Daniel Klotz

6.4k total citations · 7 hit papers
39 papers, 3.1k citations indexed

About

Daniel Klotz is a scholar working on Water Science and Technology, Environmental Engineering and Global and Planetary Change. According to data from OpenAlex, Daniel Klotz has authored 39 papers receiving a total of 3.1k indexed citations (citations by other indexed papers that have themselves been cited), including 35 papers in Water Science and Technology, 30 papers in Environmental Engineering and 28 papers in Global and Planetary Change. Recurrent topics in Daniel Klotz's work include Hydrology and Watershed Management Studies (35 papers), Hydrological Forecasting Using AI (30 papers) and Flood Risk Assessment and Management (25 papers). Daniel Klotz is often cited by papers focused on Hydrology and Watershed Management Studies (35 papers), Hydrological Forecasting Using AI (30 papers) and Flood Risk Assessment and Management (25 papers). Daniel Klotz collaborates with scholars based in Austria, United States and Germany. Daniel Klotz's co-authors include Frederik Kratzert, Mathew Herrnegger, Grey Nearing, Karsten Schulz, Claire Brenner, Alden Keefe Sampson, Sepp Hochreiter, Martin Gauch, Jonathan Frame and Hoshin V. Gupta and has published in prestigious journals such as Nature, Water Resources Research and Geophysical Research Letters.

In The Last Decade

Daniel Klotz

38 papers receiving 3.0k citations

Hit Papers

Rainfall–runoff modelling using Long Short-Term Memory (L... 2018 2026 2020 2023 2018 2019 2020 2022 2024 250 500 750 1000
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.

  1. Rainfall–runoff modelling using Long Short-Term Memory (LSTM) networks
    2018 1.1k citations Frederik Kratzert, Daniel Klotz et al. Hydrology and earth system sciences profile →
  2. Toward Improved Predictions in Ungauged Basins: Exploiting the Power of Machine Learning
    2019 484 citations Frederik Kratzert, Daniel Klotz et al. Water Resources Research profile →
  3. What Role Does Hydrological Science Play in the Age of Machine Learning?
    2020 391 citations Grey Nearing, Frederik Kratzert et al. Water Resources Research profile →
  4. Deep learning rainfall–runoff predictions of extreme events
    2022 156 citations Jonathan Frame, Frederik Kratzert et al. Hydrology and earth system sciences profile →
  5. Global prediction of extreme floods in ungauged watersheds
    2024 134 citations Grey Nearing, Déborah Cohen et al. profile →
  6. Caravan - A global community dataset for large-sample hydrology
    2023 124 citations Frederik Kratzert, Grey Nearing et al. Scientific Data profile →
  7. 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 — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Daniel Klotz Austria 17 2.3k 2.3k 1.9k 473 145 39 3.1k
Frederik Kratzert United States 17 2.4k 1.0× 2.4k 1.0× 2.0k 1.0× 481 1.0× 150 1.0× 43 3.1k
Grey Nearing United States 32 2.5k 1.1× 2.3k 1.0× 2.4k 1.2× 888 1.9× 181 1.2× 72 3.9k
A. K. Gosain India 19 1.3k 0.6× 1.1k 0.5× 1.2k 0.6× 424 0.9× 153 1.1× 80 2.1k
Zhongmin Liang China 25 1.3k 0.6× 916 0.4× 1.3k 0.7× 381 0.8× 139 1.0× 92 2.1k
Zhijia Li China 28 1.6k 0.7× 949 0.4× 1.7k 0.9× 732 1.5× 100 0.7× 141 2.6k
Elena Toth Italy 20 1.2k 0.5× 1.1k 0.5× 1.1k 0.6× 262 0.6× 114 0.8× 50 1.8k
Robert J. Abrahart United Kingdom 25 1.7k 0.8× 1.8k 0.8× 1.3k 0.6× 203 0.4× 224 1.5× 60 2.5k
Caihong Hu China 21 1.1k 0.5× 1.0k 0.5× 1.1k 0.6× 264 0.6× 85 0.6× 68 1.8k
Zhenliang Yin China 29 1.1k 0.5× 754 0.3× 1.1k 0.6× 497 1.1× 130 0.9× 70 2.0k
Shengzhi Huang China 33 2.0k 0.9× 788 0.3× 2.7k 1.4× 477 1.0× 127 0.9× 88 3.9k

Countries citing papers authored by Daniel Klotz

Since Specialization
Citations

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

Fields of papers citing papers by Daniel Klotz

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Daniel Klotz

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

All Works

20 of 20 papers shown
1.
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
2.
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.
3.
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
4.
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 →
5.
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
6.
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 →
7.
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
8.
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
9.
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
10.
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 →
11.
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
12.
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
13.
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
14.
Herrnegger, Mathew, et al.. (2021). Regionalisierung hydrologischer Modelle mit Function Space Optimization. Österreichische Wasser- und Abfallwirtschaft. 73(7-8). 281–294. 1 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.
19.
Kratzert, Frederik, et al.. (2021). Large-scale river network modeling using Graph Neural Networks. 4 indexed citations
20.
Herrnegger, Mathew, et al.. (2020). Function Space Optimization: A Symbolic Regression Method for Estimating Parameter Transfer Functions for Hydrological Models. Water Resources Research. 56(10). e2020WR027385–e2020WR027385. 24 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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