Alison Appling

3.4k total citations · 2 hit papers
39 papers, 2.0k citations indexed

About

Alison Appling is a scholar working on Water Science and Technology, Environmental Engineering and Nature and Landscape Conservation. According to data from OpenAlex, Alison Appling has authored 39 papers receiving a total of 2.0k indexed citations (citations by other indexed papers that have themselves been cited), including 29 papers in Water Science and Technology, 23 papers in Environmental Engineering and 13 papers in Nature and Landscape Conservation. Recurrent topics in Alison Appling's work include Hydrology and Watershed Management Studies (26 papers), Hydrological Forecasting Using AI (21 papers) and Fish Ecology and Management Studies (13 papers). Alison Appling is often cited by papers focused on Hydrology and Watershed Management Studies (26 papers), Hydrological Forecasting Using AI (21 papers) and Fish Ecology and Management Studies (13 papers). Alison Appling collaborates with scholars based in United States, Spain and China. Alison Appling's co-authors include Jordan S. Read, Robert O. Hall, Samantha K. Oliver, Maite Arroita, Charles B. Yackulic, Emily S. Bernhardt, James B. Heffernan, William H. McDowell, Xiaowei Jia and Vipin Kumar and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Environmental Science & Technology and The American Naturalist.

In The Last Decade

Alison Appling

39 papers receiving 1.9k citations

Hit Papers

The metabolic regimes of flowing waters 2017 2026 2020 2023 2017 2024 50 100 150 200 250
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. The metabolic regimes of flowing waters
    2017 268 citations Emily S. Bernhardt, James B. Heffernan et al. Limnology and Oceanography profile →
  2. Deep learning for water quality
    2024 118 citations Wei Zhi, Alison Appling et al. Nature Water profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Alison Appling United States 20 992 734 618 617 475 39 2.0k
Cláudio Clemente Faria Barbosa Brazil 27 844 0.9× 226 0.3× 379 0.6× 350 0.6× 675 1.4× 87 2.3k
G. B. Sahoo United States 18 750 0.8× 202 0.3× 253 0.4× 486 0.8× 138 0.3× 34 1.3k
Jacob A. Zwart United States 19 419 0.4× 338 0.5× 438 0.7× 338 0.5× 288 0.6× 48 1.4k
Yi Luo China 21 478 0.5× 173 0.2× 260 0.4× 598 1.0× 223 0.5× 104 1.7k
Stephen J. Dugdale United Kingdom 23 897 0.9× 982 1.3× 92 0.1× 540 0.9× 1.0k 2.1× 48 1.9k
Ming Shen China 22 670 0.7× 99 0.1× 420 0.7× 222 0.4× 344 0.7× 64 1.6k
Huichao Dai China 19 466 0.5× 302 0.4× 155 0.3× 143 0.2× 247 0.5× 88 1.1k
Xiankun Yang China 22 663 0.7× 133 0.2× 242 0.4× 184 0.3× 456 1.0× 74 1.8k
Charles S. Melching United States 20 754 0.8× 116 0.2× 213 0.3× 448 0.7× 279 0.6× 47 1.4k
Michael J. Salé United States 18 505 0.5× 604 0.8× 180 0.3× 174 0.3× 620 1.3× 42 1.5k

