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In this paper, a combined model is proposed to predict spare parts inventory in accordance with equipment characteristics and defect elimination records. Fourier series is employed to process the periodicity of the data, autoregressive moving average (ARMA) is used to deal with the linear autocorrelation of the data, and backpropagation (BP) neural network is used to settle the nonlinearity of the data. The prediction results, comparisons, and error analyses show that the combined model is accurate and meets the practical requirements. The combined model not only fully utilizes the information contained in the data but also provides a reasonable decision basis for the procurement of spare parts, making the inventory in a safe state and saving holding costs.

The quantitative study of earthquake-triggered rockfall debris along seismogenic fault zones has proven to be a valid approach for use in identifying paleo-earthquakes and faulting activities. After the 2010 Yushu earthquake, paleo-rockfall research employed U-series dating of calcareous coats on the rockfall surface, and the results provided chronological evidence of multi-period features relating to earthquake-triggered rockfalls that have occurred along the Yushu fault. In this study, the mechanism involved in forming the calcareous coat on seismic rockfall surfaces was analyzed to further explain the significance of conducting associated U–Th aging analyses. For earthquake-triggered rockfall regions, the U–Th age of the calcareous coat can be used as a proxy age of the rockfall formation, and the timing of the related paleo-earthquake event can be constrained. Based on all current existing age data, an in-depth comparison was made between the U–Th ages of calcareous coats obtained from five seismic rockfall sites and other 14C dating results obtained from trenches and paleo-seismic landslides relating to paleo-earthquakes. Combined with the results of field investigations, four individual paleo-earthquakes that triggered rockfalls of different scales in historical time were identified. These results imply that multi-period earthquake-triggered rockfalls along the Yushu fault may be valid time indicators of seismic events and reflect the intensity and meizoseismal scale of relevant paleo-earthquake events. This exploratory research on the U-series dating of calcareous coats from earthquake-triggered rockfalls in Yushu can be used as a valuable reference for paleo-earthquake studies in other tectonically active regions.

Parkinson’s disease (PD) is characterized by the degeneration of dopaminergic neurons and excessive microglial activation in the substantia nigra pars compacta (SNpc). In the present study, we aimed to demonstrate the therapeutic effectiveness of the potent sphingosine-1-phosphate receptor antagonist fingolimod (FTY720) in an animal model of PD induced by 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP), and to identify the potential mechanisms underlying these therapeutic effects. C57BL/6J mice were orally administered FTY720 before subcutaneous injection of MPTP. Open-field and rotarod tests were performed to determine the therapeutic effect of FTY720. The damage to dopaminergic neurons and the production of monoamine neurotransmitters were assessed using immunohistochemistry, high-performance liquid chromatography, and flow cytometry. Immunofluorescence (CD68- positive) and enzyme-linked immunosorbent assay were used to analyze the activation of microglia, and the levels of activated signaling molecules were measured using Western blotting. Our findings indicated that FTY720 significantly attenuated MPTP-induced behavioral deficits, reduced the loss of dopaminergic neurons, and increased dopamine release. FTY720 directly inhibited MPTP-induced microglial activation in the SNpc, suppressed the production of interleukin (IL)-6, IL-1β, and tumor necrosis factor-α in BV-2 microglial cells treated with 1-methyl-4-phenylpyridinium (MPP+), and subsequently decreased apoptosis in SH-SY5Y neuroblastoma cells. Moreover, in MPP+-treated BV-2 cells and primary microglia, FTY720 treatment significantly attenuated the increases in the phosphorylation of PI3K/AKT/GSK-3β, reduced ROS generation and p65 activation, and also inhibited the activation of NLRP3 inflammasome and caspase-1. In conclusion, FTY720 may reduce PD progression by inhibiting NLRP3 inflammasome activation via its effects on ROS generation and p65 activation in microglia. These findings provide novel insights into the mechanisms underlying the therapeutic effects of FTY720, suggesting its potential as a novel therapeutic strategy against PD.

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.

