The Gridded Surface Subsurface Hydrologic Analysis (GSSHA) model is an integrated system of physics-based hydrologic process models. This approach of using a system of physics-based models allows GSSHA to be a good fit for many watershed management decision support applications, such as understanding changing flood dynamics from urban growth as well as using natural and nature-based solutions, such as wetlands or artificial beaver dams, to ameliorate flooding potential. One of the key roles GSSHA has played is modeling compound coastal storm flooding – extreme rainfall combined with storm surge from tropical systems. Simulating such events requires coupling of overland, riverine, coastal, and weather modeling systems. A challenge facing the USACE modeling community is enabling the programmatic connections between models to reduce manual input errors and increase the throughput in time-sensitive situations. In this talk we will discuss how GSSHA has been modernized to enable programmatic-level access to GSSHA. This allows GSSHA to be used in systems of computational models as well as opens up GSSHA to a wide level of data science integration applications, particularly as a part of the NextGen Framework.

Prado Dam, located near Los Angeles California, is used to control flows for water supply and flood control in the Sana Ana River with a basin area of 6,216 km2. Existing operations at Prado Dam are supported by streamflow forecasts issued by the California Nevada River Forecast Center (CNRFC). The CNRFC uses a well-tested set of semi-lumped models developed in the 1970s to simulate and predict flows upstream of Prado as well as Prado reservoir inflows. The models are calibrated to current watershed conditions and executed in a modern forecasting framework. Nonetheless, there remains a potential to improve streamflow forecasts using a contemporary physics-based, gridded hydrologic model that can be coupled with a mesoscale weather forecast model run on a similar scale (West-WRF), which may perform better in an arid region like the Santa Ana River Watershed where there are a limited number of events for model tuning. Additional benefits for reservoir operations may be derived from integrated simulation of the watershed, streams, and reservoir. To test this hypothesis, the Santa Ana River watershed and Prado Dam were simulated with the Gridded Surface Subsurface Hydrologic Analyses (GSSHA) model, as an alternative to other watershed models such as those used by the CNRFC. The model is calibrated/verified to observed streamflows and changes in reservoir volume using observed rainfall data, then tested against observed reservoir inflows for an extended simulation period. Utilizing changes in reservoir volume in the calibration allowed for better estimates of reservoir volume without significantly deteriorating flow estimates. The GSSHA model, developed for larger flood events, outperformed the current system for larger events, while performing comparable for other events, indicating it might provide additional information in support of reservoir operations An experimental operational model was developed to run on the Center for Western Weather and Water Extremes (CW3E) computer resources using a West-WRF forecast; output is downloaded to and displayed on the US Army Corps of Engineers UMIP system, which shows all types of measured and simulated hydrologic data online. Pending funding, this system could be operationalized to provide additional reservoir operational guidance, especially during extreme events.

Arizona State University, Earth Genome, and USACE' Engineer Research and Development Center has been working together under a Network for Engineering With Nature project to develop a Web Application tool for regional screening of Managed Aquifer Recharge (MAR) Projects. The objective of the project is to provide relative, regional indices for appropriate siting of MAR, which would be an innovative approach to flood management and restore hydrological and ecological processes linked to aquifer recharge. Developing a rigorous decision-making framework includes physical characteristics such as surface water runoff, connection with groundwater aquifers; as well as social considerations, such as land use, engaging stakeholders in floodplain management, and distribution of risks and benefits. A suite of analyses, from CONUS-wide Machine Learning for groundwater attributes and statistical estimation of flood volumes to surveys about perceptions of MAR were implemented to inform development of a decision support tool and supplemental guidance in an effort to increase the accessibility of this innovative flood management approach to floodplain managers. This research is funded by the Engineering With Nature ® and Network for Engineering With Nature® programs. https://ewn.erdc.dren.mil/