This study set up a WRF-Hydro domain centered over the Sierra Madre and Medicine Bow mountain ranges in southern Wyoming, along with the watersheds that these regions feed into. For the winters of the 2010 and 2012 water years, a series of cloud seeding experiments were simulated in the WRF model using a specialized cloud-seeding parameterization developed by the National Center for Atmospheric Research (NCAR). For each experiment, a control simulation (without seeding) and seeding simulation were performed, allowing the precipitation change due to simulated seeding to be calculated. These changes were applied to a control WRF simulation, which were processed for ingest into to an uncoupled instance of the WRF-Hydro modeling system using Earth System Modeling Framework (ESMF) regridding tools. From this, a set of control forcings with no cloud seeding impacts and a set with the impact of seeding operations were generated for WRF-Hydro. Since the majority of the cloud seeding events were designed to enhance wintertime snowfall locally, the impacts on streamflow do not manifest until months into the melt season. These impacts lead to differences in streamflow downstream well into the water year and beyond the primary melt season. For these reasons, the WRF-Hydro simulations were carried out to the end of the water year, which is October 1st. Hydrologic verification occurred at streamflow points in the modeling domain using discharge observations at United States Geological Survey (USGS). Analysis was also done comparing gridded snow water equivalent from the National Weather Service (NWS) Snow Data Assimilation System (SNODAS) against the gridded snow states in WRF-Hydro. Additional analysis compared gridded precipitation fields to the gridded SWE fields to assess the impact of cloud seeding throughout the accumulation and ablation periods.
This study provides the first coupled simulations of the WRF model with the cloud seeding parameterization and WRF-Hydro. The benefits of this method are that it explicitly connects the spatial and temporal precipitation patterns resulting from simulated seeding to drive an process-based streamflow model. This work lays the foundation to explore additional experiments using the WRF-Hydro framework driven by output from the cloud seeding parameterization in WRF.