Flood Impacts on Critical Infrastructure in a Coastal Floodplain in Western Puerto Rico during Hurricane María
1.2. Flood Prediction Modelling
1.3. Previous Hydrologic Studies Conducted in Puerto Rico
1.4. Study Objective
2. Case Study Description
2.1. Hurricane María
2.2. Study Area
Land Use Land Cover and Soil Distribution
3.1. Hydrologic Model
- Delineation of the watershed area above the USGS stream discharge station (Sub-watershed 2): A Digital Elevation Model (DEM) of 10 m resolution was required to delineate the watershed and obtain the flow path accumulation. Subsequently, an outlet point was placed in the exact location of the USGS stream gauge (Latitude 18°17′03.00″, Longitude 67°03′03.46″ NAD27) to obtain the delineated watershed above the USGS station.
- Generation of the stream channel cross-section: The 3DEP Lidar Explorer tool was utilized from the USGS website to download the Light Detection and Ranging (LiDAR) data for the area of interest. The data acquired was from 2015 with a horizontal resolution of 100 cm and a vertical resolution of 10 cm. Subsequently, the data were processed using QGIS, and the cross-sections were created using QGIS and a python script. It should be mentioned that Lake Guayo collects water that is diverted to southwest PR through tunnels; however, water is released into the watershed when there are events with high rainfall intensity and duration, such as hurricanes or tropical storms. Thus, a downstream cross-section channel was used as the cross-section of the lake; this hypothesis assumes that the precipitation that falls into the lake area will be counted as a volume in the outlet point previously defined. Additionally, the smooth stream option was used to prevent adverse slopes.
- Grid Selection: The grid spacing is essential to define the model’s resolution; small grid cells lead to a high-resolution model, which requires high computational resources and increases computational time. A 100 m grid spacing was chosen, based on watershed size and the time required to run the simulation.
- Land Use and Soil Data: The soil type and land use maps were added to the model using the datasets previously described. It should be noted from Table A1 and Table A2 that the land and soil categories for Sub-watershed 1 (Figure 2) and Sub-watershed 2 (Figure 2) were predominantly Mixed Forest and clay, respectively. The percentages were calculated using a python script. On the other hand, the initial values of the roughness for each land class were assumed using the values in Table A1 . In addition, Table A3 shows the values of the soil parameters required to use the Green Ampt method, such as hydraulic conductivity, capillary head, and porosity, among others .
- Soil Moisture Saturation: The soil moisture saturation product from GOES-PRWEB (pragwater.com, ) was employed to estimate the average initial soil moisture for the study area, which was determined to be 98.9%. Therefore, initial soil moisture saturation was assumed to be 98.9% of the soils’ porosity values.
- Precipitation Data: Hurricane Irma precipitation data was chosen to calibrate the model, representing an extreme weather event for PR. Therefore, NTP (Storm Total Precipitation) Level 3 product from NEXRAD was used to obtain the rainfall distribution from 6 September at 12:00 to 8 September at 11:00 of 2017 (AST). According to NOAA, the NTP product, which uses the Precipitation Processing System (PPS) algorithm, is useful for locating flood potential over urban or rural areas and estimating the total basin runoff . In addition, a python code was implemented to convert the cumulative rainfall into hourly rainfall so that GSSHA can easily read it.
- Calibration process: The Secant Levenberg–Marquardt (SLM) method is an efficient enhancement to the Levenberg–Marquardt (LM) method . The Multilevel Single Linkage (MLSL) method was created to reduce the probability of not finding a local minimum. The SLM and the MLSL methods are implemented in GSSHA for conducting model calibrations. These two methods use the observed and the simulated hydrograph to find the objective function minimum, varying the parameters to be calibrated, where the objective function consists of the sum of weighted squared differences between modeled and transformed flow values, with all weights assigned a value of 1 . The Box–Cox transformation with λ = 0.3 [74,75] was employed to transform the observed and modeled flows. Additionally, a time step of 10 s was used for the computational process, and overbank flow was allowed. The following parameters were selected to calibrate the model:
- Roughness parameter for land use land cover: The Mixed Forest class was selected because, as shown in Table A1, this class is the most representative of the land classes for Sub-watershed 1 and Sub-watershed 2 of the Añasco watershed. To define the ranges, it was assumed a range of values between 0.1 and 0.16 which corresponds to areas dominated by trees generally greater than 5 m tall and greater than 20% of total vegetation cover. Neither deciduous nor evergreen species are greater than 75 percent of total tree cover . It should be mentioned that the urban and suburban drainage systems were not explicitly modelled in this study. Overland runoff in these areas was modelled using roughness values associated with high and medium developed land use land covers, respectively.
- Soil parameters: The hydraulic conductivity was identified in previous studies  as one of the most sensitive soil parameters. Therefore, it was decided to calibrate the hydraulic conductivity for the most representative soil type (clay) in the watershed.
- Streambed roughness values: Two-channel bed roughness values were proposed for the Añasco watershed; channel roughness 1 corresponds to the streams of the basin and subbasins surrounding the mainstream. Channel 1 values were assumed to be between 0.02 and 0.05, based on an initial value used in previous studies . On the other hand, channel roughness 2 was proposed for the upper area of Sub-watershed 2 with values ranging between 0.051 and 0.075, because this area is composed of shallow water with some gravel and rock and irregular cross-sectional channels, which generate significant friction .
