Citations per year, relative to Anthony S. Kiem Anthony S. Kiem (= 1×)
peers
Jacob Schewe
Countries citing papers authored by Anthony S. Kiem
Since
Specialization
Citations
This map shows the geographic impact of Anthony S. Kiem'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 Anthony S. Kiem with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Anthony S. Kiem more than expected).
This network shows the impact of papers produced by Anthony S. Kiem. 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 Anthony S. Kiem. The network helps show where Anthony S. Kiem may publish in the future.
Co-authorship network of co-authors of Anthony S. Kiem
This figure shows the co-authorship network connecting the top 25 collaborators of Anthony S. Kiem.
A scholar is included among the top collaborators of Anthony S. Kiem 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 Anthony S. Kiem. Anthony S. Kiem is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Willgoose, Garry, et al.. (2015). Modelling daily rainfall along the east coast of Australia using a compound distribution Markov Chain model. NOVA (University of Newcastle Australia). 625.3 indexed citations
9.
Magee, Andrew D., Danielle C. Verdon‐Kidd, & Anthony S. Kiem. (2015). Can Indian Ocean SST variability impact TC activity in the South Pacific? A Spatial Analysis. EGU General Assembly Conference Abstracts. 3126.1 indexed citations
10.
Kiem, Anthony S., et al.. (2015). Spatial analysis of Australian seasonal rainfall anomalies and their relation to east coast lows on the Eastern-seaboard. NOVA (University of Newcastle Australia). 1205.1 indexed citations
11.
Kiem, Anthony S., et al.. (2015). The impact of East Coast Lows (ECL) on eastern Australia's hydroclimate - do we need to consider sub-categories of ECLs?. NOVA (University of Newcastle Australia). 1506.1 indexed citations
12.
Willgoose, Garry, et al.. (2015). Use of NARCliM rainfall data for simulating streamflow in the Williams River catchment. NOVA (University of Newcastle Australia). 617.1 indexed citations
13.
Willgoose, Garry, et al.. (2015). Testing the statistics of dynamically downscaled rainfall data for the east coast of NSW. 1051.2 indexed citations
14.
Kiem, Anthony S. & Danielle C. Verdon‐Kidd. (2011). Adapting to Climate Variability and Change: Limitations of Relying on Climate Model Outputs. 802.1 indexed citations
15.
Kiem, Anthony S., et al.. (2008). Assessing the Vulnerability of Victoria's Water Resources Due to Climate Variability and Change. 759.1 indexed citations
16.
Verdon‐Kidd, Danielle C., et al.. (2005). Multi-Decadal Variability of Rainfall and Streamflow Across Eastern Australia. NOVA (University of Newcastle Australia). 42–52.15 indexed citations
17.
Kiem, Anthony S., et al.. (2005). Relationship between ENSO and snow covered area in the Mekong and Yellow river basins.. IAHS-AISH publication. 255–264.17 indexed citations
18.
Kiem, Anthony S. & Stewart W. Franks. (2003). The impact of climate variability on flood risk. IAHS-AISH publication. 11–17.2 indexed citations
19.
Kiem, Anthony S. & Stewart W. Franks. (2003). Elevated drought risk due to multi-decadal climate variability. IAHS-AISH publication. 165–172.2 indexed citations
20.
Micevski, Tom, Anthony S. Kiem, Stewart W. Franks, & George Kuczera. (2003). Multidecadal Variability in New South Wales Flood Data. 1.2 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.