Resource

Cracking Complex Conundrums: Hydrologic and Hydraulic Modeling of Newark’s High Hazard Dams

Resource Type
ASDSO Conference Papers
Reference Title
Cracking Complex Conundrums: Hydrologic and Hydraulic Modeling of Newark’s High Hazard Dams
Author/Presenter
Gamble, Rex
Hildebrandt, Mia
Organization/Agency
Association of State Dam Safety Officials
Publisher Name
Association of State Dam Safety Officials
Year
2025
Date
09/21/2025
Event Name
Dam Safety 2025
Event Location
Cleveland, Ohio
ASDSO Session Title
Session 32: Hydrology in an Uncertain World
Topic Location
New Jersey
Abstract/Additional Information

ABSTRACT ONLY - Similar to many cities in the United States, the City of Newark, the largest in New Jersey, owns and operates several reservoirs in a highland region to provide clean drinking water to its residences. The City of Newark initiated a dam safety project on its inventory of dams, in which GZA was contracted to provide engineering services. While this project encompassed a comprehensive suite of services, this presentation will focus on GZA’s H&H analyses which included hydrological modeling and calibration in the software HEC-HMS, and incremental damage analyses, spillway and channel adequacy evaluation, and dam break hydraulic modeling in the software HEC-RAS. Newark’s inventory of dams, including six high hazard dams, presented unique logistical and H&H challenges due to their hydrological interconnectivity, the presence of fifteen dams in total in the watershed, large modeling extents, and unique hydraulic conditions such as spillway submergence at some of the dams and major inflows from river confluences in downstream areas. GZA will explore the logistical and H&H approaches that emerged including but not limited to discretizing the project area to balance between runtime and needed accuracy, phasing H&H tasks, and checking differences between HEC-HMS and HEC-RAS results. The presentation will discuss how the project team adapted to challenges presented by modeling Newark’s complex dam system, and provide lessons learned for future large-scale modeling.