3.1.3. Performance Evaluation Criteria
- The coefficient of determination (R2) indicates the degree of correlation between the simulated and the observed data, describing the portion of the variance in the observed data explained by the simulation; the ranges are between 0 and 1, where 1 indicates less error in variance .
- The Nash–Sutcliffe efficiency coefficient (NSE) measures the perfect match between the predicted values and the observed values; the ranges are between −∞ to 1, where a value of 1 means a perfect match and values less than zero indicate unacceptable performance .
- Percent Bias (PBIAS) measures the average tendency of the simulated data to be larger or smaller than their observed counterparts, with the optimal value being 0; negative values indicate overestimation bias and positive values underestimation bias .
3.2. Flooding Event
- Delineation of the watershed area below the USGS stream discharge station (Sub-watershed 1): Using the same methodology as for Sub-watershed 2, a DEM of 10 m resolution was utilized to delineate the area below the USGS stream discharge station (Sub-watershed 1 of Figure 2). During this process, an outlet point was placed at the end of the Añasco watershed in the coastal zone to obtain a first approximation of the delineated area. Subsequently, the Arc tool from GSSHA was used to extend the delineated area, ensuring the total cover of Sub-watershed 1. This process is required for watershed delineation when the results from the automated delineation option from GSSHA do not match the actual delineated area.
- The same procedure as described for Sub-watershed 2 was used to generate the cross-sections of the stream channels for Sub-watershed 1 and define land use, soil type, and grid selection. It should be mentioned that the best-calibrated parameters obtained from Sub-watershed 2 were also used for Sub-watershed 1.
- Definition of the inflow hydrograph and rainfall during Hurricane María: An inflow node was placed at the location of the USGS stream gauge in Sub-watershed 1 to simulate the water coming from Sub-watershed 2. Additionally, the hourly time series of the WRF simulated rainfall from Hurricane María was used as the precipitation data.
- Definition of the storm surge: In the grid cells located along the coast, a variable stage (water surface elevation) boundary condition was created to simulate the storm surge during Hurricane María. For the simulations without using storm surge, a constant stage boundary condition was used.
3.2.1. Rainfall and Inflow Hydrograph Definition during Hurricane María
- During Hurricane María, the USGS stream discharge gauge stopped working when it reported a maximum discharge measurement of almost 5200 m3 s−1, assumed to be the peak discharge for the whole event. Consequently, there is a gap of nearly 24 h where there is no data available, and after that, the USGS reported an estimated discharge. A simple linear regression was employed to interpolate the data to fill the gap and obtain time series values every 15 min. Figure 8a shows the final built hydrograph superimposed on the USGS discharge data. The time series, limited to 48 h (Figure 8b), was used to simulate the inflow boundary condition at the upstream location in Sub-watershed 1 for two scenarios (defined below).
- The calibrated parameters obtained in the calibration process were used for Hurricane María to obtain the output hydrograph for Sub-watershed 2 using the WRF rainfall and the corrected rainfall separately. Following this, each output hydrograph obtained was used as an inflow hydrograph at the upstream location for Sub-watershed 1 to reproduce the flooding event during Hurricane María.
3.2.2. Storm Surge
3.2.3. Hydrologic Modelling Approaches (HMAs)
3.2.4. Flood Depth Validation
3.2.5. Methodology for Assessing Impacts to Critical Infrastructure
4.1. Calibration Results
4.2. Validation Results
4.3. Flooding Results
4.3.1. Flood Validation Using Survey Data
4.3.2. Extent of Flooded Area
4.4. Critical Infrastructure
- The rating curve used by the USGS to convert river stage to discharge may not have been accurate during the Hurricane Irma event.
- Although an effort was made to accurately account for the water released from the lakes in the upper Añasco watershed, the PR Energy and Power Authority (PREPA), the agency that manages the lakes, could not provide any information on discharges from the lakes.
- NEXRAD radar rainfall data may also introduce errors into the hydrologic simulation. Rojas Gonzalez  reported a simulation of streamflow for the Añasco River in which NEXRAD significantly overestimated the rainfall. A known problem associated with radar rainfall in Western PR is related to the radar’s inability to see low clouds (less than 3000 m) at large distances from the radar facility due to the earth’s curvature . This problem would be expected to produce underestimates in rainfall. However, one of the authors of this paper has considerable experience using radar rainfall in Western PR and has observed both under and overestimation compared with NWS rain gauges.
5.1. Model Limitations
- The model used in this study was developed for large storm events proceeded by saturated soil conditions. Consequently, the model has not been calibrated for conditions when soil saturation is low and infiltration becomes a significant factor. Future efforts should consider calibrating the average matric suction at the wetting front during periods of low soil saturation.
- The hydrologic model of Sub-watershed 1 was not calibrated in this study. However, the depths and extent of the flood were compared with data obtained from a homeowner survey. Although the analysis did not yield a high correlation between the observed and simulated flood depths, we applied the resulting regression equations, which significantly improved the performance of the flood depth estimation. The flood depths obtained from the face-to-face interviews were limited to flood plain areas where people were present during Hurricane María.
- The model was based on a simplified conceptualization of the land surface parameters. Single values of roughness and hydraulic conductivity were used for the dominant land use/soil in Sub-watershed 2. More accurate distribution of these parameters in the watershed may help to improve the model calibrations.
