Storm Peak Research Articles

The Impact of Coastline and Bathymetry Changes on the Storm Tides in Zhejiang Coasts

Coastal evolutions are expected to have a significant impact on storm tides, disproportionately aggravating coastal flooding. In this study, we utilize a nested storm tide model to provide an integrated investigation of storm tide responses to changes in coastline and bathymetry along the Zhejiang coasts. We selected coastline and bathymetry data from 1980 and 2016, as well as data from three typical typhoon events (i.e., Winnie, Haikui, and Chan-hom) for simulating the storm surge processes. The results indicate that changes in the coastline and bathymetry from 1980 to 2016 have resulted in an increase in storm tides in the northern part and a decrease in the central part of Zhejiang. Specifically, storm tides in Hangzhou Bay have increased significantly, with an average increase of about 0.3 m in the maximum storm tides primarily attributed to coastline changes. On the contrary, in smaller basins like Sanmen Bay, while reclamation itself has reduced peak storm surges, rapid siltation has consequently exacerbated the storm surge. By decomposing storm tides into astronomical tides and storm surges, we discovered that the change in tidal levels was twice as significant as the surge change. Moreover, the nonlinear tide–surge interaction was nearly four times that of the pure surge, significantly contributing to storm surge variation. Alterations in the momentum balance reveal that the water depth-induced bottom friction and wind stress increase contributes to the local enlargement of storm tides at the bay head, while the coastline changes exaggerate nearshore storm tides through an increase in the advection term.

EXTREME WAVE CLIMATE OF THE NEW SOUTH WALES COAST

The NSW coast is subject to a relatively moderate wave climate that is periodically affected by large coastal storm events arising from a range of synoptic weather systems. Extreme wave events may cause coastal inundation, beach erosion, damage to property and marine structures, and risks to public safety. This paper describes the statistical analysis of the extreme directional wave climate along the NSW coast based on historical wave buoy data extending back to 1974. From the analysis, long-term trends in the extreme wave climate (storm peaks, storm duration and peak directions) are also identified. The work is largely an extension of the analysis completed by Shand et al (2011), that incorporates an additional 13 years of wave data up to 2022.

Thinking inside the box: Investigating peak storm response in a simplified outdoor slope setup

Hillslopes are the fundamental building blocks of catchments, however, fully disentangling their hydrological response remains an outstanding challenge. In natural settings, hillslope hydrology is challenged by the interplay of several factors, such as, heterogeneous morphology, vegetation, and complex soils. Herein, we attempt to clarify the mechanisms that control the rainfall-runoff relationship by investigating the response of an artificial outdoor slope setup with uniform geometry, soil properties, topography, and in the absence of vegetation located in a Mediterranean climate. Based on the analysis of more than 100 storms across six consecutive hydrological years, our results demonstrate that either pre-event soil water content or rainfall conditions alone are not sufficient to explain the slope response. Seasonality played a major role through soil moisture conditions, and pre-event soil moisture, peak rainfall intensity and rainfall duration influenced the slope runoff response. Observations suggest that in fall and winter subsurface flow was the prevalent runoff mechanism, while infiltration excess overland flow mostly occurred in the dry season. Our findings show that continuous measurement of runoff could be beneficial to understand the peak rainfall-peak runoff relationship.

Ionospheric Weather at Two Starlink Launches during Two-Phase Geomagnetic Storms.

