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1. Effect of Varying Wind Intensity, Forward Speed, and Surface Pressure on Storm Surges of Hurricane Rita

   https://doi.org/10.3390/jmse9020128  

   Musinguzi, Abram ; Akbar, Muhammad K. ( February 2021 , Journal of Marine Science and Engineering)

   Hurricane storm surges are influenced by several factors, including wind intensity, surface pressure, forward speed, size, angle of approach, ocean bottom depth and slope, shape and geographical features of the coastline. The relative influence of each factor may be amplified or abated by other factors that are acting at the time of the hurricane’s approach to the land. To understand the individual and combined influence of wind intensity, surface pressure and forward speed, a numerical experiment is conducted using Advanced CIRCulation + Simulating Waves Nearshore (ADCIRC + SWAN) by performing hindcasts of Hurricane Rita storm surges. The wind field generated by Ocean Weather Inc. (OWI) is used as the base meteorological forcing in ADCIRC + SWAN. All parameters are varied by certain percentages from those in the OWI wind field. Simulation results are analyzed for maximum wind intensity, wind vector pattern, minimum surface pressure, forward speed, maximum water elevation, station water elevation time series, and high water marks. The results for different cases are compared against each other, as well as with observed data. Changes in the wind intensity have the greatest impact, followed by the forward speed and surface pressure. The combined effects of the wind intensity and forwardmore »speed are noticeably different than their individual effects.« less
2. Florida mangrove saltmarsh reference surface soils

   https://doi.org/10.6073/pasta/0e08cbe07c84488cb7b9dd16669946d0  

   Lewis, David Bruce ( January 2021 )

