Future Flood Research Articles

Identifying the Contributing Sources of Uncertainties in Urban Flood Vulnerability in South Korea Considering Multiple GCMs, SSPs, Weight Determination Methods, and MCDM Techniques

This study quantified uncertainties involved in assessing the future flood vulnerability in 33 urban areas with population exceeding designated thresholds in South Korea. The driver-pressure-state-impact-response (DPSIR) framework was utilized as the study procedure, integrating social, economic, and environmental factors. In addition, a total of 220 cases of combinations were examined, encompassing twenty general circulation models combined with shared socioeconomic pathway scenarios, five weight determination methods, and three multi-criteria decision-making (MCDM) techniques, as sources of inherent uncertainties in the process. The rankings of urban flood vulnerability (UFV) for the selected cities were comprehensively assessed considering all combinations, followed by an analysis of variance test to investigate contributing sources of uncertainties. As a result, Incheon and Busan were found to be vulnerable to flooding, while Yeongcheon and Andong appeared to be safe cities. Some cities exhibited wide ranges in their rankings, such as Daegu, Yangpyeon, and Jeongeup. The identified contributing sources were weighting (58%), MCDM (27%), and the combination of weighting and MCDM methods together (15%). This study revealed that weight determination methods and MCDM techniques are the primary sources of uncertainties in the assessment of future UFV instead of multiple GCMs and SSPs. This finding underscores the importance for decision-makers and stakeholders to carefully consider these uncertainties for sustainable flood risk management and prevention.

Projection of climate change effects on flood inundation in the Gin River basin, Sri Lanka

Abstract. Human-induced climate change has altered the frequency and severity of heavy precipitation, droughts, and floods worldwide. Sri Lanka, a developing country in the Indian Ocean, has suffered frequent floods, affecting over 64 % of the population in the past decade. The Gin River, the primary drinking water source of the densely populated Galle district with over 1 million residents, faces recurrent flood disasters. Therefore, this study employed a multi-model ensemble to simultaneously simulate the impact of climate change on both river flow and inundation extent in the frequently flooded Gin River basin located in humid tropical monsoon region. Bias-corrected climate projections of an ensemble of five General Circulation Models (GCMs) from the Coupled Model Inter-comparison Project Phase 5 (CMIP5) under Representative Concentration Pathway (RCP) 4.5 emission scenario were used to project future river flow and flood inundation in the Gin River basin, Sri Lanka. The validated Rainfall–Runoff–Inundation (RRI) model projected a 5 % increase in high flows (flows exceeding a 5 % probability of exceedance (<Q5)) at the downstream (Baddegama) gauging station and a 16 % increase at the upstream (Thawalma) gauging station in the future (2040–2060) in comparison to the past (1980–2000), which can be attributed to the elevated rainfall projected during the two monsoon seasons (southwest monsoon (May–September) and northeast monsoon (December–February)) by the GCM ensemble. In addition to the projected changes in high flows revealed by the RRI simulations, the model also yielded critical information about the expansion of annual maximum inundated extent in the basin by 3.5 % that will aid flood risk projection and sustainable management in the basin.

A Method for Municipalities to Enhance Disaster Resilience for Potential Future Floods Using a Climate Projection Database

A Method for Municipalities to Enhance Disaster Resilience for Potential Future Floods Using a Climate Projection Database

