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Digital elevation models (DEMs) derived from remote sensing data provide a valuable and consistent data source for mapping coastal flooding at local and global scales. Mapping of flood risk requires quantification of the error in DEM elevations and its effect on delineation of flood zones. The ASTER, SRTM, ALOS, and TanDEM-X (TDX) DEMs for the island of Hispaniola were examined by comparing them with GPS and LiDAR measurements. The comparisons were based on a series of error measures including root mean square error (RMSE) and absolute error at 90% quantile (LE90). When compared with >2000 GPS measurements with elevations below 7 m, RMSE and LE90 values for ASTER, SRTM, ALOS, TDX DEMs were 8.44 and 14.29, 3.82 and 5.85, 2.08 and 3.64, and 1.74 and 3.20 m, respectively. In contrast, RMSE and LE90 values for the same DEMs were 4.24 and 6.70, 4.81 and 7.16, 4.91 and 6.82, and 2.27 and 3.66 m when compared to DEMs from 150 km2 LiDAR data, which included elevations as high as 20 m. The expanded area with LiDAR coverage included additional types of land surface, resulting in differences in error measures. Comparison of RMSEs indicated that the filtering of TDX DEMs using four methods improved the accuracy of the estimates of ground elevation by 20–43%. DTMs generated by interpolating the ground pixels from a progressive morphological filter, using an empirical Bayesian kriging method, produced an RMSE of 1.06 m and LE90 of 1.73 m when compared to GPS measurements, and an RMSE of 1.30 m and LE90 of 2.02 m when compared to LiDAR data. Differences in inundation areas based on TDX and LiDAR DTMs were between −13% and −4% for scenarios of 3, 5, 10, and 15 m water level rise, a much narrower range than inundation differences between ASTER, SRTM, ALOS and LiDAR. The TDX DEMs deliver high resolution global DEMs with unprecedented elevation accuracy, hence, it is recommended for mapping coastal flood risk zones on a global scale, as well as at a local scale in developing countries where data with higher accuracy are unavailable.

Intertidal environments, including bare mudflats, tidal creeks, and vegetated salt marshes, are of significant physical and ecological importance in estuaries. Their morphodynamics are closely linked by mudflats and creek networks. Understanding water motion and sediment transport in mudflats and tidal creeks is fundamental to understand intertidal morphodynamics in intertidal environments. To explore dynamic interactions between tidal creeks and mudflats, we conducted field campaigns monitoring water depths, tidal currents, waves, suspended sediments, and bed-level changes at sites in both mudflats and tidal creeks in the Eastern Chongming tidal wetland in the Yangtze Delta for a full spring-neap tidal cycle. We saw that under fair weather conditions, the bed-level changes of the tidal creek site displayed a contrary trend compared with those of the mudflat site, indicating the source-sink relationship between tidal creek and mudflat. During over-marsh tides, the tidal creek site with relatively high bed shear stresses (averagely, 0.37 N/m2) was eroded by 35 mm whereas the mudflat site was accreted by 29 mm under low bed shear stresses (averagely, 0.18 N/m2). To the contrast, during creek-restricted tides, deposition occurred in the tidal creek site by 20 mm under low bed shear stresses (averagely, 0.09 N/m2) whereas erosion occurred in the mudflat site by 25 mm under relatively high bed shear stresses (averagely, 0.21 N/m2). Over a spring-neap tidal cycle, the net bed level changes were −15 mm (erosion) and 4 mm (deposition) in tidal creeks and mudflats, respectively. These results suggested that there were alternated erosion-deposition patterns in spring and neap tides, and a sediment source and sink shift between mudflats and creeks. We found that the eroded sediments in mudflats were transported landward into tidal creeks and deposited therein in neap tides, and these newly deposited sediments would be resuspended and transported to surrounding marshes (over-marsh deposition) at spring tides. The coherent sediment transport and associated erosion-deposition pattern within the mudflat-creek system at spring-neap tidal time scales thus played a fundamental role in intertidal morphodynamic development. These findings suggest that management and restoration of intertidal ecosystem need to take the entire mudflat-creek-marsh system as a unit into consideration rather than focusing on single elements.

