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Civil and Construction Engineering
Hydroinformatics Lab

Publications

2026

  • Shi, J., Jiao, T., Ames, D. P., Chen, Y., & Xie, Z. (2026). Improved Lightweight Marine Oil Spill Detection Using the YOLOv8 Algorithm. Applied Sciences, 16(2), 780. https://doi.org/10.3390/app16020780
  • Tanner, K. B., Cardall, A. C., Taggart, J. B., & Williams, G. P. (2026). An Earth Observation Data-Driven Investigation of Algal Blooms in Utah Lake: Statistical Analysis of the Effects of Turbidity and Water Temperature. Remote Sensing, 18(3), 394. https://doi.org/10.3390/rs18030394
  • Williams, G. P., Taggart, J. B., Smith, K. E., Miller, T. G., & Nelson, S. T. (2026). Toward Sustainable Restoration of Utah Lake: A Synthesis of the Existing Literature with New Active Dust Sampling Data and Analyses. Sustainability, 18(4), 2125. https://doi.org/10.3390/su18042125

2025

  • Moshina, U., Chick, I., Carlisle, J., Ames, D.P., 2025. Large Language Models to Support Socially Responsible Solar Energy Siting in Utah. Solar, vol 5, iss 4, art 52. https://doi.org/10.3390/solar5040052.
  • Chapagain, A.R., Maghami, I., Ames, D.P., 2025, Evaluating the U.S. National Water Model Retrospective Evapotranspiration Simulation using Eddy-Covariance Flux Tower Measurements. J. Hydrology: Regional Studies, vol 62, art 102826. https://doi.org/10.1016/j.ejrh.2025.102826.
  • Barbosa, S.A., Jones, N.L., Williams, G.P., Yidana, S.M., Pulla, S.T., Sanchez Lozano, J.L., Nelson, E.J., Ames, D.P., Miller, A.W., 2025, A Multi-Source Approach to Groundwater Storage and Recharge Assessment in the Volta Basin. Science of the Total Environment,                                                   https://doi.org/10.1016/j.scitotenv.2025.180421.
  • Patel, A., Halgren, J., Wills, Z., Frazier, N., Lee, B., Cunningham, J., Laser, J., Karimiziaran, M., Patel, T., Romero, G., Denno, M., Lamont, S., Maghami, I., Jajula, H., Alam, M., Koriche, S., Singh, M., Minor, N., Duvvuri, B., Neisary, S., Lee, Q., Burian, S., Ogden, F., Bangalore, P., Carver, J., Ames, D.P. (2025). NextGen In A Box (NGIAB): Open-Source Containerization of the NextGen Framework to Enable Community-Driven Hydrology Modeling. Environmental Modelling & Software, vol 193, art 106666. https://doi.org/10.1016/j.envsoft.2025.106666.
  • Rojas-Lesmes, D., Sanchez-Lozano, J., Nelson, E.J., Chancay-Sanchez, J., Rodriquiz-Chaves, J., Larco-Erazo, K., Romero, E.G., Trujillo-Vela, M., Hales, R.C., Ames, D.P., Gutierrez, A.L., 2025. National Water Level Forecast (NWLF): An Open-Source Customizable Web Application for the GEOGLOWS ECMWF Global Hydrological Model. Information Geography, vol. 1 iss. 1 art. 100008, https://doi.org/10.1016/j.infgeo.2025.100008.
  • Williams, G, Miller, A.W., Aghababaei, A., Chapagain, A.R., Wagle, P., Baaniya, Y., Magoffin, R.H., Li, X., Miskin, T., Oldham, P.D., Oldham, S.J., Peterson, T., Prince, L., Ames, D.P., 2025. Precipitation-Related Atmospheric Nutrient Deposition to Farmington Bay: Analysis of Spatial and Temporal Patterns. Hydrology vol 12, art 131. https://doi.org/10.3390/hydrology12060131.
  • Miller, A. W., Williams, G. P., Magoffin, R. H., Li, X., Miskin, T., Aghababaei, A., Wagle, P., Chapagain, A. R., Baaniya, Y., Oldham, P. D., Oldham, S. J., Peterson, T., Prince, L., Tanner, K. B., Cardall, A. C., & Ames, D. P., 2025. Trophic State Evolution of 45 Yellowstone Lakes Over Two Decades: Field Data and a Longitudinal Study. Water, 17(11), art 1627. https://doi.org/10.3390/w17111627.
  • Prince, L., Hales, R.C., Markert, K.N., Nelson, E.J., Williams, G.P., Ames, D.P., Lee, H., Rostami, A., 2025. Assessing Coincidence of Satellite Acquisitions and Flood Events to Predict for Flood Map Synthesis. Remote Sensing, 17(9), art 1648. https://doi.org/10.3390/rs17091648.
  • Sowby, R., South, A., Hopkins, E., Jones, N.L., Ames, D.P., 2025. More than modeling: Building trust for positive change in water resources management. Environmental Modelling & Software, Art 106465. https://doi.org/10.1016/j.envsoft.2025.106465.
  • Stevens, M.D., Ramirez, S.G., Martin, E.H., Jones, N.L., Williams, G.P., Adams, K.H., Ames, D.P., Pulla, S.T., 2025, Groundwater storage loss in the central valley analysis using a novel method based on in situ data compared to GRACE-derived data. Environmental Modelling & Software, vol. 186, art. 106368, https://doi.org/10.1016/j.envsoft.2025.106368.
  • Sanchez Lozano, J., Rojas Lesmes, D., Romero Bustamante, E.G., Hales, R.C., Nelson, E.J., Williams, G.P., Ames, D.P., Jones, N.L., Gutierrez, A.L., Almeida, C.C., 2025, Historical Simulation Performance Evaluation and Monthly Flow Duration Curve Quantile-Mapping (MFDC-QM) of the GEOGLOWS ECMWF Streamflow Hydrologic Model. Environmental Modelling & Software, vol. 183, art. 106235, https://doi.org/10.1016/j.envsoft.2024.106235.
