Google Scholar: https://scholar.google.com/citations?user=cBIcYY0AAAAJ&hl=en
[1] R. C. Hales et al., “The Second Generation Geoglows River Forecast System,” Jun. 01, 2025. doi: 10.2139/ssrn.5257837.
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[4] T. A. Maughan, R. Hotchkiss, and R. C. Hales, “Drainage Area Limitations of Single Watershed, Peak Flow Estimates from NRCS Methods,” Nebraska DOT, Mar. 2025.
[5] R. H. Magoffin et al., “Hydrologic Decision Support in the Nile Basin: Creating Status Products from the GEOGLOWS Hydrologic Model,” Hydrology, vol. 12, no. 3, p. 43, Feb. 2025, doi: 10.3390/hydrology12030043.
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[7] D. J. R. Lesmes et al., “Discharge-to-water level transformation (DWLT) using monthly duration curves: enhancing the utility of the GEOGLOWS ECMWF hydrological model,” Dec. 27, 2024. doi: 10.22541/essoar.173532353.33602073/v1.
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[9] R. Huber Magoffin, R. C. Hales, B. Erazo, E. J. Nelson, K. Larco, and T. J. Miskin, “Evaluating the Performance of Satellite Derived Temperature and Precipitation Datasets in Ecuador,” Remote Sensing, vol. 15, no. 24, p. 5713, Dec. 2023, doi: 10.3390/rs15245713.
[10] M. M. Brown et al., “Nutrient Loadings to Utah Lake from Precipitation-Related Atmospheric Deposition,” Hydrology, vol. 10, no. 10, p. 200, Oct. 2023, doi: 10.3390/hydrology10100200.
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[12] R. C. Hales, G. P. Williams, E. James Nelson, R. B. Sowby, D. P. Ames, and J. L. S. Lozano, “Bias correcting discharge simulations from the GEOGloWS global hydrologic model,” Journal of Hydrology, vol. 626, p. 130279, Oct. 2023, doi: 10.1016/j.jhydrol.2023.130279.
[13] A. C. Cardall, R. C. Hales, K. B. Tanner, G. P. Williams, and K. N. Markert, “LASSO (L1) Regularization for Development of Sparse Remote-Sensing Models with Applications in Optically Complex Waters Using GEE Tools,” Remote Sensing, vol. 15, no. 6, p. 1670, Mar. 2023, doi: 10.3390/rs15061670.
[14] R. Hales, “Sustainably Providing Accurate Local River Discharge Data with Global Hydrologic Modeling and Bias Corrections,” Doctoral Dissertation, Brigham Young University, Provo, Utah, USA, 2023. [Online]. Available: https://scholarsarchive.byu.edu/etd/9826
[15] J. E. Jones et al., “Building and Validating Multidimensional Datasets in Hydrology for Data and Mapping Web Service Compliance,” Water, vol. 15, no. 3, p. 411, Jan. 2023, doi: 10.3390/w15030411.
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[17] S. G. Ramirez, R. C. Hales, G. P. Williams, and N. L. Jones, “Extending SC-PDSI-PM with neural network regression using GLDAS data and Permutation Feature Importance,” Environmental Modelling & Software, vol. 157, p. 105475, Nov. 2022, doi: 10.1016/j.envsoft.2022.105475.
[18] R. B. Sowby and R. C. Hales, “Projected Effects of Climate Change on the Energy Footprints of U.S. Drinking Water Utilities,” Hydrology, vol. 9, no. 10, p. 182, Oct. 2022, doi: 10.3390/hydrology9100182.
[19] R. C. Hales et al., “Advancing global hydrologic modeling with the GEOGloWS ECMWF streamflow service,” J Flood Risk Management, Oct. 2022, doi: 10.1111/jfr3.12859.
[20] A. Lemnitzer, “Geotechnical Reconnaissance of the 2022 Yellowstone Floods V 1.0,” GEER Association (Report - 79), Sep. 2022. doi: 10.18118/G66M2D.
[21] R. C. Hales et al., “SABER: A Model-Agnostic Postprocessor for Bias Correcting Discharge from Large Hydrologic Models,” Hydrology, vol. 9, no. 7, p. 113, Jun. 2022, doi: 10.3390/hydrology9070113.
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[23] R. Khattar, R. Hales, D. P. Ames, E. J. Nelson, N. L. Jones, and G. Williams, “Tethys App Store: Simplifying deployment of web applications for the international GEOGloWS initiative,” Environmental Modelling & Software, vol. 146, p. 105227, Dec. 2021, doi: 10.1016/j.envsoft.2021.105227.
[24] R. C. Hales, E. J. Nelson, G. P. Williams, N. Jones, D. P. Ames, and J. E. Jones, “The Grids Python Tool for Querying Spatiotemporal Multidimensional Water Data,” Water, vol. 13, no. 15, p. 2066, Jul. 2021, doi: 10.3390/w13152066.
[25] K. Ashby, R. Hales, J. Nelson, D. Ames, and G. Williams, “Hydroviewer: A Web Application to Localize Global Hydrologic Forecasts,” Open Water Journal, vol. 7, no. 1, p. 9, Jun. 2021, [Online]. Available: https://scholarsarchive.byu.edu/openwater/vol7/iss1/9
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[27] R. Hales, “A New Method and Python Toolkit for General Access to Spatiotemporal N-Dimensional Raster Data,” Master’s Thesis, Brigham Young University, Provo, Utah, USA, 2021. [Online]. Available: https://scholarsarchive.byu.edu/etd/8882
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