1.5℃ and 2.0℃ of global warming intensifies the hydrological extremes in China

Global warming has been shown to have a profound impact on hydrological process, especially on hydrological extremes. To avoid the potential risk of extreme hazards, it is essential to project changes in hydroclimatic extremes under the 1.5℃ and 2.0℃ targets of the Paris Agreement, especially in China, which is geographically, climatologically, economically, and socially complex. Here, this study projects future changes in hydroclimatic extremes in China under 1.5℃ and 2.0℃ warming targets based on a distributed hydrological model driven by an ensemble of downscaled general circulation models. The study shows that global warming of 1.5℃ and 2.0℃ would perceptibly affect the spatial patterns of extreme rainfall and extreme heat in China. The intensification of events such as extreme rainfall, meteorological drought, and extreme heat would intensify the hydrological extremes, including floods and hydrological droughts. The risk of future extreme floods and hydrological droughts will increase simultaneously, with a 34.6% (38.0%) probability of acceleration (more severe floods and droughts) in a 1.5℃ (2.0℃) warming world. Correspondingly, global warming tends to exacerbate the water cycle, with a 92.5% (94.2%) probability that at least one extreme event will exhibit intensification characteristics in the 1.5℃ (2.0℃) warming scenario. The acceleration of the water cycle and the intensification of the hydrological extremes may have adverse impacts on the balanced development of China's ecological and economic society.

Using public participation gis to understand texas coastal communities’ perceptions and preferences for urban green space development in connection to their perceptions of flood risk

Urban green spaces, a form of green infrastructure, provide cities with a number of opportunities to build urban resilience to withstand a wide range of urban challenges, such as land-use change, climate change, biodiversity loss, and physical and mental health. Despite their benefits to communities, urban green spaces are among the most valuable and attractive areas for development and are most susceptible to urban development. Therefore, careful planning of the green space network is needed,requiring diverse voices from not only city policymakers and planning experts but also local residents. This study investigated residents’ perceptions and preferences for green space development and its association with perceived flood risk. Data were collected from residents residing in two Texas Gulf Coast cities via a Public Participation Geographic Information System (PPGIS) platform. Our analyses revealed that Texas Gulf Coast residents assigned a full spectrum of landscape values to their existing urban green spaces with aesthetic and recreation values being most frequently identified. Notably, across the city landscape, urban green spaces that were most favored by residents tended to be green spaces with water bodies, large in scale and offered a broader array of facilities/amenities. These spaces were also identified as flood-prone areas, presenting opportunities for green space improvement, flood protection, and preservation. In addition, we observed a significant association between residents’ risk perception and the number of locations across the city landscape they perceived as posing the greatest flooding threat. Our findings illustrate a need to promote multiple landscape values of urban green spaces, especially in its ability to protect the city from future flooding. This research highlights how PPGIS can support green infrastructure planning as well as enhance public participation in green infrastructure planning processes.

Hydraulic Simulation for Flow One Dimension of Shatt Al-Hilla River

Floods are considered the most serious disaster among all-natural catastrophes because they occur more frequently than any other natural hazard and strongly impact more individuals than all other natural disasters. Therefore, it is necessary to study flood risk. Hillah River is critical in securing industrial, agricultural, and civil water in three Iraqi governorates: Babil, Diwaniyah, and Muthanna. Its primary source is the Euphrates River, which extends approximately 100 kilometers within Babil Governorate – the study area. This research aims to evaluate and study the river's capacity and predict future floods by developing scenarios for anticipated events. Also, a hydraulic model was developed to assess the manning coefficient for Shatt Al-Hillah. The one-dimensional HEC-RAS 6.0.1 program has been used to simulate water flow in the river, incorporating over 350 cross-sections spaced at 250-meter intervals surveyed in 2018 by the Department of Water Resources in Babil Governorate. The model was calibrated using observed discharge data from 2004 to 2022 in Shatt al-Hillah. Subsequently, it was compared with a range of water levels by varying manning factors. The calibration results indicated that a roughness coefficient of 0.023 was suitable for unstable flow conditions, and the least mean square root error between the measured and simulated water levels was 0.053. The simulation results showed that the current capacity of Al-Hilla River was 205 m3/s, such that it cannot pass the design discharge of 303 m3/s. After conducting scenarios greater than 205 m3/s, the results showed that increasing the discharge increased the areas exposed to flooding so that when the discharge was 450 m3/s, flooding of the submerged areas increased. With a percentage of 92.2%, the northern side of Babil Governorate will be more vulnerable to flooding than the southern side because the southern part levels are lower than the northern part.