  • Hamidi, E., Henrichsen, H., Sandquist, A., Zhang, H., Moftakhari, H., Ames, D.P., Bao, S., Ferreira, C., Mandil, K.T., 2025, Coupling Coastal and Hydrologic Models Through Next Generation National Water Model Framework. ASCE J. Hydrologic Engineering, vol 30, No 2, https://doi.org/10.1061/JHYEFF.HEENG-6343. Selected as “Editor’s Choice” in this issue.
  • Choi, Y., Goodall, J.L., Maghami, I., Goodall, J.L., Band, L., Nassar, A., Line, L., Saby, L., Li, Z., Wang, S., Calloway, C., Yi, H., Seul, M., Ames, D.P., and Tarboton, D.G., 2025, Toward reproducible and interoperable environmental modeling: Integration of HydroShare with server-side methods for exposing large-extent spatial datasets to models. Environmental Modelling & Software, vol. 183, art. 106239, https://doi.org/10.1016/j.envsoft.2024.106239.
  • Miller, A. W., Wagle, P., Aghababaei, A., Chapagain, A. R., Baaniya, Y., Oldham, P. D., Oldham, S. J., Peterson, T., Prince, L., Magoffin, R. H., Li, X., Miskin, T., Tanner, K. B., Cardall, A. C., Jones, N. L., & Williams, G. P. (2025). Grand Teton National Park Trophic State Evolution at 33 Locations in 29 Lakes over Three Decades: Field Data and Analysis. Hydrology, 12(12), 321.                           https://doi.org/10.3390/hydrology12120321
  • Aghababaei, A., Jones, N. L., Williams, G. P., Webster-Esho, E., van der Heijden, R., Li, X., Clement, T. P., & Rizzo, D. M. (2025). Development and Comparison of Methods for Identification of Baseflow-Dominant Periods in Streamflow Records. Water, 17(21), 3083. https://doi.org/10.3390/w17213083
  • Markert, K., Williams, G. P., Jones, N. L., Sowby, R. B., & Morgan, G. R. (2025). Assessing Coniferous Forest Cover Change and Associated Uncertainty in a Subbasin of the Great Salt Lake Watershed: A Stochastic Approach Using Landsat Imagery and Random Forest Models. Environments, 12(10), 387. https://doi.org/10.3390/environments12100387
  • Miskin, T. J., Rosas, L. R., Hales, R. C., Nelson, E. J., Follum, M. L., Gutenson, J. L., Williams, G. P., & Jones, N. L. (2025). Impact of Elevation and Hydrography Data on Modeled Flood Map Accuracy Using ARC and Curve2Flood. Hydrology, 12(8), 202. https://doi.org/10.3390/hydrology12080202
  • Shepard, D., Jones, N. L., & Williams, G. P. (2025). Application of the Groundwater Data Mapper Tool to Assess Storage Changes in a Groundwater-Driven Basin in the Klamath Watershed, Oregon, USA. Hydrology, 12(6), 140. https://doi.org/10.3390/hydrology12060140
  • Rostami, A., Lee, H., Wan, H.-H., Du, T. L. T., Williams, G. P., Nelson, E. J., & Chen, H. (2025). Simulating hurricane-induced compound flooding via spatiotemporal analysis of satellite-derived inundation maps. Remote Sensing Applications: Society and Environment, 40, 101771.                                    https://doi.org/10.1016/j.rsase.2025.101771
  • Wan, H.-H., Lee, H., Thuy Du, T. L., Rostami, A., Chang, C.-H., Markert, K. N., Nelson, E. J., Williams, G. P., Li, S., Straka, W., Helfrich, S. R., & Meyer, F. J. (2025). An interpretable and scalable model for rapid flood extent forecasting using satellite imagery and machine learning with rotated EOF analysis. Environmental Modelling & Software, 192, 106562.                                                       https://doi.org/10.1016/j.envsoft.2025.106562
  • Magoffin, R. H., Hales, R. C., Nelson, E. J., Wara, C., Williams, G. P., South, A., & Challa, Z. K. (2025). Hydrologic Decision Support in the Nile Basin: Creating Status Products from the GEOGLOWS Hydrologic Model. Hydrology, 12(3), 43. https://doi.org/10.3390/hydrology12030043
  • Williams, G. P. (2025). Friends don’t let friends use Nash-Sutcliffe Efficiency (Nse) or KGE for hydrologic model accuracy evaluation: A rant with data and suggestions for better practice. Environmental Modelling & Software, 194, 106665. https://doi.org/10.1016/j.envsoft.2025.106665
  • Tanner, K. B., Cardall, A. C., & Williams, G. P. (2025). A Six-Year, Spatiotemporally Comprehensive Dataset and Data Retrieval Tool for Analyzing Chlorophyll-a, Turbidity, and Temperature in Utah Lake Using Sentinel and MODIS Imagery. Data, 10(8), 128. https://doi.org/10.3390/data10080128
  • Taggart, J. B., Ryan, R. L., Miller, A. W., Miller, T. G., & Williams, G. P. (2025). A Phosphorus Microfractionation (P-MF) Method for Measuring Phosphorus Fractions in Small Quantities of Suspended Solids and Sediments: Detailed Method and Example Application. Environments, 12(7), 218.          https://doi.org/10.3390/environments12070218
  • Porter, B. W., Sowby, R. B., Williams, G. P., Limb, B. J., Quinn, J. C., Johnson, A., & Thomas, E. A. (2025). Mitigating wildfire impact on water quality through climate-based financing: A case study of the provo river watershed. ACS ES&T Water, 5(2), 649–658. https://doi.org/10.1021/acsestwater.4c00727