   Abstract

   Site description. This data package consists of data obtained from sampling surface soil (the 0-7.6 cm depth profile) in black mangrove (Avicennia germinans) dominated forest and black needlerush (Juncus roemerianus) saltmarsh along the Gulf of Mexico coastline in peninsular west-central Florida, USA. This location has a subtropical climate with mean daily temperatures ranging from 15.4 °C in January to 27.8 °C in August, and annual precipitation of 1336 mm. Precipitation falls as rain primarily between June and September. Tides are semi-diurnal, with 0.57 m median amplitudes during the year preceding sampling (U.S. NOAA National Ocean Service, Clearwater Beach, Florida, station 8726724). Sea-level rise is 4.0 ± 0.6 mm per year (1973-2020 trend, mean ± 95 % confidence interval, NOAA NOS Clearwater Beach station). The A. germinans mangrove zone is either adjacent to water or fringed on the seaward side by a narrow band of red mangrove (Rhizophora mangle). A near-monoculture of J. roemerianus is often adjacent to and immediately landward of the A. germinans zone. The transition from the mangrove to the J. roemerianus zone is variable in our study area. An abrupt edge between closed-canopy mangrove and J. roemerianus monoculture may extend for up to several hundred meters in some locations, while other stretches of ecotone present a gradual transition where smaller, widely spaced trees are interspersed into the herbaceous marsh. Juncus roemerianus then extends landward to a high marsh patchwork of succulent halophytes (including Salicornia bigellovi, Sesuvium sp., and Batis maritima), scattered dwarf mangrove, and salt pans, followed in turn by upland vegetation that includes Pinus sp. and Serenoa repens. Field design and sample collection. We established three study sites spaced at approximately 5 km intervals along the western coastline of the central Florida peninsula. The sites consisted of the Salt Springs (28.3298°, -82.7274°), Energy Marine Center (28.2903°, -82.7278°), and Green Key (28.2530°, -82.7496°) sites on the Gulf of Mexico coastline in Pasco County, Florida, USA. At each site, we established three plot pairs, each consisting of one saltmarsh plot and one mangrove plot. Plots were 50 m^2 in size. Plots pairs within a site were separated by 230-1070 m, and the mangrove and saltmarsh plots composing a pair were 70-170 m apart. All plot pairs consisted of directly adjacent patches of mangrove forest and J. roemerianus saltmarsh, with the mangrove forests exhibiting a closed canopy and a tree architecture (height 4-6 m, crown width 1.5-3 m). Mangrove plots were located at approximately the midpoint between the seaward edge (water-mangrove interface) and landward edge (mangrove-marsh interface) of the mangrove zone. Saltmarsh plots were located 20-25 m away from any mangrove trees and into the J. roemerianus zone (i.e., landward from the mangrove-marsh interface). Plot pairs were coarsely similar in geomorphic setting, as all were located on the Gulf of Mexico coastline, rather than within major sheltering formations like Tampa Bay, and all plot pairs fit the tide-dominated domain of the Woodroffe classification (Woodroffe, 2002, "Coasts: Form, Process and Evolution", Cambridge University Press), given their conspicuous semi-diurnal tides. There was nevertheless some geomorphic variation, as some plot pairs were directly open to the Gulf of Mexico while others sat behind keys and spits or along small tidal creeks. Our use of a plot-pair approach is intended to control for this geomorphic variation. Plot center elevations (cm above mean sea level, NAVD 88) were estimated by overlaying the plot locations determined with a global positioning system (Garmin GPS 60, Olathe, KS, USA) on a LiDAR-derived bare-earth digital elevation model (Dewberry, Inc., 2019). The digital elevation model had a vertical accuracy of ± 10 cm (95 % CI) and a horizontal accuracy of ± 116 cm (95 % CI). Soil samples were collected via coring at low tide in June 2011. From each plot, we collected a composite soil sample consisting of three discrete 5.1 cm diameter soil cores taken at equidistant points to 7.6 cm depth. Cores were taken by tapping a sleeve into the soil until its top was flush with the soil surface, sliding a hand under the core, and lifting it up. Cores were then capped and transferred on ice to our laboratory at the University of South Florida (Tampa, Florida, USA), where they were combined in plastic zipper bags, and homogenized by hand into plot-level composite samples on the day they were collected. A damp soil subsample was immediately taken from each composite sample to initiate 1 y incubations for determination of active C and N (see below). The remainder of each composite sample was then placed in a drying oven (60 °C) for 1 week with frequent mixing of the soil to prevent aggregation and liberate water. Organic wetland soils are sometimes dried at 70 °C, however high drying temperatures can volatilize non-water liquids and oxidize and decompose organic matter, so 50 °C is also a common drying temperature for organic soils (Gardner 1986, "Methods of Soil Analysis: Part 1", Soil Science Society of America); we accordingly chose 60 °C as a compromise between sufficient water removal and avoidance of non-water mass loss. Bulk density was determined as soil dry mass per core volume (adding back the dry mass equivalent of the damp subsample removed prior to drying). Dried subsamples were obtained for determination of soil organic matter (SOM), mineral texture composition, and extractable and total carbon (C) and nitrogen (N) within the following week. Sample analyses. A dried subsample was apportioned from each composite sample to determine SOM as mass loss on ignition at 550 °C for 4 h. After organic matter was removed from soil via ignition, mineral particle size composition was determined using a combination of wet sieving and density separation in 49 mM (3 %) sodium hexametaphosphate ((NaPO_3)_6) following procedures in Kettler et al. (2001, Soil Science Society of America Journal 65, 849-852). The percentage of dry soil mass composed of silt and clay particles (hereafter, fines) was calculated as the mass lost from dispersed mineral soil after sieving (0.053 mm mesh sieve). Fines could have been slightly underestimated if any clay particles were burned off during the preceding ignition of soil. An additional subsample was taken from each composite sample to determine extractable N and organic C concentrations via 0.5 M potassium sulfate (K_2SO_4) extractions. We combined soil and extractant (ratio of 1 g dry soil:5 mL extractant) in plastic bottles, reciprocally shook the slurry for 1 h at 120 rpm, and then gravity filtered it through Fisher G6 (1.6 μm pore size) glass fiber filters, followed by colorimetric detection of nitrite (NO_2^-) + nitrate (NO_3^-) and ammonium (NH_4^+) in the filtrate (Hood Nowotny et al., 2010,Soil Science Society of America Journal 74, 1018-1027) using a microplate spectrophotometer (Biotek Epoch, Winooski, VT, USA). Filtrate was also analyzed for dissolved organic C (referred to hereafter as extractable organic C) and total dissolved N via combustion and oxidation followed by detection of the evolved CO_2 and N oxide gases on a Formacs HT TOC/TN analyzer (Skalar, Breda, The Netherlands). Extractable organic N was then computed as total dissolved N in filtrate minus extractable mineral N (itself the sum of extractable NH_4-N and NO_2-N + NO_3-N). We determined soil total C and N from dried, milled subsamples subjected to elemental analysis (ECS 4010, Costech, Inc., Valencia, CA, USA) at the University of South Florida Stable Isotope Laboratory. Median concentration of inorganic C in unvegetated surface soil at our sites is 0.5 % of soil mass (Anderson, 2019, Univ. of South Florida M.S. thesis via methods in Wang et al., 2011, Environmental Monitoring and Assessment 174, 241-257). Inorganic C concentrations are likely even lower in our samples from under vegetation, where organic matter would dilute the contribution of inorganic C to soil mass. Nevertheless, the presence of a small inorganic C pool in our soils may be counted in the total C values we report. Extractable organic C is necessarily of organic C origin given the method (sparging with HCl) used in detection. Active C and N represent the fractions of organic C and N that are mineralizable by soil microorganisms under aerobic conditions in long-term soil incubations. To quantify active C and N, 60 g of field-moist soil were apportioned from each composite sample, placed in a filtration apparatus, and incubated in the dark at 25 °C and field capacity moisture for 365 d (as in Lewis et al., 2014, Ecosphere 5, art59). Moisture levels were maintained by frequently weighing incubated soil and wetting them up to target mass. Daily CO_2 flux was quantified on 29 occasions at 0.5-3 week intervals during the incubation period (with shorter intervals earlier in the incubation), and these per day flux rates were integrated over the 365 d period to compute an estimate of active C. Observations of per day flux were made by sealing samples overnight in airtight chambers fitted with septa and quantifying headspace CO_2 accumulation by injecting headspace samples (obtained through the septa via needle and syringe) into an infrared gas analyzer (PP Systems EGM 4, Amesbury, MA, USA). To estimate active N, each incubated sample was leached with a C and N free, 35 psu solution containing micronutrients (Nadelhoffer, 1990, Soil Science Society of America Journal 54, 411-415) on 19 occasions at increasing 1-6 week intervals during the 365 d incubation, and then extracted in 0.5 M K_2SO_4 at the end of the incubation in order to remove any residual mineral N. Active N was then quantified as the total mass of mineral N leached and extracted. Mineral N in leached and extracted solutions was detected as NH_4-N and NO_2-N + NO_3-N via colorimetry as above. This incubation technique precludes new C and N inputs and persistently leaches mineral N, forcing microorganisms to meet demand by mineralizing existing pools, and thereby directly assays the potential activity of soil organic C and N pools present at the time of soil sampling. Because this analysis commences with disrupting soil physical structure, it is biased toward higher estimates of active fractions. Calculations. Non-mobile C and N fractions were computed as total C and N concentrations minus the extractable and active fractions of each element. This data package reports surface-soil constituents (moisture, fines, SOM, and C and N pools and fractions) in both gravimetric units (mass constituent / mass soil) and areal units (mass constituent / soil surface area integrated through 7.6 cm soil depth, the depth of sampling). Areal concentrations were computed as X × D × 7.6, where X is the gravimetric concentration of a soil constituent, D is soil bulk density (g dry soil / cm^3), and 7.6 is the sampling depth in cm.
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3. Co-evolution of wetland landscapes, flooding, and human settlement in the Mississippi River Delta Plain

