Martin Gauch

2.4k total citations · 4 hit papers
25 papers, 1.0k citations indexed

About

Martin Gauch is a scholar working on Water Science and Technology, Environmental Engineering and Global and Planetary Change. According to data from OpenAlex, Martin Gauch has authored 25 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Water Science and Technology, 20 papers in Environmental Engineering and 17 papers in Global and Planetary Change. Recurrent topics in Martin Gauch's work include Hydrology and Watershed Management Studies (22 papers), Hydrological Forecasting Using AI (20 papers) and Flood Risk Assessment and Management (15 papers). Martin Gauch is often cited by papers focused on Hydrology and Watershed Management Studies (22 papers), Hydrological Forecasting Using AI (20 papers) and Flood Risk Assessment and Management (15 papers). Martin Gauch collaborates with scholars based in United States, Austria and Switzerland. Martin Gauch's co-authors include Daniel Klotz, Frederik Kratzert, Grey Nearing, Oren Gilon, Juliane Mai, Guy Shalev, Jimmy Lin, Jonathan Frame, Sella Nevo and Hoshin V. Gupta and has published in prestigious journals such as Nature, Water Resources Research and Geophysical Research Letters.

In The Last Decade

Martin Gauch

24 papers receiving 992 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.

Deep learning rainfall–runoff predictions of extreme events

2022 156 citations Jonathan Frame, Frederik Kratzert et al. Hydrology and earth system sciences profile →
Global prediction of extreme floods in ungauged watersheds

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

2023 124 citations Frederik Kratzert, Grey Nearing et al. Scientific Data profile →
HESS Opinions: Never train a Long Short-Term Memory (LSTM) network on a single basin

2024 58 citations Frederik Kratzert, Martin Gauch et al. Hydrology and earth system sciences profile →

Peers

Martin Gauch

WST

GPC

ECOLO

Alden Keefe Sampson United States

Jonathan Frame United States

Dapeng Feng United States

Cristina Prieto Spain

Marie‐Amélie Boucher Canada

Patricia Jimeno‐Sáez Spain

Guy Shalev United States

Kabir Rasouli Canada

Murat Ay Türkiye

Kuai Fang United States

Alden Keefe Sampson United States

Martin Gauch

814 ×1.1

WST

766 ×1.1

658 ×1.0

GPC

152 ×0.8

39 ×1.2

ECOLO

Citations per year, relative to Martin Gauch Martin Gauch (= 1×) peers Alden Keefe Sampson

Countries citing papers authored by Martin Gauch

Since Specialization

Citations

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

Fields of papers citing papers by Martin Gauch

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Martin Gauch

This figure shows the co-authorship network connecting the top 25 collaborators of Martin Gauch. A scholar is included among the top collaborators of Martin Gauch 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 Martin Gauch. Martin Gauch 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

Kratzert, Frederik, et al.. (2025). Technical note: An approach for handling multiple temporal frequencies with different input dimensions using a single LSTM cell. Hydrology and earth system sciences. 29(6). 1749–1758. 3 indexed citations

Gauch, Martin, Frederik Kratzert, Daniel Klotz, et al.. (2025). How to deal w___ missing input data. Hydrology and earth system sciences. 29(21). 6221–6235.

Loritz, Ralf, et al.. (2025). Analyzing the generalization capabilities of a hybrid hydrological model for extrapolation to extreme events. Hydrology and earth system sciences. 29(5). 1277–1294. 3 indexed citations

Klotz, Daniel, Martin Gauch, Frederik Kratzert, Grey Nearing, & Jakob Zscheischler. (2024). Technical Note: The divide and measure nonconformity – how metrics can mislead when we evaluate on different data partitions. Hydrology and earth system sciences. 28(15). 3665–3673. 4 indexed citations

Kratzert, Frederik, Martin Gauch, Daniel Klotz, & Grey Nearing. (2024). HESS Opinions: Never train a Long Short-Term Memory (LSTM) network on a single basin. Hydrology and earth system sciences. 28(17). 4187–4201. 58 indexed citations breakdown →

Auer, Andreas, Martin Gauch, Frederik Kratzert, et al.. (2024). A data-centric perspective on the information needed for hydrological uncertainty predictions. Hydrology and earth system sciences. 28(17). 4099–4126. 5 indexed citations

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 →

Gauch, Martin, Frederik Kratzert, Oren Gilon, et al.. (2023). In Defense of Metrics: Metrics Sufficiently Encode Typical Human Preferences Regarding Hydrological Model Performance. Water Resources Research. 59(6). e2022WR033918–e2022WR033918. 14 indexed citations

Mai, Juliane, Hongren Shen, Bryan A. Tolson, et al.. (2022). The Great Lakes Runoff Intercomparison Project Phase 4: the Great Lakes (GRIP-GL). Hydrology and earth system sciences. 26(13). 3537–3572. 64 indexed citations

10.

Lees, Thomas, Steven Reece, Frederik Kratzert, et al.. (2022). Hydrological concept formation inside long short-term memory (LSTM) networks. Hydrology and earth system sciences. 26(12). 3079–3101. 95 indexed citations

11.

Frame, Jonathan, Frederik Kratzert, Daniel Klotz, et al.. (2022). Deep learning rainfall–runoff predictions of extreme events. Hydrology and earth system sciences. 26(13). 3377–3392. 156 indexed citations breakdown →

12.

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

13.

Klotz, Daniel, Frederik Kratzert, Martin Gauch, et al.. (2022). Uncertainty estimation with deep learning for rainfall–runoff modeling. Hydrology and earth system sciences. 26(6). 1673–1693. 104 indexed citations

14.

Kratzert, Frederik, Martin Gauch, Grey Nearing, & Daniel Klotz. (2022). NeuralHydrology — A Python library for Deep Learningresearch in hydrology. The Journal of Open Source Software. 7(71). 4050–4050. 50 indexed citations

15.

Lees, Thomas, Steven Reece, Frederik Kratzert, et al.. (2021). Hydrological Concept Formation inside Long Short-Term Memory (LSTM) networks. 12 indexed citations

16.

Klotz, Daniel, Frederik Kratzert, Martin Gauch, et al.. (2021). Uncertainty Estimation with Deep Learning for Rainfall–Runoff Modelling. 13 indexed citations

17.

Klotz, Daniel, Frederik Kratzert, Martin Gauch, et al.. (2021). Uncertainty estimation with LSTM based rainfall-runoff models. 1 indexed citations

18.

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

19.

Kratzert, Frederik, et al.. (2021). Large-scale river network modeling using Graph Neural Networks. 4 indexed citations

20.

Gauch, Martin, Juliane Mai, & Jimmy Lin. (2020). The proper care and feeding of CAMELS: How limited training data affects streamflow prediction. Environmental Modelling & Software. 135. 104926–104926. 109 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.

Explore authors with similar magnitude of impact