2024

  • Kel N. Markert, Hyongki Lee, Gustavious P. Williams, E. James Nelson, Ames, D.P., Robert E. Griffin, and Franz J. Meyer, 2024. Evaluating the Feasibility of Scaling the FIER Framework for Large-Scale Flood Inundation Prediction. Hydrology and Earth Systems Science,                                               https://doi.org/10.5194/egusphere-2024-3491
  • Lamichhane, M., Chapagain, A.R., Mehan, S., Ames, D.P., Kafle, S., 2024, Integrating solar-induced chlorophyll fluorescence with traditional remote sensing and environmental variables for enhanced rice yield prediction in Nepal using machine learning, Remote Sensing Applications: Society and Environment, vol. 36, art. 101371, https://doi.org/10.1016/j.rsase.2024.101371.
  • Markert, K., da Silva, G., Ames, D.P., Maghami, I., Halgren, J., Patel, J., Williams, G.P., Nelson, E.J., Santos, A., Ames, M.J., 2024, Design and Implementation of a Big Query Database and Application Programmer Interface (API) for the U.S. National Water Model. Environmental Modelling & Software, vol 179, art. 106123, https://doi.org/10.1016/j.envsoft.2024.106123.
  • Markert, K., Williams, G.P., Nelson, E.J., Ames, D.P., Lee, H., Griffin, R.E., 2024, Dense Time Series Generation of Surface Water Extents through Optical–SAR Sensor Fusion and Gap Filling, Remote Sensing, vol 16, iss. 7, https://doi.org/10.3390/rs16071262.
  • Tarboton, D.G., Ames, D.P., Horsburgh, J., Goodall, J., Couch, A., Hooper, R., Bales, J., Wang, S., Castronova, A., Seul, M., Idaszak, R., Li, Z., Dash, P., Black, S., Ramirez, M., Yi, H., Calloway, C., Cogswell, C., 2024, HydroShare Retrospective: Science and Technology Advances of a Comprehensive Data and Model Publication Environment for the Water Science Domain. Environmental Modelling & Software, vol. 172, art. 105902, https://doi.org/10.1016/j.envsoft.2023.105902.
  • Rostami, A., Chang, C.-H., Lee, H., Wan, H.-H., Du, T. L. T., Markert, K. N., Williams, G. P., Nelson, E. J., Li, S., Straka III, W., Helfrich, S., & Gutierrez, A. L. (2024). Forecasting Flood Inundation in U.S. Flood-Prone Regions Through a Data-Driven Approach (FIER): Using VIIRS Water Fractions and the National Water Model. Remote Sensing, 16(23), 4357.                                                                               https://doi.org/10.3390/rs16234357
  • Turman, A. M., Sowby, R. B., Williams, G. P., & Hansen, N. C. (2024). Remote Sensing of Residential Landscape Irrigation in Weber County, Utah: Implications for Water Conservation, Image Analysis, and Drone Applications. Sustainability, 16(21), 9356. https://doi.org/10.3390/su16219356
  • Valek, R. A., Tanner, K. B., Taggart, J. B., Ryan, R. L., Cardall, A. C., Woodland, L. M., Oxborrow, M. J., Williams, G. P., Miller, A. W., & Sowby, R. B. (2024). Regulated Inductively Coupled Plasma–Optical Emission Spectrometry Detectable Elements in Utah Lake: Characterization and Discussion. Water, 16(15), 2170. https://doi.org/10.3390/w16152170
  • Taggart, J. B., Ryan, R. L., Williams, G. P., Miller, A. W., Valek, R. A., Tanner, K. B., & Cardall, A. C. (2024). Historical Phosphorus Mass and Concentrations in Utah Lake: A Case Study with Implications for Nutrient Load Management in a Sorption-Dominated Shallow Lake. Water, 16(7), 933.                                        https://doi.org/10.3390/w16070933
  • Rapp, A. H., Sowby, R. B., & Williams, G. (2024). Economy of Scale of Energy Intensity in Aquifer Storage and Recovery (ASR). Water, 16(3), 503. https://doi.org/10.3390/w16030503

