Flood Losses Research Articles

Linking direct rainfall hydrodynamic and fuzzy loss models for generating flood damage map

ABSTRACT This research work proposes a combined method for mapping flood loss in catchment scale in which direct rainfall modelling and fuzzy approach are linked. The direct rainfall modelling was carried out using HEC-RAS 2D in which rainfall event hyetograph was defined as the boundary condition, and infiltration layer and roughness layer were other main inputs of the model. The fuzzy loss model was developed to assess direct-tangible damages of the flood in which expert opinions were applied to generate verbal fuzzy rules of flood loss. In this model, depth and velocity are inputs and normalized flood loss (between 0 and 1) is output. The results of the direct rainfall model and the fuzzy loss model were combined to generate loss map using python scripting in geographical information system. The output of direct rainfall model was verified based on recorded depths at downstream hydrometric station in which the Nash–Sutcliffe efficiency (NSE) and root mean square error (RMSE) were applied as the evaluation indices. Due to acceptability of indices (NSE = 0.75, RMSE = 0.83 m), the direct rainfall model was reliable. Maximum flood loss was 0.91 in the case study. Using the proposed approach is recommendable for to improve flood damage assessment in the catchments.

Development of a hazard risk map for assessing pedestrian risk in urban flash floods: A case study in Cúcuta, Colombia

AbstractThe rapid growth of impervious areas in urban basins worldwide has increased the number of impermeable surfaces in cities, leading to severe flooding and significant economic losses for civilians. This trend highlights the urgent need for methodologies that assess flood hazards and specifically address the direct impact on pedestrians, which is often overlooked in traditional flood hazard analyses. This study aims to evaluate a methodology for assessing the risk to pedestrians from hydrodynamic forces during urban floods, with a specific focus on Cúcuta, Colombia. The methodology couples research outcomes from other studies on the impact of floodwaters on individuals of different ages and sizes with 1D/2D hydrological modeling. Advanced computational algorithms for image recognition were used to measure water levels at 5‐s intervals on November 6, 2020, using drones for digital elevation model data collection. In Cúcuta, where flood risk is high and drainage infrastructure is limited, the PCSWMM (Computer‐based Urban Stormwater Management Model) was calibrated and validated to simulate extreme flood events. The model incorporated urban infrastructure details and geomorphological parameters of Cúcuta's urban basin. Four return periods (5, 10, 50, 100), with extreme rainfall of 3 h, were used to estimate the variability of the risk map. The output of the model was analyzed, and an integrated and time‐varying comparison of the results was done. Results show that the regions of high‐water depth and high velocity could vary significantly along the duration of the different extreme events. Also, from 5 to 100 years return period, the percentage of area at risk increased from 9.6% to 16.6%. The pedestrian sensitivity appears much higher than the increase in velocities or water depth individually. This study identified medium to high‐risk locations, which are dynamic in time. We can conclude dynamics are spatiotemporal, and the added information layer of pedestrians brings vulnerability information that is also dynamic. Areas of immediate concern in Cúcuta can enhance pedestrian safety during flash flood events. The spatiotemporal variation of patterns requires further studies to map trajectories and sequences that machine learning models could capture.

Investigating the role of refining urban flood direct loss in enhancing the accuracy of indirect economic loss assessment

