Matthew S. Gibbs

2.1k total citations
51 papers, 1.2k citations indexed

About

Matthew S. Gibbs is a scholar working on Water Science and Technology, Global and Planetary Change and Environmental Engineering. According to data from OpenAlex, Matthew S. Gibbs has authored 51 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 33 papers in Water Science and Technology, 20 papers in Global and Planetary Change and 17 papers in Environmental Engineering. Recurrent topics in Matthew S. Gibbs's work include Hydrology and Watershed Management Studies (30 papers), Flood Risk Assessment and Management (14 papers) and Hydrological Forecasting Using AI (13 papers). Matthew S. Gibbs is often cited by papers focused on Hydrology and Watershed Management Studies (30 papers), Flood Risk Assessment and Management (14 papers) and Hydrological Forecasting Using AI (13 papers). Matthew S. Gibbs collaborates with scholars based in Australia, New Zealand and Italy. Matthew S. Gibbs's co-authors include Holger R. Maier, Graeme C. Dandy, Greer B. Humphrey, Stefano Galelli, Andrea Castelletti, John B. Nixon, David McInerney, Luke M. Mosley, Dmitri Kavetski and Mark Thyer and has published in prestigious journals such as The Science of The Total Environment, Water Resources Research and Journal of Hydrology.

In The Last Decade

Matthew S. Gibbs

50 papers receiving 1.1k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Matthew S. Gibbs Australia 18 587 467 427 181 153 51 1.2k
Yiqing Guan China 16 423 0.7× 386 0.8× 364 0.9× 84 0.5× 81 0.5× 38 866
Jungang Luo China 15 541 0.9× 491 1.1× 612 1.4× 193 1.1× 89 0.6× 35 1.1k
C. Sivapragasam India 14 476 0.8× 585 1.3× 357 0.8× 113 0.6× 69 0.5× 62 1.0k
Joel O. Paz United States 27 330 0.6× 542 1.2× 523 1.2× 81 0.4× 327 2.1× 93 2.2k
Xiaotao Hu China 29 362 0.6× 275 0.6× 1.2k 2.9× 99 0.5× 156 1.0× 89 2.8k
Sujay Raghavendra Naganna India 20 583 1.0× 864 1.9× 470 1.1× 94 0.5× 123 0.8× 57 1.8k
Emanuele Barca Italy 19 343 0.6× 341 0.7× 191 0.4× 101 0.6× 158 1.0× 52 914
Hung Soo Kim South Korea 22 839 1.4× 787 1.7× 1.1k 2.6× 167 0.9× 94 0.6× 216 1.8k
Tianfang Xu United States 14 301 0.5× 488 1.0× 365 0.9× 98 0.5× 328 2.1× 32 1.1k
Samad Emamgholizadeh Iran 19 421 0.7× 590 1.3× 193 0.5× 60 0.3× 188 1.2× 52 1.1k

Countries citing papers authored by Matthew S. Gibbs

Since Specialization
Citations

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

Fields of papers citing papers by Matthew S. Gibbs

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Matthew S. Gibbs. 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 Matthew S. Gibbs. The network helps show where Matthew S. Gibbs may publish in the future.