2023

  • Jones, J. E., Hales, R. C., Larco, K., Nelson, E. J., Ames, D. P., Jones, N. L., & Iza, M. (2023). Building and Validating Multidimensional Datasets in Hydrology for Data and Mapping Web Service Compliance. Water, 15(3), 411. https://doi.org/10.3390/w15030411
  • Huber Magoffin, R., Hales, R. C., Erazo, B., Nelson, E. J., Larco, K., & Miskin, T. J. (2023). Evaluating the Performance of Satellite Derived Temperature and Precipitation Datasets in Ecuador. Remote Sensing, 15(24), 5713.   https://doi.org/10.3390/rs15245713
  • Mohammed, I. N., Bustamante, E. G. R., Bolten, J. D., & Nelson, E. J. (2023). Technical note: NASAaccess – a tool for access, reformatting, and visualization of remotely sensed earth observation and climate data. Hydrology and Earth System Sciences, 27(19), 3621–3642.                                           https://doi.org/10.5194/hess-27-3621-2023
  • Nguyen, N. T., Du, T. L. T., Park, H., Chang, C.-H., Choi, S., Chae, H., Nelson, E. J., Hossain, F., Kim, D., & Lee, H. (2023). Estimating the Impacts of Ungauged Reservoirs Using Publicly Available Streamflow Simulations and Satellite Remote Sensing. Remote Sensing, 15(18), 4563.                                                        https://doi.org/10.3390/rs15184563
  • Telfer, J. T., Brown, M. M., Williams, G. P., Tanner, K. B., Miller, A. W., Sowby, R. B., & Miller, T. G. (2023). Source Attribution of Atmospheric Dust Deposition to Utah Lake. Hydrology, 10(11), 210. https://doi.org/10.3390/hydrology10110210
  • Brown, M. M., Telfer, J. T., Williams, G. P., Miller, A. W., Sowby, R. B., Hales, R. C., & Tanner, K. B. (2023). Nutrient Loadings to Utah Lake from Precipitation-Related Atmospheric Deposition. Hydrology, 10(10), 200. https://doi.org/10.3390/hydrology10100200
  • Capener, A. M., Sowby, R. B., & Williams, G. P. (2023). Pathways to Enhancing Analysis of Irrigation by Remote Sensing (AIRS) in Urban Settings. Sustainability, 15(17), 12676. https://doi.org/10.3390/su151712676
  • Cardall, A. C., Hales, R. C., Tanner, K. B., Williams, G. P., & Markert, K. N. (2023). LASSO (L1) Regularization for Development of Sparse Remote-Sensing Models with Applications in Optically Complex Waters Using GEE Tools. Remote Sensing, 15(6), 1670.                                                                                 https://doi.org/10.3390/rs15061670
  • Berrett, C., Gurney, B., Arthur, D., Moon, T., & Williams, G. P. (2023). A Bayesian change point modeling approach to identify local temperature changes related to urbanization. Environmetrics, 34(3), e2794. https://doi.org/10.1002/env.2794