Characterizing future changes in compound flood risk by capturing the dependence between rainfall and river flow: An application to the Yangtze River Basin, China

Compound floods induced by co-occurring multiple drivers may exacerbate the flood impacts and lead to larger flood damage. Exploring future changes in compound flood risk is imperative for flood management and disaster reduction. This study attempts to investigate future changes in compound flood risk across the Yangtze River Basin during 2030 ∼ 2100. Future river flow was projected using an improved hydrological model and pairwise series of extremes of rainfall and river flow were extracted from both observed and projected series. Subsequently, stochastic pairs of rainfall and river flow characterizing compound floods were proportionally sampled from their bivariate joint distributions. The damage from each compound flood was obtained from the flood damage function constructed by Random Forests (RF). Further, the expected annual damage (EAD) was calculated to investigate future changes in compound flood risk. Results show that: (1) Future annual maximums of rainfall and river flow are expected to increase by 14.51 % ∼ 66.13 % and 1.72 % ∼ 55.73 % in the mainstream and northern tributaries, while future annual peak discharge in the southern tributaries (except for the Dongting Lake Basin) is expected to decrease by 4.18 % ∼ 12.30 %. A similar spatial distribution of future changes is also found in the bivariate joint distribution of rainfall and river flow. (2) The high coefficient of determination (R2) of 0.84 indicates the satisfactory simulation and projection capacity of the constructed flood damage function. The positive stepped relationship between flood damage and rainfall or river flow reflects the superposition of multiple flood damage processes. (3) The Han River Basin, the Jialing River Basin, and the two-lakes (the Dongting and Poyang Lakes) area face great threat from compound floods in both historical and future periods. Future compound flood risk is expected to increase by 13.43 % ∼ 46.04 % in these regions except for the Poyang Lake Basin, while future risk is expected to increase by 2.03 % ∼ 46.04 % in the whole basin. The findings help improve the understanding of future flood risk variations in the Yangtze River Basin and provide essential information for damage reduction.

On the Use of Weather Generators for the Estimation of Low-Frequency Floods under a Changing Climate

The present work presents a methodology based on the use of stochastic weather generators (WGs) for the estimation of high-return-period floods under climate change scenarios. Applying the proposed methodology in a case study, Rambla de la Viuda (Spain), satisfactory results were obtained through the regionalization of the bias-corrected EUROCORDEX climate projections and the integration of this information into the parameterization of the WG. The generated synthetic data series fed a fully distributed hydrological model to obtain the future flood quantiles. The results obtained show a clear increase in the precipitation extreme quantiles for the two analyzed projections. Although slightly reducing the annual amount of precipitation, variations between 4.3% for a return period of 5 years in the mid-term projection and 19.7% for a return period of 100 years in the long-term projection have been observed. In terms of temperatures, the results point to clear increases in the maximum and minimum temperatures for both projections (up to 3.6 °C), these increases being greater for the long-term projection, where the heat waves intensify significantly in both magnitude and frequency. Finally, although rivers may present, in general, with lower flows during the year, flood quantiles experience an increase of 53–58% for high return periods, which reach values of up to 145% when we move to smaller catchments. All this combined translates into substantial shifts in the river flow regimes, increasing the frequency and magnitude of extreme flood events.