   https://doi.org/10.1007/s11625-016-0374-4  

   Twilley, Robert R. ; Bentley, Samuel J. ; Chen, Qin ; Edmonds, Douglas A. ; Hagen, Scott C. ; Lam, Nina S.-N. ; Willson, Clinton S. ; Xu, Kehui ; Braud, DeWitt ; Hampton Peele, R. ; et al ( May 2016 , Sustainability Science)

   Abstract River deltas all over the world are sinking beneath sea-level rise, causing significant threats to natural and social systems. This is due to the combined effects of anthropogenic changes to sediment supply and river flow, subsidence, and sea-level rise, posing an immediate threat to the 500–1,000 million residents, many in megacities that live on deltaic coasts. The Mississippi River Deltaic Plain (MRDP) provides examples for many of the functions and feedbacks, regarding how human river management has impacted source-sink processes in coastal deltaic basins, resulting in human settlements more at risk to coastal storms. The survival of human settlement on the MRDP is arguably coupled to a shifting mass balance between a deltaic landscape occupied by either land built by the Mississippi River or water occupied by the Gulf of Mexico. We developed an approach to compare 50 % L:W isopleths (L:W is ratio of land to water) across the Atchafalaya and Terrebonne Basins to test landscape behavior over the last six decades to measure delta instability in coastal deltaic basins as a function of reduced sediment supply from river flooding. The Atchafalaya Basin, with continued sediment delivery, compared to Terrebonne Basin, with reduced river inputs, allow us tomore »test assumptions of how coastal deltaic basins respond to river management over the last 75 years by analyzing landward migration rate of 50 % L:W isopleths between 1932 and 2010. The average landward migration for Terrebonne Basin was nearly 17,000 m (17 km) compared to only 22 m in Atchafalaya Basin over the last 78 years (p\0.001), resulting in migration rates of 218 m/year (0.22 km/year) and\0.5 m/year, respectively. In addition, freshwater vegetation expanded in Atchafalaya Basin since 1949 compared to migration of intermediate and brackish marshes landward in the Terrebonne Basin. Changes in salt marsh vegetation patterns were very distinct in these two basins with gain of 25 % in the Terrebonne Basin compared to 90 % decrease in the Atchafalaya Basin since 1949. These shifts in vegetation types as L:W ratio decreases with reduced sediment input and increase in salinity also coincide with an increase in wind fetch in Terrebonne Bay. In the upper Terrebonne Bay, where the largest landward migration of the 50 % L:W ratio isopleth occurred, we estimate that the wave power has increased by 50–100 % from 1932 to 2010, as the bathymetric and topographic conditions changed, and increase in maximum storm-surge height also increased owing to the landward migration of the L:W ratio isopleth. We argue that this balance of land relative to water in this delta provides a much clearer understanding of increased flood risk from tropical cyclones rather than just estimates of areal land loss. We describe how coastal deltaic basins of the MRDP can be used as experimental landscapes to provide insights into how varying degrees of sediment delivery to coastal deltaic floodplains change flooding risks of a sinking delta using landward migrations of 50 % L:W isopleths. The nonlinear response of migrating L:W isopleths as wind fetch increases is a critical feedback effect that should influence human river-management decisions in deltaic coast. Changes in land area alone do not capture how corresponding landscape degradation and increased water area can lead to exponential increase in flood risk to human populations in low-lying coastal regions. Reduced land formation in coastal deltaic basins (measured by changes in the land:water ratio) can contribute significantly to increasing flood risks by removing the negative feedback of wetlands on wave and storm-surge that occur during extreme weather events. Increased flood risks will promote population migration as human risks associated with living in a deltaic landscape increase, as land is submerged and coastal inundation threats rise. These system linkages in dynamic deltaic coasts define a balance of river management and human settlement dependent on a certain level of land area within coastal deltaic basins (L).« less
4. Surface viscous stress over wind-driven waves with intermittent airflow separation