The launch of a series of Starlink internet satellites on 3 February 2022 (S-36), and 7 July 2022 (S-49), coincided with the development of two-phase geomagnetic storms. The first launch S-36 took place in the middle of the moderate two-phase space weather storm, which induced significant technological consequences. After liftoff on 3 February at 18:13 UT, all Starlink satellites reached an initial altitude of 350 km in perigee and had to reach an altitude of ~550 km after the maneuver. However, 38 of 49 launched spacecrafts did not reach the planned altitude, left orbit due to increased drag and reentered the atmosphere on 8 February. A geomagnetic storm on 3-4 February 2022 has increased the density of the neutral atmosphere up to 50%, increasing drag of the satellites and dooming most of them. The second launch of S-49 at 13:11 UT on 7 July 2022 was successful at the peak of the two-phase geomagnetic storm. The global ionospheric maps of the total electron content (GIM-TEC) have been used to produce the ionospheric weather GIM-W index maps and Global Electron Content (GEC). We observed a GEC increment from 10 to 24% for the storm peak after the Starlink launch at both storms, accompanying the neutral density increase identified earlier. GIM-TEC maps are available with a lag (delay) of 1-2 days (real-time GIMs have a lag less than 15 min), so the GIMs forecast is required by the time of the launch. Comparisons of different GIMs forecast techniques are provided including the Center for Orbit Determination in Europe (CODE), Beijing (BADG and CASG) and IZMIRAN (JPRG) 1- and 2-day forecasts, and the Universitat Politecnica de Catalunya (UPC-ionSAT) forecast for 6, 12, 18, 24 and 48 h in advance. We present the results of the analysis of evolution of the ionospheric parameters during both events. The poor correspondence between observed and predicted GIM-TEC and GEC confirms an urgent need for the industry-science awareness of now-casting/forecasting/accessibility of GIM-TECs during the space weather events.

Statistical Properties of Long‐Period Plasmapause Surface Waves From Van Allen Probes Observations

AbstractRecent work revealed the existence of plasmapause surface waves (PSWs) with Van Allen Probes observations, which are characterized by the periodic modulations of both magnetic/electric fields and plasma densities, and suggested to be the driver of giant undulations (GUs). In this study, six years data from Van Allen Probes were used to investigate the spatial distributions and occurrence conditions of PSWs in the period of 5–30 min. PSWs are found to be mainly distributed at L = 3.5–7 and their occurrence rate is increasing with larger L shells. In the azimuth direction, the spatial distribution of PSWs exhibits an obvious dawn‐dusk asymmetry with highest occurrence rates at MLT = 15–21, which is consistent with the spatial distribution of GUs revealed in the previous study. This further demonstrates that PSWs and GUs are connected, and indicates that PSWs differ from Ps6 waves which have a similar wave period and irregular waveform but mainly concentrate in the dawnside and connect with Ω bands. PSWs preferentially occur under the condition of high solar wind velocities (VSW > 500 km/s) and high dynamic pressures (Pdyn > 5 nPa), and their occurrence rate has a negative correlation with IMF Bz, SYMH and AL. Statistical results also reveal that PSWs preferentially occur around the peak of both magnetic storms and substorms. These findings may shed new lights on the further understanding of PSWs' generation and propagation.

Comparison of on-site versus NOAA’s extreme precipitation intensity-duration-frequency estimates for six forest headwater catchments across the continental United States

Urgency of Precipitation Intensity-Duration-Frequency (IDF) estimation using the most recent data has grown significantly due to recent intense precipitation and cloud burst circumstances impacting infrastructure caused by climate change. Given the continually available digitized up-to-date, long-term, and fine resolution precipitation dataset from the United States Department of Agriculture Forest Service’s (USDAFS) Experimental Forests and Ranges (EF) rain gauge stations, it is both important and relevant to develop precipitation IDF from onsite dataset (Onsite-IDF) that incorporates the most recent time period, aiding in the design, and planning of forest road-stream crossing structures (RSCS) in headwaters to maintain resilient forest ecosystems. Here we developed Onsite-IDFs for hourly and sub-hourly duration, and 25-yr, 50-yr, and 100-yr design return intervals (RIs) from annual maxima series (AMS) of precipitation intensities (PIs) modeled by applying Generalized Extreme Value (GEV) analysis and L-moment based parameter estimation methodology at six USDAFS EFs and compared them with precipitation IDFs obtained from the National Oceanic and Atmospheric Administration Atlas 14 (NOAA-Atlas14). A regional frequency analysis (RFA) was performed for EFs where data from multiple precipitation gauges are available. NOAA’s station-based precipitation IDFs were estimated for comparison using RFA (NOAA-RFA) at one of the EFs where NOAA-Atlas14 precipitation IDFs are unavailable. Onsite-IDFs were then evaluated against the PIs from NOAA-Atlas14 and NOAA-RFA by comparing their relative differences and storm frequencies. Results show considerable relative differences between the Onsite- and NOAA-Atlas14 (or NOAA-RFA) IDFs at these EFs, some of which are strongly dependent on the storm durations and elevation of precipitation gauges, particularly in steep, forested sites of H. J. Andrews (HJA) and Coweeta Hydrological Laboratory (CHL) EFs. At the higher elevation gauge of HJA EF, NOAA-RFA based precipitation IDFs underestimate PI of 25-yr, 50-yr, and 100-yr RIs by considerable amounts for 12-h and 24-h duration storm events relative to the Onsite-IDFs. At the low-gradient Santee (SAN) EF, the PIs of 3- to 24-h storm events with 100-yr frequency (or RI) from NOAA-Atlas14 gauges are found to be equivalent to PIs of more frequent storm events (25–50-yr RI) as estimated from the onsite dataset. Our results recommend use of the Onsite-IDF estimates for the estimation of design storm peak discharge rates at the higher elevation catchments of HJA, CHL, and SAN EF locations, particularly for longer duration events, where NOAA-based precipitation IDFs underestimate the PIs relative to the Onsite-IDFs. This underscores the importance of long-term high resolution EF data for new applications including ecological restorations and indicates that planning and design teams should use as much local data as possible or account for potential PI inconsistencies or underestimations if local data are unavailable.

