Inundation Risk Research Articles

Predictability of Coastal Extreme Wave Heights Based on a Nonstationary Hierarchical Bayesian Model: The Role of the Sea Surface Temperature

Kim, Y.-T.; Uranchimeg, S.; Park, M.H., and Kwon, H.-H., 2021. Predictability of coastal extreme wave heights based on a nonstationary hierarchical Bayesian model: The role of the sea surface temperature. In: Lee, J.L.; Suh, K.-S.; Lee, B.; Shin, S., and Lee, J. (eds.), Crisis and Integrated Management for Coastal and Marine Safety. Journal of Coastal Research, Special Issue No. 114, pp. 191–195. Coconut Creek (Florida), ISSN 0749-0208. The increasing frequency of extreme wave heights has been observed in many parts of the world due to recent global warming. Especially, the variability of extreme wave heights in the summer season has increased during the last three decades over the coast of South Korea that is partially attributable to the increase of the climate variability, leading to the changes in frequency and intensity of the extreme wave heights. Further, coastal areas are particularly vulnerable to the increased climate variability, and the associated risk can be exacerbated by the rapid coastal development over South Korea. Most of the extreme wave heights in South Korea are largely caused by a typhoon during the summer season. Recent studies revealed that the typhoon frequency and intensity in the Asian monsoon region are modulated by Sea Surface Temperature (SST) during the summer season from May to November. Traditional frequency analysis methods do not consider the year to year shifts in wave heights risk distributions that are attributed to changes in climate variability that affect the causal structure of coastal inundation risk. In this perspective, a climate-informed nonstationary frequency analysis model is proposed to predict the extreme wave heights and explore the role of the SST in a Bayesian regression framework. The parameters of the nonstationary frequency model are obtained using a Markov Chain Monte Carlo algorithm. The proposed model shows strong potential for predicting the extreme wave heights by linking large-scale climate patterns, providing motivation for developing dynamic coastal flood risk management strategies.

Evaluation of methods for connecting InSAR to a terrestrial reference frame in the Latrobe Valley, Australia

Deformation measurements from satellite-borne synthetic aperture radar interferometry (InSAR) are usually measured relative to an arbitrary reference point (RP) of assumed stability over time. For InSAR rates to be reliably interpreted as uplift or subsidence, they must be connected to a defined Earth-centred terrestrial reference frame (TRF), usually made through GNSS continuously operating reference stations (CORS). We adapt and compare three methods of TRF connection proposed by different studies which we term the single CORS RP (SCRP), plane-fit multiple CORS (PFMC), and the multiple CORS RP (MCRP). We generalise equations for these methods, and importantly, develop equations to propagate InSAR and GNSS uncertainties through the transformation process. This is significant, because it is important to not only estimate the InSAR uncertainties, but also to account for the uncertainties that are introduced when connecting to the CORS so as to better inform our interpretation of the deformation field and the limitation of the measurements. We then test these methods using Sentinel-1 data in the Latrobe Valley, Australia. These results indicate that differences among the three TRF connection methods may be greater than their estimated uncertainties. MCRP appears the most reliable method, although it may be limited in large study areas with sparse CORS due to long wavelength InSAR errors and that gaps and/or steps may appear at the spatial limit from the CORS. SCRP relies on the quality of the single CORS connection, but can be validated by unconnected CORS in the study area. The PFMC method is suited to larger areas undergoing slow, constant deformation covering large spatial extents where there are evenly distributed CORS across the study area. Selecting an optimal method of TRF connection is dependent on local site conditions, CORS network geometry and the characteristics of the deformation field. Hence, the choice of TRF connection method should be carefully considered, because different methods may result in significantly different transformed deformation rates. We confirm slow subsidence across the Latrobe Valley relative to the vertical component of the ITRF2014, with localised high subsidence rates near open cut mining activities. Subsidence of ~ -6 mm/year is observed in the adjacent coastal region which may exacerbate relative sea level rise along the coastline, increasing future risks of coastal inundation.

