Julia Kowalski

2.6k total citations · 1 hit paper
60 papers, 1.6k citations indexed

About

Julia Kowalski is a scholar working on Atmospheric Science, Management, Monitoring, Policy and Law and Global and Planetary Change. According to data from OpenAlex, Julia Kowalski has authored 60 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 28 papers in Atmospheric Science, 20 papers in Management, Monitoring, Policy and Law and 13 papers in Global and Planetary Change. Recurrent topics in Julia Kowalski's work include Cryospheric studies and observations (22 papers), Landslides and related hazards (20 papers) and Fire effects on ecosystems (6 papers). Julia Kowalski is often cited by papers focused on Cryospheric studies and observations (22 papers), Landslides and related hazards (20 papers) and Fire effects on ecosystems (6 papers). Julia Kowalski collaborates with scholars based in Germany, United States and Austria. Julia Kowalski's co-authors include Perry Bartelt, Marc Christen, Brian W. McArdell, Jan‐Thomas Fischer, Shiva P. Pudasaini, Jim McElwaine, Yves Bühler, Bernd Dachwald, Tomoo Sato and Thomas Baumgartner and has published in prestigious journals such as Journal of Fluid Mechanics, Journal of Computational Physics and Geophysical Research Letters.

In The Last Decade

Julia Kowalski

54 papers receiving 1.5k 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.

RAMMS: Numerical simulation of dense snow avalanches in three-dimensional terrain

2010 520 citations Marc Christen, Julia Kowalski et al. profile →

Peers

Julia Kowalski

MMPL

AS

GPC

ECOLO

CM

Yves Bühler Switzerland

Giulio Iovine Italy

Y. Fujii Japan

Jian Guo Liu United Kingdom

Kate E. Allstadt United States

Xudong Fu China

Richard G. LaHusen United States

Olga Mavrouli Spain

Paolo Mazzanti Italy

Xueliang Wang China

Yves Bühler Switzerland

Julia Kowalski

1.5k ×1.4

MMPL

2.1k ×3.0

AS

764 ×1.7

GPC

203 ×0.6

ECOLO

74 ×0.3

CM

Citations per year, relative to Julia Kowalski Julia Kowalski (= 1×) peers Yves Bühler

Countries citing papers authored by Julia Kowalski

Since Specialization

Citations

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

Fields of papers citing papers by Julia Kowalski

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Julia Kowalski

This figure shows the co-authorship network connecting the top 25 collaborators of Julia Kowalski. A scholar is included among the top collaborators of Julia Kowalski 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 Julia Kowalski. Julia Kowalski 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:

20 of 20 papers shown

1.

Nagel, Thomas, Wolfram Rühaak, Florian Amann, et al.. (2025). Deep geological disposal. Environmental Earth Sciences. 84(3). 1 indexed citations

2.

Rauter, Matthias & Julia Kowalski. (2024). OpenFOAM-avalanche 2312: depth-integrated models beyond dense-flow avalanches. Geoscientific model development. 17(17). 6545–6569.

3.

Roscher, Ribana, et al.. (2024). A modular framework for FAIR shallow landslide susceptibility mapping based on machine learning. Natural Hazards. 120(9). 8953–8982. 2 indexed citations

4.

Elgeti, Stefanie, et al.. (2023). A scale-coupled numerical method for transient close-contact melting. Computers & Mathematics with Applications. 143. 277–288. 1 indexed citations

5.

Nouri, Bijan, Stefan Wilbert, Niklas Blum, et al.. (2023). Combining Deep Learning and Physical Models: A Benchmark Study on All‐Sky Imager‐Based Solar Nowcasting Systems. Solar RRL. 8(4). 7 indexed citations

6.

Mikucki, Jill A., Ilya Digel, Julia Kowalski, et al.. (2023). Field-Based Planetary Protection Operations for Melt Probes: Validation of Clean Access into the Blood Falls, Antarctica, Englacial Ecosystem. Astrobiology. 23(11). 1165–1178. 2 indexed citations

7.

Varghese, Philip L., et al.. (2023). Unsteady Stefan problem with kinetic interface conditions for rarefied gas deposition. International Journal of Heat and Mass Transfer. 217. 124696–124696.

8.

Kowalski, Julia, et al.. (2023). Dispersion in Shallow Moment Equations. Communications on Applied Mathematics and Computation. 6(4). 2155–2195. 1 indexed citations

9.

Jacobs, Georg, et al.. (2022). Model Signatures for the Integration of Simulation Models into System Models. Systems. 10(6). 199–199. 16 indexed citations

10.

Aaron, Jordan, Scott McDougall, Julia Kowalski, A. Mitchell, & Natalia Nolde. (2022). Probabilistic prediction of rock avalanche runout using a numerical model. Landslides. 19(12). 2853–2869. 12 indexed citations

11.

Jacobs, Georg, et al.. (2022). Automated Identification of Valid Model Networks Using Model-Based Systems Engineering. Systems. 10(6). 250–250. 8 indexed citations

12.

Betancourt, Clara, et al.. (2022). Global, high-resolution mapping of tropospheric ozone – explainable machine learning and impact of uncertainties. Geoscientific model development. 15(11). 4331–4354. 16 indexed citations

13.

Chen, Qian, et al.. (2022). Ice Transit and Performance Analysis for Cryorobotic Subglacial Access Missions on Earth and Europa. Astrobiology. 23(11). 1135–1152. 4 indexed citations

14.

Betancourt, Clara, et al.. (2021). Global fine resolution mapping of ozone metrics through explainable machine learning. 1 indexed citations

15.

Kowalski, Julia, et al.. (2018). Topographic uncertainty in avalanche simulations. 690–695. 1 indexed citations

16.

Kowalski, Julia, D. Heinen, Ilya Digel, et al.. (2017). Clean Sampling of an Englacial Conduit at Blood Falls, Antarctica - Some Experimental and Numerical Results. EGUGA. 12926. 1 indexed citations

17.

Mikucki, Jill A., Ilya Digel, Marcus A.H. Chua, et al.. (2016). Cleaning the IceMole: collection of englacial samples from Blood Falls, Antarctica. AGU Fall Meeting Abstracts. 2016. 1 indexed citations

18.

Fischer, Jan‐Thomas, Julia Kowalski, Shiva P. Pudasaini, & Stephen A. Miller. (2009). Dynamic Avalanche Modeling in Natural Terrain. DORA WSL (Swiss Federal Institute for Forest, Snow and Landscape Research). 448–453. 2 indexed citations

19.

Christen, Marc, et al.. (2008). Calculation of Dense Snow Avalanches in Three-Dimensional Terrain with the Numerical Simulation Program Ramms. 709–716. 39 indexed citations

20.

Kowalski, Julia, et al.. (1984). Ambient monitoring of selected halogenated hydrocarbons and benzene in the California South Coast Air Basin. 1. 4 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.

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