Inland Flooding Research Articles

TYPHOON JEBI-INDUCED FLOOD MODELING DUE TO WAVE OVERTOPPING/RUNUP AND REVERSE FLOW AT KANSAI AIRPORT

Typhoon-induced high waves and precipitations cause severe flood damage in low-lying coastal areas. Typhoon Jebi (2018) induced a compound flood at Kansai Airport by stormy waves, and seawater reversed flow through sewer systems. However, the cause of damage at Kansai Airport has been presented incompletely in various studies that analyzed individual flood factors and simply summed each result. A valid method for simulating the compound flood has not yet been established due to insufficient discussions. It means that we might encounter serious difficulties in adequately addressing the potential compound flood risk in the future. Therefore, this study validates the applicability of the compound flood simulation model by reproducing the flooding process of Kansai Airport. A series of numerical experiments were conducted to reproduce Jebi-induced inland flood at Kansai Airport by using a coupled coastal flood model of the storm surge, high wave, wave overtopping/runup, precipitation, and sewer backflow (Jo et al., 2021).

SUITABLE BOUNDARY LOCATION IDENTIFCATION FOR RAINFALL-RUNOFF AND SURGE MODEL COUPLING TO EVALUATE COMPOUND FLOOD HAZARDS IN COASTAL REGIONS

Flooding in many coastal regions is exacerbated due to complex interactions of multiple flood drivers such as rainfall-runoff and coastal surge which can occur simultaneously or sequentially during storm events, leading to compound flooding hazards. The Texas Gulf Coasts (USA) are especially vulnerable from compound flooding due to frequent occurrence of tropical storms which bring strong winds to drive storm surge and heavy rainfall to generate inland flooding. Large scale studies {Coastal Texas Restoration and Protection Study (CTX, 2021), Texas General Land Office’s Texas River Basin Study, Base Level Engineering Study} are being conducted by state and federal agencies for flood hazard assessment and for development of mitigation and abatement strategies for reducing this risk and increasing community resilience. Traditionally, large scale storm surge models do not account for runoff contributions to water levels in the interior of their domain. Riverine models for their part use either normal depth or mean high water levels to assign downstream boundary conditions within tidally influenced area, thus ignoring effects of storm surge. In this Planning Assistance to States (PAS) study, located in the Lower Clear Creek and Dickinson Bayou watersheds (Texas, USA), we have evaluated rainfall-runoff and storm surge interactions to determine suitable locations to use as model boundary conditions for exchanging data between rainfall-runoff and surge models under compound flooding conditions.

Flood mitigation: Regulatory and hydrologic effectiveness of multicomponent runoff detention at a Southwest Florida site

AbstractThis research investigates the capability of hydrological site design to mitigate inland flooding. Empirical data for a target watershed characterize interaction among three hydrologic components: stormwater detention ponds; seasonal wetlands; and soils/groundwater. Findings are (a) stormwater ponds' elevation change in response to precipitation events of a given magnitude varies sharply among storms, such that ponds' pre‐event elevation and forecast precipitation are not reliable to predict ponds' ability to detain runoff sufficiently to avoid downstream flooding; (b) water table elevation is governed partly by long‐term seasonal variation but also responds quickly to specific events, and powerfully affects the system's capacity to detain runoff; (c) water table elevation during wet weather periods common to Southwest Florida can be high enough to breach the soil surface for extended periods, severely reducing the capacity of the system to detain runoff; (d) in the target watershed of the Florida Gulf Coast University campus, depressed surface storage in seasonal wetlands compensates for reduced wet season capacity of ponds and soil storage. That mechanism explains why the campus has successfully mitigated flooding including from high‐precipitation events most prone to produce flooding (intense rate, late wet season events), while some downstream communities with components designed to meet the regulatory minimum have experienced inundation.