In this study, the first ever Sea, Lake, Overland Surges from Hurricanes (SLOSH) grid was built for the Yucatan Peninsula. The SLOSH model was used to simulate storm surges in the coastal area of the states of Yucatan and Campeche (Mexico). Based on climatology, more than 39,900 hypothetical hurricanes covering all possible directions of motion were synthesized. The storm intensity (category), forward speed, radius of maximum winds and the tide anomaly were varied for each hypothetical track. According to these scenarios, the potential storm surge and associated inundation threat were computed. Subsequently, the Maximum Envelope of Water (MEOW) and the Maximum of the MEOWs (MOMs) were calculated to assess the flood hazard induced by tropical cyclones under varying conditions. In addition, for each MOM, the socioeconomic vulnerability aspects were taken into account in order to assess the hurricane flood risk for the states of Yucatan and Campeche. Results show that the most vulnerable areas are the surroundings of Terminos lagoon, Campeche City and its neighboring areas in the state of Campeche. For Yucatan, the towns located in the Northwest (Celestun, Hunucma and Progreso) and the eastern part of the state presented the highest risk values. The methodology used in this study can be applied to other coastal zones of Mexico as well as places with similar attributes. Furthermore, the MEOW and MOM are very useful as a decision-making tool for prevention, preparedness, evacuation plans, mitigation of the flood hazard and its associated risk, and also for insurance companies.

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.

Crustal material eastward extrusion from the Tibetan Plateau is closely related to the strike-slip faults in the SE margin of the Tibetan Plateau. The left-lateral strike-slip Xianshuihe–Xiaojiang ...

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.

This study evaluated the impact of human modifications and natural variations (e.g., seasonal riverine fluxes, plant growth, and estuarine environments) on the short-term morphological changes in estuarine tidal flats. Central to the study was an embankment, constructed in 2014, which changed the path of a tidal flat evolution in the Yangtze Estuary and resulted in a remarkable shift of the erosion-deposition pattern in the study area. To examine the embankment’s impact, we used a terrestrial laser scanner to collect seven topographic data sets for the tidal flat, during different seasons from 2012 to 2016. The rates of elevation change varied from − 19.3 cm year−1 before embankment construction to 17.2 cm year−1 after embankment construction. The field measurements also showed that the new embankment led to an increase of suspended sediment concentration from 1.4 to 2.7 kg m−3 on average in the mudflat and from 1.0 to 2.6 kg m−3 in the salt marsh. These results indicated that the increase in sediment availability caused by human modifications played an important role in tidal flat evolution and were able to promote the accretion of the tidal flats. Furthermore, there were significant spatiotemporal variations in the morphological changes in different regions of the tidal flat. Depositional changes were more likely to occur during the wet seasons and salt marshes were more stable than mudflats and tidal creeks.

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.

Hurricanes Frances, Jeanne, and Wilma passed over Lake Okeechobee, Florida, in September 2004 and October 2005, respectively. Strong winds caused a large surface seiche on the lake during all three storms. These storms resulted in erosion damage to the Herbert Hoover Dike (HHD) on Lake Okeechobee. In this paper, we use the Fully Adaptive Storm Tide (FAST) model (Kelly et al. in Coast Eng J 57(4):1–30, 2015, Nat Hazards 83:53–74, 2016) to study the response of the lake (in terms of the water level fluctuations and induced currents) to hurricanes Frances, Jeanne, and Wilma. Comparisons of the modeled surface water level with the observations are in overall good agreement for all three hurricanes. The modeled results suggest that the strong currents induced by the storm winds may be the dominant factor controlling the dike erosion observed at the lake side. The locations of erosion damage to the dike are consistent with the modeled high velocity zones during these three storms. In addition, numerical experiments have been conducted with eight hypothetical category 5 hurricanes approaching from different directions to investigate the erosion-prone zones related to high velocities in the vicinity of the dike. The results of the study should help to provide insight into vulnerable reaches of the HHD and inform flood control in the Okeechobee region.

The degradation of optical microscopic imaging is space-variant, and how to fast restore optical degraded image remains a special problem. Based on point spread function (PSF) estimation under each field of view (FOV), a L0 gradient-constrained image restoration method is proposed to solve optical degradation in microscopic imaging. Firstly, the whole scene is segmented into several different regions according to different FOV. The PSFs for each region are estimated from modulation transfer function (MTF) measured in advance. Secondly, a penalty function is designed using L0 gradient constraint to deblur the degraded images of each sub-FOV. Finally, a weighted stitching approach is used to stitch the restored images of multiple FOV (m-FOV). Experimental results indicate that the m-FOV analysis could well solve the problem of space-variant degradation. Compared with the other methods, both subjective and objective evaluation results prove that the L0 norm idea could rapidly and effectively restore the degraded image. The approach could be well applied to a real product.

