oleh: I.P. Gustave S. Pariartha
Climate change and urbanization play critical roles in compounding future flood risk due to their adverse impacts on the rainfall regime and sea level rise.
Although past studies have predicted the spatiotemporal variations in flood risk, these have appreciable limitations, viz. (i) flood risk is predicted mainly by accounting for one driver at a time (either ocean flooding or fluvial flooding); and (ii) monetization of flood damage due to future flooding had not been investigated.
However, multiple drivers could lead to flooding in coastal areas. This study presents an innovative approach for investigating the cumulative effects of urbanization, changes to the rainfall regime, and sea level rise on consequential flood damage in a coastal urban area. A comprehensive flood damage and hazard prediction model was developed by integrating 1D-2D aspects of MIKE FLOOD and GIS technology to assess the flood scenarios for 2040, 2070, and 2100 by investigating three predictor variables: urbanization, rainfall regime, and sea level rise.
The factorial design approach was used to construct a total of 27 future flood scenarios. Time horizons of 30 years provided for effectively capturing climate change and its influence on the hydrologic regime. The Generalized Linear Model (GLM) was applied to create a statistical model based on future scenarios for each time horizon. Results confirmed that changes to the rainfall regime significantly influence the average annual damage (AAD) caused by flooding for all time horizons.
At the same time, the significance of the effects of urbanization and sea level rise was found to vary. The model predicts that by 2040, urbanization would exacerbate AAD, with a significant contribution from sea level rise.
In contrast, sea level rise would provide a marginally greater and more significant contribution to AAD compared to urbanization in 2040 and 2070. Compared to the base year 2017, AAD was 78%, 197%, and 351% higher in 2040, 2070, and 2100, respectively.
The proposed flood damage prediction model developed can guide modelers and decision-makers in assessing the compounding flood damage for future flood management in any geographic location.
Introduction
The main cause of flooding in urban areas is rainfall, which affects people and property in the form of pluvial and fluvial flooding. Fluvial flooding occurs when high water levels in river channels exceed bank heights, while pluvial flooding occurs when the intensity of rainfall exceeds the infiltration capacity.
Urban areas can be severely damaged by both pluvial and fluvial floods, although in different ways (Tanaka et al., 2020). Urban regions are at risk of pluvial floods because a significant fraction of the land is converted to impervious surfaces that prevent infiltration (Netzel et al., 2021, Thrysøe et al., 2021, Lee and Kim, 2018).
The effects of fluvial flooding have been investigated in numerous past studies. There are, however, only a limited number of studies that have focused on the evaluation of damage due to combined pluvial and fluvial flooding.
Reasons include the need for high-resolution data, the number of stakeholders involved in assessing pluvial floods, the complexity of flooding processes in urban settings and the issues associated with the flood source (Muthusamy et al., 2019). Past studies report that worldwide flood disasters from 1900 to 2013 have caused around seven million deaths and a monetary loss of about US $600 billion (Disaster Profiles, 2013).
Dong (2006) quantifying the relationships between extreme precipitation and flood hazards in the Pearl River Basin found that extreme precipitation exerts a significant influence on the spatiotemporal distribution of floods.
The reduction in infiltration translates to an increase in surface runoff and flood peak (Huong and Pathirana, 2013, Blair et al., 2014). Climate change will result in radical modifications to the hydrological cycle, leading to variations in temperature and moisture, wind shear, and atmospheric instability which may cause severe storm surges and coastal floods (IPCC, 2014).
Trenberth (2011) noted that future temperature increases could alter the natural water cycle and atmospheric moisture content, leading to changes in rainfall characteristics. The changes to rainfall characteristics are expected to result in more frequent extreme rainfall events, which in turn are expected to increase the frequency of occurrence and severity of inland flooding (Kvočka et al., 2018, Chen and Zhang, 2021).