Co-authorship network of co-authors of Matthew S. Gibbs

This figure shows the co-authorship network connecting the top 25 collaborators of Matthew S. Gibbs. A scholar is included among the top collaborators of Matthew S. Gibbs 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 Matthew S. Gibbs. Matthew S. Gibbs 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.
Guo, Danlu, Anna Lintern, Alexander H. Elliott, et al.. (2025). A commentary on strategic community-led directions for water quality modelling in Australia and New Zealand. Journal of Hydrology. 656. 132978–132978.
2.
Wu, Wenyan, et al.. (2025). Moving from single values to trade-off curves for assessing the performance of water resources systems under uncertainty. Environmental Modelling & Software. 193. 106644–106644. 1 indexed citations
3.
Wassens, Skye, et al.. (2024). Stochastic metapopulation dynamics of a threatened amphibian to improve water delivery. Ecosphere. 15(1). 3 indexed citations
4.
Gibbs, Matthew S., et al.. (2023). The SWTools R package for SILO data acquisition, homogeneity testing and correction. Australasian Journal of Water Resources. 28(1). 123–135. 3 indexed citations
5.
Gibbs, Matthew S., Lei Gao, Klaus Joehnk, et al.. (2023). Using hydraulics to evaluate ecological benefits, risks, and trade‐offs from engineered flooding. Hydrological Processes. 37(11). 8 indexed citations
6.
Tibby, John, Deborah Haynes, Matthew S. Gibbs, et al.. (2022). The terminal lakes of the Murray River, Australia, were predominantly fresh before large-scale upstream water abstraction: Evidence from sedimentary diatoms and hydrodynamical modelling. The Science of The Total Environment. 835. 155225–155225. 9 indexed citations
7.
Maier, Holger R., et al.. (2018). Framework for developing hybrid process-driven, artificial neural network and regression models for salinity prediction in river systems. Hydrology and earth system sciences. 22(5). 2987–3006. 51 indexed citations
8.
McInerney, David, Mark Thyer, Dmitri Kavetski, et al.. (2018). A simplified approach to produce probabilistic hydrological model predictions. Environmental Modelling & Software. 109. 306–314. 27 indexed citations
9.
Gibbs, Matthew S., David McInerney, Greer B. Humphrey, et al.. (2018). State updating and calibration period selection to improve dynamic monthly streamflow forecasts for an environmental flow management application. Hydrology and earth system sciences. 22(1). 871–887. 34 indexed citations
10.
Maier, Holger R., et al.. (2017). Modelling salinity in river systems using hybrid process and data-drivenmodels. 1 indexed citations
11.
Gibbs, Matthew S., David McInerney, Greer B. Humphrey, et al.. (2017). State Updating and Calibration Period Selection to Improve Dynamic Monthly Streamflow Forecasts for a Wetland Management Application. 2 indexed citations
12.
Gibbs, Matthew S., et al.. (2015). The effect of barrage flow manipulation on sediment deposition and scouring patterns at the River Murray mouth. 733. 1 indexed citations
13.
Gibbs, Matthew S., et al.. (2011). Evaluating Parameter Sensitivity for Surface Water Modelling of Ungauged Catchments. Adelaide Research & Scholarship (AR&S) (University of Adelaide). 1379. 1 indexed citations
14.
Gibbs, Matthew S.. (2011). Trade Associations in Roman Egypt: Their "raison d'etre". Ancient Society. 291–315. 2 indexed citations
15.
Gibbs, Matthew S., et al.. (2011). Integrated modelling of water delivery options for the Coorong South Lagoon. Chan, F., Marinova, D. and Anderssen, R.S. (eds) MODSIM2011, 19th International Congress on Modelling and Simulation.. 1 indexed citations
16.
Gibbs, Matthew S., Holger R. Maier, & Graeme C. Dandy. (2011). Runoff and salt transport modelling to maximise environmental outcomes in the upper south east of South Australia. Chan, F., Marinova, D. and Anderssen, R.S. (eds) MODSIM2011, 19th International Congress on Modelling and Simulation.. 1 indexed citations
17.
Gibbs, Matthew S., et al.. (2008). Dryland Salinity Decision Support Systems in Data-scarce Regions. Adelaide Research & Scholarship (AR&S) (University of Adelaide). 2653. 4 indexed citations
18.
Gibbs, Matthew S., et al.. (2006). Investigation into the relationship between chlorine decay and water distribution parameters using data driven methods. Mathematical and Computer Modelling. 44(5-6). 485–498. 67 indexed citations
19.
20.
Gibbs, Matthew S. & George F Ronan. (1985). The effects of induced mood states on social problem solving. 2 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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