San Francisco Bay and its watersheds are polluted by legacy polychlorinated biphenyls (PCBs), resulting in the establishment of a total maximum daily load (TDML) that requires a 90% PCB load reduction from municipal stormwater. Green infrastructure (GI) is a multibenefit solution for stormwater management, potentially addressing the TMDL objectives, but planning and implementing GI cost-effectively to achieve management goals remains a challenge and requires an integrated watershed approach. This study used the nondominated sorting genetic algorithm (NSGA-II) coupled with the Stormwater Management Model (SWMM) to find near-optimal combinations of GIs that maximize PCB load reduction and minimize total relative cost at a watershed scale. The selection and placement of three locally favored GI types (bioretention, infiltration trench, and permeable pavement) were analyzed based on their cost and effectiveness. The results show that between optimal solutions and nonoptimal solutions, the effectiveness in load reduction could vary as much as 30% and the difference in total relative cost could be well over $100 million. Sensitivity analysis of both GI costs and sizing criteria suggest that the assumptions made regarding these parameters greatly influenced the optimal solutions.
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With rising sea levels and the increased likelihood of extreme weather events, it is important for regional agencies and local municipalities in the San Francisco Bay Area to have a clear understanding of the status, composition, condition, and elevation of our current Bay shore, including both natural features and built infrastructure.
The purpose of this Bay shore inventory is to create a comprehensive and consistent picture of today’s Bay shore features to inform regional planning. This dataset includes both structures engineered expressly for flood risk management (such as accredited levees) and features that affect flooding at the shore but are not designed or maintained for this purpose (such as berms, road embankments, and marshes). This mapping covers as much of the ‘real world’ influence on flooding and flood routing as possible, including the large number of non-accredited structures.
This information is needed to:
- identify areas vulnerable to flooding.
- identify adaptation constraints due to present Bay shore alignments; and
- suggest opportunities where beaches, wetlands, and floodplains can be maintained or restored and integrated into flood risk management strategies.
The primary focus of the project is therefore to inform regional planners and managers of Bay shore characteristics and vulnerabilities. The mapping presented here is neither to inform FEMA flood designation nor is it a replacement for site-specific analysis and design.
The mapping consists of two main elements:
- Mapping of Bay shore features (levees, berms, roads, railroads, embankments, etc.) which could affect flooding and flood routing.
- Attributing Bay shore features with additional information including elevations, armoring, ownership (when known), among others.
SFEI delineated and characterized the Bay shore inland to 3 meters (10ft) above mean higher high water (MHHW) to accommodate observed extreme water levels and the commonly used range of future sea level rise (SLR) scenarios. Elevated Bay shore features were mapped and classified as engineered levees, berms, embankments, transportation structures, wetlands, natural shoreline, channel openings, or water control structures. Mapped features were also attributed with elevation (vertical accuracy of <5cm reported in 30 meter (100ft) segments from LiDAR derived digital elevation models (DEMs), FEMA accreditation status, fortification (e.g., riprap, buttressing), frontage (e.g., whether a feature was fronted by a wetland or beach), ownership, and entity responsible for maintenance. Water control structures, ownership, and maintenance attributes were captured where data was available (not complete for entire dataset). The dataset was extensively reviewed and corrected by city, county, and natural resource agency staff in each county around the Bay. This report provides further description of the Bay shore inventory and methods used for developing the dataset. The result is a publicly accessible GIS spatial database.