Comparing spatially explicit approaches to assess social vulnerability dynamics to flooding

Madagascar is one of the poorest countries in the world, and therefore highly vulnerable to tropical cyclone-induced coastal and inland flooding. This study aims to assess Madagascar's social vulnerability to flooding resulting from tropical storms, in a spatially explicit manner ,accounting for dynamics in vulnerability over time. For this, the research applied three different social vulnerability models in a data-scarce context, for the years 2009 and 2018. Around these years, two tropical cyclones hit the island (in 2008 and 2018). The three models are 1) inductive (social vulnerability index: SoVI), 2) deductive (Weighted Median, WM), and 3) social vulnerability profiling (SVP). Our results show that the most vulnerable regions are in the south of Madagascar, which is consistent across all three models. While the calculated vulnerability score (based on hazard and exposure data) indicated a decrease in vulnerability over time, only the SVP predicted a similar vulnerability decrease, but this comparison is surrounded by uncertainty since the 2008 and 2018 events differ in flood hazard characteristics. The application of the SoVI method in such regions has some limitations, for example, introducing subjective modeling decisions. The application of the WM method could be suitable, but only if relations with variables and social vulnerability are known and understood. The SVP model seems a suitable approach for a first scoping study of social vulnerability, but it provides less insight into spatial variation. The main recommendation from this study is to further focus future research on model validation in data-scarce regions and to assess model accuracy by exploring validation data.

Establishment of warm‐season turfgrasses in a Mediterranean transition zone under simulated waterlogging

AbstractThe Mediterranean region is expected to experience increased extreme precipitation events with the risk of inland flooding because of climate change. This may adversely affect the establishment of warm‐season turf species, usually selected for low water consumption. A simulated waterlogging experiment was conducted at the University of Padua in northeastern Italy during the summers of 2017 and 2018. Four bermudagrass (Cynodon spp.) cultivars (La Paloma, Transcontinental, SR 9554, and Jackpot), ‘Pure Dynasty’ seashore paspalum (Paspalum vaginatum Swartz), and ‘SWI 2000’ buffalograss [Buchloë dactyloides (Nutt.) Engelm.] were compared under waterlogging conditions. The response of the three warm‐season species to four waterlogging events during establishment at two different seeding rates was investigated. The number of seedlings, percentage green coverage, and NDVI were measured. At the end of the experimental periods, the dry weights of the above‐ground biomass and roots were also determined. All the cultivars tested demonstrated high tolerance to waterlogging in the early stages of establishment. Moreover, they produced a significantly higher number of seedlings per unit area when sown at the higher seeding rate.

Temporal changes in dependence between compound coastal and inland flooding drivers around the contiguous United States coastline

Flooding in low-lying coastal zones arises from coastal (storm surge, tides, and waves), fluvial (excessive river discharge), and pluvial (excessive surface runoff) drivers. We analyse changes in compound flooding potential around the contiguous United States (CONUS) coastline stemming from select combinations of these flooding drivers using long observational records with at least 55 years of data. We assess temporal changes in the tail (extremal) dependence (χ) using a 30-year sliding time window. Periods of strong tail dependence are found for the windows centered between the 1960s and 1980s/1990s at several locations for surge-discharge (S-Q) and surge-precipitation (S–P) combinations. Changes in dependence are associated with large-scale climate indices such as the Arctic Oscillation (AO) and El Nino Southern Oscillation indices (Niño 1.2 and Niño 3), among others. The significance of potential changes in the dependence structure is subsequently tested using Kullback–Leibler (KL) divergence. We find that changes are mostly not significant. Finally, we perform a complete multivariate statistical analysis exemplarily for one selected pair of variables at one location (S-Q in Washington, DC), allowing for varying dependence strength and structure as well as changes in the marginal distributions. Combined changes with increase in the dependence and marginals exacerbate the predicted compound flood potential. The comprehensive analysis presented here provides new insights into how and where compound flooding potential has changed with time, demonstrates associated links with large-scale climate indices, and highlights the effects of changes in the dependence and marginals in a multivariate statistical framework.

