David M. Bjerklie

2.1k total citations
33 papers, 1.2k citations indexed

About

David M. Bjerklie is a scholar working on Water Science and Technology, Global and Planetary Change and Ecology. According to data from OpenAlex, David M. Bjerklie has authored 33 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 27 papers in Water Science and Technology, 24 papers in Global and Planetary Change and 18 papers in Ecology. Recurrent topics in David M. Bjerklie's work include Hydrology and Watershed Management Studies (26 papers), Flood Risk Assessment and Management (20 papers) and Hydrology and Sediment Transport Processes (17 papers). David M. Bjerklie is often cited by papers focused on Hydrology and Watershed Management Studies (26 papers), Flood Risk Assessment and Management (20 papers) and Hydrology and Sediment Transport Processes (17 papers). David M. Bjerklie collaborates with scholars based in United States, Italy and France. David M. Bjerklie's co-authors include Stephen Dingman, Carl H. Bolster, L. C. Smith, Delwyn Moller, Russell G. Congalton, Charles J Vörösmarty, John W. Fulton, Jacqueline D. LaPerriere, Tommaso Moramarco and John W. Jones and has published in prestigious journals such as Water Resources Research, Journal of Hydrology and Remote Sensing.

In The Last Decade

David M. Bjerklie

32 papers receiving 1.2k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
David M. Bjerklie United States 17 897 819 604 212 197 33 1.2k
Ayan Santos Fleischmann Brazil 21 984 1.1× 947 1.2× 347 0.6× 175 0.8× 267 1.4× 66 1.4k
Jeison Sosa United Kingdom 8 650 0.7× 517 0.6× 267 0.4× 318 1.5× 238 1.2× 11 1.1k
Hailong Wang China 21 672 0.7× 412 0.5× 195 0.3× 286 1.3× 169 0.9× 46 967
Mary Yaeger United States 16 584 0.7× 1.0k 1.2× 178 0.3× 160 0.8× 282 1.4× 24 1.3k
János Józsa Hungary 19 475 0.5× 456 0.6× 263 0.4× 158 0.7× 185 0.9× 58 1.1k
Denis Dartus France 19 559 0.6× 580 0.7× 283 0.5× 134 0.6× 193 1.0× 44 930
Diogo Costa Buarque Brazil 12 683 0.8× 600 0.7× 222 0.4× 266 1.3× 174 0.9× 31 920
Eliisa Lotsari Finland 21 400 0.4× 329 0.4× 529 0.9× 347 1.6× 242 1.2× 49 1.1k
S. P. Aggarwal India 20 808 0.9× 595 0.7× 235 0.4× 290 1.4× 350 1.8× 84 1.3k
Kyle W. Blasch United States 15 272 0.3× 622 0.8× 278 0.5× 123 0.6× 373 1.9× 30 943

Countries citing papers authored by David M. Bjerklie

Since Specialization
Citations

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

Fields of papers citing papers by David M. Bjerklie

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by David M. Bjerklie. 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 David M. Bjerklie. The network helps show where David M. Bjerklie may publish in the future.