The Evolutions of the Seed and Relativistic Electrons in the Earth's Outer Radiation Belt During the Geomagnetic Storms: A Statistical Study

AbstractUsing the Van Allen Probes data, we statistically study the evolutions of the seed and relativistic electrons in the Earth's outer radiation belt during the storms. Based on the different storm intensities and durations of the main phase, the storm events are divided into “moderate and short (MS) events,” “moderate and long (ML) events,” “strong and short (SS) events,” and “strong and long (SL) events.” The results from a superposed epoch analysis show that (a) For most MS and SS events, the seed electron fluxes can exceed pre‐storm levels, and enhanced fluxes mainly occur around the storm peak. (b) For the ML and SL events, the seed electron fluxes greatly exceed pre‐storm levels by more than an order of magnitude, and enhanced fluxes mainly occur during the main phase. (c) For most MS events, MeV electron fluxes are difficult to return to or exceed pre‐storm levels. While MeV electron fluxes at some L* shells for some SS events can exceed pre‐storm levels. (d) For most ML or SL events, MeV electron fluxes at L* ≥4.0 or ≥3.5 can exceed pre‐storm levels, and enhanced fluxes mainly start to occur from the main phase. These results suggest that the duration of the main phase and storm intensity play important roles in the evolutions of the seed and MeV electrons in the Earth's outer radiation belt during the storms. These are important for our further understanding of the electron dynamics of the outer radiation belt.

Storm-driven variations in depositional environments modify pyrite sulfur isotope records

The sulfur isotopic composition of marine pyrite (δ34Spyr) is one of the major geochemical tools to reconstruct global changes in Earth's surface environment. Storm-driven variations in depositional environments, however, can play key roles in modifying the δ34Spyr signal. Here we present δ34Spyr values in a complete Holocene muddy storm deposit on the East China coastal plain, which is identified by comprehensive analyses of sediment components, organic matter composition, bulk organic carbon isotopic ratios, carbon and oxygen isotopic ratios of carbonates and trace element concentrations. We find that positive (+5.8‰ to +15.8‰) and negative (−9.0‰ to −19.7‰) δ34Spyr values were preserved in two successive intervals of the muddy storm deposit (ca. 62 cm long), corresponding to the high-energy storm peak phase (HESPP) and the waning-energy late storm phase (WELSP), respectively. We propose that the 34S enrichment in pyrite from the HESPP is most likely due to a combination of storm reworking of sediments and high sedimentation rates, which involves various physical and chemical processes leading to the accumulation of 34S-enriched pyrite, such as oxidation of near-surface sulfides, reduced exchange of sulfate between sediment pore fluids and the overlying water column, and/or input of excess reactive iron minerals. In contrast, lower sedimentation rates and limited sedimentary remobilization during the WELSP allow the isotopic signal of early-formed 32S-enriched pyrite to be preserved. The striking shift in δ34Spyr values, therefore, reflects a dramatic change in the local depositional environment between the HESPP and WELSP, suggesting that non-steady-state deposition induced by storm activity facilitates the formation of isotopically “heavy” pyrite. This sharp shift can be further generalized as a characteristic feature of δ34Spyr values in storm deposits. Our work highlights the critical impact of weather- and climate-event-driven depositional variations on δ34Spyr values, adding to the growing evidence emphasizing the local environmental and diagenetic controls on these records.

