Great Flood Research Articles

Population exposure to flooding in Small Island Developing States under climate change

Abstract Estimates of current and future population exposure to both coastal and inland flooding do not exist consistently in all Small Island Developing States (SIDS), despite these being some of the places most at risk to climate change. This has primarily been due to a lack of suitable or complete data. In this paper, we utilise a ∼30 m global hydrodynamic flood model to estimate population exposure to coastal and inland flood hazard in all SIDS under present day, as well as under low, intermediate, and very high emissions climate change scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5). Our analysis shows that present day population exposure to flooding in SIDS is high (19.5% total population: 100 year flood hazard), varies widely depending on the location (3%–66%), and increases under all three climate scenarios—even if global temperatures remain below 2 °C warming (range in percentage change between present day and SSP1-2.6: −4.5%–44%). We find that levels of flood hazard and population exposure are not strongly linked, and that indirect measures of exposure in common vulnerability or risk indicators do not adequately capture the complex drivers of flood hazard and population exposure in SIDS. The most exposed places under the lowest climate change scenario (SSP1-2.6) continue to be the most exposed under the highest climate change scenario (SSP5-8.5), meaning investment in adaptation in these locations is likely robust to climate scenario uncertainty.

A framework and pilot study for assessing usability of flood data portals for interdisciplinary research

There is a lack of datasets to study the climate and human outcomes nexus. There are many flood data portals due to recent improvements in flood identification using satellites, providing opportunities to study the human impacts. The development of these portals is rapid and there is currently no standard for evaluating their usability for interdisciplinary research. This paper addresses this important data gap. We put forth a usability framework that includes data availability, approaches to flood identification, alignment, velocity, variety, and user feasibility aspects. We piloted it through an in-depth review and user survey of NASA Worldview (NW), Global Flood Awareness System (GloFAS), Global Flood Monitoring System (GFMS), Global Surface Water Explorer (GSWE), and Dartmouth Flood Observatory (DFO) GSWE and GloFAS were rated most favorably. Respondents had discrepancies in their opinions on the clarity of the goals and platform accessibility for GFMS, DFO, and NW, and in data and visualization quality for all portals. Historical data and measures of flood recurrence and other characteristics are needed. Flood data products should be provided in multiple formats, aggregated by sub-national boundaries, with mechanisms that delineate incomplete or unreliable data. Flood data portals should include interdisciplinary research as part of their mission. Their longevity and maintenance should be secured to preserve these important data sources for future research. This framework can be adapted and used to enable interdisciplinary spatial and survey data linkages.

Climate Risk Assessment Framework in Real Estate: A Focus on Flooding

Climate change exacerbates flood hazards, resulting in risks to real estate values that should be considered by long-term investors. Flood risk presents two major financial risks: market risk and credit risk. Market risk refers to potential property value loss, and credit risk increases the likelihood of mortgage defaults. However, methods and comprehensive data for quantifying global real estate flood risks are lacking. To address this problem, this paper proposes two flood risk assessment frameworks: the local-oriented approach (LOA) and global-oriented approach (GOA). Two hazard and three vulnerability assessment methods are also introduced to support these frameworks. The LOA vulnerability estimates of regions with complete records are required to support the GOA. Taiwan was selected as an example for the LOA assessment, and the results were used to estimate vulnerability overseas in GOA assessments. The results of case studies for buildings located in four cities in different countries were compared. The proposed framework enables investors and asset owners to globally quantify climate risks in real estate, even when the available data are incomplete. Users can choose the most appropriate approach on the basis of the available data and their tolerance for uncertainty.

Aboriginal flood narratives and the thunder complex in Southeast Asia

AbstractThe ancestors of the dingo were brought to mainland Australia around 4000 years ago by people who arrived by boat. The identity of these voyagers from the north, however, and the nature of their interactions with the Aboriginal population of Australia, are unknown. Here, we propose that Indigenous flood narratives from the Kimberley contain evidence for contact between Aboriginal people and early Asian seafarers in the form of the “thunder complex”. The latter is a very specific repertoire of taboos, rituals and stories that occurs widely among ethnographically known societies of Indonesia, the Philippines and peninsular Malaysia, but has not previously been identified in Australia. Among Southeast Asian groups, this cultural complex revolves around the idea that certain prohibited acts perpetrated against animals – especially “mocking” them by treating them as though they were human – precipitate a punitive storm and/or flooding. We show that in some oral traditions of the Kimberley region animal mockery is similarly held to be the causative agent behind disastrous flooding events that took place in the past creationary epoch. We contend that this localised Aboriginal variant of the thunder complex reflects an episode of close interaction with early Austronesian‐speaking voyagers who introduced ancestral dingoes to mainland Australia, apparently via the Kimberley coast.