5.2. Future Work
- Couple the flood model with a WRF rainfall forecast model to obtain 2- to 3-day advanced flooding warnings.
- Couple the flood modelling results with a Power/Water network model.
- Expand the model domain to include the Culebrinas, Yagüez, and Guanajibo watersheds. This will expand the area of coverage from 350 km2 to over 900 km2.
- Continue to work with the water and electric authorities to refine our approach for identifying impacted infrastructure.
- Use the model to investigate ways to mitigate flooding in Sub-watershed 1. For example, evaluate changes in agricultural land-use practices or incorporate natural infrastructure.
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
|Land Class||Sub-Watershed 1 (%)||Sub-Watershed 2 (%)||Manning’s Values|
|Low Intensity Developed||4.7||8.4||0.05|
|Palustrine Forested Wetland||3.0||0.5||0.1|
|Medium Intensity Developed||1.0||4.6||0.1|
|High Intensity Developed||0.3||2.8||0.13|
|Developed Open Space||0.1||1.0||0.02|
|Palustrine Scrub/Shrub Wetland||0.0||0.4||0.048|
|Palustrine Emergent Wetland||0.0||3.0||0.045|
|Estuarine Forested Wetland||0.0||0.2||0.1|
|Estuarine Scrub/Shrub Wetland||0.0||0.0||0.048|
|Estuarine Emergent Wetland||0.0||0.1||0.045|
|Soil Class||Sub-Watershed 1 (%)||Sub-Watershed 2 (%)|
|silt clay loam||1.6||10.8|
|Infiltration Variables||Clay Loam||Loamy Sand||Clay||Silt Clay Loam||Sandy Loam||Loam||Silty Clay||Silty Loam||Sand|
|Hydraulic conductivity (cm/h)||0.2||5.98||0.06||0.2||2.18||1.32||0.1||0.68||23.6|
|Capillary head (cm)||20.88||6.13||31.6||27.3||11.01||8.89||29.22||16.68||4.95|
|Pore Distribution Index (cm/cm)||0.242||0.553||0.17||0.177||0.378||0.25||0.15||0.234||0.69|
|Residual Saturation (m3/m3)||0.075||0.035||0.09||0.04||0.041||0.03||0.056||0.015||0.02|
|Field Capacity (m3/m3)||0.318||0.125||0.4||0.366||0.207||0.27||0.387||0.33||0.09|
|Wilting Point (m3/m3)||0.197||0.055||0.27||0.208||0.095||0.12||0.25||0.133||0.03|
|0||18.24994||−67.1605||1.22||Shell Station on Road 2|
|1||18.25932||−67.1604||1.525||West Power Company Road 2|
|2||18.26726||−67.1576||1.525||First Medical Cannabis|
|3||18.27699||−67.1633||1.525||Grooming Machine near Road 2|
|6||18.27804||−67.1883||0.915||In street at HP beach home|
|7||18.27354||−67.1874||0.915||Close to small waterway north of Añasco River|
|11||18.28054||−67.1365||1.0675||Auto Supply Store|
|13||18.27883||−67.1305||1.22||Back yard of home next to hardware store|
|14||18.2871||−67.1308||0||Urbanization on north side of Añasco|
|15||18.28142||−67.1407||1.6775||Daycare Center behind Municipal Building|
|19||18.2981||−67.1513||0||Pharmacy Road 402|
|23||18.30017||−67.1585||0.61||Oil Change Garage|
|25||18.29912||−67.1665||0.61||Road 402 in front of Coffee Shop|
|26||18.26535||−67.1835||1.22||Urbanization close to Añasco River|
|27||18.26223||−67.1811||1.22||Urbanization close to Añasco River|
|28||18.25013||−67.1758||1.22||Grocery Store on Road 341|
|29||18.256||−67.1612||3.355||Mazda Dealership on Road 2|
|30||18.24323||−67.1602||0.07625||Church’s Chicken on Road 2|
|31||18.2995||−67.1711||0.27755||House on Hwy 115|
- Tanaka, K.; Fujihara, Y.; Hoshikawa, K.; Fujii, H. Development of a flood water level estimation method using satellite images and a digital elevation model for the Mekong floodplain. Hydrol. Sci. J. 2019, 64, 241–253. [Google Scholar] [CrossRef][Green Version]
- Harmsen, E.W.; Goyal, M.R. Flood Assessment: Modeling and Parameterization; Apple Academic Press: Cambridge, MA, USA, 2018. [Google Scholar]
- Wu, H.; Adler, R.F.; Tian, Y.; Huffman, G.J.; Li, H.; Wang, J. Real-time global flood estimation using satellite-based precipitation and a coupled land surface and routing model. Water Resour. Res. 2014, 50, 2693–2717. [Google Scholar] [CrossRef][Green Version]
- Ramos-Scharrón, C.; Garnett, C.; Arima, E. A Catalogue of Tropical Cyclone Induced Instantaneous Peak Flows Recorded in Puerto Rico and a Comparison with the World’s Maxima. Hydrology 2021, 8, 84. [Google Scholar] [CrossRef]
- Nicholls, R.; Zanuttigh, B.; Vanderlinden, J.P.; Weisse, R.; Silva, R.; Hanson, S.; Narayan, S.; Hoggart, S.; Thompson, R.C.; de Vries, W.; et al. Developing a Holistic Approach to Assessing and Managing Coastal Flood Risk. In Coastal Risk Management in a Changing Climate; Elsevier: Amsterdam, The Netherlands, 2015; pp. 9–53. [Google Scholar]
- U.S. DHS Science and Technology Critical Infrastructure. DHS. Available online: https://www.dhs.gov/science-and-technology/critical-infrastructure (accessed on 27 May 2021).