Interactions between Surface Waves, Tides, and Storm-Induced Currents over Shelf Waters of the Northwest Atlantic

A coupled wave–tide–circulation model is used to investigate wave–current interactions (WCIs) over the shelf waters of the Northwest Atlantic (NWA) during Hurricane Earl (2010). WCIs have substantial impacts on hydrodynamics in the upper ocean. The significant wave heights are modulated by WCIs, particularly over regions with strong current gradients, with a reduction up to ~2.1 m (20%) during the storm. Noticeable decreases in surface elevations and tidal currents occur in regions with strong tides such as the Gulf of Maine, mainly due to the wave-enhanced bottom stress. Over regions with weak tidal currents, wave effects on currents are dominated by two competitive processes between wave-induced forces and wave-enhanced mixing. The former strengthens surface currents (up to ~0.55 m/s) and increases the peak storm surge (up to ~0.48 m). The latter is responsible for the reduction in storm-induced surface currents (up to ~0.94 m/s) and anticyclonic modulation of current directions. Vertically, WCIs extend the strong vertical current shear and shift it downward during the storm, which enhances the local mixing and changes the structures of near-inertial oscillations (NIOs). Moreover, tidal currents also change the magnitudes of the NIOs and subtidal currents and affect the intensity of WCIs.

North Atlantic Tropical Cyclone Size and Storm Surge Reconstructions From 1950‐Present

AbstractTropical cyclones (TCs) are one of the greatest threats to coastal communities along the US Atlantic and Gulf coasts due to their extreme wind, rainfall and storm surge. Analyzing historical TC climatology and modeling TC hazards can provide valuable insight to planners and decision makers. However, detailed TC size information is typically only available from 1988 onward, preventing accurate wind, rainfall, and storm surge modeling for TCs occurring earlier in the historical record. To overcome temporally limited TC size data, we develop a database of size estimates that are based on reanalysis data and a physics‐based model. Specifically, we utilize ERA5 reanalysis data to estimate the TC outer size, and a physics‐based TC wind model to estimate the radius of maximum wind. We evaluate our TC size estimates using two high‐resolution wind data sets as well as Best Track information for a wide variety of TCs. Using the estimated size information plus the TC track and intensity, we reconstruct historical storm tides from 1950 to 2020 using a basin‐scale hydrodynamic model and show that our reconstructions agree well with observed peak storm tide and storm surge. Finally, we demonstrate that incorporating an expanded set of historical modeled storm tides beginning in 1950 can enhance our understanding of US coastal hazard. Our newly developed database of TC sizes and associated storm tides/surges can aid in understanding North Atlantic TC climatology and modeling TC wind, storm surge, and rainfall hazard along the US Atlantic and Gulf coasts.

Real-time control enhanced blue-green infrastructure towards torrential events: A smart predictive solution

Climate change-induced storms have posed a threat to current urban stormwater systems. As a critical flood control measure, passive urban blue-green infrastructure (BGI) is incapable of effectively utilizing available storage capacity to mitigate peak flows during cloudburst events. At storm peak times, landscape-based BGI may overflow to the drainage system, contributing to downstream flooding risks. To alleviate the effect, this research investigated an adaptive RTC solution that would predischarge the BGI and initiate runoff detention during the most vulnerable period of peak intensity. A case study of a RTC-enhanced landscape detention basin in Tongzhou, Beijing, was conducted using 2D1D modeling. To minimize peak-time overflow under extreme conditions, the studied smart BGI equipped with sensors and electronically movable gates was actuated by both rule-based control (RBC) and model predictive control (MPC) algorithms. Modeling results indicate that the proposed predictive solutions reduced catchment peak outflow by 7.6% to 45.1% over the passive system during three historical cloudburst events. This proposal proves that similar RTC applications could be scaled up to additional landscape projects to increase urban sustainability towards climate change and extreme precipitation.

