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.
A Nearest Neighbor Bootstrap For Resampling Hydrologic Time Series
1996602 citationsUpmanu Lall et al.Water Resources Researchprofile →
Causes, impacts and patterns of disastrous river floods
2021401 citationsBruno Merz, Heidi Kreibich et al.profile →
National trends in drinking water quality violations
This map shows the geographic impact of Upmanu Lall'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 Upmanu Lall with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Upmanu Lall more than expected).
This network shows the impact of papers produced by Upmanu Lall. 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 Upmanu Lall. The network helps show where Upmanu Lall may publish in the future.
Co-authorship network of co-authors of Upmanu Lall
This figure shows the co-authorship network connecting the top 25 collaborators of Upmanu Lall.
A scholar is included among the top collaborators of Upmanu Lall 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 Upmanu Lall. Upmanu Lall is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Lall, Upmanu, et al.. (2017). Assessing Risks of Mine Tailing Dam Failures. AGU Fall Meeting Abstracts. 2017.3 indexed citations
11.
Lall, Upmanu, et al.. (2017). ENSO Dynamics and Trends, AN Alternate View. AGU Fall Meeting Abstracts. 2017.1 indexed citations
12.
Josset, Laureline, et al.. (2017). Application of Deep Learning and Supervised Learning Methods to Recognize Nonlinear Hidden Pattern in Water Stress Levels from Spatiotemporal Datasets across Rural and Urban US Counties. AGUFM. 2017.1 indexed citations
13.
Cioffi, Francesco, et al.. (2014). A statistical forecast model for Tropical Cyclone Rainfall and flood events for the Hudson River. IRIS Research product catalog (Sapienza University of Rome). 3568.1 indexed citations
14.
Troy, Tara J., Naresh Devineni, Carlos Lima, & Upmanu Lall. (2013). Moving towards a new paradigm for global flood risk estimation. EGUGA.
15.
Merz, Bruno, Heidi Kreibich, & Upmanu Lall. (2012). What are the important flood damage-influencing parameters? A data mining approach. Publication Database GFZ (GFZ German Research Centre for Geosciences). 7518.
16.
Longuevergne, Laurent, R. J. Harding, Martin C. Todd, et al.. (2010). Groundwater and global hydrological change – current challenges and new insight. In: Hydrocomplexity: New Tools for Solving Wicked Water Problems. UCL Discovery (University College London).
17.
Lall, Upmanu, et al.. (2008). Water in the 21st Century: Defining the Elements of Global Crises and Potential Solutions. Journal of international affairs. 61(2). 1.14 indexed citations
18.
Siegfried, Tobias, Ram Fishman, Vijay Modi, & Upmanu Lall. (2008). An Entitlement Approach to Address the Water-Energy-Food Nexus in Rural India. AGU Fall Meeting Abstracts. 2008.6 indexed citations
19.
Lima, Carlos, Upmanu Lall, & Francisco Assis Souza Filho. (2006). Influences of ENSO Events on the Hydropower Production in Brazil. AGU Fall Meeting Abstracts. 2006.1 indexed citations
20.
Cook, E. R. & Upmanu Lall. (2002). North American Drought and Wetness Reconstructed From Long Tree-Ring Records. AGUFM. 2002.1 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.