Especially, in coastal urban areas, the sea level rise would exacerbate the flooding caused by inland rainfall (Chowdhury et al., 2020, Kopp et al., 2021). Coastal floods in urban regions have the potential to cause enormous economic impacts and in the past have resulted in the loss of lives in many parts of the world (Zheng et al., 2014, Bevacqua et al., 2019, Macías-Tapia et al., 2021).
In the case of flooding, sea level rise as a result of climate change acts as a magnifying factor by restricting streamflow discharge into the ocean (Balica et al., 2012). Such a scenario will aggravate flood risk and the consequential flood damage in coastal areas (Nicholls & Cazenave, 2010).
Without appropriate flood prevention planning, an annual flood damage loss of about 0.3 to 9.3% of the global gross domestic product is expected by 2100 (Hinkel et al., 2014). Sea level rise (SLR) is expected to increase to 1 m or greater considering the trends in anthropogenic activities and greenhouse gas emissions by the year 2100 (Rahmstorf, 2007; Kopp et al., 2014).
Physically, SLR will increase the height of storm tides, reduce pressure gradients essential for transporting fluvial water to the ocean, and enable more significant upstream tide/wave propagation (Guo et al., 2015, Hoitink and Jay, 2016, Loftis et al., 2019).
Consequently, flood risk assessment of coastal urban regions is critical. The significance of this finding needs to be viewed by the fact that 37% of the global population as of 2017 live in coastal communities (UN, 2017). This further highlights the need to assess flood damage as a consequence of multiple drivers such as sea level rise (SLR), extreme rainfall events and land use change (Chen et al., 2021, Thrysøe et al., 2021).
In turn, flood damage assessment is essential as it can enhance decision-making in relation to flood risk assessment for the formulation of effective strategies for damage reduction and/or flood mitigation by broadening the awareness of catchment management authorities and enhancing the well-being of flood-vulnerable communities.
Computational flood models used for simulating the extent of flooding, depth, duration and flow velocity have been of paramount importance for determining flood-associated damages.
Usually, these are applied to investigate a range of city development scenarios and climate parameters for developing a strategic plan to implement appropriate adaptation measures.
This requires the incorporation of uncertain future conditions including their possible consequences and resulting damages in order to design flood risk mitigation measures in a timely and effective manner (Apel et al., 2004, Jamali et al., 2018).
In Scotland, a Grid-to-Grid hydrological flood forecasting model has been developed using 24-hour collective rainfall predictions and static flood risk maps (Speight et al., 2016). By segmenting the drainage region into smaller areas and employing flood volumes and damage data, Lee and Kim (2018) created a multi-dimensional flood damage assessment technique.
Recently, flood modellers have explored a combination of 1D-2D hydrodynamic models to simulate the drainage network as well as overall flow. SWMM or MOUSE (Jamali et al., 2018), SOBEK (Deltares, 2017) and XPSWMM (XPSolutions, 2017) are some of the commercially available models used for elaborately simulating flood characteristics.
These models involve intensive computations and are sometimes subjected to numerical instability (Lhomme et al., 2006, Leandro and Martins, 2016).
Considering this fact, 1D-2D flood models, which demand large computational details and time need some modification (Teng et al., 2017), as the high computational time makes the model unsuitable for application in large regions which require multiple simulations and scenario assessment.
The application process of conventional 1D-2D models is often complex as it needs a significant amount of training and optimization, and the flood prediction intervals are quite large and unsuitable for realistically estimating flood damages (Li et al., 2017).
The complexity of flood damage assessment is commonly exacerbated due to the involvement of multiple factors including flood depth, flow velocity, flood duration, lead time for flood warning and the built environment characteristics (Merz et al., 2010, Jamali et al., 2018).
The complexity also arises from a large number of drivers, the training and optimization involved and a large number of time steps which leads to high computation time. Thus, it is challenging for practitioners to estimate flood damage in the targeted catchment.
Future flood risks and associated flood damages are expected to be strongly influenced by the increase in urbanization and climate change-driven changes to rainfall characteristics and sea level rise (Jamali et al., 2018, Chen et al., 2021).