Intensification of Hurricane Sally (2020) over the Mississippi River Plume

Abstract Tropical Cyclone (TC) Sally formed on 11 September 2020, traveled through the Gulf of Mexico (GMX), and intensified rapidly before making landfall on the Alabama coast as a devastating category-2 TC with extensive coastal and inland flooding. In this study, using a combination of observations and idealized numerical model experiments, we demonstrate that the Mississippi River plume played a key role in the intensification of Sally near the northern Gulf Coast. As the storm intensified and its translation slowed before landfall, sea surface cooling was reduced along its track, coincident with a pronounced increase in SSS. Further analysis reveals that TC Sally encountered a warm Loop Current eddy in the northern GMX close to the Mississippi River plume. Besides deepening the thermocline, the eddy advected low-salinity Mississippi River plume water into the storm’s path. This resulted in the development of strong upper-ocean salinity stratification, with a shallow layer of freshwater lying above a deep, warm “barrier layer.” Consequently, TC-induced mixing and the associated sea surface cooling were reduced, aiding Sally’s intensification. These results suggest that the Mississippi River plume and freshwater advection by the Loop Current eddies can play an important role in TC intensification near the U.S. Gulf Coast.

Challenges in and Opportunities for International Collaboration: Costing Flood Damages and Losses across Canada, Mexico, and the United States

Abstract Flooding, including inland and coastal flooding, is one of the most devastating and costly natural hazards in Canada, Mexico, and the United States. Recent research conducted by an international team has focused on understanding the true and comprehensive economic costs of floods, with an eye toward addressing their impacts, allocating adequate resources for monitoring and preparedness, and building resilient communities. Flood-costing methods vary greatly among federal and subnational jurisdictions across the three North American countries. Because the rigor and consistency of existing datasets across the three countries vary significantly, it is also difficult to determine the economic impacts of cross-border events. This paper aims to critically analyze the research methods used to conduct this trinational project and develop recommendations for enhancing impacts of such work in the future. We discuss three major research barriers: gaps in knowledge and research capacity, differences in data collation and analysis methods across the three countries, and linguistic barriers in working across disciplines and economic sectors. We also explore how the COVID-19 pandemic significantly added to these three barriers. We propose creation of new institutional mechanisms that can play a major role in developing comprehensive, consistent, and cohesive data gathering approaches in Canada, Mexico, and the United States.

Geospatial Mapping of Inland Flood Susceptibility Based on Multi-Criteria Analysis – A Case Study in the Final Flow of Busu River Basin, Papua New Guinea

Geospatial Mapping of Inland Flood Susceptibility Based on Multi-Criteria Analysis – A Case Study in the Final Flow of Busu River Basin, Papua New Guinea

Inland Flooding and Rainfall from Hurricane Irene and Tropical Storm Lee (2011): Coupled Atmosphere–Wave–Ocean Model Simulations and Remote Sensing and In Situ Observations with a Machine Learning Tool

Abstract Inland flooding from landfalling tropical cyclones (TCs) is a major cause of death and damage to property and infrastructure worldwide. The mid-Atlantic region of the United States was devastated by Hurricane Irene and Tropical Storm Lee during late August–early September 2011, when the two storms produced sequential heavy rainfall and record flooding. Many rivers and streams reached their all-time record discharge to date. This study aims at 1) better understanding and predicting TC rainfall using various observed rainfall products and a high-resolution coupled atmosphere–wave–ocean model, namely, the Unified Wave Interface-Coupled Model (UWIN-CM), 2) characterizing inland flooding using streamflow data, and 3) improving prediction of TC-induced inland flooding using UWIN-CM and a machine learning K-nearest-neighbor (KNN) model. The results show that there is a wide range of uncertainty in satellite and radar–gauge-observed rainfall products in terms of rain-rate distribution and cumulative rainfall over the mid-Atlantic region. UWIN-CM rainfall is closer to the radar–gauge data than satellite data over land. Streamflow in most large rivers (>500 cfs) peaked after Lee, which reflects the sequential rainfall contributions of the two storms. The rainfall–streamflow–discharge response times were dependent on the size of the stream and the peak rain rates. To better predict rainfall and flooding, UWIN-CM and observed rainfall are used with the machine learning KNN regression model for prediction of severity of TC-induced inland flooding hazard. These results demonstrate the value of a stepped approach for rainfall and flood prediction toward a fully coupled atmosphere–ocean–land/hydrology model in the future.