Integrating Evacuation and Storm Surge Modeling Considering Potential Hurricane Tracks: The Case of Hurricane Irma in Southeast Florida

Hurricane Irma, in 2017, made an unusual landfall in South Florida and the unpredictability of the hurricane’s path challenged the evacuation process seriously and left many evacuees clueless. It was likely to hit Southeast Florida but suddenly shifted its path to the west coast of the peninsula, where the evacuation process had to change immediately without any time for individual decision-making. As such, this study aimed to develop a methodology to integrate evacuation and storm surge modeling with a case study analysis of Irma hitting Southeast Florida. For this purpose, a coupled storm surge and wave finite element model (ADCIRC+SWAN) was used to determine the inundation zones and roadways with higher inundation risk in Broward, Miami-Dade, and Palm Beach counties in Southeast Florida. This was fed into the evacuation modeling to estimate the regional clearance times and shelter availability in the selected counties. Findings show that it takes approximately three days to safely evacuate the populations in the study area. Modeling such integrated simulations before the hurricane hit the state could provide the information people in hurricane-prone areas need to decide to evacuate or not before the mandatory evacuation order is given.

Sea-level rise impact and future scenarios of inundation risk along the coastal plains in Campania (Italy)

Sea-level rise as a consequence of global warming increases the need to analyze coastal risks to conceive adaptation strategies aimed at coping with marine impacts at both short- and long-term scales. In this context, this study presents future scenarios of inundation risk evaluated along the main alluvial coastal plains of the Campania region (Italy). Due to their geomorphological and stratigraphical setting, the investigated areas are characterized by low topography and relevant but variable subsidence rates. Based on the upgrade of already published data and the new analysis of available datasets derived by multi-temporal interferometric processing of satellite Synthetic Aperture Radar (SAR) images, future scenarios of local sea level for the years 2065 and 2100 have been evaluated coupling global projections with local subsidence trends. Furthermore, aspects related with the distribution of natural and anthropic assets, as well as the local social vulnerability, have been taken into account to calculate the overall risk. The inundation risk maps here proposed can effectively address the request to improve the knowledge of policymakers and local administrators and to raise their awareness about the potential impacts of climate change in coastal areas.

To Rebuild or Relocate? Long-Term Mobility Decisions of Hazard Mitigation Grant Program (HMGP) Recipients

Limited funds and the demand for disaster assistance call for a broader understanding of how homeowners decide to either rebuild or relocate from their disaster-affected homes. This study examines the long-term mobility decisions of homeowners in Lumberton, North Carolina, USA, who received federal assistance from the Hazard Mitigation Grant Program (HMGP) for property acquisition, elevation, or reconstruction following Hurricane Matthew in 2016. The authors situate homeowners’ decisions to rebuild or relocate in the context of property attributes and neighborhood characteristics. Logit and probit regressions reveal that homeowners with lower-value properties are less likely to relocate, and those subjected to higher flood and inundation risks are more likely to relocate. Additionally, homeowners in neighborhoods of higher social vulnerability—those with a higher proportion of minorities and mortgaged properties—are more likely to rebuild their disaster-affected homes. The authors discuss homeowners’ mobility decisions in the context of the social vulnerability of neighborhoods. Our results contribute to an ongoing policy discussion that seeks to articulate the housing and neighborhood attributes that affect the long-term mobility decisions of recipients of HMGP assistance. The authors suggest that local governments prioritize the mitigation of properties of homeowners of higher physical and social vulnerability to reduce socioeconomic disparities in hazard mitigation and build equitable community resilience.

