Celso M. Ferreira

Uncertainty in hurricane surge simulation due to land cover specification

Hurricane storm surge is one of the most costly natural hazards in the United States. Numerical modeling to predict and estimate hurricane surge flooding is currently widely used for research, planning, decision making, and emergency response. Land cover plays an important role in hurricane surge numerical modeling because of its impacts on the forcing (changes in wind momentum transfer to water column) and dissipation (bottom friction) mechanisms of storm surge. In this study, the hydrodynamic model ADCIRC was used to investigate predicted surge response in bays on the central and lower Texas coast using different land cover data sets: (1) Coastal Change Analysis Program for 1996, 2001, and 2006; (2) the National Land Cover Dataset for 1992, 2001, and 2006; and (3) the National Wetlands Inventory for 1993. Hypothetical storms were simulated with varying the storm track, forward speed, central pressure, and radius to maximum wind, totaling 140 simulations. Data set choice impacts the mean of maximum surges throughout the study area, and variability in the surge prediction due to land cover data set choice strongly depends on storm characteristics and geographical location of the bay in relation to storm track. Errors in surge estimation due to land cover choice are approximately 7% of the surge value, with change in surge prediction varying by as much as 1 m, depending on location and storm condition. Finally, the impact of land cover choice on the accuracy of simulating surges for Hurricane Bret in 1999 is evaluated.

Using Non-authoritative Sources During Emergencies in Urban Areas

During emergencies in urban areas, it is paramount to assess damage to people, property, and environment in order to coordinate relief operations and evacuations. Remote sensing has become the de facto standard for observing the Earth and its environment through the use of air-, space-, and ground-based sensors. These sensors collect massive amounts of dynamic and geographically distributed spatiotemporal data daily and are often used for disaster assessment, relief, and mitigation. However, despite the quantity of big data available, gaps are often present due to the specific limitations of the instruments or their carrier platforms. This chapter presents a novel approach to filling these gaps by using non-authoritative data including social media, news, tweets, and mobile phone data. Specifically, two applications are presented for transportation infrastructure assessment and emergency evacuation.

Quantification of the Attenuation of Storm Surge Components by a Coastal Wetland of the US Mid Atlantic

Coastal wetlands are receiving increased consideration as natural defenses for coastal communities from storm surge. However, there are gaps in storm surge measurements collected in marsh areas during extreme events as well as understanding of storm surge processes. The present study evaluates the importance and variation of different processes (i.e., wave, current, and water level dynamics with respect of the marsh topography and vegetation characteristics) involved in a storm surge over a marsh, assesses how these processes contribute to storm surge attenuation, and quantifies the storm surge attenuation in field conditions. During the Fall of 2015, morphology and vegetation surveys were conducted along a marsh transect in a coastal marsh located at the mouth of the Chesapeake Bay, mainly composed of Spartina alterniflora and Spartina patens. Hydrodynamic surveys were conducted during two storm events. Collected data included wave characteristics, current velocity and direction, and water levels. Data analysis focused on the understanding of the cross-shore evolution of waves, currents and water level, and their influence on the overall storm surge attenuation. Results indicate that the marsh area, despite its short length, attenuates waves and reduces current velocity and water level. Tides have a dominant influence on current direction and velocity, but the presence of vegetation and the marsh morphology contribute to a strong reduction of current velocity over the marsh platform relative to the currents at the marsh front. Wave attenuation varies across the tide cycle which implies a link between wave attenuation and water level and, consequently, storm surge height. Storm surge reduction, here assessed through high water level (HWL) attenuation, is linked to wave attenuation across the front edge of the marsh; this positive trend highlights the reduction of water level height induced by wave setup reduction during wave propagation across the marsh front edge. Water level attenuation rates observed here have a greater range than the rates observed or modeled by other authors, and our results suggest that this is linked to the strong influence of waves in storm surge attenuation over coastal areas.

Hurricane Isaac: A Longitudinal Analysis of Storm Characteristics and Power Outage Risk.