Instructive Surprises in the Hydrological Functioning of Landscapes

Landscapes receive water from precipitation and then transport, store, mix, and release it, both downward to streams and upward to vegetation. How they do this shapes floods, droughts, biogeochemical cycles, contaminant transport, and the health of terrestrial and aquatic ecosystems. Because many of the key processes occur invisibly in the subsurface, our conceptualization of them has often relied heavily on physical intuition. In recent decades, however, much of this intuition has been overthrown by field observations and emerging measurement methods, particularly involving isotopic tracers. Here we summarize key surprises that have transformed our understanding of hydrological processes at the scale of hillslopes and drainage basins. These surprises have forced a shift in perspective from process conceptualizations that are relatively static, homogeneous, linear, and stationary to ones that are predominantly dynamic, heterogeneous, nonlinear, and nonstationary. ▪ Surprising observations and novel measurements are transforming our understanding of the hydrological functioning of landscapes. ▪ Even during storm peaks, streamflow is composed mostly of water that has been stored in the landscape for weeks, months, or years. ▪ Streamflow and tree water uptake often originate from different subsurface storages and from different seasons’ precipitation. ▪ Stream networks dynamically extend and retract as the landscape wets and dries, and many stream reaches lose flow into underlying aquifers.

A modeling application of integrated nature based solutions (NBS) for coastal erosion and flooding mitigation in the Emilia-Romagna coastline (Northeast Italy)

Worldwide, climate change adaptation in coastal areas is a growing challenge. The most common solutions such as seawalls and breakwaters are expensive and often lead to unexpected disastrous effects on the neighboring unprotected areas. In recent years, this awareness has guided coastal managers to adopt alternative solutions with lower environmental impact to protect coastal areas, defined as Nature-Based Solutions (NBSs). NBS are quite popular around the world but are often analyzed and implemented individually at pilot sites. This contribution analyzes the effectiveness of two NBS to mitigate coastal impacts (coastal flooding and erosion) under three historical storms along the Emilia-Romagna coasts and the induced improvements due to their potential integration. Through numerical simulations with XBeach, this study demonstrated that the presence of seagrass meadows of Zostera marina produces an average attenuation of 32 % of the storm peak with a maximum attenuation of 89 % in incoming wave height. Seagrass also mitigates flooded areas and maximum inundation depths by 37 % and 58 % respectively. The artificial dune leads to higher mitigation in terms of inundation of the lagoon (up to 75 %), also avoiding any morphological variations behind it. Seagrass has also been shown to be able to reduce beach erosion volumes up to 55 %. The synergic effect of the two NBS improves the capacity to mitigate both inundation (with a benefit of up to 77 % for flooded area attenuation with respect to cases without any defenses) and coastal erosion. Results of the study suggest that the two NBS will work together to produce co-benefits in terms of preservation of their efficiency, development of habitats for organisms and vegetation species, and thereby offering an important social value in terms of possible tourism, recreation and research.

Physics‐Based Approach to Thermospheric Density Estimation Using CubeSat GPS Data

AbstractIn Low Earth Orbit (LEO), atmospheric drag is the largest contributor to trajectory prediction error. The current thermospheric density model used in operations, the High Accuracy Satellite Drag Model (HASDM), applies corrections to an empirical density model every 3 hr using observations of 75+ calibration satellites. This work aims to improve global thermospheric density estimation by utilizing a physics‐based space environment model and precise GPS‐based orbit estimates of LEO CubeSats. The data assimilation approach presented here estimates drivers of the Thermosphere‐Ionosphere‐Electrodynamics General Circulation Model (TIE‐GCM) every 1.5 hr using CubeSat GPS information. In this work, Spire Global CubeSat data are used to demonstrate the method using only 10 satellites; the true strength of the method is its potential to exploit data already collected on large LEO constellations (hundreds of CubeSats). Precise Orbit Determination (POD) information from 10 CubeSats over 12 days is used to sense a global density field when Kp historical data show a minor and moderate geomagnetic storm in succession. This paper provides a direct comparison of estimated density, derived by our new method, to HASDM and Swarm mission derived density. A propagation analysis is also executed by comparing the CubeSat POD data to orbits propagated using our estimated density versus HASDM density. The analyses show that the estimated density is within 35% of HASDM during storm‐time conditions, and that the propagation using the estimated density yields an improvement of 26% over NRLMSISE‐00 compared to HASDM, while outperforming HASDM during the second storm peak.