SEA for better climate adaptation in the face of the flood risk: Multi-scenario, strategic forecasting, nature-based solutions

Numerous scholars have warned that many cities in the Greater Bay Area (GBA) are threatened by severe flood risk, which is often underestimated. Considering this, we provide a scenario-based Strategic Environmental Assessment (SEA) approach to analyse the roles of adaptation strategies on the environment in response to climate change and flooding risk. Three strategies are proposed including a non-Nature-based Solutions strategy (non-NbS), and two Nature-based Solutions strategies (NbS 1.0 and NbS 2.0). The non-NbS strategy refers to the way of promoting urban development yet no additional environmental concerns. The NbS 1.0 strategy focuses on ensuring arable lands for food security, and the NbS 2.0 strategy acquires further regulations on ecology protection.The analysis consists of two steps. First, this paper simulates the land use scenarios in 2030 and 2050, respectively, following non-NbS and NbS (1.0 & 2.0) strategies. This process is developed based on a patch-generating land use simulation (PLUS) model, which allows users to explore various future land use scenarios on account of different spatial development strategies, environmental conditions, and socio-economic trends. Second, the study overlays the land use scenarios with potential sea flooding scopes to reveal how different landscapes (e.g., forests and wetlands) are affected by carbon emissions in RCP 2.6 and RCP 8.5. Further research is suggested to incorporate the 2020 GDP data to refine the findings of this study and consider the impacts of dykes on flood-prone area mapping.The analysis finds that the NbS (1.0 & 2.0) strategies play an active role in decreasing the future flood risk of built-up areas in the GBA regionally, especially under the high-emission RCP 8.5 projection. However, their impacts vary between cities. (1) The NbS 2.0 strategy is effective in reducing the risk of built-up areas in Shenzhen, Hong Kong, and Macao, while the effectiveness of the NbS 1.0 strategy is not apparent. (2) The NbS 1.0 and 2.0 strategies are both effective in Dongguan, Guangzhou, Huizhou, Jiangmen, Zhuhai, Zhaoqing, and Zhongshan in reducing the flood risk of built-up areas. In addition, the NbS 2.0 strategy is more productive than the NbS 1.0 strategy either in 2030 or 2050. (3) The NbS 1.0 and 2.0 strategies generate subtle different effects in Foshan in 2030 and 2050. The NbS 2.0 strategy is more productive in 2050, while less productive in 2030. This study provides a comprehensive insight into the distributed flood risk following the non-NbS or NbS strategies in the context of climate change, which is useful to policymakers and urban planners.

Predicting rainfall runoff in Southern Nigeria using a fused hybrid deep learning ensemble

Rainfall as an environmental feat can change fast and yield significant influence in downstream hydrology known as runoff with a variety of implications such as erosion, water quality, and infrastructures. These, in turn impact the quality of life, sewage systems, agriculture, and tourism of a nation to mention a few. It chaotic, complex, and dynamic nature has necessitated studies in the quest for future direction of such runoff via prediction models. With little successes in use of knowledge driven models, many studies have now turned to data-driven models. Dataset is retrieved from Metrological Center in Lagos, Nigeria for the period 1999-2019 for the Benin-Owena River Basin. Data is split: 70% for train and 30% for test. Our study adapts a spatial-temporal profile hidden Markov trained deep neural network. Result yields a sensitivity of 0.9, specificity 0.19, accuracy of 0.74, and improvement rate of classification of 0.12. Other ensembles underperformed when compared to proposed model. The study reveals annual rainfall is an effect of variation cycle. Models will help simulate future floods and provide lead time warnings in flood management.

Evaluating future flood risk mitigation capacities in major cities across China

Rapid urbanization and climate change have significantly altered urban hydrological processes. Previous research has highlighted their adverse effects on escalating risk of urban flood. Nevertheless, few assessments have quantified how future urban flood risk mitigation capacities evolve under various urban expansion and climate change scenarios. This study simulates urban expansion of major cities across China using future urban-regional environment simulation model at 30-m resolution under shared socioeconomic pathways scenarios (SSP). Concurrently, future precipitation intensity changes are assessed based on climate projection data. Finally, the urban flood risk mitigation capacity impacted by urban expansion and climate change is evaluated using the soil conservation service model. The results reveal: (1) The sizes of major cities are projected to expand 30% by 2050, and the north and east China are primary urban growth poles. (2) Major cities' precipitation intensity increase 0.4% in SSP126, but decline 5.4% and 2.3% in SSP245 and SSP585. (3) The flood risk mitigation capacities of major cities decrease 2.7% in SSP126 and 2.1% in SSP585, but increase 0.2% in SSP245. Most cities experiencing capacity decrease are located in the north and east China. These results provide valuable insights for the formulation of future urban flood adaptation and mitigation strategies.

