James Prairie

1.6k total citations
26 papers, 1.2k citations indexed

About

James Prairie is a scholar working on Water Science and Technology, Global and Planetary Change and Ocean Engineering. According to data from OpenAlex, James Prairie has authored 26 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 24 papers in Water Science and Technology, 20 papers in Global and Planetary Change and 8 papers in Ocean Engineering. Recurrent topics in James Prairie's work include Hydrology and Watershed Management Studies (22 papers), Hydrology and Drought Analysis (16 papers) and Climate variability and models (9 papers). James Prairie is often cited by papers focused on Hydrology and Watershed Management Studies (22 papers), Hydrology and Drought Analysis (16 papers) and Climate variability and models (9 papers). James Prairie collaborates with scholars based in United States, Netherlands and Canada. James Prairie's co-authors include Balaji Rajagopalan, Upmanu Lall, Kenneth Nowak, Terrance J. Fulp, Benjamin L. Harding, Edith Zagona, Andrew W. Wood, J. J. Barsugli, Taesam Lee and José D. Salas and has published in prestigious journals such as Water Resources Research, Journal of Hydrology and Bulletin of the American Meteorological Society.

In The Last Decade

James Prairie

25 papers receiving 1.1k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
James Prairie United States 17 803 746 313 263 218 26 1.2k
Sungwook Wi United States 21 715 0.9× 808 1.1× 287 0.9× 259 1.0× 195 0.9× 39 1.2k
Elmira Hassanzadeh Canada 16 835 1.0× 640 0.9× 343 1.1× 177 0.7× 180 0.8× 29 1.4k
Eylon Shamir United States 19 627 0.8× 603 0.8× 227 0.7× 389 1.5× 221 1.0× 51 1.1k
Jean‐Michel Perraud Australia 14 1.1k 1.4× 1.2k 1.6× 117 0.4× 262 1.0× 382 1.8× 27 1.5k
Marie Minville Canada 11 716 0.9× 842 1.1× 172 0.5× 282 1.1× 124 0.6× 15 1.0k
N. V. Umamahesh India 24 1.2k 1.5× 701 0.9× 217 0.7× 418 1.6× 282 1.3× 73 1.6k
Manuela I. Brunner Switzerland 25 1.4k 1.8× 1.1k 1.5× 112 0.4× 392 1.5× 262 1.2× 73 1.8k
William Farmer United States 17 538 0.7× 759 1.0× 115 0.4× 116 0.4× 326 1.5× 46 1.0k
Juraj M. Cunderlik Canada 20 1.2k 1.5× 1.0k 1.4× 106 0.3× 267 1.0× 201 0.9× 27 1.5k
Yingchun Ge China 17 598 0.7× 569 0.8× 137 0.4× 327 1.2× 225 1.0× 32 1.1k

Countries citing papers authored by James Prairie

Since Specialization
Citations

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

Fields of papers citing papers by James Prairie

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of James Prairie

This figure shows the co-authorship network connecting the top 25 collaborators of James Prairie. A scholar is included among the top collaborators of James Prairie 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 James Prairie. James Prairie 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.
Pokharel, Binod, Kripa Jagannathan, Shih‐Yu Wang, et al.. (2024). Can we rely on drought‐ending “miracles” in the Colorado River Basin?. JAWRA Journal of the American Water Resources Association. 60(3). 813–824.
3.
Towler, Erin, et al.. (2022). Incorporating Mid-Term Temperature Predictions into Streamflow Forecasts and Operational Reservoir Projections in the Colorado River Basin. Journal of Water Resources Planning and Management. 148(4). 8 indexed citations
4.
Marston, Landon, Adel Abdallah, Kenneth J. Bagstad, et al.. (2022). Water‐Use Data in the United States: Challenges and Future Directions. JAWRA Journal of the American Water Resources Association. 58(4). 485–495. 24 indexed citations
5.
Rajagopalan, Balaji, Rebecca Smith, James Prairie, et al.. (2021). Stochastic Decadal Projections of Colorado River Streamflow and Reservoir Pool Elevations Conditioned on Temperature Projections. Water Resources Research. 57(12). 11 indexed citations
6.
Bruce, Breton W., et al.. (2018). Comparison of U.S. Geological Survey and Bureau of Reclamation water-use reporting in the Colorado River Basin. Scientific investigations report. 9 indexed citations
7.
Prairie, James, et al.. (2012). Colorado River Basin Water Supply and Demand Study. AGU Fall Meeting Abstracts. 2012. 100 indexed citations
8.
Harding, Benjamin L., Andrew W. Wood, & James Prairie. (2012). The implications of climate change scenario selection for future streamflow projection in the Upper Colorado River Basin. Hydrology and earth system sciences. 16(11). 3989–4007. 87 indexed citations
9.
Rangwala, Imtiaz, J. J. Barsugli, Karen Cozzetto, Jason C. Neff, & James Prairie. (2012). Mid-21st century projections in temperature extremes in the southern Colorado Rocky Mountains from regional climate models. Climate Dynamics. 39(7-8). 1823–1840. 42 indexed citations
10.
Lee, Taesam, José D. Salas, & James Prairie. (2010). An enhanced nonparametric streamflow disaggregation model with genetic algorithm. Water Resources Research. 46(8). 55 indexed citations
11.
Bracken, Cameron, Balaji Rajagopalan, & James Prairie. (2010). A multisite seasonal ensemble streamflow forecasting technique. Water Resources Research. 46(3). 33 indexed citations
12.
Nowak, Kenneth, James Prairie, Balaji Rajagopalan, & Upmanu Lall. (2010). A nonparametric stochastic approach for multisite disaggregation of annual to daily streamflow. Water Resources Research. 46(8). 103 indexed citations
13.
Rajagopalan, Balaji, Kenneth Nowak, James Prairie, et al.. (2009). Water supply risk on the Colorado River: Can management mitigate?. Water Resources Research. 45(8). 125 indexed citations
14.
Barsugli, J. J., Kenneth Nowak, Balaji Rajagopalan, James Prairie, & Benjamin L. Harding. (2009). Comment on “When will Lake Mead go dry?” by T. P. Barnett and D. W. Pierce. Water Resources Research. 45(9). 18 indexed citations
15.
Prairie, James, Kenneth Nowak, Balaji Rajagopalan, Upmanu Lall, & Terrance J. Fulp. (2008). A stochastic nonparametric approach for streamflow generation combining observational and paleoreconstructed data. Water Resources Research. 44(6). 66 indexed citations
16.
Prairie, James & Balaji Rajagopalan. (2007). A basin wide stochastic salinity model. Journal of Hydrology. 344(1-2). 43–54. 5 indexed citations
17.
Prairie, James. (2006). *Stochastic nonparametric framework for basin wide streamflow and salinity modeling: Application for the Colorado River Basin. 9 indexed citations
18.
Prairie, James, et al.. (2006). Modified K-NN Model for Stochastic Streamflow Simulation. Journal of Hydrologic Engineering. 11(4). 371–378. 92 indexed citations
19.
Prairie, James, et al.. (2005). Natural Flow and Salt Computation Methods, Calendar Years 1971-1995. Digital Commons - USU (Utah State University). 24 indexed citations
20.
Prairie, James, Balaji Rajagopalan, Terrance J. Fulp, & Edith Zagona. (2005). Statistical Nonparametric Model for Natural Salt Estimation. Journal of Environmental Engineering. 131(1). 130–138. 42 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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