Indirect Economic Loss (IEL) constitutes a significant component of urban flood loss, particularly in disasters affecting densely populated cities with a substantial concentration of assets and industrial supply chains. Information on the scope of disaster-induced damage is vital for assessing IEL, and its accuracy largely depends on the precision of information regarding direct loss. However, existing studies usually rely solely on administrative statistical data such as sectoral GDP or employed population. Few studies have focused on the impact of differences in the accuracy of direct information on IEL. Here, we improve the AMIL model and conduct a comparative study on the “7.6 Wuhan flood disaster” in 2016, measuring the role of refining the following three dimensions in enhancing the accuracy of IEL assessment results - direct loss data, industry subdivision number, and ripple region number -. The results indicate that (1) Using multiple sources of data, such as geographic and spatial data, to refine Direct Economic Loss (DEL) and the affected labor population can increase IEL by up to 10.49 %; (2) Subdividing industries with more severe loss to detailed sectors can improve IEL accuracy by up to 12.50 % compared to non-subdivision; (3) Refining direct loss statistics for sectors with higher proportions of intermediate input and output can balance statistical costs and precision requirements. This study aims to enhance the accuracy of IEL assessments resulting from disasters by proposing a more efficient system for direct loss statistics and providing theoretical and empirical support for such improvement.

Flood timing and flood loss impact on effectiveness of florpyrauxifen-benzyl coated on urea in rice

Abstract Florpyrauxifen-benzyl has generated complaints and concerns around rice injury and off-target movement to soybean since its commercial launch in 2018. Developing a precise method for applying florpyrauxifen-benzyl was imperative for its continued use. Experiments were conducted in 2020 and 2021 to evaluate rice weed control as influenced by preflood application interval and flood loss following florpyrauxifen-benzyl at 30 g ai ha−1 applied as a spray or coated on urea. In a preflood application experiment, coating florpyrauxifen-benzyl on urea and applying it the day of flood establishment and 5 and 10 d prior to flooding (DPTF) resulted in lower yellow nutsedge, broadleaf signalgrass, and barnyardgrass control than when the herbicide was spray at 3 and 5 wk after final treatment (WAFT). Coating florpyrauxifen-benzyl onto urea provided only 61% to 63% yellow nutsedge control at 3 and 5 WAFT, which was 35 to 37 percentage points lower than when the spray was applied at 5 or 10 DPTF. Likewise, rice yields following applications of florpyrauxifen-benzyl coated onto urea were 1,200 kg ha−1 less than yields following spray applications. Florpyrauxifen-benzyl coated onto urea and clomazone provided lower levels of weed control than spraying the herbicide alone, suggesting an explanation for the yield losses. The timing of flood loss experiment suggested that when florpyrauxifen-benzyl coated onto urea at 30 g ai ha−1 was applied preflood and flood was relinquished at 2 h, 24 h, and 7 d after flood establishment, hemp sesbania and yellow nutsedge control were not affected. However, loss of floodwater 2 h after flood establishment resulted in lower barnyardgrass control than when the flood was lost 24 h and 7 d after flooding. Generally, the period between a herbicide application and flooding completion should be minimized to aid in weed control. These results indicate the importance of maintaining a flood for weed control and nutrient management.

Flash flood susceptibility mapping of north-east depression of Bangladesh using different GIS based bivariate statistical models

Flash flood causes severe damage to the environment and human life across the world, no exception is Bangladesh. Severe flash floods affect the northeastern portion of Bangladesh in the early monsoon and pose a serious threat to every aspect of socioeconomic development and environmental sustainability. To manage the threat and reduce flood loss, the map of flash flood susceptible zones plays a key role. Thus, the aim of this research is to map the flash flood-susceptible areas of the northeastern haor areas of Bangladesh utilizing GIS-based bivariate statistical models. The models utilized are frequency ratio (FR), weights of evidence (WoE), certainty factor (CF), Shanon’s entropy (SE) and information value (IV). Among the 250 identified flash flood locations, 80 % data was used for training purposes and 20 % data for testing purposes. Eleven selected conditioning factors of flash flood include elevation, slope, aspect, curvature, TWI, TRI, SPI, distance to stream, stream density, rainfall and physiography. The calculated weights are assigned to the conditioning factors using ArcGIS environment to prepare the final flash flood maps. Results of AUC of ROC indicate WoE (success rate = 0.833 and prediction rate = 0.925) is the best model for flash flood susceptibility mapping followed by FR (success rate = 0.828 and prediction rate = 0.928) and SE (success rate = 0.827 and prediction rate = 0.923). According to the models, topographic (flat area) and hydrologic factors significantly control flash flood occurrence in the study area. The prepared flash flood susceptibility maps will be helpful for disaster managers and haor master planners of the study area.