Blue carbon ecosystems (BCEs) provide many ecosystem services as “the benefits people obtain from ecosystems.” These include important recreational and tourism opportunities, key fishery habitat, water quality improvements, and flood and erosion mitigation. These are co-benefits of BCEs in addition to carbon sequestration. The monetary value of these services are best known for coastal protection and carbon sequestration, with monetary value of coastal protection services estimated at \23 billion year− 1 between 1980 and 2008.

Kelly et al. (Coast Eng J 57(4):1–30, 2015) present a finite volume dynamic adaptive mesh model based on Osher’s approximate Riemann solver for the prediction of storm surges over complex landscapes. Here, the model described in that paper is extended to use distributed memory parallel block tree-based mesh refinement via the PARAMESH library. The resulting model, called the fully adaptive storm tide (FAST) model, can thus be run on massively parallel supercomputers. In this paper, we validate the FAST model by conducting numerical simulations of the storm surges due to hurricanes Wilma (2005) on Lake Okeechobee and Ike (2008) in the Gulf of Mexico. The storm surge due to Wilma on Lake Okeechobee is interesting as it can be considered as an almost idealized case which comprises a closed system. The case of hurricane Ike is more complex as it involves a coastline and additional features such as barrier islands and tidally controlled boundaries. For both cases, results obtained using the FAST model compare favorably with the measured water elevation and high-water mark data. Moreover, we show that, with sufficient computational resource, low runtimes are possible for real-world surge simulations. The FAST model therefore has the potential to run the ensemble predictions necessary to account for the variability that is inherent in hurricane forecasting.

Zhang, K.; Zhang, M.C.; Leatherman, S.B., and Leatherman, S.P., 2020. Rip current hazard analysis. In: Malvarez, G. and Navas, F. (eds.), Global Coastal Issues of 2020. Journal of Coastal Research, Special Issue No. 95, pp. 969–973. Coconut Creek (Florida), ISSN 0749-0208.The effect of rip currents on swimmers was analyzed based on the drag force acting on swimmers and the power they need to generate to overcome the drag force. The drag force and power increase quadratically and cubically, respectively, with increase of rip current and swimming speeds. An additional 50% increase in rip current speed above swimmer's speed results in a 125% increase in drag force, requiring a 238% increase in power by the swimmer to overcome the additional drag. Hence, even rip currents of low velocity can be dangerous and swimming against the current should be avoided.

Parkinson's disease (PD) is characterized by the degeneration of dopaminergic neurons and excessive microglial activation in the substantia nigra pars compacta (SNpc). In the present study, we aimed to demonstrate the therapeutic effectiveness of the potent sphingosine-1-phosphate receptor antagonist fingolimod (FTY720) in an animal model of PD induced by 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP), and to identify the potential mechanisms underlying these therapeutic effects. C57BL/6J mice were orally administered FTY720 before subcutaneous injection of MPTP. Open-field and rotarod tests were performed to determine the therapeutic effect of FTY720. The damage to dopaminergic neurons and the production of monoamine neurotransmitters were assessed using immunohistochemistry, high-performance liquid chromatography, and flow cytometry. Immunofluorescence (CD68- positive) and enzyme-linked immunosorbent assay were used to analyze the activation of microglia, and the levels of activated signaling molecules were measured using Western blotting. Our findings indicated that FTY720 significantly attenuated MPTP-induced behavioral deficits, reduced the loss of dopaminergic neurons, and increased dopamine release. FTY720 directly inhibited MPTP-induced microglial activation in the SNpc, suppressed the production of interleukin (IL)-6, IL-1β, and tumor necrosis factor-α in BV-2 microglial cells treated with 1-methyl-4-phenylpyridinium (MPP

The geographic area that the Joint Airborne LiDAR Bathymetry Technical Center of Expertise (JALBTCX) covers with their Light Detection and Ranging (LiDAR) program allows for researchers to quantify coastal metrics on national, regional and local scales. As these studies progressed, it has become apparent that software needs to be developed to quantify coastal change from LiDAR at multiple scales. The purpose of this research is to provide coastal managers with quantities and locations of change that occurred on eastern US coastlines, and if the researcher would like additional information, provide the tools necessary for additional metrics to be quantified and additional questions to be answered.