2022


2021


2020


2019


  • Souffront Alcantara, M. A., Nelson, E. J., Shakya, K., Edwards, C., Roberts, W., Krewson, C., Ames, D. P., Jones, N. L., & Gutierrez, A. (2019). Hydrologic modeling as a service (Hmaas): A new approach to address hydroinformatic challenges in developing countries. Frontiers in Environmental Science, 7. https://doi.org/10.3389/fenvs.2019.00158
  • McDonald, S., Mohammed, I. N., Bolten, J. D., Pulla, S., Meechaiya, C., Markert, A., Nelson, E. J., Srinivasan, R., & Lakshmi, V. (2019). Web-based decision support system tools: The Soil and Water Assessment Tool Online visualization and analyses (Swatonline) and NASA earth observation data downloading and reformatting tool (Nasaaccess). Environmental Modelling & Software, 120, 104499. https://doi.org/10.1016/j.envsoft.2019.104499
  • Qiao, X., Nelson, E. J., Ames, D. P., Li, Z., David, C. H., Williams, G. P., Roberts, W., Sánchez Lozano, J. L., Edwards, C., Souffront, M., & Matin, M. A. (2019). A systems approach to routing global gridded runoff through local high-resolution stream networks for flood early warning systems. Environmental Modelling & Software, 120, 104501. https://doi.org/10.1016/j.envsoft.2019.104501
  • Sikder, M. S., David, C. H., Allen, G. H., Qiao, X., Nelson, E. J., & Matin, M. A. (2019). Evaluation of available global runoff datasets through a river model in support of transboundary water management in south and southeast asia. Frontiers in Environmental Science, 7. https://doi.org/10.3389/fenvs.2019.00171
  • Nelson, E. J., Pulla, S. T., Matin, M. A., Shakya, K., Jones, N., Ames, D. P., Ellenburg, W. L., Markert, K. N., David, C. H., Zaitchik, B. F., Gatlin, P., & Hales, R. (2019). Enabling stakeholder decision-making with earth observation and modeling data using tethys platform. Frontiers in Environmental Science, 7. https://doi.org/10.3389/fenvs.2019.00148
  • Jackson, E. K., Roberts, W., Nelsen, B., Williams, G. P., Nelson, E. J., & Ames, D. P. (2019). Introductory overview: Error metrics for hydrologic modelling – A review of common practices and an open source library to facilitate use and adoption. Environmental Modelling & Software, 119, 32–48. https://doi.org/10.1016/j.envsoft.2019.05.001
  • McDonald, S., Mohammed, I. N., Bolten, J. D., Pulla, S., Meechaiya, C., Markert, A., Nelson, E. J., Srinivasan, R., & Lakshmi, V. (2019). Web based decision support system tools: The Soil and Water Assessment Tool Online visualization and analyses (Swatonline) and NASA earth observation data downloading and reformatting tool (Nasaaccess). Environmental Modelling & Software, 120, 104499. https://doi.org/10.1016/j.envsoft.2019.104499
  • Qiao, X., Li, Z., Ames, D. P., Nelson, E. J., & Swain, N. R. (2019). Simplifying the deployment of OGC web processing services (Wps) for environmental modelling – Introducing Tethys WPS Server. Environmental Modelling & Software, 115, 38–50. https://doi.org/10.1016/j.envsoft.2019.01.021
  • Purdy, A. J., David, C. H., Sikder, M. S., Reager, J. T., Chandanpurkar, H. A., Jones, N. L., & Matin, M. A. (2019). An open-source tool to facilitate the processing of grace observations and gldas outputs: An evaluation in Bangladesh. Frontiers in Environmental Science, 7.                                                                https://doi.org/10.3389/fenvs.2019.00155
  • Williams, G. P., & Walton, A. C. (2019). Method for Estimating Sediment Mass Movement from Delta Recutting: A Case Study Using Single Beam Sonar in Deer Creek Reservoir. Water, 11(11), 2222. https://doi.org/10.3390/w11112222
  • Nelson, R. W., & Williams, G. P. (2019). Bounding of Flow and Transport Analysis in Heterogeneous Saturated Porous Media: A Minimum Energy Dissipation Principle for the Bounding and Scale-Up. Hydrology, 6(2), 33. https://doi.org/10.3390/hydrology6020033

2018


2017


  • Souffront Alcantara, M., Crawley, S., Stealey, M., Nelson, E., Ames, D., & Jones, N. (2017). Open water data solutions for accessing the national water model. Open Water Journal, 4(1). https://scholarsarchive.byu.edu/openwater/vol4/iss1/3
  • Kadlec, J., & Ames, D. P. (2017). Using crowdsourced and weather station data to fill cloud gaps in MODIS snow cover datasets. Environmental Modelling & Software, 95, 258–270. https://doi.org/10.1016/j.envsoft.2017.06.002
  • Crawley, S., Ames, D., Li, Z., & Tarboton, D. (2017). Hydroshare gis: Visualizing spatial data in the cloud. Open Water Journal, 4(1). https://scholarsarchive.byu.edu/openwater/vol4/iss1/2
  • Christensen, S. D., Swain, N. R., Jones, N. L., Nelson, E. J., Snow, A. D., & Dolder, H. G. (2017). A comprehensive python toolkit for accessing high‐throughput computing to support large hydrologic modeling tasks. JAWRA Journal of the American Water Resources Association, 53(2), 333–343. https://doi.org/10.1111/1752-1688.12455
  • Souffront Alcantara, M. A., Kesler, C., Stealey, M. J., Nelson, E. J., Ames, D. P., & Jones, N. L. (2018). Cyberinfrastructure and web apps for managing and disseminating the national water model. JAWRA Journal of the American Water Resources Association, 54(4), 859–871. https://doi.org/10.1111/1752-1688.12608
  • Buahin, C. A., Sangwan, N., Fagan, C., Maidment, D. R., Horsburgh, J. S., Nelson, E. J., Merwade, V., & Rae, C. (2017). Probabilistic flood inundation forecasting using rating curve libraries. JAWRA Journal of the American Water Resources Association, 53(2), 300–315.                                                               https://doi.org/10.1111/1752-1688.12500
  • Hansen, C. H., Burian, S. J., Dennison, P. E., & Williams, G. P. (2017). Spatiotemporal Variability of Lake Water Quality in the Context of Remote Sensing Models. Remote Sensing, 9(5), 409. https://doi.org/10.3390/rs9050409