Rapid damage assessment caused by the flooding event 2021 in Limburg, Netherlands

The floods in the Netherlands in the summer of 2021 led to severe economic damage and losses in the affected area. A first estimate in September 2021 showed that more than 2,500 houses, more than 5,000 inhabitants and around 600 businesses were affected. Using the Dutch standard Flood Damage and Loss Model (SSM2017), and based on figures from international literature, the total damage in the Netherlands is estimated in the order of € 350 – 600 million. Physical damage to houses and businesses, business interruption, damage to infrastructure and crop losses were the most substantial. The differences in damage to individual structures (residential and commercial) were large. The estimated damage in the affected area is clearly larger than for the Meuse river floods in 1993 and 1995 which occurred in the same region (converted to 2021 prices: around € 200 million and € 125 million, respectively, excluding damage due to business interruption). It is important to note that the largest damages and losses in 2021 occurred the smaller regional rivers, mainly in the Geul floodplain, while in 1993 and 1995 most damage and losses were recorded in the Meuse floodplain.
 This paper aims to improve the preliminary damage estimates made right after the floods in 2021 by comparing them with actual damage reports from insurance companies and information acquired for the implementation of the national Act “Compensation of damage due to disasters” (WTS). We conclude that the value of the damage is about € 400 - 500 million, which is in line with the initial estimate. It is however seen that there is a gap between the assessments of damage by experts and the damage as perceived by citizens and companies.

Impacts of an upper tropospheric cold low on the extreme precipitation in Henan Province, China in July 2021

From 19 to 21 July 2021, Henan province of China experienced an extreme precipitation event that caused massive flooding and great loss of lives. This event is thus far the second heaviest precipitation event observed by rain gauges in this region. Based on the ERA5 reanalysis data, the ECMWF operational global ensemble forecasts and numerical simulations using the ARW-WRF model, impacts of an upper tropospheric cold low (UTCL) on the extreme precipitation are examined. It is found that due to the influence of the persistent intrusion of stratospheric high potential vorticity (PV) air, a long-lived UTCL was detached from the upper level flow a week prior to the extreme precipitation event. The UTCL then moved westward, reaching the Yellow Sea and the East China Sea and maintaining there until the precipitation event ended. During this event, a broad northeast-southwest oriented area of ascending motion associated with the UTCL could be observed in front of the UTCL and strong ascending motions developed in the upper troposphere above Henan province. Analysis of the ECMWF operational global ensemble forecasts reveals that the amount of precipitation over Henan is positively correlated with the UTCL intensity. The UTCL impact on the extreme precipitation and the underlying mechanisms are further investigated based on results of numerical experiments. The control experiment reasonably reproduces the UTCL location as well as the distribution and evolution of the extreme precipitation. When the UTCL intensity is reduced in the initial condition using the piecewise PV inversion for sensitivity experiment, the upper tropospheric divergence reduces correspondingly and the dynamical ascending motion weakens in the second precipitation stage. As a result, the amount and intensity of precipitation both decrease. When the UTCL is completely removed from the initial condition, the sensitivity experiment indicates that the upper tropospheric divergence and dynamical ascending motion further weaken, resulting in a large decrease in precipitation intensity during the whole precipitation period. These findings highlight that the occurrence of the long-lived UTCL is a crucial factor that affects the intensity of the extreme precipitation event.