Airborne Light Detection and Ranging (LIDAR) technology has become the primary method to derive high-resolution Digital Terrain Models (DTMs), which are essential for studying Earth's surface processes, such as flooding and landslides. The critical step in generating a DTM is to separate ground and non-ground measurements in a voluminous point LIDAR dataset, using a filter, because the DTM is created by interpolating ground points. As one of widely used filtering methods, the progressive morphological (PM) filter has the advantages of classifying the LIDAR data at the point level, a linear computational complexity, and preserving the geometric shapes of terrain features. The filter works well in an urban setting with a gentle slope and a mixture of vegetation and buildings. However, the PM filter often removes ground measurements incorrectly at the topographic high area, along with large sizes of non-ground objects, because it uses a constant threshold slope, resulting in "cut-off" errors. A novel cluster analysis method was developed in this study and incorporated into the PM filter to prevent the removal of the ground measurements at topographic highs. Furthermore, to obtain the optimal filtering results for an area with undulating terrain, a trend analysis method was developed to adaptively estimate the slope-related thresholds of the PM filter based on changes of topographic slopes and the characteristics of non-terrain objects. The comparison of the PM and generalized adaptive PM (GAPM) filters for selected study areas indicates that the GAPM filter preserves the most "cut-off" points removed incorrectly by the PM filter. The application of the GAPM filter to seven ISPRS benchmark datasets shows that the GAPM filter reduces the filtering error by 20% on average, compared with the method used by the popular commercial software TerraScan. The combination of the cluster method, adaptive trend analysis, and the PM filter allows users without much experience in processing LIDAR data to effectively and efficiently identify ground measurements for the complex terrains in a large LIDAR data set. The GAPM filter is highly automatic and requires little human input. Therefore, it can significantly reduce the effort of manually processing voluminous LIDAR measurements.

The snake algorithm has been proposed to solve many remote sensing and computer vision problems such as object segmentation, surface reconstruction, and object tracking. This paper introduces a framework for 3-D building model construction from LIDAR data based on the snake algorithm. It consists of nonterrain object identification, building and tree separation, building topology extraction, and adjustment by the snake algorithm. The challenging task in applying the snake algorithm to building topology adjustment is to find the global minima of energy functions derived for 2-D building topology. The traditional snake algorithm uses dynamic programming for computing the global minima of energy functions which is limited to snake problems with 1-D topology (i.e., a contour) and cannot handle problems with 2-D topology. In this paper, we have extended the dynamic programming method to address the snake problems with a 2-D planar topology using a novel graph reduction technique. Given a planar snake, a set of reduction operations is defined and used to simplify the graph of the planar snake into a set of isolated vertices while retaining the minimal energy of the graph. Another challenging task for 3-D building model reconstruction is how to enforce different kinds of geometric constraints during building topology refinement. This framework proposed two energy functions, deviation and direction energy functions, to enforce multiple geometric constraints on 2-D topology refinement naturally and efficiently. To examine the effectiveness of the framework, the framework has been applied on different data sets to construct 3-D building models from airborne LIDAR data. The results demonstrate that the proposed snake algorithm successfully found the global optima in polynomial time for all of the building topologies and generated satisfactory 3-D models for most of the buildings in the study areas.

Fingolimod (FTY720), an immunomodulator, is approved as an oral treatment for patients with relapsing forms of multiple sclerosis. Its effects are largely attributed to its mechanism of selectively retaining lymphocytes in the lymph nodes to reduce autoreactive T-cell recruitment in the CNS. In this study, we investigated the therapeutic effect of FTY720 on an animal model of CNS inflammation induced by intracerebral ventricle LPS injection. We found that FTY720 treatment significantly prevented LPS-induced neutrophil recruitment in the CNS by inhibiting leukocyte recruitment in cerebral microvessels. Furthermore, FTY720 also inhibited the expressions of adhesion molecules on the cerebral endothelium, but did not affect the expression levels of pro-inflammatory cytokines (TNF-α and IL-6) and chemokines (CXCL1 and CXCL2) in the CNS parenchyma. The inhibition of endothelial activation was accompanied by reduced phosphorylation of signaling molecules, including serine/threonine-specific protein kinase (Akt), STAT6, and nuclear factor-κB. This FTY720-attenuated inhibition of leukocyte recruitment and endothelial activation was reversed by blocking the functions of sphingosine kinase 2 or sphingosine-1-phosphate receptor 1. Our study demonstrated, for the first time, that FTY720 directly inhibits the phosphorylation of multiple signaling molecules in endothelial cells, thereby effectively blocking leukocyte recruitment in the CNS.