Seasonal significance of new particle formation impacts on cloud condensation nuclei at a mountaintop location

Abstract. New particle formation (NPF) events are defined as a sudden burst of aerosols followed by growth and can impact climate by growing to larger sizes and under proper conditions, potentially forming cloud condensation nuclei (CCN). Field measurements relating NPF and CCN are crucial in expanding regional understanding of how aerosols impact climate. To quantify the possible impact of NPF on CCN formation, it is important to not only maintain consistency when classifying NPF events but also consider the proper timeframe for particle growth to CCN-relevant sizes. Here, we analyze 15 years of direct measurements of both aerosol size distributions and CCN concentrations and combine them with novel methods to quantify the impact of NPF on CCN formation at Storm Peak Laboratory (SPL), a remote, mountaintop observatory in Colorado. Using the new automatic method to classify NPF, we find that NPF occurs on 50 % of all days considered in the study from 2006 to 2021, demonstrating consistency with previous work at SPL. NPF significantly enhances CCN during the winter by a factor of 1.36 and during the spring by a factor of 1.54, which, when combined with previous work at SPL, suggests the enhancement of CCN by NPF occurs on a regional scale. We confirm that events with persistent growth are common in the spring and winter, while burst events are more common in the summer and fall. A visual validation of the automatic method was performed in the study. For the first time, results clearly demonstrate the significant impact of NPF on CCN in montane North American regions and the potential for widespread impact of NPF on CCN.

Thermospheric Neutral Density Variation During the “SpaceX” Storm: Implications From Physics‐Based Whole Geospace Modeling

AbstractThe Starlink satellites launched on 3 February 2022 were lost before they fully arrived in their designated orbits. The loss was attributed to two moderate geomagnetic storms that occurred consecutively on 3–4 February. We investigate the thermospheric neutral mass density variation during these storms with the Multiscale Atmosphere‐Geospace Environment (MAGE) model, a first‐principles, fully coupled geospace model. Simulated neutral density enhancements are validated by Swarm satellite measurements at the altitude of 400–500 km. Comparison with standalone TIEGCM and empirical NRLMSIS 2.0 and DTM‐2013 models suggests better performance by MAGE in predicting the maximum density enhancement and resolving the gradual recovery process. Along the Starlink satellite orbit in the middle thermosphere (∼200 km altitude), MAGE predicts up to 150% density enhancement near the second storm peak while standalone TIEGCM, NRLMSIS 2.0, and DTM‐2013 suggest only ∼50% increase. MAGE also suggests altitudinal, longitudinal, and latitudinal variability of storm‐time percentage density enhancement due to height dependent Joule heating deposition per unit mass, thermospheric circulation changes, and traveling atmospheric disturbances. This study demonstrates that a moderate storm can cause substantial density enhancement in the middle thermosphere. Thermospheric mass density strongly depends on the strength, timing, and location of high‐latitude energy input, which cannot be fully reproduced with empirical models. A physics‐based, fully coupled geospace model that can accurately resolve the high‐latitude energy input and its variability is critical to modeling the dynamic response of thermospheric neutral density during storm time.

Modeling the hydrodynamics of a tidal inlet during bay-side storms

Bay-side storms, defined here as storms with tracks on the landward side of barrier islands, may disturb the hydrodynamics of inner bays to a larger extent than on the ocean side. These storms are common in large-scale O(>100,000 m) estuarine systems and have the potential to modify the circulation in bays and within tidal inlets. Here, we provide an overview of the hydrodynamic response of a tidal inlet under forcings caused by bay-side storms and explore the role of waves in modulating the release of storm surge from the back-barrier regions into the ocean. A two-dimensional horizontal numerical model including wave-current interactions is calibrated and validated against field observations of water levels and depth-averaged velocities at Oregon Inlet, NC. The model is then used to investigate the effect of synthetic bay-side storms with varying wave conditions and water levels based on those generated by Hurricane Irene (2011), which is the strongest bay-side storm to hit the Outer Banks of North Carolina in the last two decades. Effect of timing of the peak storm surge during the ebb and flood phases of the tide is also explored.Results from synthetic storms indicate that, during bay-side storms, the water level gradient along the inlet favors ebbing flows regardless of the timing of the storms relative to tidal phase. These results suggest that waves might be responsible for any influx of volume to the bay during high bay-side surge events. Wave blocking effects were found to be stronger along the ebb shoal and only reached the flood delta when bay water levels were nearly the same as the ocean water levels. Reduction of currents by waves in the inlet have the potential to extend the duration of the inundation period in the back barrier region. Bay-side storms also caused flux enhancement over inlet shoals and channels in the flood delta, which could have implications for circulation patterns as well as the morphodynamics of the system.

