Enhancing management of flood forecasting in Southern New Jersey: a HEC-HMS model development for Maurice River and Raccoon Creek Watersheds

Abstract

Southern New Jersey, the USA, particularly its coastal regions, faces rising flood risks due to frequent and extreme weather events associated with climate change. Interactions between rainfall and runoff remain challenging due to data limitations and variability in hydrological conditions. This study integrates precipitation data and watershed characteristics in a hydrologic model to assess flood vulnerability of two watersheds (i.e., Maurice River and Raccoon Creek) in southern New Jersey. A sensitivity analysis revealed that land imperviousness exerts the strongest influence on peak runoff predicted by the model. The model was calibrated using past precipitation data and validated against observed peak runoff records. Model performance assessed using the fitting criteria of Normalized Root Mean Square Error and the Nash–Sutcliffe Efficiency demonstrated good agreement between calculated and observed peak runoff data. Model simulations based on 5-, 25-, 50-, 100- and 200-year return period precipitations were employed to predict the peak runoff from the watersheds. Precipitation projections using different scenarios including the high-emissions pathway (i.e., scenario where greenhouse gas emissions continue to increase throughout the twenty-first century, leading to significant climate-related changes) in global climate model were used to calculate the peak runoff. The results indicated a notable increase in peak runoff associated with high-emission precipitation projections from global climate model compared to return period-based peak runoff. These results highlight the redistribution of weather extremes, increased winter precipitation, and heightened flood risks in the watersheds. Overall, the study establishes a practical, data-driven framework for assessing future flood hazards, supporting both technical decision-making and long-term climate adaptation strategies in flood-prone regions.