- Dept. for Transportation Great Britain. Transport Resilience Review: A Review of the Resilience of the Transport Network to Extreme Weather Events; Dept. for Transportation Great Britain: London, UK, 2014. [Google Scholar]
- AECOM. Natural Infrastructure—Coastal Resources, Freshwater & Forestry Briefing Document; Resilient Puerto Rico-Advisory Commision: San Juan, Puerto Rico, 2018. [Google Scholar]
- NOAA. NOAA Digital Coast, Office of Coastal Managment, Natural Infrastructure Resources. Available online: https://coast.noaa.gov/data/digitalcoast/pdf/natural-infrastructure.pdf (accessed on 30 May 2021).
- Pant, R.; Thacker, S.; Hall, J.W.; Alderson, D.; Barr, S. Critical infrastructure impact assessment due to flood exposure. J. Flood Risk Manag. 2018, 11, 22–33. [Google Scholar] [CrossRef]
- Dano, U.L. Flash Flood Impact Assessment in Jeddah City: An Analytic Hierarchy Process Approach. Hydrology 2020, 7, 10. [Google Scholar] [CrossRef][Green Version]
- Lu, Q.-C.; Peng, Z.-R.; Zhang, J. Identification and Prioritization of Critical Transportation Infrastructure: Case Study of Coastal Flooding. J. Transp. Eng. 2015, 141, 04014082. [Google Scholar] [CrossRef]
- Chisolm, E.I.; Matthews, J.C. Impact of Hurricanes and Flooding on Buried Infrastructure. Leadersh. Manag. Eng. 2012, 12, 151–156. [Google Scholar] [CrossRef]
- Unterberger, C. How Flood Damages to Public Infrastructure Affect Municipal Budget Indicators. Econ. Disasters Clim. Chang. 2017, 2, 5–20. [Google Scholar] [CrossRef][Green Version]
- Murdock, H.J.; De Bruijn, K.M.; Gersonius, B. Assessment of Critical Infrastructure Resilience to Flooding Using a Response Curve Approach. Sustainability 2018, 10, 3470. [Google Scholar] [CrossRef]
- Sánchez, J.G.A.; Houmanfar, R.A.; Alavosius, M.P. A Descriptive Analysis of the Effects of Weather Disasters on Community Resilience. Behav. Soc. Issues 2019, 28, 298–315. [Google Scholar] [CrossRef]
- Pokhrel, R.; Ramírez-Beltran, N.D.; González, J.E. On the assessment of alternatives for building cooling load reductions for a tropical coastal city. Energy Build. 2019, 182, 131–143. [Google Scholar] [CrossRef]
- López, G.G. The Multiple Layers of Environmental Injustice in Contexts of (Un)natural Disasters: The Case of Puerto Rico Post-Hurricane Maria. Environ. Justice 2018, 11, 101–108. [Google Scholar] [CrossRef]
- Puerto Rico Emergency Management Bureau. Joint Operational Catastrophic Incident Plan; Puerto Rico Emergency Management Bureau: San Juan, Puerto Rico, 2019; Version 1.10; p. 244. [Google Scholar]
- Georgescu, M.; Broadbent, A.M.; Wang, M.; Krayenhoff, E.S.; Moustaoui, M. Precipitation response to climate change and urban development over the continental United States. Environ. Res. Lett. 2021, 16, 044001. [Google Scholar] [CrossRef]
- Glenn, E.; Comarazamy, D.; González, J.E.; Smith, T. Detection of recent regional sea surface temperature warming in the Caribbean and surrounding region. Geophys. Res. Lett. 2015, 42, 6785–6792. [Google Scholar] [CrossRef]
- Gould, W.A.; Diaz, E.L.; Álvarez-Berríos, N.L.; Aponte-González, F.; Archibald, W.; Bowden, J.H.; Carrubba, L.; Crespo, W.; Fain, S.J.; González, G.; et al. US Caribbean, in Impacts, Risks and Adaptation in the United States: 4th National Climate Assessment; Reidmiller, D., Avery, C.W., Easterling, D.R., Kunkel, K.E., Lewis, K.L.M., Maycock, T.K., Stewart, B.C., Eds.; US Global Change Research Program: Washington, DC, USA, 2018; Volume 2, pp. 809–871. [Google Scholar] [CrossRef]
- Jury, M. Resolution-Dependent Perspectives on Caribbean Hydro-Climate Change. Hydrology 2020, 7, 93. [Google Scholar] [CrossRef]
- Hunter, J. A simple technique for estimating an allowance for uncertain sea-level rise. Clim. Chang. 2012, 113, 239–252. [Google Scholar] [CrossRef]
- van der Sande, C.; De Jong, S.; de Roo, A. A segmentation and classification approach of IKONOS-2 imagery for land cover mapping to assist flood risk and flood damage assessment. Int. J. Appl. Earth Obs. Geoinf. 2003, 4, 217–229. [Google Scholar] [CrossRef]
- Grek, E.; Zhuravlev, S. Simulation of Rainfall-Induced Floods in Small Catchments (the Polomet’ River, North-West Russia) Using Rain Gauge and Radar Data. Hydrology 2020, 7, 92. [Google Scholar] [CrossRef]
