Sella Nevo

935 total citations · 2 hit papers
10 papers, 314 citations indexed

About

Sella Nevo is a scholar working on Water Science and Technology, Global and Planetary Change and Environmental Engineering. According to data from OpenAlex, Sella Nevo has authored 10 papers receiving a total of 314 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Water Science and Technology, 6 papers in Global and Planetary Change and 5 papers in Environmental Engineering. Recurrent topics in Sella Nevo's work include Hydrology and Watershed Management Studies (6 papers), Flood Risk Assessment and Management (6 papers) and Hydrological Forecasting Using AI (5 papers). Sella Nevo is often cited by papers focused on Hydrology and Watershed Management Studies (6 papers), Flood Risk Assessment and Management (6 papers) and Hydrological Forecasting Using AI (5 papers). Sella Nevo collaborates with scholars based in United States, Austria and Israel. Sella Nevo's co-authors include Grey Nearing, Daniel Klotz, Martin Gauch, Frederik Kratzert, Guy Shalev, Oren Gilon, Yossi Matias, Avinatan Hassidim, Lukas Gudmundsson and Nans Addor and has published in prestigious journals such as Nature, Scientific Reports and Hydrology and earth system sciences.

In The Last Decade

Sella Nevo

8 papers receiving 303 citations

Hit Papers

align trajectories

What are hit papers?

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.

Global prediction of extreme floods in ungauged watersheds

2024 134 citations Grey Nearing, Déborah Cohen et al. Nature profile →
Caravan - A global community dataset for large-sample hydrology

2023 124 citations Frederik Kratzert, Grey Nearing et al. Scientific Data profile →

Peers

Sella Nevo

WST

GPC

EE

AS

AI

Amar Kant Gautam India

Pierre Brigode France

Dong-Sin Shih Taiwan

F. Salas United States

Arno Adi Kuntoro Indonesia

Ralf Loritz Germany

Nitesh Patidar India

Aditya P. Nilawar India

Amiya Basak India

Hamid Mohebzadeh Canada

Amar Kant Gautam India

Sella Nevo

220 ×1.1

WST

220 ×1.1

GPC

273 ×1.7

EE

46 ×0.7

AS

16 ×0.6

AI

Citations per year, relative to Sella Nevo Sella Nevo (= 1×) peers Amar Kant Gautam

Countries citing papers authored by Sella Nevo

Since Specialization

Citations

This map shows the geographic impact of Sella Nevo'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 Sella Nevo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sella Nevo more than expected).

Fields of papers citing papers by Sella Nevo

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sella Nevo. 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 Sella Nevo. The network helps show where Sella Nevo may publish in the future.

Co-authorship network of co-authors of Sella Nevo

This figure shows the co-authorship network connecting the top 25 collaborators of Sella Nevo. A scholar is included among the top collaborators of Sella Nevo 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 Sella Nevo. Sella Nevo is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

10 of 10 papers shown

1.

Nearing, Grey, Déborah Cohen, Martin Gauch, et al.. (2024). Global prediction of extreme floods in ungauged watersheds. Nature. 627(8004). 559–563. 134 indexed citations breakdown →

2.

Kratzert, Frederik, Grey Nearing, Nans Addor, et al.. (2023). Caravan - A global community dataset for large-sample hydrology. Scientific Data. 10(1). 61–61. 124 indexed citations breakdown →

3.

Elidan, Gal, Avinatan Hassidim, Yossi Matias, et al.. (2023). A neural encoder for earthquake rate forecasting. Scientific Reports. 13(1). 12350–12350. 17 indexed citations

4.

Nevo, Sella, et al.. (2023). Securing Artificial Intelligence Model Weights: Interim Report. RAND Corporation eBooks.

5.

Nearing, Grey, Daniel Klotz, Jonathan Frame, et al.. (2022). Technical note: Data assimilation and autoregression for using near-real-time streamflow observations in long short-term memory networks. Hydrology and earth system sciences. 26(21). 5493–5513. 26 indexed citations

6.

Pierce, Damien M., et al.. (2022). Accelerating physics simulations with tensor processing units: An inundation modeling example. The International Journal of High Performance Computing Applications. 36(4). 510–523. 3 indexed citations

7.

Nearing, Grey, Daniel Klotz, Alden Keefe Sampson, et al.. (2021). Technical Note: Data assimilation and autoregression for using near-real-time streamflow observations in long short-term memory networks. 7 indexed citations

8.

Metzger, Asher, Frederik Kratzert, Efrat Morin, et al.. (2020). HydroNets: Leveraging River Network Structure and Deep Neural Networks for Hydrologic Modeling. 1 indexed citations

9.

Kratzert, Frederik, Daniel Klotz, Guy Shalev, et al.. (2020). Towards deep learning based flood forecasting for ungauged basins. 2 indexed citations

10.

Nevo, Sella, et al.. (2020). Spectral Algorithm for Shared Low-rank Matrix Regressions. 2. 1–5.

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.

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