Global Projection of Flood Risk With a Bivariate Framework Under 1.5–3.0°C Warming Levels

AbstractGlobal warming increases the atmospheric water‐holding capacity, consequently altering the frequency, and intensity of extreme hydrological events. River floods characterized by large peak flow or prolonged duration can amplify the risk of social disruption and affect ecosystem stability. However, previous studies have mostly focused on univariate flood magnitude characteristics, such as flood peak or volume, and there is still limited understanding of how these joint flood characteristics (i.e., magnitude and duration) might co‐evolve under different warming levels. Here, we develop a systematical bivariate framework to project future flood risk in 11,528 catchments across the globe. By constructing the joint distribution of flood peak and duration with copulas, we examine global flood risk with a bivariate framework under varying levels of global warming (i.e., within a range of 1.5–3.0°C above pre‐industrial levels). The flood projections are produced by driving five calibrated lumped hydrological models (HMs) using the simulations with bias adjustment of five global climate models (GCMs) under three shared socioeconomic pathways (SSP126, SSP370, and SSP585). On average, global warming from 1.5 to 3.0°C tends to amplify flood peak and lengthen flood duration across almost all continents, but changes are not unidirectional and vary regionally around the globe. The joint return period (JRP) of the historical (1985–2014) 50‐year flood event is projected to decrease to a median with 36 years under a medium emission pathway at the 1.5°C warming level. Finally, we evaluate the drivers of these JRP changes in the future climate and quantify the uncertainty arising from the different GCMs, SSPs, and HMs. Our findings highlight the importance of limiting greenhouse gas emission to slow down global warming and developing climate adaptation strategies to address future flood hazards.

Hydrological Drought and Flood Projection in the Upper Heihe River Basin Based on a Multi-GCM Ensemble and the Optimal GCM

To ensure water use and water resource security along “the Belt and Road”, the runoff and hydrological droughts and floods under future climate change conditions in the upper Heihe River Basin were projected in this study, based on the observed meteorological and runoff data from 1987 to 2014, and data from 10 GCMs from 1987 to 2014 and from 2026 to 2100, using the SWAT model, the Standardized Runoff Index, run length theory, and the entropy-weighted TOPSIS method. Both the multi-GCM ensemble (MME) and the optimal model were used to assess future hydrological drought and flood responses to climate change. The results showed that (1) the future multi-year average runoff from the MME was projected to be close to the historical period under the SSP245 scenario and to increase by 2.3% under the SSP585 scenario, and those from the optimal model CMCC-ESM2 were projected to decrease under both scenarios; (2) both the MME and the optimal model showed that drought duration and flood intensity in the future were projected to decrease, while drought intensity, drought peak, flood duration, and flood peak were projected to increase under both scenarios in their multi-year average levels; (3) drought duration was projected to decrease most after 2080, and drought intensity, flood duration, and flood peak were projected to increase most after 2080, according to the MME. The MME and the optimal model reached a consensus on the sign of hydrological extreme characteristic responses to climate change, but showed differences in the magnitude of trends.