The increasing integration of renewable energy sources has brought about a great computational burden for the traditional methods that assess the power system reliability. To reduce the computational costs, an extendable Latin hypercube importance sampling (ELHIS) method that combines importance sampling (IS) and Latin hypercube sampling (LHS) is proposed in this paper. First, the challenge of combining IS and LHS is analysed, and a customized sampling process of LHS is designed accordingly. Then, in the IS part of ELHIS, the cross entropy theory and Gaussian mixture model are adopted in the stage that constructs the quasi-optimal probability distribution of random variables. In addition, the samples in this stage are also utilized in the estimation of reliability indices to reduce the waste of computational efforts. In the LHS part, an extendable LHS approach is used to make ELHIS adaptive and flexible in determining the sample size to reach the required accuracy. Finally, numerical tests are performed on the modified IEEE-RTS 79 test system, where the real historical data from three wind farms and a photovoltaic station in Northwest China are employed. The results show that ELHIS is obviously faster than recent IS methods when used to assess the power system reliability.

Mangroves have been proven to be effective in storm surge attenuation but it remains an important challenge to accurately quantify such bio-shielding effects using numerical simulations, as it is very difficult to comprehensively represent the ecological characteristics of mangroves at both large and small scales. In this study, a numerical method is developed and implemented in the Coastal and Estuarine Storm Tide (CEST) model in order to investigate the attenuation effect of mangroves on storm surge. This numerical method employs an improved drag force formula, which involves the development of new abstract tree models and use of a landscape scale data map of mean mangrove tree height for the study area. The storm surge observed in the South Florida mangrove zone caused by Hurricane Wilma (2005) is used to verify the numerical model. The numerical results indicate a maximum surge of approximately 4.3 m, and a decay rate of peak storm surge height of approximately 18 cm/km across the areas with a mixture of mangrove islands and open water, and nearly 24 cm/km through areas with dense mangrove forest. Results also show that short mangroves ( 4 m) can outperform tall mangroves on surge attenuation when the water depth is low ( 4 m). Extensive comparisons are also made with the conventional Manning coefficient based method that incorporates the mangrove drag force into bed friction; it is found that the current method predicts better inundation extents for Wilma (2005), hence a more accurate quantification of the attenuation of storm surge due to mangroves.

Quantification of tidal flat morphological changes at varying timescales is critical from a management point of view. High-resolution tidal flat morphology data, including those for mudflats and salt-marshes, are rare due to monitoring difficulty by traditional methods. Recent advances in Terrestrial Laser Scanner (TLS) technology allow rapid acquisition of high-resolution and large-scale morphological data, but it remains problematic for its application on salt-marshes due to the presence of dense vegetation. In this study, we applied a TLS system to retrieve high-accuracy digital elevation models in a tidal flat of the Yangtze Estuary by using a robust and accurate Progressive Morphological filter (PM) to separate ground and non-ground points. Validations against GPS-supported RTK measurements suggested remarkable performance. In this case the average estimation error was about 0.3 cm, while the Root Mean Square Error (RMSE) was 2.0 cm. We conducted three TLS surveys on the same field including salt-marshes and mudflats at the time points 5 days before, 3 days after, and 45 days after a typhoon event. The retrieved data showed that the mudflats suffered from profound erosion while the salt-marshes slightly accreted during the typhoon period. The average elevation change of the total area was about − 4 cm (− 0.28 cm per day). However, both the mudflats and salt-marshes deposited in the post-typhoon period and the accretion over salt-marshes occurred at a higher rate than that during the typhoon. The elevation of the total area increased by 15.9 cm (0.37 cm per day), suggesting fast recovery under calm conditions. Quantification of the erosion and deposition rates was aided by the high quality TLS data. This study shows the effectiveness of TLS in quantifying morphological changes of tidal flats at an event (and post-event) timescale. The data and analysis also provide sound evidence on vegetation impact in stimulating salt-marsh development and restoration, shedding lights on bio-morphological interactions.