Flood economic vulnerability and risk assessment at the urban mesoscale based on land use: A case study in Changsha, China

With climate change and urbanization, flood disasters have significantly affected urban development worldwide. In this study, we developed a paradigm to assess flood economic vulnerability and risk at the urban mesoscale, focusing on urban land use. A hydrological simulation was used to evaluate flood hazards through inundation analyses, and a hazard-vulnerability matrix was applied to assess flood risk, enhancing the economic vulnerability assessment by quantifying the differing economic value and flood losses associated with different land types. The case study of Wangchengpo, Changsha, China, found average total economic losses of 126.94 USD/m2, with the highest risk in the settlement core. Residential areas had the highest flood hazard, vulnerability, and losses (61.10% of the total loss); transportation areas accounted for 27.87% of the total economic losses due to their high flooding depth. Despite low inundation, industrial land showed greater economic vulnerability due to higher overall economic value (10.52% of the total). Our findings highlight the influence of land types and industry differences on flood vulnerability and the effectiveness of land-use inclusion in urban-mesoscale analyses of spatial flood characteristics. We identify critical areas with hazard and economic vulnerability for urban land and disaster prevention management and planning, helping to offer targeted flood control strategies to enhance urban resilience.

Assessment of Flood Economic Losses Under Climate Change: A Case Study in the Ngan Sau River Basin, Ha Tinh Province and Vietnam

The Ngan Sau River basin, which is situated in Ha Tinh Province of Vietnam, experiences flooding during the rainy season, resulting in significant loss of property and human life. This research aimed to investigate the impact of climate change and land-use variation on flood losses. The study began by simulating the heavy rainfall events in August 2007 using the Weather Research and Forecast model with an ensemble method. Future rainfall was examined through numerical simulation based on pseudo-global warming constructed using six CMIP5 models (MIROC-ESM, MRI-CGM3, GISS-E2-H, HadGEM2-ES, HadGEM2-ES, and CNRM-CM5), and the variation in land-use was obtained from local authorities. Inundations caused by rainfall in 2007 and rainfall in the future were determined by the rainfall-runoff-inundation model. Finally, based on flood maps, land-use, and flood depth-damage functions, the economic losses were computed. The results of the average flood economic loss were $380 million in CTL, whereas the local authorities report an estimated loss of over $300 million. Under the impact of climate change and land-use variation, economic losses ranged from $380 million to $526 million in six CMIP5 models. The result of INMCM4 showed the highest value of $526 million, the results of MRI-CGM3, GISS-E2-H, HadGEM2-ES, and CNRM-CM5 fluctuated around $500 million, and the MIROC-ESM recorded the lowest at $380 million. The damage maps showed that the losses would be highest in urban areas, followed by forest areas, and lowest in agricultural areas. This information is essential for decision-makers to improve solutions for preventing economic losses caused by floods.

To What Extent Have Nature-Based Solutions Mitigated Flood Loss at a Regional Scale in the Philadelphia Metropolitan Area?

Globally, floods are becoming more severe, lasting longer, and occurring more frequently because of changes in climate, rapid urbanization, and changing human demographics. Although traditional structural flood mitigation infrastructures (e.g., drainage systems, levees) are effective in urban areas, their functionalities in the face of extreme rainfall events and increased development largely depend on the capacity and location of such systems, making complementary solutions such as nature-based solutions (NBS) important. The concept of NBS within the context of flood mitigation has gained traction in the last decade; however, the success of NBS depends on their effectiveness and distribution over urban regions. This article seeks to examine the potential of NBS as a flood loss mitigation tool in one of the fastest-growing and flood-prone counties of Pennsylvania, Montgomery County, using Generalized Linear Model (GLR) and Geographically Weighted Regression (GWR) techniques. The analysis integrates the National Risk Index dataset for river flooding, a 100-year flood zone layer from National Flood Hazard Layer (NFHL) provided by FEMA, with land use and impervious surface percent data derived from National Land Cover Database (NLCD) for 2019 and socioeconomic data at the U.S. census tract level from the 2019 U.S. Census. This study’s findings partially contradict previous research by revealing an unexpected relationship between NBS quantity in floodplains and expected annual loss. Findings also suggest that small size and disconnected patches of NBS in floodplains in some dense urban areas effectively reduce total losses from flood events.