   https://doi.org/10.1017/jfm.2020.760  

   Buckley, M. P. ; Veron, F. ; Yousefi, K. ( December 2020 , Journal of Fluid Mechanics)

   The small-scale physics within the first centimetres above the wavy air–sea interface are the gateway for transfers of momentum and scalars between the atmosphere and the ocean. We present an experimental investigation of the surface wind stress over laboratory wind-generated waves. Measurements were performed at the University of Delaware's large wind-wave-current facility using a recently developed state-of-the-art wind-wave imaging system. The system was deployed at a fetch of 22.7 m, with wind speeds from 2.19 to $16.63\ \textrm {m}\ \textrm {s}^{-1}$ . Airflow velocity fields were acquired using particle image velocimetry above the wind waves down to $100\ \mathrm {\mu }\textrm {m}$ above the surface, and wave profiles were detected using laser-induced fluorescence. The airflow intermittently separates downwind of wave crests, starting at wind speeds as low as $2.19\ \textrm {m}\ \textrm {s}^{-1}$ . Such events are accompanied by a dramatic drop in tangential viscous stress past the wave's crest, and a gradual regeneration of the viscous sublayer upon the following (downwind) crest. This contrasts with non-airflow separating waves, where the surface viscous stress drop is less significant. Airflow separation becomes increasingly dominant with increasing wind speed and wave slope $a k$ (where $a$ and $k$ are peak wave amplitudemore »and wavenumber, respectively). At the highest wind speed ( $16.63\ \textrm {m}\ \textrm {s}^{-1}$ ), airflow separation occurs over nearly 100 % of the wave crests. The total air–water momentum flux is partitioned between viscous stress and form drag at the interface. Viscous stress (respectively form drag) dominates at low (respectively high) wave slopes. Tangential viscous forcing makes a minor contribution ( ${\sim }3\,\%$ ) to wave growth.« less
5. Integrating Evacuation and Storm Surge Modeling Considering Potential Hurricane Tracks: The Case of Hurricane Irma in Southeast Florida

   https://doi.org/10.3390/ijgi10100661  

   Ghorbanzadeh, Mahyar ; Vijayan, Linoj ; Yang, Jieya ; Ozguven, Eren Erman ; Huang, Wenrui ; Ma, Mengdi ( October 2021 , ISPRS International Journal of Geo-Information)

   Hurricane Irma, in 2017, made an unusual landfall in South Florida and the unpredictability of the hurricane’s path challenged the evacuation process seriously and left many evacuees clueless. It was likely to hit Southeast Florida but suddenly shifted its path to the west coast of the peninsula, where the evacuation process had to change immediately without any time for individual decision-making. As such, this study aimed to develop a methodology to integrate evacuation and storm surge modeling with a case study analysis of Irma hitting Southeast Florida. For this purpose, a coupled storm surge and wave finite element model (ADCIRC+SWAN) was used to determine the inundation zones and roadways with higher inundation risk in Broward, Miami-Dade, and Palm Beach counties in Southeast Florida. This was fed into the evacuation modeling to estimate the regional clearance times and shelter availability in the selected counties. Findings show that it takes approximately three days to safely evacuate the populations in the study area. Modeling such integrated simulations before the hurricane hit the state could provide the information people in hurricane-prone areas need to decide to evacuate or not before the mandatory evacuation order is given.