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.

The operational forecast demands and constraints of the National Hurricane Center require that a storm surge model in research mode be tested against a benchmark model such as Sea, Lake, and Overland Surges from Hurricanes (SLOSH) for accuracy, computation time, and numerical stability before the model is used for operational forecasts. Additionally, the simulated results must be in a geographic information system format to facilitate the usage of computed storm surge for various applications. This paper presents results from a demonstration project to explore the pathway for the transition of the Coastal and Estuarine Storm Tide (CEST) model to an operational forecast model by testing CEST over SLOSH basins in Florida. The performance and stability of CEST were examined by conducting simulations for Hurricane Andrew (1992) and more than 100 000 synthetic hurricanes for nine SLOSH basins covering the Florida coast and Lake Okeechobee. The results show that CEST produces peak surge heights similar to those from SLOSH. Additionally, CEST has proven to be numerically stable against all synthetic hurricanes and the computation time of CEST is comparable to that of SLOSH. Therefore, CEST has the potential to be used for operational forecasts of storm surge. The potential of producing more detailed real-time surge inundation forecasts was also investigated through the simulations of Andrew's surge on various grids with different cell sizes. The results indicate that CEST can produce 48-h forecasts using a single processor in about 40 min over a grid generated by reducing the cell edge size of the SLOSH grid by 4 times.

Two linear methods, including the simple linear addition and linear addition by expansion, and numerical simulations were employed to estimate storm surges and associated flooding caused by Hurricane Andrew for scenarios of sea level rise (SLR) from 0.15 m to 1.05 m with an interval of 0.15 m. The interaction between storm surge and SLR is almost linear at the open Atlantic Ocean outside Biscayne Bay, with slight reduction in peak storm surge heights as sea level rises. The nonlinear interaction between storm surges and SLR is weak in Biscayne Bay, leading to small differences in peak storm surge heights estimated by three methods. Therefore, it is appropriate to estimate elevated storm surges caused by SLR in these areas by adding the SLR magnitude to storm surge heights. However, the magnitude and extent of inundation at the mainland area by Biscayne Bay estimated by numerical simulations are, respectively, 22–24 % and 16–30 % larger on average than those generated by the linear addition by expansion and the simple linear addition methods, indicating a strong nonlinear interaction between storm surge and SLR. The population and property affected by the storm surge inundation estimated by numerical simulations differ up to 50–140 % from that estimated by two linear addition methods. Therefore, it is inappropriate to estimate the exacerbated magnitude and extent of storm surge flooding and affected population and property caused by SLR by using the linear addition methods. The strong nonlinear interaction between surge flooding and SLR at a specific location occurs at the initial stage of SLR when the water depth under an elevated sea level is less than 0.7 m, while the interaction becomes linear as the depth exceeds 0.7 m.

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 (

The potential impacts of sea level rise (SLR) on 95% of the land areas of the Florida Keys were estimated through analysis of a digital elevation model (DEM) derived from airborne light detection and ranging (LiDAR) measurements in a geographic information system. The topographic detail of the LiDAR DEM allowed projections of land, population, and property inundation in 0.15 m increments across a broad range of SLR scenarios for the next century. The results showed that a 0.6 m SLR by 2100 would inundate about 70% of the total land surface, but smaller percentages of the population (17%) and real property (12%). A 1.5 m rise in sea level during the same period would inundate 91% of the land surface, 71% of the population and 68% of property in the study area. Comparison of inundation dynamics indicates that the Lower Florida Keys are more susceptible to SLR than the Upper Florida Keys. The inundation dynamics exhibit non-linear behavior and demonstrate tipping points in inundation processes beyond which the inundation of land, population, and property speeds up. Acceleration of SLR will amplify the non- linear inundation, causing tipping points to be reached sooner.

