Ryan T. Bailey

4.0k total citations
134 papers, 2.9k citations indexed

About

Ryan T. Bailey is a scholar working on Water Science and Technology, Environmental Engineering and Global and Planetary Change. According to data from OpenAlex, Ryan T. Bailey has authored 134 papers receiving a total of 2.9k indexed citations (citations by other indexed papers that have themselves been cited), including 88 papers in Water Science and Technology, 62 papers in Environmental Engineering and 40 papers in Global and Planetary Change. Recurrent topics in Ryan T. Bailey's work include Hydrology and Watershed Management Studies (87 papers), Groundwater flow and contamination studies (51 papers) and Groundwater and Isotope Geochemistry (37 papers). Ryan T. Bailey is often cited by papers focused on Hydrology and Watershed Management Studies (87 papers), Groundwater flow and contamination studies (51 papers) and Groundwater and Isotope Geochemistry (37 papers). Ryan T. Bailey collaborates with scholars based in United States, Spain and Denmark. Ryan T. Bailey's co-authors include Mazdak Arabi, Timothy K. Gates, John W. Jenson, Tyler Wible, Rosemary M. Records, Domenico Baù, Seonggyu Park, Katrin Bieger, Jeffrey G. Arnold and Mehdi Ahmadi and has published in prestigious journals such as The Science of The Total Environment, Water Research and Scientific Reports.

In The Last Decade

Ryan T. Bailey

129 papers receiving 2.9k citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Ryan T. Bailey 1.7k 1.3k 804 694 464 134 2.9k
Jason J. Gurdak 1.6k 0.9× 1.3k 1.0× 930 1.2× 1.2k 1.7× 283 0.6× 47 2.9k
Richard G. Niswonger 2.8k 1.6× 2.4k 1.8× 1.1k 1.3× 902 1.3× 464 1.0× 69 4.0k
Edwin H. Sutanudjaja 1.9k 1.1× 954 0.7× 1.3k 1.6× 486 0.7× 440 0.9× 78 3.2k
Kristine Walraevens 1.1k 0.6× 1.5k 1.1× 499 0.6× 1.4k 2.1× 364 0.8× 190 3.0k
Laurent Ruiz 1.6k 1.0× 1.0k 0.8× 685 0.9× 791 1.1× 229 0.5× 99 3.2k
David E. Prudic 1.7k 1.0× 1.7k 1.3× 621 0.8× 858 1.2× 285 0.6× 55 2.6k
Nerantzis Kazakis 1.8k 1.0× 2.3k 1.7× 1.4k 1.8× 1.5k 2.2× 369 0.8× 81 4.3k
Helen E. Dahlke 1.0k 0.6× 716 0.5× 515 0.6× 376 0.5× 218 0.5× 98 2.0k
Jerker Jarsjö 1.2k 0.7× 621 0.5× 696 0.9× 463 0.7× 309 0.7× 115 3.0k
Micòl Mastrocicco 862 0.5× 1.5k 1.1× 407 0.5× 1.5k 2.1× 192 0.4× 145 3.1k

Countries citing papers authored by Ryan T. Bailey

Since Specialization
Citations

This map shows the geographic impact of Ryan T. Bailey'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 Ryan T. Bailey with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ryan T. Bailey more than expected).

Fields of papers citing papers by Ryan T. Bailey

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ryan T. Bailey. 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 Ryan T. Bailey. The network helps show where Ryan T. Bailey may publish in the future.