- Moftakhari, H.R.; Salvadori, G.; AghaKouchak, A.; Sanders, B.; Matthew, R.A. Compounding effects of sea level rise and fluvial flooding. Proc. Natl. Acad. Sci. USA 2017, 114, 9785–9790. [Google Scholar] [CrossRef][Green Version]
- Silva-Araya, W.F.; Santiago-Collazo, F.L.; Gonzalez-Lopez, J.; Maldonado-Maldonado, J. Dynamic Modeling of Surface Runoff and Storm Surge during Hurricane and Tropical Storm Events. Hydrology 2018, 5, 13. [Google Scholar] [CrossRef][Green Version]
- McInnes, K.; Hubbert, G.D.; Abbs, D.J.; Oliver, S.E. A numerical modelling study of coastal flooding. Theor. Appl. Clim. 2002, 80, 217–233. [Google Scholar] [CrossRef]
- Nofal, O.M.; van de Lindt, J.W.; Do, T.Q. Multi-variate and single-variable flood fragility and loss approaches for buildings. Reliab. Eng. Syst. Saf. 2020, 202, 106971. [Google Scholar] [CrossRef]
- Santiago-Collazo, F.L.; Bilskie, M.V.; Hagen, S.C. A comprehensive review of compound inundation models in low-gradient coastal watersheds. Environ. Model. Softw. 2019, 119, 166–181. [Google Scholar] [CrossRef]
- Santiago-Collazo, F.; Silva Araya, W.; González-López, J.; Maldonado-Maldonado, J. Flooding Effects Combining Storm Surge and Surface Runoff during Hurricane Georges on the Eastern Coast of Puerto Rico. Available online: https://www.scipedia.com/public/Santiago-Collazo_et_al_2017a (accessed on 1 April 2021).
- Chen, W.-B.; Liu, W.-C. Assessment of storm surge inundation and potential hazard maps for the southern coast of Taiwan. Nat. Hazards 2016, 82, 591–616. [Google Scholar] [CrossRef]
- Bates, P.D.; Dawson, R.J.; Hall, J.W.; Horritt, M.S.; Nicholls, R.J.; Wicks, J.; Hassan, M.A.A.M. Simplified two-dimensional numerical modelling of coastal flooding and example applications. Coast. Eng. 2005, 52, 793–810. [Google Scholar] [CrossRef]
- Dietrich, J.; Zijlema, M.; Westerink, J.; Holthuijsen, L.; Dawson, C.; Luettich, R.; Jensen, R.; Smith, J.; Stelling, G.; Stone, G. Modeling hurricane waves and storm surge using integrally-coupled, scalable computations. Coast. Eng. 2011, 58, 45–65. [Google Scholar] [CrossRef]
- Bhaskaran, P.K.; Gayathri, R.; Murty, P.; Bonthu, S.; Sen, D. A numerical study of coastal inundation and its validation for Thane cyclone in the Bay of Bengal. Coast. Eng. 2014, 83, 108–118. [Google Scholar] [CrossRef]
- Graeme, D.H.; McInnes, K.L. A Storm Surge Inundation Model for Coastal Planning and Impact Studies. J. Coast. Res. 1999, 15, 168–185. [Google Scholar]
- Ikeuchi, H.; Hirabayashi, Y.; Yamazaki, D.; Muis, S.; Ward, P.J.; Winsemius, H.; Verlaan, M.; Kanae, S. Compound simulation of fluvial floods and storm surges in a global coupled river-coast flood model: Model development and its application to 2007 Cyclone Sidr in Bangladesh. J. Adv. Model. Earth Syst. 2017, 9, 1847–1862. [Google Scholar] [CrossRef]
- Nguyen, H.Q.; Degener, J.; Kappas, M. Flash Flood Prediction by Coupling KINEROS2 and HEC-RAS Models for Tropical Regions of Northern Vietnam. Hydrology 2015, 2, 242–265. [Google Scholar] [CrossRef][Green Version]
- Kornilova, E.; Krylenko, I.; Rets, E.; Motovilov, Y.; Bogachenko, E.; Krylenko, I.; Petrakov, D. Modeling of Extreme Hydrological Events in the Baksan River Basin, the Central Caucasus, Russia. Hydrology 2021, 8, 24. [Google Scholar] [CrossRef]
- Jamrussri, S.; Toda, Y. Simulating past severe flood events to evaluate the effectiveness of nonstructural flood countermeasures in the upper Chao Phraya River Basin, Thailand. J. Hydrol. Reg. Stud. 2017, 10, 82–94. [Google Scholar] [CrossRef]
- Giovanni-Prieto, M. Development of a Regional Integrated Hydrologic Model for a Tropical Watershed. Ph.D. Thesis, University of Puerto Rico, Mayaguez Campus, Mayagüez, Puerto Rico, 2007. Available online: https://hdl.handle.net/20.500.11801/1764 (accessed on 15 May 2021).
- Rojas González, A.M. Flood Prediction Limitations in Small Watersheds with Mountainous Terrain and High Rainfall Variability; University of Puerto Rico-Mayaguez Campus: Mayagüez, Puerto Rico, 2012; Available online: https://hdl.handle.net/20.500.11801/1085 (accessed on 15 May 2021).