By analyzing a digital elevation model (DEM) derived from airborne light detection and ranging (LIDAR) data and airborne height finder measurements, this study demonstrates that a 1.5 m sea-level rise by 2100 would cause inundation of large areas of Miami-Dade County, southern Broward County, and Everglades National Park. Inundation processes are non-linear: inundation is gradual before reaching a threshold, and speeds up rapidly afterwards due to the regional topography. Accelerated sea-level rise will cause the threshold to be reached sooner by amplifying the non-linear inundation, and must be considered in policy-making. Comparison of inundated areas extracted from 30 m LIDAR and USGS DEMs indicates that the vertical accuracy of a DEM has a great effect on delineation of inundation areas. For a 1.5 m sea-level rise, the inundated area delineated by USGS DEM for Broward County is 1.65 times greater than that indicated by the LIDAR DEM.

The effectiveness of simulating surge inundation using the Eulerian–Lagrangian circulation (ELCIRC) model over multi-scale unstructured grids was examined in this study. The large domain model grid encompasses the western North Atlantic Ocean, the Gulf of Mexico, and the Caribbean Sea to appropriately account for remote and resonance effects during hurricane events and simplify the specification of the open boundary condition. The U.S. East and Gulf Coasts were divided into 12 overlapping basins with fine-resolution (up to 30 × 30 m) grids to model overland surge flooding. These overlapping basins have different fine-resolution grids near the coastal region, but have an identical coarse-resolution grid in the offshore region within the large model domain. Thus, the storm surge prediction can be conducted without reducing computation efficiency by executing multiple model runs with local fine-resolution grids where potential hurricane landfalls may occur. The capability of the multi-scale approach was examined by simulating storm surge caused by Hurricanes Andrew (1992) and Isabel (2003) along the South Florida coast and in the Chesapeake Bay. Comparisons between simulated and observed results suggest that multi-scale models proficiently simulated storm surges in the Biscayne Bay and the Chesapeake Bay during two hurricanes. A series of sensitivity tests demonstrated that the simulation of surge flooding was improved when LiDAR topographic data and special bottom drag coefficient values for mangrove forests were employed. The tests also showed that appropriate representation of linear hydrologic features is important for computing surge inundation in an urban area.

The distribution of mangrove biomass and forest structure along Shark River estuary in the Florida Coastal Everglades (FCE) has been correlated with elevated total phosphorus concentration in soils thought to be associated with storm events. The passage of Hurricane Wilma across Shark River estuary in 2005 allowed us to quantify sediment deposition and nutrient inputs in FCE mangrove forests associated with this storm event and to evaluate whether these pulsing events are sufficient to regulate nutrient biogeochemistry in mangrove forests of south Florida. We sampled the spatial pattern of sediment deposits and their chemical properties in mangrove forests along FCE sites in December 2005 and October 2006. The thickness (0.5 to 4.5 cm) of hurricane sediment deposits decreased with distance inland at each site. Bulk density, organic matter content, total nitrogen (N) and phosphorus (P) concentrations, and inorganic and organic P pools of hurricane sediment deposits differed from surface (0–10 cm) mangrove soils at each site. Vertical accretion resulting from this hurricane event was eight to 17 times greater than the annual accretion rate (0.30 ± 0.03 cm year−1) averaged over the last 50 years. Total P inputs from storm-derived sediments were equivalent to twice the average surface soil nutrient P density (0.19 mg cm−3). In contrast, total N inputs contributed 0.8 times the average soil nutrient N density (2.8 mg cm−3). Allochthonous mineral inputs from Hurricane Wilma represent a significant source of sediment to soil vertical accretion rates and nutrient resources in mangroves of southwestern Everglades. The gradient in total P deposition to mangrove soils from west to east direction across the FCE associated with this storm event is particularly significant to forest development due to the P-limited condition of this carbonate ecosystem. This source of P may be an important adaptation of mangrove forests in the Caribbean region to projected impacts of sea-level rise.

Sea-level rise presents an imminent threat to freshwater-dependent ecosystems on small oceanic islands, which often harbor rare and endemic taxa. Conservation of these assemblages is complicated by feedbacks between sea level and recurring pulse disturbances (eg hurricanes, fire). Once sea level reaches a critical level, the transition from a landscape characterized by mesophytic upland forests and freshwater wetlands to one dominated by mangroves can occur suddenly, following a single storm-surge event. We document such a trajectory, unfolding today in the Florida Keys. With sea level projected to rise substantially during the next century, ex-situ actions may be needed to conserve individual species of special concern. However, within existing public conservation units, managers have a responsibility to conserve extant biodiversity. We propose a strategy that combines the identification and intensive management of the most defensible core sites within a broader reserve system, in which refugia for biota facing local extirpation may be sought.

