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.
Development of a large-sample watershed-scale hydrometeorological data set for the contiguous USA: data set characteristics and assessment of regional variability in hydrologic model performance
2015417 citationsAndrew J. Newman, Martyn Clark et al.Hydrology and earth system sciencesprofile →
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 K. M. Sampson'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 K. M. Sampson with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites K. M. Sampson more than expected).
This network shows the impact of papers produced by K. M. Sampson. 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 K. M. Sampson. The network helps show where K. M. Sampson may publish in the future.
Co-authorship network of co-authors of K. M. Sampson
This figure shows the co-authorship network connecting the top 25 collaborators of K. M. Sampson.
A scholar is included among the top collaborators of K. M. Sampson 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 K. M. Sampson. K. M. Sampson is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
McAllister, Mary Louise, David Gochis, Michael Barlage, et al.. (2020). The Community WRF-Hydro Modeling System Version 5.2 Updates & New Community Focused Testbed. AGU Fall Meeting Abstracts. 2020.1 indexed citations
5.
Knievel, Jason C., Jennifer Boehnert, Maria Frediani, et al.. (2020). A Modeling System for Predicting the Behavior of Wildland Fires by Simulating Their Two-Way Interaction with the Atmosphere. AGU Fall Meeting Abstracts. 2020.1 indexed citations
6.
Vandecrux, Baptiste, Robert S. Fausto, Dirk van As, et al.. (2019). Heat budget of Greenland firn: observed and simulated changes from 1998-2015. EGU General Assembly Conference Abstracts. 19148.
Cosgrove, B., A. L. Dugger, K. M. Sampson, et al.. (2018). Multi-variate evaluation of the NOAA National Water Model. AGU Fall Meeting Abstracts. 2018.2 indexed citations
9.
McAllister, Mary Louise, David Gochis, Michael Barlage, et al.. (2018). The Community WRF-Hydro Modeling System Version 5 melding with the National Water Model: Enhancements and Education. AGU Fall Meeting Abstracts. 2018.1 indexed citations
Dugger, A. L., A. Rafieeinasab, David Gochis, et al.. (2016). Evaluating CONUS-Scale Runoff Simulation across the National Water Model WRF-Hydro Implementation to Disentangle Regional Controls on Streamflow Generation and Model Error Contribution. AGU Fall Meeting Abstracts. 2016.1 indexed citations
Newman, Andrew J., Martyn Clark, K. M. Sampson, et al.. (2015). Development of a large-sample watershed-scale hydrometeorological data set for the contiguous USA: data set characteristics and assessment of regional variability in hydrologic model performance. Hydrology and earth system sciences. 19(1). 209–223.417 indexed citations breakdown →
15.
Monaghan, Andrew J., Sean M. Moore, K. M. Sampson, Charles B. Beard, & Rebecca J. Eisen. (2015). Climate change influences on the annual onset of Lyme disease in the United States. AGU Fall Meeting Abstracts. 2015.1 indexed citations
16.
Gochis, David, Wei Yu, K. M. Sampson, et al.. (2015). Multi-scale model analysis and hindcast of the 2013 Colorado Flood. EGUGA. 7531.1 indexed citations
Gochis, David, Wei Yu, A. L. Dugger, et al.. (2014). Recent Developments and Applications of the WRF-Hydro Modeling System for Continental Scale Water Cycle Predictions. AGUFM. 2014.1 indexed citations
Pavelsky, Tamlin M., et al.. (2005). A Statistical Analysis of Precipitation and River Discharge Variability in the Eurasian Arctic. AGU Fall Meeting Abstracts. 2005.1 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.