Sensitivity of tides and tidal components to sea-level-rise in the Radial Sand Ridges

Sea-level-rise (SLR) induced by global climate would by changing tidal dynamics in the deltaic and coastal regimes aggravate inundation risks. In order to have a thorough understanding of the tidal responses to SLR in the RSR of the South Yellow Sea, a comprehensive analysis on the tidal and tidal components’ responses to SLR are conducted. Spatial variations of tides and tidal components between SLR scenarios and present-day scenario are examined. Strong spatial variation in tidal responses to SLR occurs over the RSR. Further investigation reveals it is the semidiurnal tide (S2) in control of the tidal range evolution under SLR in this area. Strong spatial variability in the primary tidal amplitudes’ evolution is found between northern Dongsha shoal and southern Lansha-yang channel in the RSR. The significantly enhanced shallow water tidal components, M4 and MS4, lead to increase tidal distortion(flood-dominant) in the nearshore under SLR. In addition, the natural period for tidal resonance in the RSR tends to be smaller and the amplification due to resonance enhances. The evolution in tidal asymmetry and tidal resonance adjusts the timing and height of the local high-water levels. Therefore, conclusion in this study can be used for reference to future high-water level prediction in the RSR.

Climatic changes and water management in the Ural River basin and their impact on the river water regime

The Ural River basin is a transboundary object, which is characterized by a very uneven distribution of water resources, an extensive arid areas and water-intensive socio-economic complex. There are about 3,130 reservoirs and ponds, which are evaporating 0.9 km3/yr. and carry out intra-annual regulation. Water intake peaked in the 1980s. Now the anthropogenic decrease in runoff is < 1.6 km3/yr. Climate warming has begun in 1977-1980 and has intensified since the mid-1990s, as well has the occurrence of thaws in winter. In the first half of the current period, annual, summer-autumn, and winter precipitation was higher than the average long-term values. In the second half, spring and winter precipitation increased, while total precipitation decreased due to decrease in summer-autumn precipitation. As a result, the modern water regime of rivers has clearly changed and not in all cases positively. This initiated an inter-State discussion on the efficient use of water resources. Therefore, the goal and objectives of the research were not only to assess the characteristics of the runoff, trends of their changes, but also to determine the role of climatic and anthropogenic factors and the patterns of their long-term behavior. An analysis of data at 56 gauges, 10 meteorological stations, and on water use shows that after 1977 the runoff exceeded by an average of 10-20%. 1985-1995 and 2000-2007 were high-water sub-periods. Since 2006/2008, the prolonged water shortage has been registered. The decrease in the precipitation and the increase in evaporation are the main reasons for its formation. The regulation of the Ural runoff and warming have led to an earlier commencement and ending of floods in the lower reaches, a decrease in its volume and maximum water discharges, and the inundation risk reduction. Low-water runoff has increased significantly. This is illustrated by the new data.

Regional Flood Risk Projections under Climate Change

AbstractFlood-related risks to people and property are expected to increase in the future due to environmental and demographic changes. It is important to quantify and effectively communicate flood hazards and exposure to inform the design and implementation of flood risk management strategies. Here we develop an integrated modeling framework to assess projected changes in regional riverine flood inundation risks. The framework samples climate model outputs to force a hydrologic model and generate streamflow projections. Together with a statistical and hydraulic model, we use the projected streamflow to map the uncertainty of flood inundation projections for extreme flood events. We implement the framework for rivers across the state of Pennsylvania, United States. Our projections suggest that flood hazards and exposure across Pennsylvania are overall increasing with future climate change. Specific regions, including the main stem Susquehanna River, lower portion of the Allegheny basin and central portion of Delaware River basin, demonstrate higher flood inundation risks. In our analysis, the climate uncertainty dominates the overall uncertainty surrounding the flood inundation projection chain. The combined hydrologic and hydraulic uncertainties can account for as much as 37% of the total uncertainty. We discuss how this framework can provide regional and dynamic flood-risk assessments and help to inform the design of risk-management strategies.

Understanding Residential Relocation Choices in Coastal Cities in the Face of Climate Change

Climate change poses risks of inundation to low-lying coastal cities and may cause residential relocation and change in housing demand. Taking the City of Gold Coast in Queensland, Australia as a case study, this paper reports on a survey that investigates the potential responses of residents living in the coastal city to flood risks and how the responses may relate to their socio-economic status. Through a combined online and mail-based survey, our data show that people’s perceptions of flooding have an important impact on their relocation choices. Their perceptions and relocation choices are associated with their socio-economic background. Furthermore, residents’ preferences of dwelling types appear to be affected by the level of flood risks we hypothesise in the survey. The findings from this study provide empirical evidence for future residential zoning and urban development.