- Torres-Molina, L.E. Flood Alert System Using Rainfall Data in the Mayagüez Bay Basin, Western Puerto Rico. Ph.D. Theses, University of Puerto Rico, Mayaguez Campus, Mayagüez, Puerto Rico, 2014. Available online: https://hdl.handle.net/20.500.11801/1156 (accessed on 15 May 2021).
- Ramos Scharrón, C.E.; Gilbes, F.; Torres-Pulliza, D.; Rodríguez-Guzmán, V.; Aceituno, J. Application of the Soil and Water Assessment Tool (SWAT) to Estimate Discharge and Sediment Yields from the Río Grande de Añasco Watershed, Puerto Rico; Final Report to UPR-Sea Grant; Island Resources Foundation: St. Thomas, VA, USA, 2014; 71p. [Google Scholar]
- Nilawar, A.P.; Calderella, C.P.; Lakhankar, T.Y.; Waikar, M.L.; Munoz, J. Satellite Soil Moisture Validation Using Hydrological SWAT Model: A Case Study of Puerto Rico, USA. Hydrology 2017, 4, 45. [Google Scholar] [CrossRef][Green Version]
- Vieux, B.E. Distributed Hydrologic Modeling Using GIS, 2nd ed.; Springer: Berlin/Heidelberg, Germany, 2004. [Google Scholar]
- Beven, K.J.; Binley, A. The future of distributed models: Model calibration and uncertainty prediction. Hydrol. Process. 1992, 6, 279–298. [Google Scholar] [CrossRef]
- Molina, L.S.T.; Harmsen, E.W.; Cruz-Pol, S. Flood alert system using rainfall forecast data in Western Puerto Rico. 2013 IEEE Int. Geosci. Remote Sens. Symp. IGARSS 2013, 574–577. [Google Scholar] [CrossRef]
- Sepúlveda, N.; Perez-Blair, F.; Delong, L.; Trujillo, D.L. Real-Time Rainfall-Runoff Model of the Carraizo-Reservoir Basin in Puerto Rico; US Geological Survey: Reston, VA, USA, 1996. [Google Scholar]
- Vieux, B.E.; Vieux, J.E. Evaluation of a Physics-Based Distributed Hydrologic Model for Coastal, Island and Inland Hydrologic Modeling. In Coastal Hydrology and Processes; Water Resources Publications: Highlands Ranch, CO, USA, 2006; pp. 453–464. [Google Scholar]
- Diaz-Ramirez, J.N.; Perez-Alegria, L.R.; McAnally, W.H. Hydrology and Sediment Modeling Using BASINS/HSPF in a Tropical Island Watershed. Trans. ASABE 2008, 51, 1555–1565. [Google Scholar] [CrossRef]
- Cohen, E.; Sun, G.L.; Zhang, P.; Caldwell, P.; Krieger, S. Quantifying the Role of Forested Lands in Providing Surface Drinking Water Supply for Puerto Rico; U.S. Department of Agriculture Forest Service: Newtown Square, PA, USA, 2017; p. 28. [Google Scholar]
- Zhang, L.; Sun, G.; Cohen, E.; McNulty, S.G.; Caldwell, P.V.; Krieger, S.; Christian, J.; Zhou, D.; Duan, K.; Cepero-Pérez, K.J. An Improved Water Budget for the El Yunque National Forest, Puerto Rico, as Determined by the Water Supply Stress Index Model. For. Sci. 2018, 64, 268–279. [Google Scholar] [CrossRef][Green Version]
- Miller, P.W.; Kumar, A.; Mote, T.L.; Moraes, F.D.S.; Mishra, D. Persistent Hydrological Consequences of Hurricane Maria in Puerto Rico. Geophys. Res. Lett. 2019, 46, 1413–1422. [Google Scholar] [CrossRef][Green Version]
- Harmsen, E.W.; Mecikalski, J.R.; Reventos, V.J.; Álvarez Pérez, E.; Uwakweh, S.S.; Adorno García, C. GOES-PRWEB: Development, Validation and Application. Hydrology 2021, in press. [Google Scholar]
- Delong, L.; Schoellhamer, D. Computer Program HYDRAUX: A Model for Simulating One- Dimensional, Unsteady, Open-Channel Flow; US Geological Survey: Washington, DC, USA, 1989. [Google Scholar]
- Smith, M.B.; Seo, D.-J.; Koren, V.I.; Reed, S.M.; Zhang, Z.; Duan, Q.; Moreda, F.; Cong, S. The distributed model intercomparison project (DMIP): Motivation and experiment design. J. Hydrol. 2004, 298, 4–26. [Google Scholar] [CrossRef]
- DNRA. Informe Sobre la Sequía 2014-16 en Puerto Rico, División Monitoreo del Plan de Aguas, San Juan, Puerto Rico; Departamento de Recursos Naturales y Ambientales de Puerto Rico: San Juan, Puerto Rico, 2016. Available online: http://drna.pr.gov/wp-content/uploads/2017/01/Informe-Sequia-2014-2016.compressed.pdf (accessed on 15 May 2021).