A holistic physics-based observer for online flooding and ECSA loss detection in FCEV

This paper addresses the critical need for improving fuel cell electric vehicles (FCEVs) reliability to promote their commercialization. A physics-based observer that combines an Extended Kalman Filter (EKF) with a Sequential Probability Ratio Test (SPRT) has been employed to address two major failure modes in FCEV stacks: flooding and reduction of Electrochemical Active Surface Area (ECSA). Furthermore, the observer estimates the hidden states of a standard automotive airpath system to enable state-feedback control. Therefore, the proposed observer is a novel approach which holistically tackles three coupled problems (i.e., flooding, ECSA loss, and airpath state estimation) which are typically addressed individually. Additionally, the observer is computationally affordable and relies solely on typical vehicle sensors. The observer performance has been validated through simulations, testing various dynamic scenarios in the presence of flooding, ECSA loss failures, and sensors uncertainty. The estimation errors have been confined within a 3% error band for all test cases while the health indicators coherently responded in the presence of faults.

Assam Flood and Financial Loss in Agricultural Production: A Study on Mitigation Strategy Adopted by the Farmers of Dhemaji District

Background: Assam is considered as a flood prone state, as this natural disaster creates devastating situation almost every year. The study was conducted in Dhemaji district of Assam which is in the list of highly flood hazard index. The district was selected to know the impact of flood on financial loss for agricultural production and to study the mitigation strategy adopted by the flood affected farmers in the district. Methods: For that a multistage random sampling technique was adopted to select flood affected farmers from the district. Financial loss for agricultural production due to flood was estimated by calculating financial loss for crop loss, agricultural asset loss and livestock loss for the study area. Flood situation and Non flood situation was compared while going for calculation of financial loss for crop due to flood. Henry Garret ranking technique was adopted to rank the problems face by the farmers. Result: The result reflected that net return from crop production was recorded to be less (Group I 1032.44Rs/ha and Group II 3830.99 Rs/ha) in flood situation than the non-flood situation (Group I 7051.30 Rs/ha and Group II 8330.08 Rs/ha).Total financial loss due to crop loss, asset loss and livestock loss was found to be high for size group II farmers (Rs 39094.33) then the size group I (Rs 36643.40). Majority of the respondents (79.17 per cent) preferred migration as the best coping mechanism to protect them from flood loss.

Comprehensive Performance Assessment for Sponge City Construction: A Case Study

Sponge City Construction (SCC) can effectively solve urban areas’ water problems. Green infrastructure (GI), the core of SCC, exhibits a multifaceted capacity to deliver many co-benefits, while grey infrastructure primarily serves the single function of controlling rainfall runoff. However, existing assessment indicator systems fail to either consider or evaluate comprehensive impacts. In this work, a comprehensive indicator system has been established to assess the co-benefits of five different GI measures. The system includes twelve indicators from four aspects, i.e., disaster reduction, economic, environmental, and social benefits. This newly established assessment system is applied to Jinan as a case study. Six out of twelve indicators are evaluated using the self-developed Flood Risk Analysis Software and empirical equations. The results show that the inundation reduction ratios are 11.02%, 9.32%, and 8.02% under the 24-h design rainfall with a 5-year, 10-year, and 20-year return period, respectively. In addition, the corresponding direct flood loss reduction rates are 13.86%, 4.28%, and 4.09%. That is, as the rainfall return periods increase, the disaster reduction benefits become less pronounced. On the contrary, other benefits, e.g., groundwater recharge volumes, are more obvious. The corresponding groundwater recharge volumes are 2.23 million m3, 2.86 million m3, and 2.87 million m3, respectively. The proposed indicator system can be adopted to assess the performances of the different GI combinations, which provides effective support and reference to decision-makers in SCC at the planning and design stages.

