Observation and modeling of Hurricane Maria for damage assessment

Pokhrel, Rabindra; Cos, Salvador del; Montoya-Rincon, Juan P.; Glenn, Equisha; González, Jorge E.

doi:10.1016/j.wace.2021.100331

Cited by 11 publications

(9 citation statements)

References 39 publications

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“…The maximum sustained wind speed was 69.3 m s −1 (249.5 km h −1 ) and the heavy precipitation produced serious flooding and mudslides. One of the locations in PR had a storm total of almost 38 inches (965 mm) [61]. The neutral El Niño Southern Oscillation (ENSO) and unusually high sea surface temperature (SSTs) in 2017 [62] contributed to the strength of Hurricane María.…”

Section: Hurricane Maríamentioning

confidence: 99%

“…During Hurricane María, the WSR-88D radar located in PR was damaged due to extreme winds [60]; thus, level 3 NEXRAD data were unavailable. Therefore, the rainfall was defined using data from a concurrent study [61]; a Weather Research and Forecasting Model (WRF) was used to reproduce atmospheric variables during Hurricane María, such as precipitation and wind speed. The WRF precipitation output has a 1 km spatial and 1 hr temporal resolution in this study, covering the period 09/19/2017, 12 pm to 09/21/2017, 11 am, a total of 48 h. The criteria used for choosing 48 h was to be consistent with the same number of hours used in the calibration and validation process.…”

Section: Rainfall and Inflow Hydrograph Definition During Hurricane Maríamentioning

confidence: 99%

“…Consequently, it was necessary to use a WRF model rainfall dataset. In general, the WRF model of [61] did a good job of simulating rainfall over PR, with a Normalized Root Mean Square Error (RMSE) of 0.2. However, we observed inconsistencies in the total rainfall depths within our study area compared with the NWS interpolated rain gauge data.…”

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confidence: 99%

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Flood Impacts on Critical Infrastructure in a Coastal Floodplain in Western Puerto Rico during Hurricane María

et al. 2021

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Flooding during extreme weather events damages critical infrastructure, property, and threatens lives. Hurricane María devastated Puerto Rico (PR) on 20 September 2017. Sixty-four deaths were directly attributable to the flooding. This paper describes the development of a hydrologic model using the Gridded Surface Subsurface Hydrologic Analysis (GSSHA), capable of simulating flood depth and extent for the Añasco coastal flood plain in Western PR. The purpose of the study was to develop a numerical model to simulate flooding from extreme weather events and to evaluate the impacts on critical infrastructure and communities; Hurricane María is used as a case study. GSSHA was calibrated for Irma, a Category 3 hurricane, which struck the northeastern corner of the island on 7 September 2017, two weeks before Hurricane María. The upper Añasco watershed was calibrated using United States Geological Survey (USGS) stream discharge data. The model was validated using a storm of similar magnitude on 11–13 December 2007. Owing to the damage sustained by PR’s WSR-88D weather radar during Hurricane María, rainfall was estimated in this study using the Weather Research Forecast (WRF) model. Flooding in the coastal floodplain during Hurricane María was simulated using three methods: (1) Use of observed discharge hydrograph from the upper watershed as an inflow boundary condition for the coastal floodplain area, along with the WRF rainfall in the coastal flood plain; (2) Use of WRF rainfall to simulate runoff in the upper watershed and coastal flood plain; and (3) Similar to approach (2), except the use of bias-corrected WRF rainfall. Flooding results were compared with forty-two values of flood depth obtained during face-to-face interviews with residents of the affected communities. Impacts on critical infrastructure (water, electric, and public schools) were evaluated, assuming any structure exposed to 20 cm or more of flooding would sustain damage. Calibration equations were also used to improve flood depth estimates. Our model included the influence of storm surge, which we found to have a minimal effect on flood depths within the study area. Water infrastructure was more severely impacted by flooding than electrical infrastructure. From these findings, we conclude that the model developed in this study can be used with sufficient accuracy to identify infrastructure affected by future flooding events.