An early warning system for wave-driven coastal flooding at Imperial Beach, CA

Waves overtop berms and seawalls along the shoreline of Imperial Beach (IB), CA when energetic winter swell and high tide coincide. These intermittent, few-hour long events flood low-lying areas and pose a growing inundation risk as sea levels rise. To support city flood response and management, an IB flood warning system was developed. Total water level (TWL) forecasts combine predictions of tides and sea-level anomalies with wave runup estimates based on incident wave forecasts and the nonlinear wave model SWASH. In contrast to widely used empirical runup formulas that rely on significant wave height and peak period, and use only a foreshore slope for bathymetry, the SWASH model incorporates spectral incident wave forcing and uses the cross-shore depth profile. TWL forecasts using a SWASH emulator demonstrate skill several days in advance. Observations set TWL thresholds for minor and moderate flooding. The specific wave and water level conditions that lead to flooding, and key contributors to TWL uncertainty, are identified. TWL forecast skill is reduced by errors in the incident wave forecast and the one-dimensional runup model, and lack of information of variable beach morphology (e.g., protective sand berms can erode during storms). Model errors are largest for the most extreme events. Without mitigation, projected sea-level rise will substantially increase the duration and severity of street flooding. Application of the warning system approach to other locations requires incident wave hindcasts and forecasts, numerical simulation of the runup associated with local storms and beach morphology, and model calibration with flood observations.

Development of a Simulation Model for Real-Time Urban Floods Warning: A Case Study at Sukhumvit Area, Bangkok, Thailand

Increasingly frequent, high-intensity rain events associated with climatic change are driving urban drainage systems to function beyond their design discharge capacity. It has become an urgent issue to mitigate the water resource management challenge. To address this problem, a real-time procedure for predicting the inundation risk in an urban drainage system was developed. The real-time procedure consists of three components: (i) the acquisition and forecast of rainfall data; (ii) rainfall-runoff modeling; and (iii) flood inundation mapping. This real-time procedure was applied to a drainage system in the Sukhumvit area of Bangkok, Thailand, to evaluate its prediction efficacy. The results showed precisely that the present real-time procedure had high predictability in terms of both the water level and flood inundation area mapping. It could also determine hazardous areas with a certain amount of lead time in the drainage system of the Sukhumvit area within an hour of rainfall data. These results show the real-time procedure could provide accurate flood risk warning, resulting in more time to implement flood management measures such as pumping and water gate operations, or evacuation.

Accounting for DEM Error in Sea Level Rise Assessment within Riverine Regions; Case Study from the Shatt Al-Arab River Region

Global elevation datasets such as the Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) are the best available terrain data in many parts of the world. Consequently, SRTM is widely used for understanding the risk of coastal inundation due to climate change-induced sea level rise. However, SRTM elevations are prone to error, giving rise to uncertainty in the quality of the inundation projections. This study investigated the error propagation model for the Shatt al-Arab River region (SARR) to understand the impact of DEM error on an inundation model in this sensitive, low-lying coastal region. The analysis involved three stages. First, a multiple regression model, parameterized from the Mississippi River delta region, was used to generate an expected DEM error surface for the SARR. This surface was subtracted from the SRTM DEM for the SARR to adjust it. Second, residuals from this model were simulated for the SARR. Modelled residuals were subtracted from the adjusted SRTM to produce 50 DEM realizations capturing potential elevation variation. Third, the DEM realizations were each used in a geospatial “bathtub” inundation model to estimate flooding area in the region given 1 m of sea level rise. Across all realizations, the area predicted to flood covered about 50% of the entire region, while predicted flooding using the raw SRTM covered only about 28%, indicating substantial underprediction of the affected area when error was not accounted for. This study can be an applicable approach within such environments worldwide.