Framework for considering the interactions between climate change, socio-economic development and land use planning in the assessment of future flood risk

Flood risk is expected to increase significantly around the globe, fuelled by the nexus between flooding and land use. This is because changes in land use due to socio-economic development increase runoff, as well as the “values-at-stake” exposed to flooding. At the same time, changes in flood extent and depth can impact land use via policy interventions aimed at reducing future flood risk, such as buy-backs and zoning. Existing approaches to flood risk assessment and mitigation have generally ignored this two-way interaction between flooding and land use. To address this shortcoming, an integrated framework considering the dynamics of future changes in the flood-land use nexus is introduced and applied to the Gawler River region, South Australia. Results indicate that changes in land use due to socio-economic drivers can have a significantly greater impact on flood losses than climate change and that zoning can be effective in reducing future flood risk.

Assessing community flood adaptation capacity to reduce flood losses

Flooding constitutes the largest proportion of economic losses from disasters in the US and is poised to become the main driver of disaster-driven economic loss worldwide. Community level flood planning has the potential to alleviate part of the problem. The US Community Rating System incentivizes community floodplain management practices in exchange for flood insurance premium rate discounts. The main goal of the program is to reduce and avoid flood damage to insurable property. Using multi-period matching and difference in differences this paper explores whether and to what extent this goal is achieved. Contrary to past evidence, this assessment finds mixed evidence on the potential that participating in the CRS decreases flood losses for communities that join the CRS. Further research is required to explore whether the program contributes to increasing community resilience aspects beyond decreasing damage to insurable property, and to determine who benefits from flood loss reduction.

Compound Continental Risk of Multiple Extreme Floods in the United States

AbstractUnderstanding spatially correlated floods and modeling joint hazard associated with threshold exceedances across multiple locations is crucial for accurate estimation of continental‐scale portfolio risk. This work uses a non‐parametric copula‐based spatial simulator to analyze peak floods across the United States to derive the first‐of‐its‐kind continental portfolio risk estimates at the 10‐ and 100‐year return levels. We find significant interdependence in floods across the nation, revealing the recurring pattern of extreme events affecting the Northeast, Central, West, and Northwest United States in the same year. The stochastic simulator effectively manages high‐dimensional data and offers reliable uncertainty estimates for both spatially dependent floods and the aggregated flood losses at the continental level. El Niño‐Southern Oscillation and Atlantic Multidecadal Oscillation are identified as statistically significant tele‐connectors of aggregate loss. This research aims to advance the understanding of compound continental flood hazard and the potential large‐scale climate teleconnections that lead to such compound floods.

Fine‐scale damage assessment of urban flooding based on the 1D/2D coupled hydrodynamic model

AbstractDue to the high population density and economic property in urban environments, urban flooding can result in substantial economic losses. However, accurate urban flood modeling and assessing the associated damages are challenging due to complex urban layouts and rugged topography. Furthermore, the rapid development of urban underground spaces has introduced additional complexities. To address the demanding need for accurate flood modeling and damage assessment in cities, a fine‐scale flood damage assessment method was proposed in this study, based on the 1D/2D coupled hydrodynamic model, which can not only assess the flood damage on the ground surface but also evaluate the flood loss in underground spaces. Taking the Gangxi drainage area in Wuhan City, China as a case, this study extensively discussed the flood inundation processes on the ground surface as well as in the buildings and underground spaces, under different rainfall scenarios with the return period ranging from 2 to 200 years. Based on the high spatial‐temporal hydrodynamic simulations, the flood damage degrees were evaluated for the buildings and underground spaces with different industry types. The results indicated that the inundation of buildings and underground spaces in densely built cities cannot be neglected in urban flood modeling. Buildings with public service attributes can have a higher flood damage degree, as compared with other types of buildings and underground spaces.