- Richard, J.; Pasch, A.B.P.; Berg, R. National Hurricane Center Tropical Cyclone Report Hurricane Maria (AL152017) 16–30 September 2017. NOAA NWS, 2019. Available online: https://www.nhc.noaa.gov/data/tcr/AL152017_Maria.pdf (accessed on 15 May 2021).
- Pokhrel, R.; del Cos, S.; Rincon, J.P.M.; Glenn, E.; González, J.E. Observation and modeling of Hurricane Maria for damage assessment. Weather. Clim. Extrem. 2021, 33, 100331. [Google Scholar] [CrossRef]
- Glenn, E.; Smith, T.; Gálvez, J.; Davison, M.; Hibbert, K.; González, J. Tropical Convection in the Caribbean and Surrounding Region during a Regional, Warming Sea-Surface Temperature Period, 1982–2020. Hydrology 2021, 8, 56. [Google Scholar] [CrossRef]
- Office for Coastal Management, 2021: 2010 C-CAP 30 Meter Land Cover of Puerto Rico from 2010-06-15 to 2010-08-15. NOAA National Centers for Environmental Information. Available online: https://www.fisheries.noaa.gov/inport/item/48300 (accessed on 30 May 2020).
- Web Soil Survey. Available online: http://websoilsurvey.nrcs.usda.gov (accessed on 21 February 2013).
- U.S. Army Corpes of Engineers. Gridded Surface Subsurface Hydrologic Analysis. Available online: https://www.erdc.usace.army.mil/Media/Fact-Sheets/Fact-Sheet-Article-View/Article/476714/gridded-surface-subsurface-hydrologic-analysis/#:~:text=GSSHA%20is%20a%20multidimensional%20modeling. (accessed on 19 November 2020).
- Wiki Team Gridded Surface Subsurface Hydrologic Analysis Wiki. Available online: https://www.gsshawiki.com/Gridded_Surface_Subsurface_Hydrologic_Analysis (accessed on 1 October 2020).
- Downer, C.W.; Ogden, F.L. GSSHA: Model to Simulate Diverse Stream Flow Producing Processes. J. Hydrol. Eng. 2004, 9, 161–174. [Google Scholar] [CrossRef]
- Richards, L.A. Capillary conduction of liquids through porous medium. Physics 1931, 1, 318–333. [Google Scholar] [CrossRef]
- Mattocks, C.; Forbes, C. A real-time, event-triggered storm surge forecasting system for the state of North Carolina. Ocean Model. 2008, 25, 95–119. [Google Scholar] [CrossRef]
- Rawls, W.J.; Brakensiek, D.L. Estimation of Soil Water Retention and Hydraulic Properties. In Unsaturated Flow in Hydrologic Modeling; Springer: Berlin/Heidelberg, Germany, 1989; pp. 275–300. [Google Scholar]
- NOAA NCEI. NEXRAD Level-II (Base) Data and Level-III Products. NEXRAD Products. Available online: https://www.ncdc.noaa.gov/data-access/radar-data/nexrad-products (accessed on 1 August 2020).
- Brian, E.S.; Charles, W.D.; Jeffrey, S.B. A Practical Guide to Calibration of a GSSHA Hydrologic Model Using ERDC Automated Model Calibration Software—Effective and Efficient Stochastic Global Optimization; ERDC/CHL TR-12-2; U.S. Army Corps of Engineers Coastal Hydraulics Laboratory: Vicksburg, MS, USA, 2012; Available online: https://www.gsshawiki.com/images/8/8e/ERDC_CHL_TR-12-2.pdf (accessed on 1 December 2020).
- Downer, C.; Wahl, M.; Pradhan, N.; Skahill, B.; Turnbull, S.; Pickett, R. Nested Physics-Based Watershed Modeling at Seven Mile Creek: Minnesota River Integrated Watershed Study; Engineer Research and Development Center: Vicksburg, MS, USA, 2020. [Google Scholar] [CrossRef]
- Box, G.E.P.; Jenkins, G.M.; Reinsel, G.C.; Ljung, G.M. Time Series Analysis: Forecasting and Control, 5th ed.; John Wiley & Sons, Inc.: Hoboken, NJ, USA, 2016. [Google Scholar]
- Misirli, F.; Gupta, H.V.; Sorooshian, S.; Thiemann, M. Bayesian recursive estimation of parameter and output uncertainty for watershed models. In Water Science and Application; Duan, Q., Gupta, H.V., Sorooshian, S., Rousseau, A.N., Turcotte, R., Eds.; American Geophysical Union: Washington, DC, USA, 2003; Volume 6, pp. 113–124. [Google Scholar] [CrossRef]
- NRCS. Manning’s n Values for Various Land Covers To Use for Dam Breach Analyses by NRCS in Kansas. Available online: https://www.wcc.nrcs.usda.gov/ftpref/wntsc/H&H/HecRAS/NEDC/lectures/docs/Manning%92s%20n-values%20for%20Kansas%20Dam%20Breach%20Analyses%20-%20Adopted%20071216.pdf (accessed on 1 August 2020).