Sea level rise inundation risk assessment in residential cadastral parcels along the Mediterranean Andalusian coast

The general objective of this article is to present the methodological approach for the assessment of the inundation risks associated with the rise in the mean sea level for different models and scenarios of climatic change (IPCC, 2013 and Jevrejeva et al., 2012). The approach focuses on the assessment of the hazard, exposure and physical vulnerability of the built areas, especially the residential areas along the Mediterranean coast of Andalusia (Spain). For hazards calculation a set of tide gauges’ data and climatic scenarios were analyzed to obtain future sea levels for all the spectrum of probabilities. Further, a method is proposed for mapping the probability associated with each scenario using a 5 m cell size DTM. For exposure and vulnerability assessment of built up areas, especially for residential ones, the National Cadastre, which is the most detailed set of data, have been used. Modeling the original cadastral data in a spatial database management system and their analysis through SQL sentences have made possible to identify the cadastral parcels with residential use and associated variables (area, number of residential real estate units, constructed area below ground, etc..), being therefore the cadastral parcel the spatial reference unit for mapping. The overlay of cadastral parcels and the inundation hazard maps has allowed identifying the residential cadastral parcels exposed to each climatic scenario. A method is proposed for better identification of the number of real estate units in each cadastral parcel, assuming this number as the base for residential parcel vulnerability. Finally, the risk assessment is calculated as the product of the previous variables. For the dissemination of the inundation risk assessment results, a web application (geoviewer) was developed. Results show that for the more pessimistic scenario more than 24,000 exposed cadastral parcels would be affected. Out of that, 13,000 exposed residential cadastral parcels were identified and their hazards, exposure and vulnerability calculated in order to map the final inundation risk associated with future sea levels.

Mid-Holocene to present circum-Arabian sea level database: Investigating future coastal ocean inundation risk along the Arabian plate shorelines

The Arabian Peninsula has a unique setting to resolve how proximity to tectonically diverse plate margins, eustacy, and glacial isostatic adjustment (GIA) influences coastal sea level fluctuations. New 14C AMS and U series dating of coastal sediments from the western, southern, and eastern plate margins (Al-Wajh and Thuwal, Saudi Arabia; Bar Al-Hikman, Oman; Abu Dhabi, U.A.E) have been integrated with an archive of dated coastal sediments (n = 145, 31 locations). Each dated sample is recalibrated for its elevation based on a high-resolution coastal DEM dataset, tidal and tectonic correction, and reservoir correction, to develop a new-suite of relative sea level (RSL) plots, separated into six tectonically distinct zones. A mid-Holocene highstand has been identified in most of the zones (1, 2, 4, 6), however, with complex spatial and temporal variability. No data is available for zone 3, whereas no Holocene highstand was observed in zone 5. The results suggest that eustacy, varying rates of vertical tectonic change (max 1 mm/year) and glacial isostatic adjustment (max 0.5 mm/year) were the major drivers of sea level fluctuations since Mid-Holocene albeit in different proportions along the different sections of Arabian shorelines. Furthermore, the topography of a shoreline, tidal range, and presence/absence of a coastal barrier also significantly influences the coastal inundation. Based on our findings, eastern Arabia will in the near future experience the largest coastal flooding (largest 0–10 m elevation area, > 2 m tidal amplitude, no barriers), followed by western (1–2 m tidal amplitude, discontinuous coral reef barriers) and southern Arabia shorelines (>3 m tidal amplitude, no barriers) shorelines. The presented insights are critical in supporting communities along the coasts of the Arabian Peninsula, who will have to cope with flooding from rising oceans over the next few decades due to climate change.