The sensitivity of the mangrove effect on reducing storm surge flooding to hurricane characteristics is investigated by using the numerical model Coastal and Estuarine Storm Tide (CEST). First, the attenuation of storm surge by mangroves is incorporated into the model by updating Manning’s coefficient based on the National Land Cover Dataset (NLCD) 2001. Then CEST is verified by comparing the model results with field observations in South Florida for Hurricane Wilma. Secondly, a set of numerical experiments using synthetic hurricanes with different intensity, forward speed, radius of maximum wind speed and travel direction are conducted for the sensitivity study. Results indicate that storm surge magnitudes and flooding areas are reduced by the mangrove zone more for fast moving hurricanes than slow moving hurricanes in the west coast of South Florida. In addition, increasing hurricane intensity and hurricane size lower the effect of mangroves on attenuating storm surge and reducing the flooding area. The mangrove zone plays a more effective role in reducing flooding areas from hurricanes that travel from east to west than from hurricanes that travel from west to east. The mangrove reduction effect is most sensitive to changes in hurricane forward speed. A 6.7 m/s to 2.2 m/s decrease in forward speed can result in a decrease in flood area reduction by mangroves that is equivalent to the decrease in flood area reduction by mangroves from Category 3 to 5 hurricanes.

Field observations and numerical simulations indicate that the 6-to-30-km-wide mangrove forest along the Gulf Coast of South Florida effectively attenuated storm surges from a Category 3 hurricane, Wilma, and protected the inland wetland by reducing an inundation area of 1800 km 2 and restricting surge inundation inside the mangrove zone. The surge amplitude decreases at a rate of 40e50 cm/km across the mangrove forest and at a rate of 20 cm/km across the areas with a mixture of mangrove islands with open water. In contrast, the amplitudes of storm surges at the front of the mangrove zone increase by about 10e30% because of the “blockage” of mangroves to surge water, which can cause greater impacts on structures at the front of mangroves than the case without mangroves. The mangrove forest can also protect the wetlands behind the mangrove zone against surge inundation from a Category 5 hurricane with a fast forward speed of 11.2 m/s (25 mph). However, the forest cannot fully attenuate storm surges from a Category 5 hurricane with a slow forward speed of 2.2 m/s (5 mph) and reduced surges can still affect the wetlands behind the mangrove zone. The effects of widths of mangrove zones on reducing surge amplitudes are nonlinear with large reduction rates (15e30%) for initial width increments and small rates (

Mangrove forests change frequently due to disturbances from tropical storms, frost, lightning, and insects. It has been suggested that the death and regeneration of trees in small gaps due to lightning may play a critical role in mangrove forest turnover; however, the large-scale quantification of spatial pattern and areas of gaps is lacking for investigating this issue. Airborne light detection and ranging (LIDAR) technology provides an effective way for identifying gaps by remotely obtaining direct measurements of ground and canopy elevations. A method based on an alternative sequential filter and black top-hat mathematical morphological transformation was developed to extract gap features. Comparison of identified gap polygons with raw LIDAR measurements and field surveys shows that the proposed method successfully extracted gap features in mangrove forests in Everglades National Park. There are 400–500 lightning gaps per square kilometer in mangrove forests at the study sites. The distribution of gap sizes follows an exponential form and the area of gaps with sizes larger than 100 m2 account for 55–61% of the total area of gaps. The area of gaps in the mangrove forest in Everglades National Park is about 4–5% of the total forest area and the average gap formation rate is about 0.3% of the total forest area per year, indicating that lightning gaps play an important role in mangrove forest dynamics.

This study used airborne laser data to investigate spatial variations in shoreline migration, beach width, subaerial volume, and subaqueous volume change due to a hurricane event. Five separate airborne laser data sets of Panama City, FL area beaches were collected during a seven-month period before and after landfall of Hurricane Ivan. Contour shorelines were extracted from digital elevation models interpolated from these laser measurements and were used to measure changes in shoreline position and beach width. The shoreline migrated 16 m landward due to Hurricane Ivan and migrated 10 m seaward following Hurricane Ivan. No significant spatial relationship was found between shoreline migration before and after the hurricane. Linear relationships between a time series of beach width and subaerial volume were found at many locations. However, utilization of a single coefficient to represent all relationships is problematic due to the spatial variability in the linear relationship. Differences in two bathymetric data sets for summer and fall show that only a small portion of sediments were transported beyond an active zone and most sediments remain within the active zone despite the occurrence of a hurricane.