 Publications |
Wang, J., Wang, C., Rao, V., Orr, A., Yan, E., and Kotamarthi, R.: A parallel workflow implementation for PEST version 13.6 in high-performance computing for WRF-Hydro version 5.0: a case study over the midwestern United States, Geosci. Model Dev., 12, 3523–3539, https://doi.org/10.5194/gmd-12-3523-2019, 2019.
von Ramm A., Weismüller J., Kurtz W., Neckel T. (2019) Comparing Domain Decomposition Methods for the Parallelization of Distributed Land Surface Models. In: Rodrigues J. et al. (eds) Computational Science – ICCS 2019. ICCS 2019. Lecture Notes in Computer Science, vol 11536. Springer, Cham
Ning, L., Zhan, C., Luo, Y. et al. A review of fully coupled atmosphere-hydrology simulations J. Geogr. Sci. (2019) 29: 465. https://doi-org.cuucar.idm.oclc.org/10.1007/s11442-019-1610-5
Zarekarizi, Mahkameh, "Ensemble Data Assimilation for Flood Forecasting in Operational Settings: From Noah-MP to WRF-Hydro and the National Water Model" (2018). Dissertations and Theses. Paper 4651. https://pdxscholar.library.pdx.edu/open_access_etds/4651/
Youssef Wehbe, Marouane Temimi, Michael Weston, Naira Chaouch, Oliver Branch, Thomas Schwitalla, Volker Wulfmeyer, and Abdulla Al Mandous: Analysis of an Extreme Weather Event in a Hyper Arid Region Using WRF-Hydro Coupling, Station, and Satellite data. Natural Hazards and Earth System Sciences. September 2018. https://doi.org/10.5194/nhess-2018-226
Marcelo A. Somos-Valenzuelaand Richard N. Palmer: Use of WRF-Hydro over the Northeast of the US to Estimate Water Budget Tendencies in Small Watersheds. Water 2018, 10(12), 1709; https://doi.org/10.3390/w10121709
Friedrich, K., and Coauthors, 2018: Reservoir evaporation in the western United States: Current science, challenges, and future needs. Bulletin of the American Meteorological Society, 99, 167-187, doi:10.1175/BAMS-D-15-00224.1.
Hu, H., F. Dominguez, P. Kumar, J. McDonnell, and D. Gochis, 2018: A numerical water tracer model for understanding event-scale hydrometeorological phenomena. Journal of Hydrometeorology, 19, 947-967, doi:10.1175/JHM-D-17-0202.1.
Hoar, T. J., and Coauthors, 2018: Ensemble data assimilation with the Community Land Model and the US National Water Model. International Conference on Terrestrial Systems Research: Monitoring, Prediction and High Performance Computing, University of Bonn, Bonn, DE.
Z. George Xue, David J. Gochis, Wei Yu, Barry D. Keim, Robert V. Rohli, Zhengchen Zang, Kevin Sampson, Aubrey Dugger, David Sathiaraj, and Qian Ge: Modeling Hydroclimatic Change in Southwest Louisiana Rivers.Water 2018, 10(5), 596; doi:10.3390/w10050596
Lin, P. R. L. J. Hopper, Jr., Z.-L. Yang, M. Lenz, and J. W. Zeitler, 2018: Insights into hydrometeorological factors constraining flood prediction skill during the May and October 2015 Texas Hill Country flood events, J. Hydrometeorology, 9 (8), 1339-1361.
Lin, P. R., Z.-L. Yang, D. J. Gochis, W. Yu, D. R. Maidment, M. A. Somos-Valenzuela, and C. H. David, 2018: Implementation of a vector-based river network routing scheme in the community WRF-Hydro modeling frameowork for flood discharge simulation, Environmental Modelling and Software,107, 1–11, https://doi.org/10.1016/j.envsoft.2018.05.018.
Lin, P. R., M. A. Rajib, Z.-L. Yang, M. Somos-Valenzuela, V. Merwade, D. R. Maidment, Y. Wang, and L. Chen, 2018: Spatiotemporal evaluation of simulated evapotranspiration and streamflow over Texas using the WRF-Hydro-RAPID modeling framework, Journal of the American Water Resources Association, 54 (1), 40–54, DOI: 10.1111/1752-1688.12585.
Ryu, Y., Lim, YJ., Ji, HS. et al. Applying a Coupled Hydrometeorological Simulation System to Flash Flood Forecasting over the Korean Peninsula Asia-Pacific J Atmos Sci (2017) 53: 421. https://doi-org.cuucar.idm.oclc.org/10.1007/s13143-017-0045-0
E.NaabilabB.LLampteycJ.ArnaultdA.Olufayo,aH.Kunstmann: Water resources management using the WRF-Hydro modelling system: Case-study of the Tono dam in West Africa. Journal of Hydrology: Regional Studies. Volume 12, August 2017, Pages 196-209 https://doi.org/10.1016/j.ejrh.2017.05.010
Majidzadeh, H., Uzun, H., Ruecker, A. et al. Extreme flooding mobilized dissolved organic matter from coastal forested wetlands Biogeochemistry (2017) 136: 293. https://doi-org.cuucar.idm.oclc.org/10.1007/s10533-017-0394-x
Xiang, T., E. R. Vivoni, D. J. Gochis, and G. Mascaro, 2017: On the diurnal cycle of surface energy fluxes in the North American monsoon region using the WRF-Hydro modeling system. Journal of Geophysical Research: Atmospheres, 122, 9024-9049, doi:10.1002/2017JD026472.
Brothers, K., 2017: Forecast Verification of the 2017 Flooding at Lake Oroville Using the National Water Model.