2016


  • Swain, N. R., Christensen, S. D., Snow, A. D., Dolder, H., Espinoza-Dávalos, G., Goharian, E., Jones, N. L., Nelson, E. J., Ames, D. P., & Burian, S. J. (2016). A new open source platform for lowering the barrier for environmental web app development. Environmental Modelling & Software, 85, 11–26. https://doi.org/10.1016/j.envsoft.2016.08.003
  • Perez, J. F., Swain, N. R., Dolder, H. G., Christensen, S. D., Snow, A. D., Nelson, E. J., & Jones, N. L. (2016). From global to local: Providing actionable flood forecast information in a cloud‐based computing environment. JAWRA Journal of the American Water Resources Association, 52(4), 965–978. https://doi.org/10.1111/1752-1688.12392
  • Fatichi, S., Vivoni, E. R., Ogden, F. L., Ivanov, V. Y., Mirus, B., Gochis, D., Downer, C. W., Camporese, M., Davison, J. H., Ebel, B., Jones, N., Kim, J., Mascaro, G., Niswonger, R., Restrepo, P., Rigon, R., Shen, C., Sulis, M., & Tarboton, D. (2016). An overview of current applications, challenges, and future trends in distributed process-based models in hydrology. Journal of Hydrology, 537, 45–60. https://doi.org/10.1016/j.jhydrol.2016.03.026
  • Hinton, D., Hotchkiss, R., & Ames, D. P. (2017). Comprehensive and quality-controlled bedload transport database. Journal of Hydraulic Engineering, 143(2), 06016024. https://doi.org/10.1061/(ASCE)HY.1943-7900.0001221
  • Woodbury, D. H., Ames, D. P., Kadlec, J., Duncan, S., & Gault, G. (2016). A new open‐access huc‐8 based downscaled cmip‐5 climate model forecast dataset for the conterminous united states. JAWRA Journal of the American Water Resources Association, 52(4), 906–915. https://doi.org/10.1111/1752-1688.12437
  • Kadlec, J., Miller, A. W., & Ames, D. P. (2016). Extracting snow cover time series data from open access web mapping tile services. JAWRA Journal of the American Water Resources Association, 52(4), 916–932. https://doi.org/10.1111/1752-1688.12387
  • Snow, A. D., Christensen, S. D., Swain, N. R., Nelson, E. J., Ames, D. P., Jones, N. L., Ding, D., Noman, N. S., David, C. H., Pappenberger, F., & Zsoter, E. (2016). A high‐resolution national‐scale hydrologic forecast system from a global ensemble land surface model. JAWRA Journal of the American Water Resources Association, 52(4), 950–964. https://doi.org/10.1111/1752-1688.12434
  • Joe, J. C., Hendrickson, K., Wong, M., Kane, S. L., Solan, D., Carlisle, J. E., Koehler, D., Ames, D. P., & Beazer, R. (2016). Political efficacy and familiarity as predictors of attitudes towards electric transmission lines in the United States. Energy Research & Social Science, 17, 127–134. https://doi.org/10.1016/j.erss.2016.04.010
  • Ge, M., Wu, P., Zhu, D., & Ames, D. P. (2016). Comparison between sprinkler irrigation and natural rainfall based on droplet diameter. Spanish Journal of Agricultural Research, 14(1), undefined-undefined. https://doi.org/10.5424/sjar/2016141-8076
  • Sadler, J., Ames, D.P., and Livingston, S.J. (2016).”Extending HydroShare to enable hydrologic time series data as social media.” Journal of Hydroinformatics, 18(2), 198-209. https://doi.org/10.2166/hydro.2015.331
  • Sadler, J., Ames, D.P., and Khattar, R. (2016). “A Recipe for Standards-Based Data Sharing using Open Source Software and Low-Cost Electronics.” Journal of Hydroinformatics, 18(2), 185-197. https://doi.org/10.2166/hydro.2015.092