Machine learning-based assessment of storm surge in the New York metropolitan area

Storm surge generated from low-probability high-consequence tropical cyclones is a major flood hazard to the New York metropolitan area and its assessment requires a large number of storm scenarios. High-fidelity hydrodynamic numerical simulations can predict surge levels from storm scenarios. However, an accurate prediction requires a relatively fine computational grid, which is computationally expensive, especially when including wave effects. Towards alleviating the computational burden, Machine Learning models are developed to determine long-term average recurrence of flood levels induced by tropical cyclones in the New York metropolitan area. The models are trained and verified using a data set generated from physics-based hydrodynamic simulations to predict peak storm surge height, defined as the maximum induced water level due to wind stresses on the water surface and wave setup, at four coastal sites. In the generated data set, the number of low probability high-level storm surges was much smaller than the number of high probability low-level storm surges. This resulted in an imbalanced data set, a challenge that is addressed and resolved in this study. The results show that return period curves generated based on storm surge predictions from machine learning models are in good agreement with curves generated from high-fidelity hydrodynamic simulations, with the advantage that the machine learning model results are obtained in a fraction of the computational time required to run the simulations.

Solar cycle and seasonal variability of H in the upper atmosphere of Mars

The complex seasonal, dust, and atmospheric circulation patterns at Mars affect the abundance and spatial extent of water and its photo-dissociated atomic byproduct H. Recent observations by the Mars Atmosphere and Volatile Evolution (MAVEN) mission have been used to constrain the H abundance at the exobase (200 km altitude) as well as its thermal and global escape rate. The MAVEN observations span the last seven years and encompass the effects of the declining phase of Solar Cycle 24 and activating phase of Solar Cycle 25 from mid-perihelion of Mars Year (MY) 32 through most of aphelion of MY36. Results show that there are seasonal as well as solar cycle variations in H exobase temperatures, densities, thermal escape rates, and global escape rates. During perihelion of MYs 32–35, the H exobase density fluctuates with a cadence that corresponds to the approach to perihelion as well as the onset and peak of regional dust storms. Additionally, the abundance of H atoms did not show exceptional variability during the MY34 Global Dust Storm, when compared with trends from adjacent years. Seasonal averages of H exobase densities, thermal escape rates, and global escape rates indicated an order of magnitude difference between perihelion and aphelion, and suggest a water global equivalent layer range of 0.02–13 m lost over 4 billon years. H properties are also found to vary with solar cycle to a lesser extent than with seasons across the MAVEN timeline. Trends show that the exobase density varies inversely with solar activity, while both the thermal and global escape rates vary directly with solar activity. It remains to be seen if the effects of a stronger than recent solar cycle on the upper atmospheric H properties would become more pronounced.

Compound Flooding in a Subtropical Estuary Caused by Hurricane Irma 2017

AbstractHurricane Irma affected the Florida peninsula from 10 to 12 September 2017. The peninsula's east coast was hit hardest: the city of Jacksonville flooded around the St. Johns River estuary with storm surge that exceeded 1.5 m and rainfall that surpassed 20 cm in 24 hr. This study used observations such as water and wind velocities, river discharge, and salinity data to determine whether compounding forcings influenced flood levels. Results show that flooding was initiated by the ocean (first punch) and then exacerbated by river discharge (second punch). The 1–2 punch from the ocean and then the river caused record flooding, with impacts lasting through September. Peak storm surge occurred while winds were receding, and river discharge was increasing, rather than at the peak of either process. Compound flood models should consider the phase lag between driving processes, as the individual maxima may not occur simultaneously, yet still exacerbate flooding.