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.

The Coastal and Estuarine Storm Tide (CEST) model for the boundary-fitted curvilinear grid has been developed recently to simulate a hurricane-induced storm surge. A new wetting-drying algorithm was based on accumulated volume and was created for simulating overland flooding. To evaluate the capability of the CEST model, it was compared with the well-established storm surge model—Sea, Lake, and Overland Surge from Hurricane (SLOSH)—in model grid representation and surge inundation prediction. Two models were compared: first, by carrying out storm surge simulations for Hurricanes Andrew (1992), Hugo (1989), and Camille (1969) over SLOSH's coarse polar grids with cell sizes of 500–7000 m. Results show that the CEST model agreed better with field observations of storm surge flooding. The CEST model was further evaluated by applying it to a fine-resolution curvilinear grid, which has cell sizes of 100–200 m at the hurricane landfall area, along with a superior representation of coastal topography. Co...

Nutrient loading and eutrophication in coastal waters are the causes of water quality degradation and loss of marine biota, which has led to ecological imbalance. Understanding and modeling the level of eutrophication as a function of environmental parameters can be beneficial to coastal ecosystem management. The limitation of deterministic and empirical models in accurately predicting the level of algal blooms, and the nonlinear relationship between the water quality and environmental parameters and that of the level of chlorophyll a necessitate a new approach using machine learning and data-driven modeling. A multilayer perceptron-back propagation (MLP-BP) algorithm of artificial neural network (ANN) was used to predict the level of eutrophication (chlorophyll a) from water quality parameters monitored at two Florida Bay water quality monitoring stations (FLAB03 and FLAB14). Based on the correlation of monthly nutrients (total phosphate, nitrite, ammonium) and other water data (temperature, tur...

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.

Accuracy of predicting storm surge inundation depends on the model grid resolution. The spatial resolutions of many storm surge models are on the order of hundreds of meters to kilometers, which are not sufficient to resolve irregular shorelines and small topographic features. In this paper, storm surge and inundation simulation for Hurricane Andrew of 1992 was carried out with the unstructured tidal, residual, intertidal mudflat (UnTRIM) hydrodynamic model. A high-resolution unstructured grid with cell sizes as small as 20 m was placed over the Key Biscayne Bay area near Miami to better represent irregular shorelines and small morphological features such as barrier islands, rivers, canals, and inlets. A series of experiments was conducted to test model performance with respect to model grid resolution, time step selection, open boundary condition specification, and tidal influence. Results show that the model simulated maximum surge heights and areas of inundation along the Biscayne Bay coast ve...

This paper presents a framework that applies a series of algorithms to automatically extract building footprints from airborne light detection and ranging (LIDAR) measurements. In the proposed framework, the ground and nonground LIDAR measurements are first separated using a progressive morphological filter. Then, building measurements are identified from nonground measurements using a region-growing algorithm based on the plane-fitting technique. Finally, raw footprints for segmented building measurements are derived by connecting boundary points, and the raw footprints are further simplified and adjusted to remove noise caused by irregularly spaced LIDAR measurements. Data sets from urbanized areas including large institutional, commercial, and small residential buildings were employed to test the proposed framework. A quantitative analysis showed that the total of omission and commission errors for extracted footprints for both institutional and residential areas was about 12%. The results demonstrated that the proposed framework identified building footprints well

We produced a landscape scale map of mean tree height in mangrove forests in Everglades National Park (ENP) using the elevation data from the Shuttle Radar Topography Mission (SRTM). The SRTM data was calibrated using airborne lidar data and a high resolution USGS digital elevation model (DEM). The resulting mangrove height map has a mean tree height error of 2.0 m (RMSE) over a pixel of 30 m. In addition, we used field data to derive a relationship between mean forest stand height and biomass in order to map the spatial distribution of standing biomass of mangroves for the entire National Park. The estimation showed that most of the mangrove standing biomass in the ENP resides in intermediate-height mangrove stands around 8 m. We estimated the total mangrove standing biomass in ENP to be 5.6 � 10 9 kg.