도시홍수 통합관리모델 수립연구

Urban areas with high levels of impervious cover and inadequate flood-mitigation policies are becoming increasingly vulnerable to flooding compared to other areas. Despite nationwide efforts to mitigate flood damages, which primarily focus on major rivers, smaller rivers and urban catchments still experience frequent floods. This study utilized a semi-distributed hydrologic model and SWMM to consider the potential backwater effect on inland flooding. Through complementary analysis using observed rainfall and flow data from early October 2016 (Typhoon Chaba), the study was conducted on the Yugok Catchment. The results demonstrate the advantages of the semi-distributed model, which can handle spatial-temporal rainfall distribution and infiltration parameters while maintaining relatively faster calculation speed compared to a fully-distributed model. Furthermore, this study suggests that integrating the model with the SWMM can provide sufficient, accurate, and prompt information to enable effective flood crisis management plan.

Projected changes in extreme streamflow and inland flooding in the mid-21st century over Northeastern United States using ensemble WRF-Hydro simulations

Study regionNortheastern United States (NEUS). Study focusWe investigate the potential impacts of climate change on precipitation, streamflow, and inland flooding in the NEUS during the mid-21st century. Dynamically downscaled climate projections from three global climate models for historical (1995–2004) and future (2045–2054) periods under business-as-usual scenarios were used to force the hydrologic model WRF-Hydro at 200-meter resolution and create ensemble hydrologic simulations. Additionally, an extreme value model was developed to project the risks associated with low-frequency hydrologic events. New hydrological insights for the regionResults from four major watersheds indicate a significantly wetter regime in winter months and potential drier conditions during late spring to early summer. Discharges in fall are projected to decrease in the northern watersheds and increase toward the south. Extreme flow and water depths resulting from extreme inland flooding are projected to increase by 5–20% and > 100%, respectively. The extent of the total flooded area is likely to be 20% greater by the mid-century. These increased risks can be attributed to (i) an approximate 25% increase in decadal mean and > 40% increase in decadal extreme precipitation intensity, (ii) up to 30% lower snow availability and 5–25% higher evapotranspiration throughout the year, and (iii) a projected 5% increase in soil moisture in all seasons except summer. Furthermore, rapid snow melting in winter will likely cause an earlier peak flow in the rivers.

Wave and storm surge evolutions in the Pearl River Estuary with large-scale land reclamation impacts

Globally, many estuarine areas are suffering from large-scale land reclamations to fulfill the land demands of city expansion. Understanding the wave and storm surge evolutions in response to human reclamations in estuaries is of vital importance regarding estuarine management (e.g., reclamation plan formulation, flood mitigation). The wave and storm characteristics, and their responses to 1990–2020 land reclamations in the Pearl River Estuary (PRE) were investigated by a storm surge model. Besides, the impacts of increasing typhoon intensity (TI) on the changes of waves and storm surges caused by reclamations were also illustrated under the background of global warming. Simulated results show that reclamations cause apparent variations of wave height and storm surge fields in the PRE. The wave height and surge height also increase with the enhancement of TI, consequently influencing the wave height and surge height change rates caused by reclamations. Furthermore, the combined actions of land reclamation and increased TI conduct the most remarkable effects and bring the highest surge height compared with other scenarios. Particularly, the maximum wave height and surge height can reach 0.88–2.31 m and 2.71–3.77 m along the seawalls of Zhuhai, respectively, indicating more enormous seawall submersion and inland flooding.

Comparison of estimated flood exposure and consequences generated by different event-based inland flood inundation maps

Abstract. The flooding brought about by compound coastal flooding events can be devastating. Before, during, and immediately following these events, flood inundation maps (FIMs) can provide essential information to emergency management. However, there are a number of frameworks capable of estimating FIMs during flood events. In this article, we evaluate FIMs derived from three such frameworks in the context of Hurricane Harvey. Our analysis reveals that each of the three FIM frameworks provides different FIMs that differ in their level of accuracy. Each of the three FIMs also produces different exposure and consequence estimates because of their spatial differences. This investigation highlights the need for a centralized means of vetting and adjudicating multiple FIMs during compound flood events empowered by the ability to distribute FIMs as geographic information system (GIS) services and coalesce FIMs into a common operating picture. Furthermore, we provide evidence that the ability to produce multi-model estimates of FIMs to create probabilistic FIMs may provide a better product than the use of a lone FIM.