Co-authorship network of co-authors of David M. Bjerklie

This figure shows the co-authorship network connecting the top 25 collaborators of David M. Bjerklie. A scholar is included among the top collaborators of David M. Bjerklie 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 David M. Bjerklie. David M. Bjerklie 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.
Bjerklie, David M., et al.. (2020). Fundamental Hydraulics of Cross Sections in Natural Rivers: Preliminary Analysis of a Large Data Set of Acoustic Doppler Flow Measurements. Water Resources Research. 56(3). 21 indexed citations
3.
Fulton, John W., et al.. (2020). QCam: sUAS-Based Doppler Radar for Measuring River Discharge. Remote Sensing. 12(20). 3317–3317. 30 indexed citations
4.
Parasiewicz, Piotr, J. Angus Webb, Mikołaj Piniewski, et al.. (2019). The role of floods and droughts on riverine ecosystems under a changing climate. Fisheries Management and Ecology. 26(6). 461–473. 32 indexed citations
5.
Moramarco, Tommaso, Silvia Barbetta, David M. Bjerklie, John W. Fulton, & Angelica Tarpanelli. (2019). River Bathymetry Estimate and Discharge Assessment from Remote Sensing. Water Resources Research. 55(8). 6692–6711. 59 indexed citations
6.
Bjerklie, David M., Charon Birkett, John W. Jones, et al.. (2018). Satellite remote sensing estimation of river discharge: Application to the Yukon River Alaska. Journal of Hydrology. 561. 1000–1018. 93 indexed citations
7.
Afshari, Shahab, B M Fekete, Stephen Dingman, et al.. (2017). Statistical filtering of river survey and streamflow data for improving At-A-Station hydraulic geometry relations. Journal of Hydrology. 547. 443–454. 7 indexed citations
8.
Durand, Michael, Colin J. Gleason, David M. Bjerklie, et al.. (2016). Including stage-dependent roughness coefficient in algorithms to estimate river discharge from remotely sensed water elevation, width, and slope. AGU Fall Meeting Abstracts. 2016. 1 indexed citations
9.
Parr, Dana, Guiling Wang, & David M. Bjerklie. (2015). Integrating Remote Sensing Data on Evapotranspiration and Leaf Area Index with Hydrological Modeling: Impacts on Model Performance and Future Predictions. Journal of Hydrometeorology. 16(5). 2086–2100. 33 indexed citations
10.
Durand, Michael, Laurence Smith, Colin J. Gleason, et al.. (2014). Assessing SWOT discharge algorithms performance across a range of river types. AGUFM. 2014. 1 indexed citations
11.
Bjerklie, David M., et al.. (2013). A one-dimensional diffusion analogy model for estimation of tide heights in selected tidal marshes in Connecticut. Scientific investigations report. 1 indexed citations
12.
Bjerklie, David M., et al.. (2012). Preliminary investigation of the effects of sea-level rise on groundwater levels in New Haven, Connecticut. Antarctica A Keystone in a Changing World. 50 indexed citations
13.
Bjerklie, David M., et al.. (2011). Simulations of Historical and Future Trends in Snowfall and Groundwater Recharge for Basins Draining to Long Island Sound. Earth Interactions. 15(34). 1–35. 12 indexed citations
14.
Birkett, C. M., et al.. (2010). Application of ICESat/GLAS laser altimetry to the Estimation of Surface Water Level and River Discharge. AGU Fall Meeting Abstracts. 2010. 1 indexed citations
15.
Bjerklie, David M.. (2009). Keeping (or finding) the faith.. PubMed. 173(7). 84–84. 2 indexed citations
16.
Bjerklie, David M., Delwyn Moller, L. C. Smith, & Stephen Dingman. (2005). Estimating discharge in rivers using remotely sensed hydraulic information. Journal of Hydrology. 309(1-4). 191–209. 230 indexed citations
17.
Vörösmarty, C. J., David M. Bjerklie, Stephen Dingman, et al.. (2003). River discharge strategies from space. EAEJA. 11300. 1 indexed citations
18.
Bjerklie, David M.. (1995). Telecommuting: preparing for round two. Technology Review. 98(5). 20–21. 1 indexed citations
19.
Bjerklie, David M. & Jacqueline D. LaPerriere. (1985). GOLD‐MINING EFFECTS ON STREAM HYDROLOGY AND WATER QUALITY, CIRCLE QUADRANGLE, ALASKA1. JAWRA Journal of the American Water Resources Association. 21(2). 235–242. 27 indexed citations
20.
LaPerriere, Jacqueline D., et al.. (1985). GOLD‐MINING EFFECTS ON HEAVY METALS IN STREAMS, CIRCLE QUADRANGLE, ALASKA1. JAWRA Journal of the American Water Resources Association. 21(2). 245–252. 21 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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