Ben R. Hodges, Sazzad Sharior, Edward Tiernan, Eric Jenkins, Gerardo Riaño-Briceño, Cesar Davila-Hernandez, Ehsan Madadi-Kandjani, Cheng-Wei Yu

The SWMM5+ project began with the observation that the EPA SWMM hydraulic solver: (i) is the primary consumer of computational time, (ii) is the source of model instability/mass non-conservation, (iii) limits parallelization, and (iv) cannot support mass-conservative Advection-Diffusion-Reaction (ADR) transport modeling. The new SWMM5+ solver provides the foundations for addressing these issues with a mass-conservative hydraulic model that is coded for parallel efficiency. The SWMM5+ code acts as the top-level controlling program with the EPA SWMM code providing a library of functions for hydrology and input data parsing.

Sazzad Sharior, Ben R. Hodges, Cheng-Wei Yu, Edward Tiernan, Eric Jenkins, Gerardo Riaño-Briceño, Cesar Davila-Hernandez, Abdulmuttalib Lokhandwala, Christopher Brashear

This manual provides the fundamentals for using the SWMM5+ hydraulics engine coupled to the the US Environmental Protection Agency's Storm Water Management Model (EPA SWMM). SWMM5+ provides a finite-volume solution for the dynamic wave equation (Saint-Venant equations) that replaces the link-node finite-difference approach in EPA SWMM. The principal advantages of the new model are (i) improved mass conservation and stability and (ii) efficient use of multi-core processors to reduce computational time for large systems. This manual assumes users are already familiar with EPA SWMM and have the EPA SWMM user guide available for reference.

Cesar Davila-Hernandez, Ben R. Hodges, Sazzad Sharior, Eric Jenkins, Edward Tiernan

This guide provides installation instructions for v1.0 of the SWMM5+ hydraulics engine. This engine is a companion code to the US Environmental Protection Agency’s Storm Water Management Model (EPA SWMM). The SWMM5+ code is linked with EPA SWMM code during compilation to simulate a coupled hydrology/hydraulic system. SWMM5+ is available as an executable that has been developed using the Ubuntu Linux operating system. It can be run on Windows systems through either a Docker container or using the Windows Subsystem for Linux (WSL). The code has not been tested on Mac OSX systems. The SWMM5+ source code is public domain and can be compiled with the Intel oneAPI compiler.

Cesar Davila-Hernandez, Jungseok Ho, Dongchul Kim, Abdoul Oubeidillah

During every Atlantic hurricane season, storms represent a constant risk to Texan coastal communities and other communities along the Atlantic coast of the United States. A storm surge refers to the abnormal rise of sea water level due to hurricanes and storms; traditionally, hurricane storm surge predictions are generated using complex numerical models that require high amounts of computing power to be run, which grow proportionally with the extent of the area covered by the model. In this work, a machine-learning-based storm surge forecasting model for the Lower Laguna Madre is implemented. The model considers gridded forecasted weather data on winds and atmospheric pressure over the Gulf of Mexico, as well as previous sea levels obtained from a Laguna Madre ocean circulation numerical model. Using architectures such as Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) combined, the resulting model is capable of identifying upcoming hurricanes and predicting storm surges, as well as normal conditions in several locations along the Lower Laguna Madre. Overall, the model is able to predict storm surge peaks with an average difference of 0.04 m when compared with a numerical model and an average RMSE of 0.08 for normal conditions and 0.09 for storm surge conditions.

Cesar Davila-Hernandez, Sara Davila, Martin Flores, Jungseok Ho, Dongchul Kim

Forecasting natural disasters such as inundations can be of great help for emergency bodies and first responders. In coastal communities, this risk is often associated with storm surge. To produce flood forecasts for coastal communities, a system must incorporate models capable of simulating such events based on forecasted weather conditions. In this work, a system for forecasting inundations based predominantly on storm surge is explored. An automation and a coupling strategy were implemented to produce forecasted flood maps automatically. The system leverages an ocean circulation model and a channel water flow model to estimate flood events in South Texas specially alongside the Lower Laguna Madre. The system around the models is implemented using Python and the meteorological forcing input is obtained from weather forecasting models maintained by the National Oceanic and Atmospheric Administration. The forecasted weather data retrieval, data processing and automation of the models are successful, and the complete stack of software can be deployed locally or in cloud solutions to accelerate computations. The resulting system performs as expected and successfully produces flood maps automatically providing vital information for flood emergency management in coastal communities.

Sara Davila, Cesar Davila-Hernandez, Martin Flores, Jungseok Ho

The intensification of climatic changes, mainly natural geophysical hazards like hurricanes, are of great interest to the South Texas region. Scientists and engineers must protect essential resources from coastal threats, such as storm surge. This study presents the development process and improvements of a hydrodynamic finite element model that covers the South Texas coast, specifically the Lower Laguna Madre, for the aid of local emergency management teams. Four historical tropical cyclone landfalls are evaluated and used as a means of verification of the hydrodynamic model simulation results. The parameters used to improve the accuracy of the model are the tidal harmonic constituents and the surface roughness coefficient, or manning’s n value. A total of four different scenarios that use a variety of tidal constituent combinations and nodal attribute files were developed to identify the best case. Statistical evaluation, such as regression analysis, normalized root mean square error, and scatter index, was used to determine the significance of each hydrodynamic computational storm surge result with observed historical water surface elevations. In an effort to improving all models locally, using seven tidal constituents combinations along with a surface roughness nodal attribute grid that assigns values with respect to bathymetric data improves the accuracy of the storm surge model and should, therefore, be implemented for future hydrodynamic studies in the South Texas region.