The Change in Elevation, Land Subsidence and Local Sea Level Rise Using Coastal Response Model in Jakarta

The rapid urban development activities on the coast of Jakarta has a major impact on the risk of land subsidence. This land subsidence causes significant changes in land elevation. Especially, the coastal areas of Jakarta receive the risk of inundation. The primary purpose of this research was to examine the responses of Jakarta coastal areas based on land subsidence rates of a 24-day baseline data of SAR Sentinel 1A, and local sea-level rise prediction from MOTIWALI data. The methods used in this research were an interferometric SAR (InSAR) and Coastal Response Models. The results of this study indicate that the coastal model response using adjusted elevation based on interferogram elevation and land subsidence rates of SAR Sentinel A1 in a relatively short time (monthly) escalates the evaluation of areas with potential undergoing inundation impacts. Jakarta coastal area receives a greater response with changes in the elevation adjustment based on the local sea-level rise of 0.54 m and land subsidence rates of 0.02. The model demonstrates that the coastal landscape of Jakarta was not possessing the capacity to respond to sea-level rise. Coastal dikes are quite effective as an initial defense in the event of extreme sea waves. The coastal response model using SAR Sentinel 1A data can respond to resource management decisions in coastal areas, and improve assessments quickly that consider the impacts of future land subsidence and local sea-level rise.

Vulnerability to Mega Underground Inundation and Evacuation Assuming Devastating Urban Flood

In urban areas of Japan, there are numerous underground spaces, such as subways and shopping malls. These areas hold the possibility of being inundated not only above the ground but also underground when intense rainfall causes pluvial flooding. For this reason, it is necessary to take measures to anticipate urban floods. In this study, an analysis of inundation on the ground surface and underground spaces was performed, and the risk of underground inundation was evaluated using the index of safe evacuation. In addition, as one of the countermeasures against underground inundation, the effect of flood boards on inundation prevention was examined. Furthermore, the factors for improving the success rate of evacuation are discussed by simulating the evacuation of users from an underground mall during inundation. From the results, the water inflow to underground spaces was larger as rainfall was greater, and the reduction effect of flood boards on underground inundation was only temporary if the inflow to the underground mall was large. In addition, the success rate of evacuation depended on the timing of evacuation because the flooded water spread broadly and rapidly across the underground mall. These results show that users in an underground mall should move with a quick response to flash flood from high intensity rainfall and would be useful in creating a safe and prompt evacuation plan.

Estimation of Potential Economic Losses Due to Flooding Considering Variations of Spatial Distribution of Houses and Firms in a City

In Japan, flood disasters caused by record-breaking heavy rainfall frequently cause significant damages. It is also great concern that heavy rainfall may increase and occur more frequently due to global warming. In July 2013, a heavy rainfall event caused record-flooding of the Kakehashi River in Ishikawa Prefecture. In this study, pseudo global warming (PGW) experiments were implemented for the heavy rainfall in 1998 and 2013 around the Kakehashi River basin. Based on the results of PGW simulations, rainfall with different return periods were generated. Runoff analyses and inundation simulations were carried out by forcings with multiple return periods, and the results were used to estimate the economic losses due to flood inundation. Expected values of the economic losses were calculated using two methods for multiple return periods. Differences between the two expected values indicates the importance of the weighting method for the result of each return period. In addition, variations of spatial distribution of houses and firms in a city (i.e., urban structure) were simulated using a computable urban economics (CUE) model for the area of middle-lower reach of the Kakehashi River basin to examine its impact on economic loss due to flooding. In the simulation using the CUE model, a more severe flood inundation risk and an additional insurance burden for general households were added, and possible variations of urban structure were estimated around the lower part of the Kakehashi River basin. Under the more severe risk condition, relocation proceeded from higher risk areas to safer areas, and possible economic losses decreased in the target area. This result indicates that proper recognition of risk can reduce flood damages. On the other hand, there were small variations in economic losses under the condition with the additional flood insurance burden.

