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.
Climate Change in the Mediterranean Basin (Part II): A Review of Challenges and Uncertainties in Climate Change Modeling and Impact Analyses
202371 citationsLeonardo Noto, Giuseppe Cipolla et al.profile →
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 Leonardo Noto'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 Leonardo Noto with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Leonardo Noto more than expected).
This network shows the impact of papers produced by Leonardo Noto. 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 Leonardo Noto. The network helps show where Leonardo Noto may publish in the future.
Co-authorship network of co-authors of Leonardo Noto
This figure shows the co-authorship network connecting the top 25 collaborators of Leonardo Noto.
A scholar is included among the top collaborators of Leonardo Noto 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 Leonardo Noto. Leonardo Noto is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Francipane, Antonio, et al.. (2018). Object-based image analysis technique for gully mapping using topographic data at very high resolution (VHR). Nova Science Publishers (Nova Science Publishers, Inc.). 17281.1 indexed citations
Arnone, Elisa, et al.. (2015). The role of Soil Water Retention Curve in slope stability analysis in unsaturated and heterogeneous soils.. EGUGA. 12436.1 indexed citations
Noto, Leonardo, et al.. (2015). Integration of fuzzy logic and image analysis for the detection of gullies in the Calhoun critical zone observatory using airborne LiDAR data. AGUFM. 2015.1 indexed citations
16.
Dialynas, Y. G., Satish Bastola, E. Marín-Spiotta, et al.. (2015). Influence of Soil Erosion and Landslide Occurrence on Soil Organic Carbon Storage and Loss in the Luquillo Critical Zone Observatory, Puerto Rico. 2015 AGU Fall Meeting. 2015.2 indexed citations
17.
Arnone, Elisa, Dario Pumo, Francesco Viola, Leonardo Noto, & Goffredo La Loggia. (2013). Rainfall statistics changes in Sicily. Hydrology and earth system sciences. 17(7). 2449–2458.101 indexed citations
18.
Arnone, Elisa, et al.. (2013). Effect of DEM resolution on rainfall-triggered landslide modeling within a triangulated network-based model. A case study in the Luquillo Forest, Puerto Rico. AGU Fall Meeting Abstracts. 2013.1 indexed citations
19.
Arnone, Elisa, et al.. (2008). Effect of DEM resolution and threshold area on the hydrologic response at catchment scale. Institutional Research Information System (University of Udine). 2008.1 indexed citations
20.
Candela, Angela, Leonardo Noto, & Giuseppe Tito Aronica. (2002). Influence of Roughness Surface In Hydrological Response of Semiarid Catchments. EGSGA. 5329.1 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.