Assessment of inland flood hazard sensitivity to hydrological intensification in coastal watersheds

IntroductionIntensification of the hydrologic cycle induced by climate variability and landscape modification is expected to increase the frequency of extreme flood events. Multi-jurisdictional approaches to manage inland flood risks at watershed scales demand the ability to objectively assess not only future flood potentials, but to also set priorities based upon multiple factors such as the stream channels most sensitive to hydrologic stress.MethodsThis study presents a method to estimate flood hazard sensitivities to increasing stormwater runoff due to hydrologic intensification (e.g., urbanization, climate effects) on local and watershed scales. The method is demonstrated in the low-gradient inland watershed regions of southwestern coastal Louisiana, USA. Utilizing highly detailed numerical models from the Federal Emergency Management Agency (FEMA), absolute and relative flood sensitivities were calculated for 45 flood-prone stream channels in the Lafayette, LA region. Channel sensitivities to flood hazards induced by changes in the 10-yr (10% annual exceedance probability) flood flows were quantified by analysis of 485 scenarios developed using a downward counterfactual scaling strategy.Results and discussionRelying entirely upon publicly available numerical models and input datasets, the study revealed key information about the relationship between estimable hydraulic characteristics (e.g., conveyance, resistance, and flow) and absolute and relative flood hazard sensitivity measures on a per-channel basis. Information from the subset of detailed numerical models was efficiently leveraged to provide a regional map of relative flood sensitivities. The methodology is robust and can be applied in very general settings to address the concern of hydrologic intensification in practical flood risk management applications.

Exposure to precipitation from tropical cyclones has increased over the continental United States from 1948 to 2019

AbstractExtreme precipitation from tropical cyclones can generate large-scale inland flooding and cause substantial damage. Here, we quantify spatiotemporal changes in population risk and exposure to tropical cyclone precipitation in the continental eastern United States over the period 1948–2019 using high-resolution in-situ precipitation observations. We find significant increases in the magnitude and likelihood of these extreme events due to increased rainfall rates and reduced translation speeds of tropical cyclones over land. We then develop a social exposure index to quantify exposure and risk of tropical cyclone precipitation as a function of both physical risk and socio-economic activities. Increased social exposure is primarily due to the increased risk of tropical cyclone precipitation, but there are regional differences. We identify exposure hotspots in the south-eastern United States, where rapid population growth and economic development amplify societal exposure to tropical cyclone hazards. Our multi-scale evaluation framework can help identify locations that should be targeted for mitigation and adaptation activities to increase their climate resilience.

Investigation of Waves Generated by Tropical Cyclone Kyarr in the Arabian Sea: An Application of ERA5 Reanalysis Wind Data

In this study, the wave conditions in the Arabian Sea induced by tropical cyclone Kyarr (2019) have been simulated by employing the 3rd generation wave model MIKE 21 SW. The model was run from 24 October to 1 November 2019, a total of 8 days. The MIKE 21 SW model was forced by reanalyzed ERA5 wind data from the European Centre for Medium-Range Weather Forecasts (ECMWF). The results are compared with buoy data from the Indian National Centre for Ocean Information Services (INCOIS), which is located at 67.44° E, 18.50° N. In addition, the satellite altimeter data (CryoSat-2, SARAL and Jason-3 satellite altimeter data) was utilized for validation. Three wave parameters are considered for the validation: the significant wave height; the peak wave period; and the mean wave direction. The validation results showed that the significant wave height, the peak wave period, and the mean wave direction could be reasonably predicted by the model with reanalysis wind data as input. The maximum significant wave height reached to 10.7 m (with an associated peak wave period of 12.5 s) on 28 October 2019 at 23:00:00 in the middle of the Arabian Sea. For coastal areas, the significant wave height along the Iran and Pakistan (north Arabian Sea) coasts increased to a range of 1.4–2.8 m when tropical cyclone Kyarr moved northward. This wave height along with elevated sea level may cause severe coastal erosion and nearshore inland flooding. Impacts of cyclones on coastal zones critical facilities and infrastructure can be reduced by timely and suitable action before the event, so coastal managers should understand the effect of cyclones and their destructive consequences. The validated model developed in this study may be utilized as input data of evaluating the risk to life and infrastructure in this area.

