	22	342

Countries citing papers authored by Daehyok Shin

Since Specialization

Citations

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

Fields of papers citing papers by Daehyok Shin

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Daehyok Shin

This figure shows the co-authorship network connecting the top 25 collaborators of Daehyok Shin. A scholar is included among the top collaborators of Daehyok Shin 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 Daehyok Shin. Daehyok Shin is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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10 of 10 papers shown

Woldemeskel, Fitsum, David McInerney, Julien Lerat, et al.. (2018). Evaluating residual error approaches for post-processing monthly and seasonal streamflow forecasts. Biogeosciences (European Geosciences Union). 8 indexed citations

Woldemeskel, Fitsum, David McInerney, Julien Lerat, et al.. (2018). Evaluating post-processing approaches for monthly and seasonal streamflow forecasts. Hydrology and earth system sciences. 22(12). 6257–6278. 39 indexed citations

Bari, MA, et al.. (2016). How streamflow has changed across Australia since the 1950s: evidence from the network of hydrologic reference stations. Hydrology and earth system sciences. 20(9). 3947–3965. 93 indexed citations

Lerat, Julien, Daehyok Shin, Senlin Zhou, et al.. (2015). Dynamic streamflow forecasts within an uncertainty framework for 100 catchments in Australia. 1396. 1 indexed citations

Bari, Mohammed Abdul, et al.. (2014). Hydrologic reference stations to monitor climate-driven streamflow variability and trends. 1048. 13 indexed citations

Tuteja, Narendra, et al.. (2011). Seasonal Streamflow Forecasting with a workflow-based dynamic hydrologic modelling approach. Chan, F., Marinova, D. and Anderssen, R.S. (eds) MODSIM2011, 19th International Congress on Modelling and Simulation.. 3 indexed citations

Shin, Daehyok, et al.. (2011). WAFARi: A new modelling system for Seasonal Streamflow Forecasting service of the Bureau of Meteorology, Australia. Chan, F., Marinova, D. and Anderssen, R.S. (eds) MODSIM2011, 19th International Congress on Modelling and Simulation.. 1 indexed citations

Tuteja, Narendra, Daehyok Shin, Quanxi Shao, et al.. (2011). Experimental evaluation of the dynamic seasonal streamflow forecasting approach. Adelaide Research & Scholarship (AR&S) (University of Adelaide). 12 indexed citations

Baigorria, Guillermo A., James W. Jones, Daehyok Shin, Ashok Mishra, & J. J. O’Brien. (2007). Assessing uncertainties in crop model simulations using daily bias-corrected Regional Circulation Model outputs. Climate Research. 34. 211–222. 84 indexed citations

10.

Shin, Daehyok, et al.. (1998). SPATIAL VARIABILITY OF CONDUCTWITY AS APPLIED TO DISTRIBUTED PARAMETER INFILTRATION MODELS¹. JAWRA Journal of the American Water Resources Association. 34(3). 545–558. 6 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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