Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
The probability-distributed principle and runoff production at point and basin scales
1985478 citationsR. J. MooreHydrological Sciences Journalprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of R. J. Moore'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 R. J. Moore with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites R. J. Moore more than expected).
This network shows the impact of papers produced by R. J. Moore. 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 R. J. Moore. The network helps show where R. J. Moore may publish in the future.
Co-authorship network of co-authors of R. J. Moore
This figure shows the co-authorship network connecting the top 25 collaborators of R. J. Moore.
A scholar is included among the top collaborators of R. J. Moore 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 R. J. Moore. R. J. Moore is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Wells, Steven, Steven J. Cole, R. J. Moore, et al.. (2019). Distributed hydrological modelling for forecasting water discharges from the land area draining to the Great Barrier Reef coastline. EGU General Assembly Conference Abstracts. 16408.1 indexed citations
5.
Cranston, Michael, et al.. (2012). Countrywide flood forecasting in Scotland: challenges for hydrometeorological model uncertainty and prediction. NERC Open Research Archive (Natural Environment Research Council).15 indexed citations
6.
Price, David, et al.. (2012). Representing the spatial variability of rainfall for input to the G2G distributed flood forecasting model: operational experience from the Flood Forecasting Centre. NERC Open Research Archive (Natural Environment Research Council).2 indexed citations
7.
Moore, R. J., et al.. (2010). Sources of uncertainty and probability bands for flood forecasts: an upland catchment case study. EGUGA. 15609.1 indexed citations
8.
Cole, Steven J., Alice J. Robson, Victoria A. Bell, & R. J. Moore. (2009). Model initialisation, data assimilation and probabilistic flood forecasting for distributed hydrological models. EGUGA. 8048.5 indexed citations
Cole, Steven J., R. J. Moore, & Nigel Roberts. (2007). Using high resolution Numerical Weather Prediction models to reduce and estimate uncertainty in flood forecasting. AGU Fall Meeting Abstracts. 2007.1 indexed citations
11.
Moore, R. J., Steven J. Cole, Victoria A. Bell, & David Jones. (2006). Issues in flood forecasting: ungauged basins, extreme floods and uncertainty. IAHS-AISH publication. 103–122.64 indexed citations
Moore, R. J.. (1985). The probability-distributed principle and runoff production at point and basin scales. Hydrological Sciences Journal. 30(2). 273–297.478 indexed citations breakdown →
17.
Moore, R. J.. (1982). Algorithm AS 187: Derivatives of the Incomplete Gamma Integral. Journal of the Royal Statistical Society Series A (Statistics in Society).12 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.