Abstract
Accurate and timely flood forecasts are critical for making emergency-response decisions regarding public safety, infrastructure operations, and resource allocation. One of the main challenges for coastal flood forecasting systems is a lack of reliable forecast data of large-scale oceanic and watershed processes and the combined effects of multiple hazards, such as compound flooding at river mouths. Offshore water level anomalies, known as remote Non-Tidal Residuals (NTRs), are caused by processes such as downwelling, offshore wind setup, and also driven by ocean-basin salinity and temperature changes, common along the west coast during El Niño events. Similarly, fluvial discharges can contribute to extreme water levels in the coastal area, while they are dominated by large-scale watershed hydraulics. However, with the recent emergence of reliable large-scale forecast systems, coastal models now import the essential input data to forecast extreme water levels in the nearshore. Accordingly, we have developed Hydro-CoSMoS, a new coastal forecast model based on the USGS Coastal Storm Modeling System (CoSMoS) powered by the Delft3D San Francisco Bay and Delta community model. In this work, we studied the role of fluvial discharges and remote NTRs on extreme water levels during a February 2019 storm by using Hydro-CoSMoS in hindcast mode. We simulated the storm with and without real-time fluvial discharge data to study their effect on coastal water levels and flooding extent, and highlight the importance of watershed forecast systems such as NOAA’s National Water Model (NWM). We also studied the effect of remote NTRs on coastal water levels in San Francisco Bay during the 2019 February storm by utilizing the data from a global ocean model (HYCOM). Our results showed that accurate forecasts of remote NTRs and fluvial discharges can play a significant role in predicting extreme water levels in San Francisco Bay. This pilot application in San Francisco Bay can serve as a basis for integrated coastal flood modeling systems in complex coastal settings worldwide.
Highlights
Climate change is driving several processes that will lead to increased flood frequency in many parts of the world [1,2]
As part of the AQPI framework, we developed an integrated modeling system, coupling high-resolution rapid refresh (HRRR) and National Water Model (NWM) to the hydrodynamic model of USGS Coastal Storm Modeling System (Hydro-CoSMoS, www.usgs.gov/cosmos) to achieve the ultimate goal of providing the public with an accurate coastal and inland flood forecast system
We have modeled the 2019 February Storm using four different forcing combinations to study the effect of fluvial discharges and remote Non-Tidal Residuals (NTRs) on flooding in San Francisco Bay, considering two different types of forcing for offshore and discharge boundaries
Summary
Climate change is driving several processes that will lead to increased flood frequency in many parts of the world [1,2]. Global Sea-Level Rise (SLR) has been observed to be between 1.2 to 1.7 mm/yr. Water 2020, 12, 2481 during the twentieth century [3,4] and the rate of sea-level rise is accelerating [4,5] and will continue to accelerate under most emissions scenarios [6]. Rainfall intensity and storm frequency are changing with some regional variability, but in general rising temperatures are leading to intensified rainfall [8]. This change in hydrology is putting pressure on water providers and changes flood risk. Since the past is no longer a good guide to current risks for flooding [9,10], there is an even greater need for improved forecasts in these regions
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