Great Lakes Observing & Forecasting Systems
Assimilation of lake and reservoir levels into the WRF-Hydro National Water Model to improve operational hydrologic predictions
Overview and Objectives
This project will improve the representation of reservoir mass balance and storages in the operational NOAA National Water Model (NWM) through the implementation and evaluation ofa new reservoir level data assimilation scheme and new reservoir process representations. Beginning in the summer of 2016, the NOAA National Water Center (NWC) in partnership with the National Centers for Environmental Prediction (NCEP), the National Center for Atmospheric Research (NCAR) and other academic partners will produce operational hydrologic predictions for the nation using a new National Water Model (NWM) that is based on the community WRF-Hydro modeling system (Gochis et al., 2015). This system will operationally produce a variety of hydrologic analysis and prediction products, including gridded fields of soil moisture, snowpack, shallow groundwater levels, inundated area depths, evapotranspiration as well as estimates of river flow and velocity for approximately 2.7 million river reaches. Also included in the NWM are representations of more than 1,200 reservoirs, which are linked into the national channel network defined by the USGS NHDPlusv2.0 hydrography dataset. Despite the unprecedented spatial and temporal coverage of the NWM, a number of known deficiencies exist in the representation of lakes and reservoirs, including no representation of the U.S. Great Lakes system.
The WRF-Hydro system is a physics-based, distributed hydrologic modeling system and has been used in several streamflow prediction applications in the U.S. and around the world (Yucel et al., 2015; Senatore et al., 2015; Givati et al., 2016; Xiang, et al., 2016; Lin et al., 2016). The modeling system provides users with a multi-physics and multi-scale modeling framework for representing a large range of terrestrial hydrologic processes such as infiltration, runoff, lateral flow, channel flow, soil moisture, snowpack, evapotranspiration as well as various water management components.
Publications
Anderson, E., D. Schwab, and G. Lang, 2010: Real-time hydraulic and hydrodynamic model of the St. Clair River, Lake St. Clair, Detroit River system. J. Hydraul. Eng., 136, 507–518.
Bonan, G.B., 1995: Sensitivity of a GCM simulation to inclusion of inland water surfaces. J.Climate, 8, 2691-2074.
Givati, A., D. Gochis, T. Rummler, H. Kunstmann, 2016: Comparing One-way and Two-way Coupled Hydrometeorological Forecasting Systems for Flood Forecasting in the Mediterranean Region. In press, Hydrology.
Gochis, D.J., W. Yu, D.N. Yates, 2015: The WRF-Hydro model technical description and user’s guide, version 3.0. NCAR Technical Document. 120 pages. Available online at: http://www.ral.ucar.edu/projects/wrf_hydro/.
Gronewold, A. D., & Fortin, V. (2012). Advancing Great Lakes hydrological science through targeted binational collaborative research. Bulletin of the American Meteorological Society, 93(12), 1921–1925.
Hostetler, S.W., 1993: Interactive coupling of a lake thermal model with a regional climate model. J. Geophys. Res. 98: 5045-5057.
Lee, D. H., Quinn, F. H., Sparks, D., & Rassam, J. C. (1994). Modification of Great Lakes regulation plans for simulation of maximum Lake Ontario outflows. Journal of Great Lakes Research, 20(3), 569–582.
Lin, P., Z.-L. Yang, D.J. Gochis, W. Yu, D. R. Maidment, M.A. Somos-Valenzuela, C. David, 2016: Development and evaluation of a hybrid framework (WRF-Hydro-RAPID) for flash flood modeling: A case study for Hurricane Ike flooding in 2008. Submitted to Env. Modeling and Software, March, 2016.
Lofgren, B.M., T. Hunter, T. S.; Wilbarger, 2011: Effects of using air temperature as a proxy for potential evapotranspiration in climate change scenarios of Great Lakes basin hydrology. J. Great Lakes Res.
Mallard, M. S., C. G. Nolte, T. L. Spero, O. R. Bullock, K. Alapaty, J. A. Herwehe, J. Gula, and J. H.Bowden, 2015: Technical challenges and solutions in representing lakes when using WRF in downscaling applications. Geosci. Model Dev., 8, 1085–1096.
Mironov, D. L. Rontu, et al., 2010: Towards improved representation of lakes in numerical weather prediction and climate models: introduction to the special issue of Boreal Environment Research. Boreal Env. Res., 15, 97-99.
Obenour, D. R., Gronewold, A. D., Stow, C. A., & Scavia, D. (2014). Using a Bayesian hierarchical model to improve Lake Erie cyanobacteria bloom forecasts. Water Resources Research, 50(10), 7847–7860.
Senatore, A., G. Mendicino, D. J. Gochis, W. Yu, D. N. 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, J. Adv. Model. Earth Syst., 07, doi:10.1002/2015MS000510.
Subin, Z.M., W.J. Riley, D. Mironov, 2012: An improved lake model for climate simulations:model structure, evaluation and sensitivity analyses in CESM1. J. Adv. Model Earth Sys., 4,M02001.
Sun, X, Lian Xie, Fredrick H. M. Semazzi, and Bin Liu, 2014: A numerical investigation of theprecipitation over Lake Victoria basin using a coupled atmosphere-lake limited-area model.Advances in Meteorology, 2014, 15 pp. http://dx.doi.org/10.1155/2014/960924.
Thompson, L., M. Escobar, C. Mosser, D. Purkey, D. Yates and Peter B. Moyle, 2011, Water Management Adaptations to Prevent Loss of Spring-Run Chinook Salmon in California under Climate Change,” Journal of Water Resources Planning and Management, doi:10.1061/(ASCE)WR.1943-5452.0000194.
Xiang, T., Vivoni, E.R. and Gochis, D.J. 2016. On the Diurnal Cycle of Surface Energy Fluxes in the North American Monsoon Region using the WRF-Hydro Modeling System. Submitted to J. Hydrometeorology, Mar. 2016.
Yates, D., J. Sieber, D. Purkey, and A. Huber-Lee, 2005a., WEAP21 a demand, priority, and preference driven water planning model: Part 1, Model Characteristics, Water International, 30,4, pg 487-500.
Yates, D., D. Purkey, H. Galbraith, A. Huber-Lee, and J. Sieber, 2005b, WEAP21 a demand, priority, and preference driven water planning model: Part 2, Aiding freshwater ecosystem service evaluation, Water International, 30,4, pp. 501-512.
Yucel, I., Onen, A., Yilmaz, K. and Gochis, D. 2015. Calibration and evaluation of a flood forecasting system: Utility of numerical weather prediction model, data assimilation and satellite-based rainfall. J. Hydrol. 523, 49 – 66.
PrincipaI Investigator(s):
Chuliang Xiao (CILER)
David Gochis (NCAR)
David Yates (NCAR)
Andrew Wood (NCAR)
Lynn Johnson (CIRA)
NOAA Technical Lead(s):
Drew Gronewold (NOAA-GLERL)
Eric Anderson (NOAA-GLERL)
Rob Cifelli (NOAA PSD)
Ed Clark (NOAA NWC)
Conceptual diagram of WRF-Hydro components. Credit: NCAR
Schematic of the modularized multiscale, multi-physics WRF-Hydro modeling framework. Credit: NCAR