2015


  • Jones, D., Jones, N., Greer, J., & Nelson, J. (2015). A cloud-based MODFLOW service for aquifer management decision support. Computers & Geosciences, 78, 81–87. https://doi.org/10.1016/j.cageo.2015.02.014
  • Webber, B., Weber, K.T., Clark, P.E., Moffet, C.A., Ames, D.P., Taylor, J.B., Johnson, D.E., and Kiel, J.G. (2015). “Movements of Domestic Sheep in the Presence of Livestock Guardian Dogs.” Sheep & Goat Research Journal, 30, 18-23.
  • Kadlec, J., StClair, B., Ames, D. P., & Gill, R. A. (2015). WaterML R package for managing ecological experiment data on a CUAHSI HydroServer. Ecological Informatics, 28, 19–28. https://doi.org/10.1016/j.ecoinf.2015.05.002
  • Brewer, J., Ames, D. P., Solan, D., Lee, R., & Carlisle, J. (2015). Using GIS analytics and social preference data to evaluate utility-scale solar power site suitability. Renewable Energy, 81(C), 825–836. https://doi.org/10.1016/j.renene.2015.04.017
  • Dolder, H. G., Jones, N. L., & Nelson, E. J. (2015). Simple method for using precomputed hydrologic models in flood forecasting with uniform rainfall and soil moisture pattern. Journal of Hydrologic Engineering, 20(12), 04015039. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001232
  • Kern, E., Hotchkiss, R.H., and Ames, D.P. (2015). “Introducing a Low-Head Dam Fatality Database and Internet Information Portal.” Journal of the American Water Resources Association (JAWRA), 1-7. https://doi.org/10.1111/jawr.12289
  • Baloch, M., Ames, D. P., and Tanik, A. (2015). “Hydrological impacts of climate and land use change on Namnam stream in Koycegiz watershed, Turkey.” International Journal of Environmental Science and Technology, 12, 1481-1494. https://doi.org/10.1007/s13762-014-0527-x
  • Swain, N. R., Latu, K., Christensen, S. D., Jones, N. L., Nelson, E. J., Ames, D. P., & Williams, G. P. (2015). A review of open source software solutions for developing water resources web applications. Environmental Modelling & Software, 67, 108–117. https://doi.org/10.1016/j.envsoft.2015.01.014
  • Osorio-Murillo, C. A., Over, M. W., Savoy, H., Ames, D. P., & Rubin, Y. (2015). Software framework for inverse modeling and uncertainty characterization. Environmental Modelling & Software, 66, 98–109. https://doi.org/10.1016/j.envsoft.2015.01.002
  • Fan, F. M., Fleischmann, A. S., Collischonn, W., Ames, D. P., & Rigo, D. (2015). Large-scale analytical water quality model coupled with GIS for simulation of point sourced pollutant discharges. Environmental Modelling & Software, 64, 58–71. https://doi.org/10.1016/j.envsoft.2014.11.012
  • Wang, J., Zhu, D., Zhang, L., & Ames, D. P. (2015). Economic analysis approach for identifying optimal microirrigation uniformity. Journal of Irrigation and Drainage Engineering, 141(8), 04015002. https://doi.org/10.1061/(ASCE)IR.1943-4774.0000863
  • Hansen, C. H., Williams, G. P., Adjei, Z., Barlow, A., Nelson, E. J., & Woodruff Miller, A. (2015). Reservoir water quality monitoring using remote sensing with seasonal models: Case study of five central-Utah reservoirs. Lake and Reservoir Management, 31(3), 225–240. https://doi.org/10.1080/10402381.2015.1065937

2014


  • Hayden, S., Ames, D. P., Turner, D., Keene, T., & Andrus, D. (2015). Mobile, low-cost, and large-scale immersive data visualization environment for civil engineering applications. Journal of Computing in Civil Engineering, 29(6), 05014011. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000428
  • Fan, Y., Richard, S., Bristol, R. S., Peters, S. E., Ingebritsen, S. E., Moosdorf, N., Packman, A., Gleeson, T., Zaslavsky, I., Peckham, S., Murdoch, L., Fienen, M., Cardiff, M., Tarboton, D., Jones, N., Hooper, R., Arrigo, J., Gochis, D., Olson, J., & Wolock, D. (2015). DigitalCrust – a 4D data system of material properties for transforming research on crustal fluid flow. Geofluids, 15(1–2), 372–379. https://doi.org/10.1111/gfl.12114
  • Yang, P., Ames, D. P., Fonseca, A., Anderson, D., Shrestha, R., Glenn, N. F., & Cao, Y. (2014). What is the effect of LiDAR-derived DEM resolution on large-scale watershed model results? Environmental Modelling & Software, 58, 48–57. https://doi.org/10.1016/j.envsoft.2014.04.005
  • Anderson, D. L., Ames, D. P., & Yang, P. (2014). Quantitative methods for comparing different polyline stream network models. Journal of Geographic Information System, 2014. https://doi.org/10.4236/jgis.2014.62010
  • Fonseca, A., Ames, D. P., Yang, P., Botelho, C., Boaventura, R., & Vilar, V. (2014). Watershed model parameter estimation and uncertainty in data-limited environments. Environmental Modelling & Software, 51, 84–93. https://doi.org/10.1016/j.envsoft.2013.09.023
  • Williams, G. P., Obregon, O., Nelson, E. J., Miller, W., Borup, M. B., & Buahin, C. (2014). Sensitivity of water quality indicators in a large tropical reservoir to selected climate and land‐use changes. Lakes & Reservoirs: Science, Policy and Management for Sustainable Use, 19(4), 293–305.                                      https://doi.org/10.1111/lre.12079
  • Berrett, C., Williams, G. P., Moon, T., & Gunther, J. (2014). A bayesian nonparametric model for temperature-emissivity separation of long-wave hyperspectral images. Technometrics, 56(2), 200–211. https://doi.org/10.1080/00401706.2013.869262