Countries citing papers authored by Alison Appling

Since Specialization
Citations

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

Fields of papers citing papers by Alison Appling

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Alison Appling

This figure shows the co-authorship network connecting the top 25 collaborators of Alison Appling. A scholar is included among the top collaborators of Alison Appling 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 Alison Appling. Alison Appling 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.
Appling, Alison, et al.. (2024). Deep learning of estuary salinity dynamics is physically accurate at a fraction of hydrodynamic model computational cost. Limnology and Oceanography. 69(5). 1070–1085. 2 indexed citations
2.
3.
Zhi, Wei, Alison Appling, Heather E. Golden, Joel Podgorski, & Li Li. (2024). Deep learning for water quality. Nature Water. 2(3). 228–241. 118 indexed citations breakdown →
4.
Topp, Simon, Alexander Y. Sun, Xiaowei Jia, et al.. (2023). Stream Temperature Prediction in a Shifting Environment: Explaining the Influence of Deep Learning Architecture. Water Resources Research. 59(4). 25 indexed citations
5.
Zwart, Jacob A., Scott D. Hamshaw, Samantha K. Oliver, et al.. (2023). Evaluating deep learning architecture and data assimilation for improving water temperature forecasts at unmonitored locations. Frontiers in Water. 5. 5 indexed citations
6.
7.
Rahmani, Farshid, Alison Appling, Dapeng Feng, Kathryn Lawson, & Chaopeng Shen. (2023). Identifying Structural Priors in a Hybrid Differentiable Model for Stream Water Temperature Modeling. Water Resources Research. 59(12). 15 indexed citations
8.
Sadler, Jeffrey M., Alison Appling, Jordan S. Read, et al.. (2022). Multi‐Task Deep Learning of Daily Streamflow and Water Temperature. Water Resources Research. 58(4). 37 indexed citations
9.
Zwart, Jacob A., Samantha K. Oliver, W D Watkins, et al.. (2022). Near‐term forecasts of stream temperature using deep learning and data assimilation in support of management decisions. JAWRA Journal of the American Water Resources Association. 59(2). 317–337. 16 indexed citations
10.
Bernhardt, Emily S., Philip Savoy, Alison Appling, et al.. (2022). Light and flow regimes regulate the metabolism of rivers. Proceedings of the National Academy of Sciences. 119(8). 119 indexed citations
11.
Varadharajan, Charuleka, Alison Appling, Bhavna Arora, et al.. (2022). Can machine learning accelerate process understanding and decision‐relevant predictions of river water quality?. Hydrological Processes. 36(4). 56 indexed citations
12.
Willard, Jared, Jordan S. Read, Alison Appling, et al.. (2021). Predicting Water Temperature Dynamics of Unmonitored Lakes With Meta‐Transfer Learning. Water Resources Research. 57(7). 61 indexed citations
13.
Jia, Xiaowei, Yiqun Xie, Sheng Li, et al.. (2021). Physics-Guided Machine Learning from Simulation Data: An Application in Modeling Lake and River Systems. 270–279. 15 indexed citations
14.
15.
Savoy, Philip, Alison Appling, James B. Heffernan, et al.. (2019). Metabolic rhythms in flowing waters: An approach for classifying river productivity regimes. Limnology and Oceanography. 64(5). 1835–1851. 61 indexed citations
16.
Ross, Matthew, Simon Topp, Alison Appling, et al.. (2019). AquaSat: A Data Set to Enable Remote Sensing of Water Quality for Inland Waters. Water Resources Research. 55(11). 10012–10025. 112 indexed citations
17.
Read, Jordan S., Xiaowei Jia, Jared Willard, et al.. (2019). Process‐Guided Deep Learning Predictions of Lake Water Temperature. Water Resources Research. 55(11). 9173–9190. 262 indexed citations
18.
Appling, Alison, Jordan S. Read, Luke Winslow, et al.. (2018). The metabolic regimes of 356 rivers in the United States. Scientific Data. 5(1). 180292–180292. 76 indexed citations
19.
Bernhardt, Emily S., James B. Heffernan, Nancy B. Grimm, et al.. (2017). The metabolic regimes of flowing waters. Limnology and Oceanography. 63(S1). 268 indexed citations breakdown →
20.
Appling, Alison & James B. Heffernan. (2014). Nutrient Limitation and Physiology Mediate the Fine-Scale (De)coupling of Biogeochemical Cycles. The American Naturalist. 184(3). 384–406. 28 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

Breakdown of academic impact, for papers by Cláudio Clemente Faria Barbosa Breakdown of academic impact, for papers by G. B. Sahoo Breakdown of academic impact, for papers by Jacob A. Zwart Breakdown of academic impact, for papers by Yi Luo Breakdown of academic impact, for papers by Stephen J. Dugdale Breakdown of academic impact, for papers by Ming Shen Breakdown of academic impact, for papers by Huichao Dai Breakdown of academic impact, for papers by Xiankun Yang Breakdown of academic impact, for papers by Charles S. Melching Breakdown of academic impact, for papers by Michael J. Salé
Rankless by CCL
2026