Flood damage occurrence and extent vary across different regions due to the characteristics of the catchment, such as land use.
Past researchers such as Miller and Hutchins, 2017, Semadeni-Davies et al., 2008, Gu et al., 2011, and Huong and Pathirana (2013) have attempted to create methodology focused on determining flood damage by considering urbanization and climate change impacts on flooding.
These models are not appropriate for single drainage basins as they use long-term rainfall data, and a single drainage basin requires rainfall data on a per-min/per-hr scale. Also, little is known about the integrated and simultaneous influence of future changes to rainfall, urbanization, and sea level rise on urban flood damage costs (Mahmoud and Gan, 2018, Zhou et al., 2019).
Therefore, it is crucial to statistically estimate future flood damage based on rainfall, urbanization, and sea level rise. Practitioners require comprehensive and simplified flood damage assessment models that include 1D-2D model components and use rainfall data at a lower scale (per min) and can be applied across various drainage basins.
This study combines climatic and urbanization trends to present a novel modelling framework for estimating future flood damage costs in coastal urban regions.
The study's objectives were: (1) to develop a rational approach to assess the impacts of rainfall depth, sea level rise, and urbanization (%) on floods for the years 2040, 2070, and 2100 under different climate change scenarios and Representative Concentration Pathways (RCP 8.5); and (2) to assess future flood damage costs under the compounding effects of predicted changes to rainfall depth, sea level rise, and urbanization.
The study outcomes are expected to contribute to creating new knowledge on the impacts of climate change and can guide the development of future flood mitigation strategies to enhance resilience to climate change.
The methodology proposed in this study is generic and can be applied to any area irrespective of the geographic location.
Study area and data
Musgrave Catchment, located within the larger Loders Creek Catchment in the Gold Coast, Australia (Fig. 1) was selected as the study area for this research study.
The focus of the study was to assess the flood damage to residential buildings caused by the combination of climate change, sea level rise, and urbanization.
The relatively densely populated coastal residential region (1.82 km2) has predominantly flat terrain with the downstream part of the catchment prone to frequent floods. Further,
Predictor variables for future time horizons
The upper and lower boundaries obtained for sea level rise, rainfall depth increase percentage, and urbanization percentage are provided in Table 3.
The rainfall percentage increase in the upper and lower bounds was computed following the EA (2016) assumptions i.e., 5% rainfall magnitude will increase with 1 °C increase in the temperature.
The difference between the lower and upper extremes of ‘percentage increase in rainfall depth (second column, Table 3)’ was found to increase when heading
Discussion
This study combines the multiple drivers viz., rainfall, sea level rise, and urbanization to flooding in a coastal region to assess their influence cumulatively as well as individually to AAD for future time horizons. The investigation was undertaken by aggregating land use change details, sea level rise and rainfall depth increase projections using RCP 8.5 scenario, topography, and surface roughness information as inputs to the MIKE FLOOD model. The existing MIKE FLOOD model was developed
Conclusions
The study created a modelling framework for predicting the flood damage caused by climate change and changes in urbanization.
Results revealed that the AAD value increases significantly if there are no measures taken to mitigate the impacts of climate change (the RCP8.5 scenario). As an example, in the study area, it was found that AAD increases to 78% in 2040, 197% in 2070, and 351% in 2100 compared to the 2017 baseline AAD value. The average costs associated with flood damage losses in the
Funding
No funding was received for the undertaking the research discussed in this manuscript.
CRediT authorship contribution statement
I.P. Gustave S. Pariartha: Conceptualization, Methodology, Investigation, Writing – original draft. Shubham Aggarwal: Methodology, Writing – original draft, Writing – review & editing. Srinivas Rallapalli: Writing – original draft, Writing – review & editing. Prasanna Egodawatta: Methodology. James McGree: Methodology. Ashantha Goonetilleke: Conceptualization, Methodology, Writing – review & editing.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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