2013


  • Jones, N. L., Nelson, J., Williams, G., Ogden, F., Tarboton, D., & Burian, S. (2013). Ci-water: Cyberinfrastructure to advance high performance water resource modeling. World Environmental and Water Resources Congress 2013, 2737–2746. https://doi.org/10.1061/9780784412947.271
  • Jones, N. L., Lemon, A. M., & Kennard, M. J. (2014). Efficient Storage of Large MODFLOW Models. Groundwater, 52(3), 461–465. https://doi.org/10.1111/gwat.12060
  • Conner, L. G., Ames, D. P., & Gill, R. A. (2013). HydroServer Lite as an open source solution for archiving and sharing environmental data for independent university labs. Ecological Informatics, 18, 171–177. https://doi.org/10.1016/j.ecoinf.2013.08.006
  • Stites, M., Gunther, J., Moon, T., & Williams, G. (2013). Using Physically-Modeled Synthetic Data to Assess Hyperspectral Unmixing Approaches. Remote Sensing, 5(4), 1974-1997. https://doi.org/10.3390/rs5041974

2012


2011


  • Salah, A. M., Nelson, E. J., & Williams, G. P. (2011). A framework for implementing spatial and temporal uncertainty in integrated water resources modelling. Lakes & Reservoirs: Science, Policy and Management for Sustainable Use, 16(1), 77–86. https://doi.org/10.1111/j.1440-1770.2011.00465.x
  • Raza, M., Weber, K., Mannel, S., Ames, D.P., and Pattillo, R. (2011). “Geospatial analysis of tree root damage to sidewalks in southeastern Idaho.” URISA Journal, 23(1), 29-32.
  • Alexandrov, G. A., Ames, D., Bellocchi, G., Bruen, M., Crout, N., Erechtchoukova, M., Hildebrandt, A., Hoffman, F., Jackisch, C., Khaiter, P., Mannina, G., Matsunaga, T., Purucker, S. T., Rivington, M., & Samaniego, L. (2011). Technical assessment and evaluation of environmental models and software: Letter to the Editor. Environmental Modelling & Software, Thematic Issue on the Assessment and Evaluation of Environmental Models and Software, 26(3), 328–336. https://doi.org/10.1016/j.envsoft.2010.08.004
  • Anderson, D. L. and Ames, D.P. (2011). “A method for extracting stream channel flow paths directly from LiDAR point cloud data.” Journal of Spatial Hydrology, 11(1), 1-17.
  • Marchionni, B. and Ames, D.P. (2011). “A modular spatial modeling environment for GIS.” OSGeo Journal, 8, 54-64.
  • Dunsford, H. and Ames, D.P. (2011). “MapWindow 6.0: an extensible architecture for cartographic symbology.” OSGeo Journal, 8, 31-36.
  • Lowe, N. J., Hotchkiss, R. H., & Nelson, E. J. (2011). Theoretical determination of sequent depths in closed conduits. Journal of Irrigation and Drainage Engineering, 137(12), 801–810. https://doi.org/10.1061/(ASCE)IR.1943-4774.0000349
  • Richards, P. W., Williams, G., Schultz, G. G., & Nelson, E. J. (2011). Present sentiment about asce policy statement 465 among business owners, university professors, and state licensing boards. Journal of Professional Issues in Engineering Education and Practice, 137(3), 122–126. https://doi.org/10.1061/(ASCE)EI.1943-5541.0000041
  • Stephens, R., Obregon, O., Chilton, R. E., Williams, G. P., & Nelson, E. J. (2011). Field algae measurements using empirical correlations at deer creek reservoir. World Environmental and Water Resources Congress 2011, 3783–3791. https://doi.org/10.1061/41173(414)396
  • de Anda, J., Rangel-Peraza, J. G., Obregon, O., Nelson, J., Williams, G. P., Jarquín-Javier, Y., ... & Rode, M. (2011). The use of digital elevation models (dems) for bathymetry development in large tropical reservoirs. Bathymetry and its Applications, 158
  • Williams, G., Sawyer, P., Venedam, R., & Wannberg, V. E. (2011). Comparison of three air transport models for safety applications under diffusive conditions using full-scale experimental data: Epicode, aloha, and scipuff. Journal of Hazardous, Toxic, and Radioactive Waste, 15(1), 26–36.   https://doi.org/10.1061/(ASCE)HZ.1944-8376.0000052

2010


2009


  • Paudel, M., Nelson, E. J., & Scharffenberg, W. (2009). Comparison of lumped and quasi-distributed clark runoff models using the scs curve number equation. Journal of Hydrologic Engineering, 14(10), 1098–1106. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000100
  • Ames, D. P., Rafn, E. B., Van Kirk, R., & Crosby, B. (2009). Estimation of stream channel geometry in Idaho using GIS-derived watershed characteristics. Environmental Modelling & Software, 24(3), 444–448. https://doi.org/10.1016/j.envsoft.2008.08.008
  • Michaelis, C. D., & Ames, D. P. (2009). Evaluation and implementation of the ogc web processing service for use in client-side gis. GeoInformatica, 13(1), 109–120. https://doi.org/10.1007/s10707-008-0048-1
  • Williams, G. P., & Tomasko, D. (2009). A Simple Quantitative Model to Estimate Consumptive Evaporation Impacts of Discharged Cooling Water with Minimal Data Requirements. Energy & Environment, 20(7), 1155-1162. https://doi.org/10.1260/095830509789876718

2008 and earlier