Shoreline Solutions: Guiding Efficient Data Selection for Coastal Risk Modeling and the Design of Adaptation Interventions

The Caribbean is affected by climate change due to an increase in the variability, frequency, and intensity of extreme weather events. When coupled with sea level rise (SLR), poor urban development design, and loss of habitats, severe flooding often impacts the coastal zone. In order to protect citizens and adapt to a changing climate, national and local governments need to investigate their coastal vulnerability and climate change risks. To assess flood and inundation risk, some of the critical data are topography, bathymetry, and socio-economic. We review the datasets available for these parameters in Jamaica (and specifically Old Harbour Bay) and assess their pros and cons in terms of resolution and costs. We then examine how their use can affect the evaluation of the number of people and the value of infrastructure flooded in a typical sea level rise/flooding assessment. We find that there can be more than a three-fold difference in the estimate of people and property flooded under 3m SLR. We present an inventory of available environmental and economic datasets for modeling storm surge/SLR impacts and ecosystem-based coastal protection benefits at varying scales. We emphasize the importance of the careful selection of the appropriately scaled data for use in models that will inform climate adaptation planning, especially when considering sea level rise, in the coastal zone. Without a proper understanding of data needs and limitations, project developers and decision-makers overvalue investments in adaptation science which do not necessarily translate into effective adaptation implementation. Applying these datasets to estimate sea level rise and storm surge in an adaptation project in Jamaica, we found that less costly and lower resolution data and models provide up to three times lower coastal risk estimates than more expensive data and models, indicating that investments in better resolution digital elevation mapping (DEM) data are needed for targeted local-level decisions. However, we also identify that, with this general rule of thumb in mind, cost-effective, national data can be used by planners in the absence of high-resolution data to support adaptation action planning, possibly saving critical climate adaptation budgets for project implementation.

Safety design for water-carrying Lake flood control based on copula function: A Case study of the Hongze Lake, China

Lakes are typical plain reservoirs, with many similar functions to valley reservoirs; however, their flood processes differ significantly due to their specific topography. Flooding of the dam site, the entire reservoir inflow, is considered during flood control analysis of valley reservoirs. Whereas, the flood inundation risk of flood detention areas in the lake basin should be considered for lakes. Therefore, more attention should be paid to the flood processes of each sub-region. Here, based on the flood processes characteristics of large water-carrying lakes, a complete framework for establishing design floods for sizeable rivers-connected lakes is introduced. First, multiple Copula-based joint distribution functions are constructed based on the water system structure of the lake. Then, several confidence intervals are obtained using a methodology that identifies the boundary for multivariate combinations. Finally, through these confidence intervals are used in conjunction with each other, the design flood processes for water-carrying lake system can be determined. The proposed methodology was applied to the Hongze Lake, China. The results indicate that this method not only effectively avoids the randomness of the traditional method but also balances the characteristics of the flood process in each sub-region with the features of the entire flood process. The proposed method has a strong statistical theoretical foundation and expands the applicability of multi-variable flood frequency analysis techniques to water conservancy projects.

A Spatial Improved-kNN-Based Flood Inundation Risk Framework for Urban Tourism under Two Rainfall Scenarios

Urban tourism has been suffering socio-economic challenges from flood inundation risk (FIR) triggered by extraordinary rainfall under climate extremes. The evaluation of FIR is essential for mitigating economic losses, and even casualties. This study proposes an innovative spatial framework integrating improved k-nearest neighbor (kNN), remote sensing (RS), and geographic information system (GIS) to analyze FIR for tourism sites. Shanghai, China, was selected as a case study. Tempo-spatial factors, including climate, topography, drainage, vegetation, and soil, were selected to generate several flood-related gridded indicators as inputs into the evaluation framework. A likelihood of FIR was mapped to represent possible inundation for tourist sites under a moderate-heavy rainfall scenario and extreme rainfall scenario. The resultant map was verified by the maximum inundation extent merged by RS images and water bodies. The evaluation outcomes deliver the baseline and scientific information for urban planners and policymakers to take cost-effective measures for decreasing and evading the pressure of FIR on the sustainable development of urban tourism. The spatial improved-kNN-based framework provides an innovative, effective, and easy-to-use approach to evaluate the risk for the tourism industry under climate change.