In August 2012, Hurricane Isaac, a Category 1 hurricane at landfall, caused extensive power outages in Louisiana. The storm brought high winds, storm surge, and flooding to Louisiana, and power outages were widespread and prolonged. Hourly power outage data for the state of Louisiana were collected during the storm and analyzed. This analysis included correlation of hourly power outage figures by zip code with storm conditions including wind, rainfall, and storm surge using a nonparametric ensemble data mining approach. Results were analyzed to understand how correlation of power outages with storm conditions differed geographically within the state. This analysis provided insight on how rainfall and storm surge, along with wind, contribute to power outages in hurricanes. By conducting a longitudinal study of outages at the zip code level, we were able to gain insight into the causal drivers of power outages during hurricanes. Our analysis showed that the statistical importance of storm characteristic covariates to power outages varies geographically. For Hurricane Isaac, wind speed, precipitation, and previous outages generally had high importance, whereas storm surge had lower importance, even in zip codes that experienced significant surge. The results of this analysis can inform the development of power outage forecasting models, which often focus strictly on wind-related covariates. Our study of Hurricane Isaac indicates that inclusion of other covariates, particularly precipitation, may improve model accuracy and robustness across a range of storm conditions and geography.

Discriminatory Power of Heterogeneity Statistics with Respect to Error of Precipitation Quantile Estimation

AbstractAt low sample size, sampling error may be reduced by pooling multiple gauge records. This creates an error component due to heterogeneity, the degree to which the pooled regional data’s quantile estimates are different from the true at-site quantiles. Heterogeneity statistics attempt to quantify the degree to which error is added due to regional heterogeneity. They are justified through elucidation of a so-called reasonable proxy relationship with error caused by heterogeneity and through the ability of heterogeneity thresholds to detect heterogeneous regions. In this paper, previous findings regarding three heterogeneity statistics H1–H3 are revisited; a previous finding that H1 is superior to H2 and H3 is amended based on simulation experiments and upon enumeration of all possible regionalizations of a small gauge dataset across time scales from daily to monthly. Thresholds defined based on H1 are shown to be 4× too high for application to H2 and new thresholds are derived for H2. Two nonparamet...

Simulation of cyclone induced storm surges in the low lying delta of Bangladesh using coupled hydrodynamic and wave model (SWAN+ADCIRC)

Bangladesh is vulnerable to several natural disasters and cyclone-generated storm surges have resulted in the deaths of over 700 000 people since 1960. Advancing our capability to model and simulate storm surges using numerical models is utmost important to support early warning and emergency response efforts in the region. This study primarily explored the effectiveness of a hydrodynamic model (ADvanced CIRCulation, ADCIRC) coupled with wave model (Simulating WAves Nearshore, SWAN) under a high-performance computing environment to simulate storm surge and inundation in coastal regions of Bangladesh. The modelling framework was validated using data from freely available historical reports and buoy data. The model-generated storm surge water level shows good agreement with the observations with maximum R value of 0.98 and root mean square error of 0.30 m. Ultimately, research findings have highlighted the importance of the coupled wave and hydrodynamic modelling to calculate storm surges in a region with poor observational coverage.

Impact of Forecasted Land Use Change on Design Peak Discharge at Watershed and Catchment Scales: Simple Equation to Predict Changes

AbstractCommon engineering methods for the computation of peak discharge are generally based on the assumption of a stationary watershed. This assumption can potentially lead to inaccurate estimates of peak discharge when considering the lifetime of engineering structures. Future land use change is one of the possible causes of non-stationarity in watershed runoff. This study focuses on a method to integrate the readily available integrated climate and land use scenarios (ICLUS) data sets from the environmental protection agency (EPA), with geographic information system (GIS) and hydrologic modeling. This framework is applied to evaluate the impact of the forecasted land use change on the design peak discharge in the rapidly urbanizing region in Northern Virginia (US) at the watershed (Anderson formula) and catchment [calibrated storm water management model (SWMM)] scales. The results show that the impervious area in the rapidly urbanizing Difficult Run watershed is expected to increase by 99.1% (2070) fr...