Flood Impacts on Agriculture under Climate Change: The case of the Awanui Catchment, New Zealand

We assess the economic and environmental impacts on agriculture of flooding under projected climate change, including land-use change as an adaption response to floods. Our case study area is the Awanui catchment located in the far north of New Zealand, where flooding is a significant hazard. The study uses an integrated approach and combines spatial information on agricultural exposure and damage from flooding, economic land-use modelling, and valuation approaches. We analyse direct tangible and direct intangible flood impacts under various extents, frequencies, and water depth levels. Our results show areas with flood exposure and damage costs increase with projected climate change. As an adaptation measure to reduce flood damage costs, pastoral farms convert to other land uses, mainly to unfarmed land. Flooding reduces the net revenue for the catchment, while it can result in some reduction of environmental pollution. When the probability of all possible flood events are considered, the value of the changes in greenhouse gas emissions are equivalent to approximately 18% of the change in total net revenue, while the value of changes in other environmental outputs is equivalent to less than 1% of the change in total net revenue. Based on this study, the assessment of various flood impacts and adaptation responses to them can help to develop resilience strategies for the agricultural sector to future climate-induced flooding.

Examining the Effects of a Flood Event in the Lower Ceyhan Basin in 1980 Using Historical Satellite Data

Analysis of past flood events contributes forecasting of effects of future flood events. Flood maps have been created in order to assess flood hazards in planning projects and to identify flood-inundated regions with flood damage following flood occurrences. Flood mapping in the context of flood monitoring enables development of flood management strategies to protect life and property. Although conventional terrestrial observations and measurements in flood control have been constrained by topographical and meteorological circumstances, remote sensing provides decision support with quick analysis capability. The flood event that occurred in the Lower Ceyhan Basin of Turkey in 1980 was examined in this work utilizing satellite-based remote sensing techniques, and flood inundation areas were calculated using NDWI (Normalized Difference Water Index). As a result, it was determined that 3493.45 ha in the north of Karataş in the Lower Ceyhan Plain, 7799.42 ha between Bahçe, Akdeniz, and Yumurtalık, 7404.9 ha around Çatalpınar and Yakapınar in the Lower Ceyhan Plain, and approximately 24890 ha in the Upper Ceyhan were affected by the flood event in 1980.

Between Floods and Climate Change: Revisiting the Mishing Community of Majuli Island, Northeast India

The transformation of monsoon rainfall patterns in India, largely attributed to climate change, is leading to more frequent and severe floods. These escalating challenges underscore the imperative of prioritising adaptive measures, given the intrinsic link between humans and climate change. This research conducted in Majuli Island, a highly vulnerable region in India’s northeast, aims to understand current adaptive strategies and assess potential risks from impending physical exposures. Empirical evidence was collected using purposive sampling in two flood-prone villages. The objective was to revisit the Mishing community’s experiences with annual flooding and climate challenges. Thematic analysis interpreted the qualitative findings. Implications for community-based adaptation and sustainable practices are discussed for future flood and climate challenges. The study emphasises strengthening ecosystem-based adaptation through multi-sectoral networking in Majuli Island, Northeast India.

How effective is twitter (X) social media data for urban flood management?

Social media has been an instant source of information for natural disasters, such as urban floods, throughout the world. Ex-post evaluation of the information is considered more important after a flood disaster with devastating consequences on critical infrastructure, the environment, and the well-being of the society. Research proposes an evaluation framework for integrating the disorganized online public opinion on urban flood disaster events towards emotional and conceptual characteristics for better ex-post analysis and public participation. Social media posts on an urban flood were acquired using a search engine, and then sentiment analysis, topic modeling, and spatial–temporal analysis were performed to generate measures of online public opinion about the urban flood crisis. Twitter (X) is the most popular microblogging service presently available. As per the methodology, we analyzed all tweets regarding the 2022 urban flood in the cosmopolitan city (Karachi) of Pakistan from users all over the world. The evaluation results demonstrated that the distribution patterns of post intensities and emotion polarity in response to the floods emphasized crucial aspects with contradictory emotions and highlighted the strategic implications. Experimental results on real datasets show relatively better performance than the baseline and state-of-the-art approaches and achieved the highest 91% score. Online public opinion is a valuable supplement to ex post-disaster evaluation as it helps the project (e.g., flood management) better perform and provides suggestions for future flood mitigation, especially public participation management.