Silvera,*, A. Karnielia, H. Ginatc, E. Meiric, E. Fredjb,*An innovative method for determining hydrological calibrationparameters for the WRF-Hydro model in arid regions. Environmental Modelling & Software. January 2017
Kerandi, N., J. Arnault, P. Laux, S. Wagner, J. Kitheka, and H. Kunstmann, 2017: Joint atmospheric-terrestrial water balances for East Africa: a WRF-Hydro case study for the upper Tana River basin. Theoretical and Applied Climatology, doi: 10.1007/s00704-017-2050-8.
Verri, G., N. Pinardi, D. Gochis, J. Tribbia, A. Navarra, G. Coppini, and T. Vukicevic, 2017: A meteo-hydrological modelling system for the reconstruction of river runoff: the case of the Ofanto river catchment. Natural Hazards and Earth System Sciences, 17, 1741-1761, doi:10.5194/nhess-17-1741-2017.
Brothers, K., 2017: Forecast Verification of the 2017 Flooding at Lake Oroville Using the National Water Model.
Powers, J. G., and Coauthors, 2017: The Weather Research and Forecasting Model: Overview, system efforts, and future directions. Bulletin of the American Meteorological Society, 98, 1717-1737, doi:10.1175/BAMS-D-15-00308.1.
Li, L., D. J. Gochis, S. Sobolowski, and M. D. S. Mesquita, 2017: Evaluating the present annual water budget of a Himalayan headwater river basin using a high-resolution atmosphere-hydrology model. Journal of Geophysical Research: Atmospheres, 122, 4786-4807, doi:10.1002/2016JD026279.
Weber, W. J., 2017: Unidata in the cloud: A vision for future data services [presentation]. NCAR Day of Networking and Discovery 2017, National Center for Atmospheric Research (NCAR), Boulder, CO, US.
Haupt, S. E., 2016: Applications of meteorology for energy. University of Connecticut Seminar 2016, University of Connecticut, Storrs, CT, US.
Collins, N. S., 2016: A generic implementation of strongly-coupled assimilations in the DART framework. Coupled Data Assimilation Workshop 2016, Meteo France, Toulouse, FR.
Cuntz, M., J. Mai, L. Samaniego, M. Clark, V. Wulfmeyer, O. Branch, S. Attinger, and S. Thober, 2016: The impact of standard and hard-coded parameters on the hydrologic fluxes in the Noah-MP land surface model. Journal of Geophysical Research: Atmospheres, 121, 10,676-10,700, doi:10.1002/2016JD025097.
Theurich, G., and Coauthors, 2016: The Earth System Prediction Suite: Toward a coordinated U.S. modeling capability. Bulletin of the American Meteorological Society, 97, 1229-1247, doi:10.1175/BAMS-D-14-00164.1.
Mizukami, N., and Coauthors, 2016: mizuRoute version 1: A river network routing tool for a continental domain water resources applications. Geoscientific Model Development, 9, 2223-2238, doi:10.5194/gmd-9-2223-2016.
Tessendorf, S. A., B. Boe, B. Geerts, M. J. Manton, S. Parkinson, and R. M. Rasmussen, 2015: The future of winter orographic cloud seeding: A view from scientists and stakeholders. Bulletin of the American Meteorological Society, 96, 2195-2198, doi:10.1175/BAMS-D-15-00146.1.
Senatore, A., G. Mendicino, D. J. Gochis, W. Yu, D. Yates, and H. Kunstmann, 2015: Fully coupled atmosphere-hydrology simulations for the central Mediterranean: Impact of enhanced hydrological parameterization for short and long time scales. Journal of Advances in Modeling Earth Systems, 7, 1693-1715, doi:10.1002/2015MS000510.
Clark, M., and Coauthors, 2015: Improving the representation of hydrologic processes in Earth System Models. Water Resources Research, 51, 5929-5956, doi:10.1002/2015WR017096.
Prein, A., and Coauthors, 2015: A review on regional convection-permitting climate modeling: Demonstrations, prospects, and challenges. Reviews of Geophysics, 53, 323-361, doi:10.1002/2014RG000475.
Hoar, T. J., 2015: Getting to know the Data Assimilation Research Testbed [presentation]. STATMOS Summer School in Data Assimilation 2015, National Center for Atmospheric Research (NCAR), Boulder, CO, US.
Yucel, I., A. Onen, K. K. Yilmaz, and D. J. Gochis, 2015: Calibration and evaluation of a flood forecasting system: Utility of numerical weather prediction model, data assimilation and satellite-based rainfall. Journal of Hydrology, 523, 49-66, doi:10.1016/j.jhydrol.2015.01.042.
Clark, M., and Coauthors, 2015: A unified approach for process-based hydrologic modeling: 1. Modeling concept. Water Resources Research, 51, 2498-2514, doi:10.1002/2015WR017198.
Hoar, T. J., 2015: The keys to successful ensemble data assimilation [presentation]. San Diego State University Presentation, San Diego, CA, US.
Bierkens, M. F. P., and Coauthors, 2015: Hyper-resolution global hydrological modelling: What is next? Hydrological Processes, 29, 310-320, doi:10.1002/hyp.10391.
Hoar, T. J., 2014: Ensemble verification: Part II [presentation]. International Cooperative for Aerosol Prediction (ICAP/AEROCAST) Validation 2014, University of North Dakota, Boulder, CO, US.
Wolff, J., P. Jimenez, J. Dudhia, M. Harrold, G. Lackmann, and K. Mahoney, 2013: Demonstrating the utility of the Mesoscale Model Evaluation Testbed (MMET) in a research environment [poster]. 14th WRF Users Workshop, University Corporation for Atmospheric Research, Boulder, CO, US.