Co-authorship network of co-authors of Ryan T. Bailey

This figure shows the co-authorship network connecting the top 25 collaborators of Ryan T. Bailey. A scholar is included among the top collaborators of Ryan T. Bailey 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 Ryan T. Bailey. Ryan T. Bailey 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.
Molina‐Navarro, Eugenio, et al.. (2025). Assessing improvements in environmental flow estimation through the use of a coupled surface-groundwater model. Groundwater for Sustainable Development. 29. 101426–101426. 1 indexed citations
2.
3.
Bailey, Ryan T., et al.. (2025). Modeling agro-hydrological surface-subsurface processes in a semi-arid, intensively irrigated river basin. Journal of Hydrology Regional Studies. 57. 102188–102188. 1 indexed citations
4.
Bailey, Ryan T., et al.. (2025). SWAT+MODFLOW: a new hydrologic model for simulating surface-subsurface flow in managed watersheds. Geoscientific model development. 18(17). 5681–5697. 1 indexed citations
5.
Bailey, Ryan T., et al.. (2024). Quantifying climate change impacts on future water resources and salinity transport in a high semi-arid watershed. Journal of Contaminant Hydrology. 261. 104289–104289. 3 indexed citations
6.
Mirchi, Ali, Vahid Nourani, Jeffrey M. Sadler, et al.. (2024). Stream salinity prediction in data-scarce regions: Application of transfer learning and uncertainty quantification. Journal of Contaminant Hydrology. 266. 104418–104418. 1 indexed citations
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Bailey, Ryan T., et al.. (2024). Mutual impact of salinity and climate change on crop production water footprint in a semi-arid agricultural watershed: Application of SWAT-MODFLOW-Salt. The Science of The Total Environment. 955. 176973–176973. 3 indexed citations
9.
Bailey, Ryan T.. (2024). Quantifying hydrologic fluxes in an irrigated region characterized by groundwater return flows. Journal of Hydrology. 648. 132402–132402.
10.
Boithias, Laurie, Vianney Sivelle, Ryan T. Bailey, et al.. (2024). Evaluation of precipitation products for small karst catchment hydrological modeling in data-scarce mountainous regions. Journal of Hydrology. 645. 132131–132131. 2 indexed citations
11.
Bailey, Ryan T., Jeremy T. White, Jeffrey G. Arnold, et al.. (2024). A framework for parameter estimation, sensitivity analysis, and uncertainty analysis for holistic hydrologic modeling using SWAT+. Hydrology and earth system sciences. 28(1). 21–48. 27 indexed citations
12.
Grigg, Neil S., Ryan T. Bailey, & Ryan Smith. (2023). Stream-Aquifer Systems in Semi-Arid Regions: Hydrologic, Legal, and Management Issues. Hydrology. 10(12). 224–224.
13.
Park, Seonggyu, et al.. (2023). Introducing APEXMOD - A QGIS plugin for developing coupled surface-subsurface hydrologic modeling framework of APEX, MODFLOW, and RT3D-Salt. Environmental Modelling & Software. 165. 105723–105723. 3 indexed citations
14.
Bailey, Ryan T., et al.. (2021). APEX-MODFLOW: A New integrated model to simulate hydrological processes in watershed systems. Environmental Modelling & Software. 143. 105093–105093. 18 indexed citations
15.
Bailey, Ryan T., et al.. (2019). Coupled SWAT-MODFLOW model for large-scale mixed agro-urban river basins. Environmental Modelling & Software. 115. 200–210. 117 indexed citations
16.
17.
Bailey, Ryan T., et al.. (2019). A salinity module for SWAT to simulate salt ion fate and transport at the watershed scale. Hydrology and earth system sciences. 23(7). 3155–3174. 34 indexed citations
18.
Wei, Xiaoyan & Ryan T. Bailey. (2017). Using SWAT-MODFLOW to simulate groundwater flow and groundwater-surface water interactions in an intensively irrigated stream-aquifer system. AGU Fall Meeting Abstracts. 2017. 2 indexed citations
19.
Bailey, Ryan T., Mehdi Ahmadi, Timothy K. Gates, & Mazdak Arabi. (2015). Spatially distributed influence of agro-environmental factors governing nitrate fate and transport in an irrigated stream–aquifer system. Hydrology and earth system sciences. 19(12). 4859–4876. 3 indexed citations
20.
Bailey, Ryan T. & Domenico Baù. (2012). Estimating geostatistical parameters and spatially-variable hydraulic conductivity within a catchment system using an ensemble smoother. Hydrology and earth system sciences. 16(2). 287–304. 34 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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