Wetlands as a nature-based coastal defense: a numerical modeling and field data integration approach to quantify storm surge attenuation for the Mid-Atlantic region

This paper has presented the objectives and broad methodology of a 2-year effort to combine field work and numerical modeling to quantify storm surge attenuation of wetlands in the Chesapeake Bay region. The criteria considered in selection of marsh sites included exposure to storm surge based on simulations of historical storms, presence of salt marsh vegetation, topography and internal marsh geometry. Three sites have been identified and have been instrumented with pressure transducers deployed over transects of 200 to 750 m. Future work will present methods used in calibrating ADCIRC-SWAN using the water level data collected and processed from these sites, as well current profiler data. Of particular consideration in numerical modeling efforts for this study is that of computation cost. A high-resolution computational mesh (~10m) is necessary to capture the complex hydrodynamics in marsh systems. Furthermore the computational domain must stretch far enough from the coastline to adequately capture the propagation of waves and storm surge. The mesh employed here is a 1.2 million node mesh extending to the 15m inland contour, and to the 60 degree Prime Meridian in the Atlantic Ocean. To reduce computational costs significantly, while allowing for adjustments to the mesh, and to the vegetation parameterization at the chosen marsh sites, a subdomain modeling method developed by Baugh et al. will be utilized [20].

Evaluation of weather forecast systems for storm surge modeling in the Chesapeake Bay

Accurate forecast of sea-level heights in coastal areas depends, among other factors, upon a reliable coupling of a meteorological forecast system to a hydrodynamic and wave system. This study evaluates the predictive skills of the coupled circulation and wind-wave model system (ADCIRC+SWAN) for simulating storm tides in the Chesapeake Bay, forced by six different products: (1) Global Forecast System (GFS), (2) Climate Forecast System (CFS) version 2, (3) North American Mesoscale Forecast System (NAM), (4) Rapid Refresh (RAP), (5) European Center for Medium-Range Weather Forecasts (ECMWF), and (6) the Atlantic hurricane database (HURDAT2). This evaluation is based on the hindcasting of four events: Irene (2011), Sandy (2012), Joaquin (2015), and Jonas (2016). By comparing the simulated water levels to observations at 13 monitoring stations, we have found that the ADCIR+SWAN System forced by the following: (1) the HURDAT2-based system exhibited the weakest statistical skills owing to a noteworthy overprediction of the simulated wind speed; (2) the ECMWF, RAP, and NAM products captured the moment of the peak and moderately its magnitude during all storms, with a correlation coefficient ranging between 0.98 and 0.77; (3) the CFS system exhibited the worst averaged root-mean-square difference (excepting HURDAT2); (4) the GFS system (the lowest horizontal resolution product tested) resulted in a clear underprediction of the maximum water elevation. Overall, the simulations forced by NAM and ECMWF systems induced the most accurate results best accuracy to support water level forecasting in the Chesapeake Bay during both tropical and extra-tropical storms.

The relationship between Monte Carlo estimators of heterogeneity and error for daily to monthly time steps in a small Minnesota precipitation gauge network

Precipitation quantile estimates are used in engineering, agriculture, and a variety of other disciplines. Index flood regional frequency methods pool normalized gauge data in the case of homogeneity among the constituent gauges of the region. Unitless regional quantile estimates are outputted and rescaled at each gauge. Because violation of the homogeneity hypothesis is a major component of quantile estimation error in regional frequency analysis, heterogeneity estimators should be “reasonable proxies” of the error of quantile estimation. In this study, three Monte Carlo heterogeneity statistics tested in Hosking and Wallis (1997) are plotted against Monte Carlo estimates of quantile error for all five-or-more-gauge regionalizations in a 12 gauge network in the Twin Cities region of Minnesota. Upper-tail quantiles with nonexceedance probabilities of 0.75 and above are examined at time steps ranging from daily to monthly. A linear relationship between heterogeneity and error estimates is found and quantified using Pearsons r score. Two of Hosking and Walliss (1997) heterogeneity measures, incorporating the coefficient of variation in one case and additionally the skewness in the other, are found to be reasonable proxies for quantile error at the L-moment ratio values characterizing these data. This result, in addition to confirming the utility of a commonly used coefficient of variation-based heterogeneity statistic, provides evidence for the utility of a heterogeneity measure that incorporates skewness information.