Hurricane Sandy inflicted heavy damage in New York City and the New Jersey coast as the second costliest storm in history. A large-scale, unstructured grid storm tide model, Semi-implicit Eulerian Lagrangian Finite Element (SELFE), was used to hindcast water level variation during Hurricane Sandy in the mid-Atlantic portion of the U.S. East Coast. The model was forced by eight tidal constituents at the model's open boundary, 1500 km away from the coast, and the wind and pressure fields from atmospheric model Regional Atmospheric Modeling System (RAMS) provided by Weatherflow Inc. The comparisons of the modeled storm tide with the NOAA gauge stations from Montauk, NY, Long Island Sound, encompassing New York Harbor, Atlantic City, NJ, to Duck, NC, were in good agreement, with an overall root mean square error and relative error in the order of 15-20 cm and 5%-7%, respectively. Furthermore, using large-scale model outputs as the boundary conditions, a separate sub-grid model that incorporates LIDAR data for the major portion of the New York City was also set up to investigate the detailed inundation process. The model results compared favorably with USGS' Hurricane Sandy Mapper database in terms of its timing, local inundation area, and the depth of the flooding water. The street-level inundation with water bypassing the city building was created and the OPEN ACCESS J. Mar. Sci. Eng. 2014, 2 227 maximum extent of horizontal inundation was calculated, which was within 30 m of the data-derived estimate by USGS.

SummaryThe LaRC GIS Team installed a prototype water level monitoring system on Brick Kiln creek on Langley Research Center property. The purpose of this system is to monitor water level fluctuations to support local tide analysis. The system consists of a pressure sensor (calibrated to read water depth) and a battery-powered datalogger which records data to internal, non-volatile memory. Collected data is uploaded to a PDA or laptop over an on-demand Bluetooth RF connection. Initial data collected over a week correlates well with data from the same period for Sewell's Point (downloaded from NOAA).

Remote sensing analysis is routinely used to map flooding extent either retrospectively or in near-real time. For flood emergency response, remote-sensing-based flood mapping is highly valuable as it can offer continued observational information about the flood extent over large geographical domains. Information about the floodwater depth across the inundated domain is important for damage assessment, rescue, and prioritizing of relief resource allocation, but cannot be readily estimated from remote sensing analysis. The Floodwater Depth Estimation Tool (FwDET) was developed to augment remote sensing analysis by calculating water depth based solely on an inundation map with an associated digital elevation model (DEM). The tool was shown to be accurate and was used in flood response activations by the Global Flood Partnership. Here we present a new version of the tool, FwDET v2.0, which enables water depth estimation for coastal flooding. FwDET v2.0 features a new flood boundary identification scheme which accounts for the lack of confinement of coastal flood domains at the shoreline. A new algorithm is used to calculate the local floodwater elevation for each cell, which improves the tool's runtime by a factor of 15 and alleviates inaccurate local boundary assignment across permanent water bodies. FwDET v2.0 is evaluated against physically based hydrody-namic simulations in both riverine and coastal case studies. The results show good correspondence, with an average difference of 0.18 and 0.31 m for the coastal (using a 1 m DEM) and riverine (using a 10 m DEM) case studies, respectively. A FwDET v2.0 application of using remote-sensing-derived flood maps is presented for three case studies. These case studies showcase FwDET v2.0 ability to efficiently provide a synoptic assessment of floodwater. Limitations include challenges in obtaining high-resolution DEMs and increases in uncertainty when applied for highly fragmented flood inundation domains.

The democratization of ocean observation has the potential to add millions of observations every day. Though not a solution for all ocean monitoring needs, citizen scientists offer compelling examples showcasing their ability to augment and enhance traditional research and monitoring. Information they are providing is increasing the spatial and temporal frequency and duration of sampling, reducing time and labor costs for academic and government monitoring programs, providing hands-on STEM learning related to real-world issues and increasing public awareness and support for the scientific process. Examples provided here demonstrate the wide range of people who are already dramatically reducing gaps in our global observing network while at the same time providing unique opportunities to meaningfully engage in ocean observing and the research and conservation it supports. While there are still challenges to overcome before widespread inclusion in projects requiring scientific rigor, the growing organization of international citizen science associations is helping to reduce barriers. The case studies described support the idea that citizen scientists should be part of an effective global strategy for a sustained, multidisciplinary and integrated observing system.

Technological progress in ood monitoring and the proliferation of cost-e cient IoT-enabled water level sensors are enabling new streams of information for today's smart cities. StormSense is an inundation forecasting research initiative and an active participant in the GCTC seeking to enhance ood preparedness in the Hampton Roads region for ooding resulting from storm surge, rain, and tides and demonstrating replicability of the solution. Herein, we present street-level hydrodynamic modeling results at 5m resolution with conventional ood validation sources alongside new emergent techniques for validating model predictions during three prominent recent ooding events in Hampton Roads during Fall 2016: Hurricane Hermine, Tropical Storm Julia, and Hurricane Ma hew. Emerging validation techniques include: (1) IoT-water level sensors, (2) crowd-sourced GPS maximum ood extent measurements, and (3) geospatial ooded area comparisons with drone-surveyed ood extents via ESRI's Drone2Map. Model uncertainty was validated against 5 newly-established tide gauges within the domain for an aggregate vertical root mean squared error of ±8.19 cm between the sensor observations and model predictions. Also, geospatial uncertainty was assessed using mean horizontal distance di erence as ±4.97 m via 206 crowd-sourced GPS ood extents from the Sea Level Rise App. CCS CONCEPTS •Computer systems organization → Embedded and cyberphysical systems; •So ware and its engineering → Design patterns;