Three-dimensional computer visualization and animation can provide a substitute for coastal residents', lack of personal experience with hurricane-surge flooding. Tremendous progress has been made in 3D animation in the last decade, which movies such as Perfect Storm and The Day After Tomorrow have demonstrated. However, the 3D visualization and animation system for storm-surge flooding differs from those in Hollywood movies in three aspects. First, objects such as buildings, roads, and trees in a synthetic 3D visualization environment not only have to be able to duplicate the real-world feature visually, but also be georeferenced so users can find real locations through addresses or spatial coordinates. The sizes and shapes of buildings and trees have to be accurate so users can sense the severity of flooding by comparing the water level with heights of familiar objects. Second, the magnitude, extent, and process of storm-surge flooding have to be accurate enough to represent the real situation. This information has to be based on hydrodynamics of storm surge. Third, the damage extent of a property caused by storm surge and waves, such as the collapse of a house, must be determined by engineering rules. Recent advances in high-resolution remote-sensing technology and numerical modeling make it possible to provide accurate data for the earth's surface features and storm-surge flooding.

This paper compares three methods for removing non-ground measurements from airborne laser scanning data. These methods, including the elevation threshold with expanding window (ETEW), maximum local slope (MLS), and progressive morphological (PM) filters, analyze data points based on variations of local slope, and elevation. Low and high-relief data sets with various densities of trees, houses, and sand dunes were selected to test the filtering methods. The results show that all three methods can effectively remove most nonground points in both low-relief urban and high-relief forested areas. The PM filter generated the best result in coastal barrier island areas, whereas the other algorithms tended to remove the tops of steep sand dunes. Each method experienced various omission or commission errors, depending on the filtering parameters. Topographic slope is the most sensitive parameter for the three filtering methods.

Quantitative data on beach changes caused by coastal storms is critical to the understanding of coastal morphodynamics and mitigation of coastal erosion hazards. Recent advances in airborne LIDAR technology allow large-scale mapping of beach erosion, dune scarping, and overwash deposition with incredible detail. By comparing 40 km of beaches along the central Florida Atlantic coast surveyed before and after Hurricane Floyd in 1999, we found that most beaches experienced erosion; about −18 to +1 m3/m of sediment per unit shoreline length were removed or deposited. Beach erosion is not spatially uniform, and variations in magnitude occur three dimensionally. The high-density LIDAR data provided accurate information about shore changes both at small and large scales.

This paper examines the feasibility of using LIDAR surveys to update existing historical shoreline data sets by comparing contour shorelines and the high water line (HWL) at eight study locations in North and South Carolina. The analysis was based on airborne LIDAR topography and orthoimagery collected simultaneously during June 2000. The popular method of digitizing the wet-dry line from orthoimagery was used to measure the HWL. Contour shorelines were derived by using the previous high tide (HW), the mean high water datum (MHW), and the mean higher high water datum (MHHW) of nearby tide gauges. A method was developed to quantitatively compare the positions of the HWL and the contour shorelines in a GIS. The mean high water and mean higher high water contour shoreline positions were the best match to the high water line at 7 of 8 locations, and differed by less than 5.4 meters from the digitized high water line positions. This difference is well within the errors associated with past methods for measuring shoreline position. Therefore, it is deemed practical to use LIDAR data to estimate the HWL.

One of the most certain consequences of global warming is an increase of global (eustatic) sea level. The resulting inundation from rising seas will heavily impact low-lying areas; at least 100 million persons live within one meter of mean sea level and are at increased risk in the coming decades. The very existence of some island states and deltaic coasts is threatened by sea level rise. An additional threat affecting some of the most heavily developed and economically valuable real estate will come from an exacerbation of sandy beach erosion. As the beach is lost, fixed structures nearby are increasingly exposed to the direct impact of storm waves, and will ultimately be damaged or destroyed unless expensive protective measures are taken. It has long been speculated that the underlying rate of long-term sandy beach erosion is two orders of magnitude greater than the rate of rise of sea level, so that any significant increase of sea level has dire consequences for coastal inhabitants. We present in this paper an analytical treatment that indicates there is a highly multiplicative association between long-term sandy beach erosion and sea level rise, and use a large and consistent data base of shoreline position field data to show that there is reasonable quantitative agreement with observations of 19th and 20th century sea levels and coastal erosion. This result means that the already-severe coastal erosion problems witnessed in the 20th century will be exacerbated in the 21st century under plausible global warming scenarios.