Scott D. Hamshaw

405 total citations
20 papers, 251 citations indexed

About

Scott D. Hamshaw is a scholar working on Water Science and Technology, Environmental Engineering and Ecology. According to data from OpenAlex, Scott D. Hamshaw has authored 20 papers receiving a total of 251 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Water Science and Technology, 7 papers in Environmental Engineering and 6 papers in Ecology. Recurrent topics in Scott D. Hamshaw's work include Hydrology and Watershed Management Studies (7 papers), Hydrology and Sediment Transport Processes (6 papers) and Flood Risk Assessment and Management (4 papers). Scott D. Hamshaw is often cited by papers focused on Hydrology and Watershed Management Studies (7 papers), Hydrology and Sediment Transport Processes (6 papers) and Flood Risk Assessment and Management (4 papers). Scott D. Hamshaw collaborates with scholars based in United States and Australia. Scott D. Hamshaw's co-authors include Donna M. Rizzo, Mandar M. Dewoolkar, Beverley Wemple, Andrew W. Schroth, Jarlath O’Neil‐Dunne, Dustin W. Kincaid, Kristen L. Underwood, Rui Wu, Donald S. Ross and Byung Suk Lee and has published in prestigious journals such as Water Resources Research, Journal of Hydrology and Hydrological Processes.

In The Last Decade

Scott D. Hamshaw

18 papers receiving 244 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Scott D. Hamshaw United States 8 102 77 73 69 59 20 251
Hongye Cao China 13 97 1.0× 76 1.0× 69 0.9× 16 0.2× 90 1.5× 25 399
Liangzhi Li China 12 82 0.8× 76 1.0× 85 1.2× 23 0.3× 100 1.7× 33 409
Triven Koganti Denmark 11 43 0.4× 147 1.9× 65 0.9× 48 0.7× 28 0.5× 26 283
G. R. Aggett United States 8 175 1.7× 74 1.0× 76 1.0× 57 0.8× 186 3.2× 15 365
Ni-Bin Chang United States 7 69 0.7× 89 1.2× 30 0.4× 27 0.4× 60 1.0× 15 311
Arno Adi Kuntoro Indonesia 11 91 0.9× 69 0.9× 38 0.5× 16 0.2× 180 3.1× 65 321
Vladislav Ivov Ivanov Italy 13 34 0.3× 43 0.6× 73 1.0× 60 0.9× 88 1.5× 22 375
Mohamed Rached Boussema Tunisia 10 105 1.0× 116 1.5× 124 1.7× 166 2.4× 104 1.8× 34 346
Boosik Kang South Korea 10 195 1.9× 100 1.3× 38 0.5× 29 0.4× 259 4.4× 51 387
Seamus Coveney Ireland 9 27 0.3× 151 2.0× 71 1.0× 30 0.4× 81 1.4× 17 294

Countries citing papers authored by Scott D. Hamshaw

Since Specialization
Citations

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

Fields of papers citing papers by Scott D. Hamshaw

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Scott D. Hamshaw. 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 Scott D. Hamshaw. The network helps show where Scott D. Hamshaw may publish in the future.

Co-authorship network of co-authors of Scott D. Hamshaw

This figure shows the co-authorship network connecting the top 25 collaborators of Scott D. Hamshaw. A scholar is included among the top collaborators of Scott D. Hamshaw 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 Scott D. Hamshaw. Scott D. Hamshaw 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.
2.
Underwood, Kristen L., et al.. (2025). Leveraging High‐Frequency Sensor Data and U.S. National Water Model Output to Forecast Turbidity in a Drinking Water Supply Basin. JAWRA Journal of the American Water Resources Association. 61(2). 1 indexed citations
3.
Wemple, Beverley, James B. Shanley, Scott D. Hamshaw, et al.. (2025). Assessing and Enhancing National Water Model Streamflow Predictions for Montane Catchments in the Northeastern United States. JAWRA Journal of the American Water Resources Association. 61(4).
4.
5.
Kincaid, Dustin W., Kristen L. Underwood, Scott D. Hamshaw, et al.. (2024). Solute export patterns across the contiguous USA. Hydrological Processes. 38(6). 7 indexed citations
6.
Diehl, Rebecca M., et al.. (2024). Evaluating opportunities for broad-scale remote sensing of total suspended solids on small rivers. Remote Sensing Applications Society and Environment. 35. 101234–101234. 2 indexed citations
7.
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
8.
Wu, Rui, et al.. (2022). Data Imputation for Multivariate Time Series Sensor Data With Large Gaps of Missing Data. IEEE Sensors Journal. 22(11). 10671–10683. 32 indexed citations
9.
Burberry, Caroline M., et al.. (2022). Earth and Planetary Surface Processes Perspectives on Integrated, Coordinated, Open, Networked (ICON) Science. Earth and Space Science. 9(8). 1 indexed citations
10.
Zhang, Yifan, Jiahao Li, Alex K. Manda, et al.. (2021). Data Regression Framework for Time Series Data with Extreme Events. 2021 IEEE International Conference on Big Data (Big Data). 5327–5336. 6 indexed citations
11.
Hamshaw, Scott D., et al.. (2020). Multivariate event time series analysis using hydrological and suspended sediment data. Journal of Hydrology. 593. 125802–125802. 18 indexed citations
12.
Bierman, Paul R., et al.. (2019). Optimization of over-summer snow storage at midlatitudes and low elevation. ˜The œcryosphere. 13(12). 3367–3382. 4 indexed citations
13.
Hamshaw, Scott D., et al.. (2019). Application of unmanned aircraft system (UAS) for monitoring bank erosion along river corridors. Geomatics Natural Hazards and Risk. 10(1). 1285–1305. 27 indexed citations
14.
Ross, Donald S., et al.. (2018). Impact of an Extreme Storm Event on River Corridor Bank Erosion and Phosphorus Mobilization in a Mountainous Watershed in the Northeastern United States. Journal of Geophysical Research Biogeosciences. 124(1). 18–32. 27 indexed citations
15.
Hamshaw, Scott D., Mandar M. Dewoolkar, Andrew W. Schroth, Beverley Wemple, & Donna M. Rizzo. (2018). A New Machine‐Learning Approach for Classifying Hysteresis in Suspended‐Sediment Discharge Relationships Using High‐Frequency Monitoring Data. Water Resources Research. 54(6). 4040–4058. 72 indexed citations
16.
Hamshaw, Scott D., et al.. (2017). Quantifying Streambank Erosion Using Unmanned Aerial Systems at Site-Specific and River Network Scales. Geotechnical Frontiers 2017. 499–508. 5 indexed citations
17.
Hamshaw, Scott D., et al.. (2017). Quantifying streambank movement and topography using unmanned aircraft system photogrammetry with comparison to terrestrial laser scanning. River Research and Applications. 33(8). 1354–1367. 23 indexed citations
18.
Hamshaw, Scott D., Kristen L. Underwood, Beverley Wemple, & Donna M. Rizzo. (2016). Classification and Prediction of Event-based Suspended Sediment Dynamics using Artificial Neural Networks. AGUFM. 2016. 1 indexed citations
19.
Rizzo, Donna M., et al.. (2013). Estimates of Sediment Loading from Streambank Erosion Using Terrestrial LiDAR. AGUFM. 2013. 1 indexed citations
20.
Hamshaw, Scott D., et al.. (2012). Rapid Flood Exposure Assessment of Vermont Mobile Home Parks Following Tropical Storm Irene. Natural Hazards Review. 15(1). 27–37. 18 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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