Grey Nearing

8.0k total citations · 7 hit papers
72 papers, 3.9k citations indexed

About

Grey Nearing is a scholar working on Environmental Engineering, Water Science and Technology and Global and Planetary Change. According to data from OpenAlex, Grey Nearing has authored 72 papers receiving a total of 3.9k indexed citations (citations by other indexed papers that have themselves been cited), including 50 papers in Environmental Engineering, 44 papers in Water Science and Technology and 41 papers in Global and Planetary Change. Recurrent topics in Grey Nearing's work include Hydrology and Watershed Management Studies (44 papers), Hydrological Forecasting Using AI (32 papers) and Flood Risk Assessment and Management (21 papers). Grey Nearing is often cited by papers focused on Hydrology and Watershed Management Studies (44 papers), Hydrological Forecasting Using AI (32 papers) and Flood Risk Assessment and Management (21 papers). Grey Nearing collaborates with scholars based in United States, Austria and Switzerland. Grey Nearing's co-authors include Frederik Kratzert, Hoshin V. Gupta, Daniel Klotz, Alden Keefe Sampson, Sepp Hochreiter, Jonathan Frame, Cristina Prieto, Martin Gauch, Martyn Clark and Mathew Herrnegger and has published in prestigious journals such as Nature, Remote Sensing of Environment and Water Resources Research.

In The Last Decade

Grey Nearing

68 papers receiving 3.8k citations

Hit Papers

Toward Improved Predictions in Ungauged Basins: Exploitin... 2019 2026 2021 2023 2019 2020 2022 2024 2023 100 200 300 400
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. 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 →
  2. 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 →
  3. Deep learning rainfall–runoff predictions of extreme events
    2022 156 citations Jonathan Frame, Frederik Kratzert et al. Hydrology and earth system sciences profile →
  4. Global prediction of extreme floods in ungauged watersheds
    2024 134 citations Grey Nearing, Déborah Cohen et al. profile →
  5. Caravan - A global community dataset for large-sample hydrology
    2023 124 citations Frederik Kratzert, Grey Nearing et al. Scientific Data profile →
  6. Hybrid forecasting: blending climate predictions with AI models
    2023 93 citations Louise Slater, Louise Arnal et al. Hydrology and earth system sciences 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

Grey Nearing
Chaopeng Shen United States
Ercan Kahya Türkiye
Lucy Marshall Australia
Karsten Schulz Austria
Yonas Dibike Canada
Shie‐Yui Liong Singapore
Ali Danandeh Mehr Türkiye
Albrecht Weerts Netherlands
Xiaomang Liu China
Huilin Gao United States
Chaopeng Shen United States
Grey Nearing
Citations per year, relative to Grey Nearing Grey Nearing (= 1×) peers Chaopeng Shen

Countries citing papers authored by Grey Nearing

Since Specialization
Citations

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

Fields of papers citing papers by Grey Nearing

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Grey Nearing

This figure shows the co-authorship network connecting the top 25 collaborators of Grey Nearing. A scholar is included among the top collaborators of Grey Nearing 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 Grey Nearing. Grey Nearing 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.
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.
2.
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
3.
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 →
4.
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
5.
Sandoval-Solís, Samuel, et al.. (2024). Drought Awareness over Continental United States. Journal of Hydrology. 642. 131868–131868.
6.
Nearing, Grey, et al.. (2023). A Deep Learning Data Fusion Model Using Sentinel-1/2, SoilGrids, SMAP, and GLDAS for Soil Moisture Retrieval. Journal of Hydrometeorology. 24(10). 1789–1823. 15 indexed citations
7.
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 →
8.
Slater, Louise, Louise Arnal, Marie‐Amélie Boucher, et al.. (2023). Hybrid forecasting: blending climate predictions with AI models. Hydrology and earth system sciences. 27(9). 1865–1889. 93 indexed citations breakdown →
9.
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
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.
Klotz, Daniel, Frederik Kratzert, Martin Gauch, et al.. (2021). Uncertainty Estimation with Deep Learning for Rainfall–Runoff Modelling. 13 indexed citations
15.
Klotz, Daniel, Frederik Kratzert, Martin Gauch, et al.. (2021). Uncertainty estimation with LSTM based rainfall-runoff models. 1 indexed citations
16.
17.
Qiu, Jianxiu, Wade T. Crow, Jianzhi Dong, & Grey Nearing. (2020). Model representation of the coupling between evapotranspiration and soil water content at different depths. Hydrology and earth system sciences. 24(2). 581–594. 15 indexed citations
18.
Qiu, Jianxiu, Wade T. Crow, Jianzhi Dong, & Grey Nearing. (2019). Land Surface Model Representation of the Mutual Information Context between Multi-Layer Soil Moisture and Evapotranspiration. 1 indexed citations
19.
Addor, Nans, Grey Nearing, Cristina Prieto, et al.. (2018). A Ranking of Hydrological Signatures Based on Their Predictability in Space. Water Resources Research. 54(11). 8792–8812. 189 indexed citations
20.
Nearing, Grey, Soni Yatheendradas, Wade T. Crow, et al.. (2017). Nonparametric triple collocation. Water Resources Research. 53(7). 5516–5530. 10 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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