Observational Verification of the Cumulative Resilience Screening Index (CRSI) Using Hurricanes, Inland Floods, and Wildfires From 2016 to 2019.

Users can apply three processes to develop confidence in decision‐making tools like models and indices—validation, verification, and observation. The utility of the Cumulative Resilience Screening Index (CRSI) was demonstrated by combining the processes of verification and observation using real‐world natural hazard events (i.e., hurricanes, inland flooding, and wildfires). The ability of CRSI to determine the counties most vulnerable to hazards and least likely to recover quickly from natural hazards is demonstrated using these natural hazard events from outside the original index construction data set. Using Hurricane Harvey and Hurricane Michael, the counties in Texas and Florida/Georgia, respectively, experiencing the most damage and the most extended recovery intervals were determined accurately. Similarly, the most vulnerable and least recoverable counties were correctly identified as those associated with the Great Louisiana Flood of 2016. Finally, three different types of wildfires in California were examined to determine the likelihood of recovery and the strength of pre‐event planning. All models and indices developed for use by decision‐makers should consider undertaking this verification or a similar validation operation to enhance user confidence.

Gaussian process emulation of spatio-temporal outputs of a 2D inland flood model

The computational limitations of complex numerical models have led to adoption of statistical emulators across a variety of problems in science and engineering disciplines to circumvent the high computational costs associated with numerical simulations. In flood modelling, many hydraulic and hydrodynamic numerical models, especially when operating at high spatiotemporal resolutions, have prohibitively high computational costs for tasks requiring the instantaneous generation of very large numbers of simulation results. This study examines the appropriateness and robustness of Gaussian Process (GP) models to emulate the results from a hydraulic inundation model. The developed GPs produce real-time predictions based on the simulation output from LISFLOOD-FP numerical model. An efficient dimensionality reduction scheme is developed to tackle the high dimensionality of the output space and is combined with the GPs to investigate the predictive performance of the proposed emulator for estimation of the inundation depth. The developed GP-based framework is capable of robust and straightforward quantification of the uncertainty associated with the predictions, without requiring additional model evaluations and simulations. Further, this study explores the computational advantages of using a GP-based emulator over alternative methodologies such as neural networks, by undertaking a comparative analysis. For the case study data presented in this paper, the GP model was found to accurately reproduce water depths and inundation extent by classification and produce computational speedups of approximately 10,000 times compared with the original simulator, and 80 times for a neural network-based emulator.

Economically optimizing elevation of new, single-family residences for flood mitigation via life-cycle benefit-cost analysis

Construction with freeboard—vertical height of a structure above the minimum required—is commonly accepted as a sound investment for flood hazard mitigation. However, determining the optimal height of freeboard poses a major decision problem. This research introduces a life-cycle benefit-cost analysis (LCBCA) approach for optimizing freeboard height for a new, single-family residence, while incorporating uncertainty, and, in the case of insured homes, considering the costs from losses, insurance, and freeboard (if any) to the homeowner and National Flood Insurance Program (NFIP) separately. Using a hypothetical, case study home in Metairie, Louisiana and U.S. Army Corps of Engineers design depth-damage functions for generic inland flooding, results show that adding 2 ft of freeboard at the time of construction might be considered the optimal option given that it yields the highest net benefit, but the highest net benefit-cost ratio occurs for the 1 ft freeboard. Even if flood loss reduction is not considered when adding freeboard, the savings in annual insurance premiums alone are sufficient to recover the construction costs paid by the homeowner if at least one foot of freeboard is included at construction. Collectively, these results based on conservative assumptions suggest that at the time of construction, even a small amount of freeboard provides a huge savings for the homeowner and (especially) for the financially-strapped National Flood Insurance Program. For community planners, the results suggest that wise planning with reasonable expectations on the front end makes for a more sustainable community.