Quantitative flood disaster loss-resilience with the multilevel hybrid evaluation model

The severity of global flood disasters is growing, causing loss of human life and property. Building a high-resilience social system, an important means of sustainable flood control, can address these flood-related issues. Numerous studies have carried out disaster resilience evaluations and explored the correlation between flood disaster loss and intensity, but neglected to analyze the role of resilience construction in disaster loss reduction. This study proposed a research route for linking flood loss and disaster loss to quantify the relationship between the two. Take Guangdong Province, China as a study case, the mixed-effects (ME) model and multilevel hybrid evaluation model (MHEM) were established to assess disaster loss and resilience of cities, respectively. Subsequently, disaster resilience curves were built to quantitatively evaluate disaster resilience and corresponding disaster loss. The results show that (1) the ME model can concurrently build the disaster intensity-loss curves of multiple cities with high fitting accuracy. The MHEM combines multiple methods to determine the evaluation result with the highest consistency, showing high reliability. (2) The central and southern regions of Guangdong Province have low disaster loss and high resilience, while the northern regions have high disaster loss and low resilience. (3) With the improvement of disaster resistance, the reduction in disaster loss gradually decreases. Disaster loss in low-resilience cities exhibits greater randomness than that in high-resilience cities, and increasing their resilience can more significantly reduce their level of loss. This study provides a quantitative basis and available methods for comprehensive responses to natural disasters and adaptation to global climate change.

Learning inter-annual flood loss risk models from historical flood insurance claims

Flooding is a natural hazard that causes substantial loss of lives and livelihoods worldwide. Developing predictive models for flood-induced financial losses is crucial for applications such as insurance underwriting. This research uses the National Flood Insurance Program (NFIP) dataset between 2000 and 2020 to evaluate the predictive skill of past data in predicting near-future flood loss risk. Our approach applies neural networks (Conditional Generative Adversarial Networks), decision trees (Extreme Gradient Boosting), and kernel-based regressors (Gaussian Processes) to estimate pointwise losses. It aggregates them over intervals using a bias-corrected Burr–Pareto distribution to predict risk. The regression models help identify the most informative predictors and highlight crucial factors influencing flood-related financial losses. Applying our approach to quantify the county-level coastal flood loss risk in eight US Southern states results in an R2=0.807, substantially outperforming related work using stage-damage curves. More detailed testing on 11 counties with significant claims in the NFIP dataset reveals that Extreme Gradient Boosting yields the most favorable results, and bias correction significantly improves the similarity between the predicted and reference claim amount distributions. Our experiments also show that, despite the already experienced climate change, the difference in future short-term risk predictions of flood-loss amounts between historical shifting or expanding training data windows is insignificant.

Real options analysis for valuation of climate adaptation pathways with application to transit infrastructure.

Climate change and sea-level rise (SLR) are expected to increase the frequency and intensity of coastal flood events, posing risks to coastal communities and infrastructure. While regional climate adaptation investments can provide substantive flood protection, existing plans often neglect uncertainty in future climate conditions and adaptation performance, consequently neglecting the option value of flexibly implementing proposed projects. Addressing this gap, we develop and employ a generalizable real options analysis (ROA) valuation framework that considers how uncertainty in adaptation project costs, SLR, flood severity, and flood losses inform the full range of adaptation performance outcomes. We further propose and apply a novel, computationally efficient flood loss sampling algorithm to estimate the consequences of randomly arriving coastal flood events. We apply this ROA framework to assess the option value of flexibly timing adaptation investments over time, investigating an adaptation pathway proposed by the City of Boston from the perspective of the regional transit system manager. Our results suggest that flexible implementation can provide significant option value in the near- to mid-term (>30 years), with the highest option values under low-probability, high-consequence scenarios. Our results also suggest adaptation pathway performance in the latter half of the 21st century is most sensitive to uncertainty in SLR, flood loss estimates, and flood frequency, underscoring the importance of uncertainty quantification in the long-term valuation of adaptation investments.