Projected seasonal flooding in Canada under climate change with statistical and machine learning

Study regionCanada Study focusFloods are among the costliest and deadliest natural hazards in the world. To date, little is known about future seasonal flooding across all Canada. In this paper, data-driven models for flood occurrence (i.e., happening of a flood) and impact (i.e., displaced population) were calibrated for spring and summer seasons in 14,000 watersheds across Canada. Generalized Additive Models (GAM), Random Forests (RF) and Gradient Boosting Machines (GBM) were considered to model seasonal floods. The best-performing flood models were then used with regional climate models to assess the effect of climate change on flooding for three time horizons: historical, medium-term (∼2050) and long-term (∼2080). New hydrological insights for the regionGAM offered the best out-of-sample performance trade-off in both seasons for predicting flooding in Canada. Projections with GAM showed a general increase in summer flooding occurrence and impact in 2050 and 2080, mainly in the Yukon, western British Columbia, southern Prairies, Ontario and Quebec and some Atlantic provinces. Results for spring flooding were more mixed, but there seemed to be a slight decrease in the impact of spring flooding in the southern Prairies, particularly in 2080. The combination of statistical/machine learning and climate models have provided a more detailed and contrasted picture of the projected seasonal flooding situation over Canada that will help authorities better mitigate future flood risks.

Regional event-based flood quantile estimation method for large climate projection ensembles

Emerging large ensemble climate datasets produced by multiple general circulation models and their downscaling products challenge the limits of hydrodynamic models because of the immense data size. To overcome this new challenge and estimate the discharge quantiles corresponding to different return periods at all river sections in an entire region, this study proposes an event-based regional approach that uses a nationwide distributed rainfall–runoff model as well as large climate projection ensembles. This approach addresses the high computational burden associated with continuous simulations and solves the problem of conventional event-based simulations serving only a single outlet of a basin. For our analysis, we extracted 372 annual maximum 48 h rainfall events that cover the entirety of Shikoku Island and its eight major river basins. Peak discharges were estimated using a 150 m resolution rainfall–runoff–inundation model. These discharges were then screened using either the peak-over-threshold (POT) method or block maxima (BM) method, and frequency curves were subsequently constructed and evaluated. The primary reason for the necessity of POT or BM was to avoid interference from extraneous low discharges. The POT-based frequency curves showed good accuracy when using peak discharges in the range of the top 10–50%, and the results remain stable within this threshold range. The BM method, employing block sizes of 2–5 years, can generate relatively accurate frequency curves, but the choice of block size introduces significant variations in results among certain basins. Generally, the accuracy of results based on the POT method surpasses that of the BM method. Considering the accuracy, computational cost, and result stability, the POT method is preferred. The error introduced by the regional approach was acceptable with more than half of the relative root-mean-square errors remaining within 10% and basically all of the results are within 20%. The results of the regional approach exhibited good accuracy across climate scenarios and provided consistent information regarding future flood quantiles. This study serves as the foundation for high-resolution future flood risk assessment.

The value of marsh restoration for flood risk reduction in an urban estuary

The use of nature-based solutions (NBS) for coastal climate adaptation has broad and growing interest, but NBS are rarely assessed with the same rigor as traditional engineering solutions or with respect to future climate change scenarios. These gaps pose challenges for the use of NBS for climate adaptation. Here, we value the flood protection benefits of stakeholder-identified marsh restoration under current and future climate change within San Francisco Bay, a densely urbanized estuary, and specifically on the shores of San Mateo County, the county most vulnerable to future flooding in California. Marsh restoration provides a present value of $21 million which increases to over $100 million with 0.5 m of sea level rise (SLR), and to about $500 million with 1 m of SLR. There are hotspots within the county where marsh restoration delivers very high benefits for adaptation, which reach $9 million/hectare with likely future sea level and storm conditions. Today’s investments in nature and community resilience can result in increasing payoffs as climate change progresses and risk increases.