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Section: Hurricane Maríamentioning

confidence: 99%

Section: Rainfall and Inflow Hydrograph Definition During Hurricane Maríamentioning

confidence: 99%

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Flood Impacts on Critical Infrastructure in a Coastal Floodplain in Western Puerto Rico during Hurricane María

et al. 2021

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“…A good example for such a disaster and the long-lived negative effects related to slow recovery is the devastation of Puerto Rico by category 5 Hurricane Maria in September 2017 (e.g. Pasch et al, 2019;Pokherl et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Rising sea level and increasing tropical cyclone frequency are threatening the population of San Andrés Island, Colombia, western Caribbean

Bernet¹,

Acosta²

2022

BSGF - Earth Sci. Bull.

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The Colombian island of San Andrés is a popular tourist destination located about 195 km offshore of the east coast of Nicaragua in the southwestern Caribbean Sea. Together with Providencia and Santa Catalina, San Andrés is part of the UNESCO Seaflower Biosphere Reserve. With a 26 km2 surface area and 78,000 inhabitants, San Andrés is one of the most densely populated islands in the Caribbean with on average ~3000 inhabitants/km2. The majority of the population and the mass tourism are concentrated in the low-elevation (0.5 - 6 m) areas, particularly in the north and along the east coast of the island. These areas are prone to flooding during storm events such as hurricane Eta and Iota in 2020. A review of the geological, environmental and the socio-economic situation of the island, and the record of tropical cyclones since 1911, shows why the local population has become increasingly vulnerable to storm events and rising sea level. Tropical cyclones may form locally in the southwestern Caribbean or originate in the eastern Caribbean/Atlantic Ocean. The latter tend to be stronger and cause more damage when they reach San Andrés. The HURDAT2 dataset shows that the frequency of storm events affecting San Andrés has increased in recent decades, with six storms over the past 20 years, including three category 4-5 hurricanes since 2007. Increasing storm frequency and intensity may be linked to increasing sea surface temperatures caused by global warming. The growing population density since the 1950s, has augmented the potential for disaster.

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“…It is widely used in the research of forecasting wind speed, temperature, humidity and pressure [13]. Pokhrel Rabindra et al [14] used WRF to simulate the changes of wind speed, rainfall and other meteorological data caused by Hurricane Maria. This study filled the gap of the hydro-meteorological processes due to the limited observational data.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning-Based Method for Wind Fields Forecasting Utilizing GNSS Radio Occultation Data

et al. 2022

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With the development of computer technology and expanding environmental issues, machine learning has received more and more attention in the field of weather forecasting. Global Navigation Satellite System-Radio Occultation(GNSS-RO) technology is a kind of remote sensing technology. This investigation proposes an alternative to numerical weather forecasting model. The new method is based on machine learning utilizing GNSS-RO data to forecast the wind field in the Beijing-Tianjin-Hebei region of China. The dataset including temperature, humidity， pressure， wind speed and direction was obtained by numerical calculation in terms of historical monitoring data in Beijing-Tianjin-Hebei region. Then the models of wind fields forecasting based on machine learning were established with different neural network including Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN) and Deep Neural Networks (DNN). The prediction performance of different models was analyzed. The results demonstrate that LSTM and CNN have better performance on predicting the wind field than Deep Neural Networks. The wind speed error is about 1.4m/s, and the wind direction error is about 30°. Moreover, the time required for neural network to predict a new sample is about 1 second, which is only 0.2% of the prediction time compared with numerical model. Finally, the machine learning model can be used to predict the wind field effectively, with GNSS-RO data as the input in application. This paper pro-vides a new method in sight to use machine learning to forecast the regional wind field utilizing GNSS-RO data. INDEX TERMSWind fields forecasting; machine learning; GNSS-RO; Long Short-Term Memory (LSTM); Convolutional Neural Networks (CNN)

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Observation and modeling of Hurricane Maria for damage assessment

Cited by 11 publications

References 39 publications

Flood Impacts on Critical Infrastructure in a Coastal Floodplain in Western Puerto Rico during Hurricane María

Flood Impacts on Critical Infrastructure in a Coastal Floodplain in Western Puerto Rico during Hurricane María

Rising sea level and increasing tropical cyclone frequency are threatening the population of San Andrés Island, Colombia, western Caribbean

A Machine Learning-Based Method for Wind Fields Forecasting Utilizing GNSS Radio Occultation Data

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