- Barnes, H.H., Jr. Roughness Characteristics of Natural Channels; Water Supply Paper 1849; USGS: Reston, VA, USA, 1987. [Google Scholar]
- Hydrologic and Water Quality Models: Performance Measures and Evaluation Criteria. Trans. ASABE 2015, 58, 1763–1785. [CrossRef][Green Version]
- Moriasi, D.N.; Arnold, J.G.; Van Liew, M.W.; Bingner, R.L.; Harmel, R.D.; Veith, T.L. Model Evaluation Guidelines for Systematic Quantification of Accuracy in Watershed Simulations. Trans. ASABE 2007, 50, 885–900. [Google Scholar] [CrossRef]
- Gupta, H.V.; Sorooshian, S.; Yapo, P.O. Status of Automatic Calibration for Hydrologic Models: Comparison with Multilevel Expert Calibration. J. Hydrol. Eng. 1999, 4, 135–143. [Google Scholar] [CrossRef]
- FEMA. FEMA’s National Flood Hazard Layer (NFHL) Viewer. Available online: https://hazards-fema.maps.arcgis.com/apps/webappviewer/index.html?id=8b0adb51996444d4879338b5529aa9cd&extent=-67.18397885742179,18.183296085801487,-67.10982114257813,18.224063601404733 (accessed on 1 May 2021).
|Parameters Selected||Initial Value||Intervals|
|Channel Roughness 1||0.03||0.02||0.05|
|Channel Roughness 2||0.6||0.051||0.075|
|Mixed Forest Roughness||0.12||0.1||0.16|
|Hydraulic Conductivity for Clay||0.03||0.02||0.06|
|Events||Dates||Spatial Resolution (m)||Temporal Resolution (hr)||Maximum Discharge Recorded in the Station (cms)||Volume Hydrograph (USGS Station) (m3)||Volume of Rainfall from NEXRAD (m3)|
|Sub-Watershed 2||Sub-Watershed 1||Sub-Watershed 2|
|Hurricane Irma||9/6/2017 12:00–9/8/2017 11:00||769||1||594||26,727,374.00||13,337,010||35,293,9530|
|Extreme Rainfall Event||12/11/2007 00:00–12/13/2007 00:00||632||1||468||22,888,190.00||9752,113||29,394,120|
|Volume from Cumulative Rainfall (m3)||Error (%)|
|WRF Cumulative Rainfall||NWS Interpolated Rainfall||Absolute Difference (m3)|
|1||USGS Hydrograph + WRF Rainfall + No Storm Surge|
|2||USGS Hydrograph + WRF Rainfall + Storm Surge|
|3||Inflow Hydrograph from Sub-watershed 2 using WRF Rainfall + WRF Rainfall + No Storm Surge|
|4||Inflow Hydrograph from Sub-watershed 2 using WRF Rainfall + WRF Rainfall + Storm Surge|
|5||Inflow Hydrograph from Sub-watershed 2 using bias-corrected WRF Rainfall + bias-corrected WRF Rainfall + No Storm Surge|
|6||Inflow Hydrograph from Sub-watershed 2 using bias-corrected WRF Rainfall + bias-corrected WRF Rainfall + Storm Surge|
|Water infrastructure||Sewer pump stations||33|
|Potable water pump stations||40|
|Public Infrastructure||Public Schools||21|
|Channel Roughness 1||0.02||0.02|
|Channel Roughness 2||0.075||0.063|
|Mixed Forest Roughness||0.1||0.1075|
|Hydraulic Conductivity for Clay||0.02||0.02262|
|Evaluation Metrics||Volume (m3)||Evaluation Criteria|
|R2||NSE||PBIAS (%)||Observed||Modelled||Error (%)||R2 > 0.6||NSE > 0.5||PBIAS < ±15%|
|Evaluation Metrics||Volume (m3)||Evaluation Criteria|
|Time Step(s)||R2||NSE||PBIAS (%)||Observed||Modelled||Error (%)||R2 > 0.6||NSE > 0.5||PBIAS < ±15%|
|HMA||Flooded Area (m2)||Comparison with the FEMA Flood Extent Area|
|Absolute Difference (m2)||Difference (%)|
|Water Infrastructure||HMA||Total Facilities|
|Number of Facilities that Failed|
|Sewer pump stations||9||12||4||7||7||10||33|
|Potable water pump stations||4||4||4||4||4||4||40|
|Public and Electrical Infrastructure||HMA||Total Facilities|
|Number of Facilities that Failed|
|Water Infrastructure||HMA||Total Facilities|
|Number of Facilities that Failed|
|Sewer pump stations||18||13||11||33|
|Potable water pump stations||6||6||6||40|
|Public and Electrical Infrastructure||HMA||Total Facilities|
|Number of Facilities that Failed|
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Mejia Manrique, S.A.; Harmsen, E.W.; Khanbilvardi, R.M.; González, J.E. Flood Impacts on Critical Infrastructure in a Coastal Floodplain in Western Puerto Rico during Hurricane María. Hydrology 2021, 8, 104. https://doi.org/10.3390/hydrology8030104
Mejia Manrique SA, Harmsen EW, Khanbilvardi RM, González JE. Flood Impacts on Critical Infrastructure in a Coastal Floodplain in Western Puerto Rico during Hurricane María. Hydrology. 2021; 8(3):104. https://doi.org/10.3390/hydrology8030104Chicago/Turabian Style
Mejia Manrique, Said A., Eric W. Harmsen, Reza M. Khanbilvardi, and Jorge E. González. 2021. "Flood Impacts on Critical Infrastructure in a Coastal Floodplain in Western Puerto Rico during Hurricane María" Hydrology 8, no. 3: 104. https://doi.org/10.3390/hydrology8030104