With the populations of anadromous salmonids in steep decline throughout California, many river restoration projects attempt to bring fish back to tributaries by enabling fish passage and creating spawning habitat. Carneros Creek, a tributary of the Napa River, is an incised and sinuous stream which poses a challenge for restoration planning land use management, as the watershed supports steelhead runs and valuable agricultural land. We documented the physical channel morphology of a 150 meter long reach in the Upper Carneros Creek using ground based Light Detection and Ranging (LiDAR) scans and assessed grain size using pebble counts in order to gain insight into restoration and management opportunities. These data provide a baseline geomorphic assessment for future restoration projects and allowed us to compare velocities predicted by 1-dimensional (1D) and 2-dimensional (2D) models. For the 1D model, we simulated flows by pulling out cross-sectional points from the LiDAR scans. Using a Manning’s n value of 0.033 for clean, sinuous channels with some pools and riffles, we found 1D velocities at four cross-sections corresponded to 3.3 m/s, 2.3 m/s, 2.5 m/s, and 2.8 m/s with a mean velocity of 2.73 m/s. For the 2D model, we used FaSTMECH in U.S. Geological Survey’s (USGS) Multi-Dimensional Surface Water Modeling System (MD_SWMS) based on LiDAR data. Our 2D velocity results decreased to an average of 0.85 m/s and ranged from 0 to 4.53 m/s based on local slope changes from the detailed channel morphology measurements. By adding grain size variable roughness to the 2D model, we saw a range of velocities from 0 to 1.98 m/s with an average of 0.65 m/s. We found that because 1D modeling of cross-sectional data using Manning’s equation does not simulate flow curvature in bends, our 2D model can provide betterdefined velocities than a 1D model. Because Carneros Creek is listed as a viable migration passage for steelhead, restoration managers concerned about the level of incision and the ‘flashy’ nature of the stream should consider how the variability in channel morphology and geomorphology models influence velocity predictions that are important drivers of habitat quality for migrating fish and juveniles.
Abstract Globally, rising seas threaten massive numbers of people and significant infrastructure. Adaptation strategies increasingly incorporate nature-based solutions. New science can illuminate where these solutions are appropriate in urban environments and what benefits they provide to people. Together with stakeholders in San Mateo County, California, USA, we co-developed nature-based solutions to support adaptation planning. We created six guiding principles to shape planning, summarized vulnerability to sea-level rise and opportunities for nature-based solutions, created three adaptation scenarios, and compared multiple benefits provided by each scenario. Adaptation scenarios that included investments in nature-based solutions deliver up to eight times the benefits of a traditionally engineered baseline as well as additional habitat for key species. The magnitude and distribution of benefits varied at subregional scales along the coastline. Our results demonstrate practical tools and engagement approaches to assessing the multiple benefits of nature-based solutions in an urban estuary that can be replicated in other regions.
Mapping channel geomorphology, riparian vegetation and the extent of anthropogenic disturbance of river corridors has traditionally been conducted laboriously in the field. With the implementation of the EU Water Framework Directive (WFD), member states are mandated to complete river basin management plans requiring such fieldwork in order to achieve good ecological status by 2015. Thus, deriving wider land cover information from remotely sensed data will be an integral addition to fieldwork in order to meet the requirements of the WFD. The objective of this study was to use two types of remote sensing data, LiDAR and ortho-imagery, to delineate channel morphologies and to field check the analysis in the field to test the accuracy of the remote sensing techniques and assess their applicability for the WFD. Using Carneros Creek, in Napa, CA as a testing ground because of the publicly available LiDAR and ortho-imagery datasets, I achieved 80% accuracy in identifying large terrace features from the Digital Elevation Model (DEM), and a lower percentage at 66% accuracy in identifying steep or vertical banks. Field checking the data not only helped clarify existence of the morphological feature but it also allowed for more complex data gathering such vegetation patterns and species, bedrock outcroppings and land use evidence such as outlets of drainage systems and in-stream pumps, all of which I could not see from the DEM. Preliminary conclusions point towards effective usage of remote sensing data in the WFD. LiDAR scans portend to be specifically useful in Mediterranean climates that are dry most of the year, as opposed to wetter climates where water impedes accuracy of LiDAR mapping.