Leveraging synthetic aperture radar (SAR) with the National Water Model (NWM) to improve above-normal flow prediction in ungauged basins

Abstract Effective flood prediction supports developing proactive risk management strategies, but its application in ungauged basins faces tremendous challenges due to limited/no streamflow record. This study investigates the potential for integrating streamflow derived from synthetic aperture radar (SAR) data and U.S. National Water Model (NWM) reanalysis estimates to develop improved predictions of above-normal flow (ANF) over the coterminous US. Leveraging the SAR data from the Global Flood Detection System to estimate the antecedent conditions using principal component regression, we apply the spatial-temporal hierarchical model (STHM) using NWM outputs for improving ANF prediction. Our evaluation shows promising results with the integrated model, STHM-SAR, significantly improving NWE, especially in 60% of the sites in the coastal region. Spatial and temporal validations underscore the model’s robustness, with SAR data contributing to explained variance by 24% on average. This approach not only improves NWM prediction, but also uniquely combines existing remote sensing data with national-scale predictions, showcasing its potential to improve hydrological modeling, particularly in regions with limited stream gauges.

For resilient rural shorelines: reviewing Nature-based Solutions for flood risk reduction in small coastal communities

Coastal flood risk poses a serious, existential threat to shoreline populations around the world both now and in the future. Unsurprisingly, global decision makers are considering their options – one of these being Nature-based Solutions – for effective disaster risk reduction which specifically targets coastal flooding. While strides have been made in the field of Nature-based Solutions for coastal flooding, much of this attention has been directed towards the urban setting, with a wealth of scholastic documentation to support this notion. The sizeable rural populations scattered throughout the world's small coastal communities, meanwhile, have been largely neglected in academic literature. Without this information, it is impossible to properly capture the full potential of Nature-based Solutions in (global) flood risk modelling endeavours or understand their role in the future of equitable disaster risk reduction. In light of this gap, we have reviewed the limited amount of existing literature from around the world involving the implementation and effectiveness of Nature-based Solutions in small coastal communities. We analysed 28 peer-reviewed studies to gather common themes and insights about the barriers and opportunities unique to these rural shorelines. Takeaways we have identified include a near consistent scarcity of resources (e.g., technical, financial, institutional) to implement disaster risk reduction measures; an abundance of space and opportune land use regimes which make Nature-based Solutions a highly plausible option; amplified nature contributions to people leading to larger benefits reaped from investments into Nature-based Solutions; and the presence of local knowledge regarding societal norms, climate patterns, and ecosystem capabilities. We argue that these four common themes point to the fact that more attention must be given to coastal flooding-focused Nature-based Solutions in the rural setting. As such, we present this collation as a starting point for future projects of similar setting and scope. We also recommend improving benefit-cost analysis methods as well as including local knowledge and other perspectives in future global assessments of coastal flood risk.

Identification of Flood-Prone Areas Using Geo-Informatics: A Case Study of Erbil City, Kurdistan Region, Iraq

Global flood events, intensified by an intricate interplay of natural dynamics and human activities, pose severe environmental and safety threats, driving the demand for advanced vulnerability assessments. This research innovates with a holistic geo-informatics methodology, pinpointing flood-prone regions in Erbil City, Kurdistan Region, Iraq. It harnesses the Analytic Hierarchy Process and Geographic Information System synergies, enriched with insights from multispectral satellite observations, to conduct an exhaustive analysis of key flood susceptibility indicators. These encompass geological, hydrological, meteorological, and anthropogenic parameters, integrated into a robust Geographic Information System analytical matrix. Through strategic factor analysis and prioritization, the study distills a detailed flood susceptibility cartogram, classifying regional vulnerabilities into distinct risk echelons: high (encompassing 25%), moderate (38.1%), low (28.4%), and very low (8.5%). The final flood map highlights an acute susceptibility epicenter in the northeast, contrasting with the minimal threat southwest. This demarcation reaffirms the Analytic Hierarchy Process-Geographic Information System fusion's precision in translating multifaceted data into strategic intelligence, proving instrumental for scholars, administrative frameworks, and developmental strategists. The insights garnered are pivotal in sculpting informed urban development and flood counteractive blueprints, instrumental in mitigating flood repercussions and preserving ecological and human integrities.

Concurrent droughts across Major River Basins of the World modulated by El Niño–Southern Oscillation

Droughts are among the costliest natural disasters with far-reaching impacts. Focusing on the influence of El Niño–Southern Oscillation (ENSO) on worldwide droughts, this paper presents a formulation of the complex network of concurrent droughts across the Major River Basins (MRBs) of the World. Specifically, concurrent droughts are measured by event synchronization and interconnections of concurrent droughts are determined by significance test. Event coincidence analysis is incorporated into interconnections to quantify the effects of El Niño and La Niña events. A case study is devised for the 3-monthly standardized precipitation index (SPI) of Global Drought and Flood Catalogue during the period from 1950 to 2016. The results highlight that owing to the synchronization of droughts, there exist nine communities of the MRBs, each covering MRBs across different continents. While the correlation between SPI and ENSO exhibits mixed patterns for MRBs grouped by continent, there exist distinct patterns of correlation for MRBs grouped by community. Overall, 28.88% of concurrent droughts tend to be modulated by El Niño events, especially over MRBs in South-West Pacific, South Africa, northern South America and South Asia. In the meantime, 23.51% of concurrent droughts tend to be modulated by La Niña events, especially over MRBs in the United States, southwestern South America, northern China and Central Asia. The modulation of El Niño and La Niña is confirmed by composite analysis of SPI and detailed analysis of the Yangtze River Basin. Overall, the complex network provides useful information for drought forecasting and disaster mitigation.

Comprehensive Zoning Strategies for Flood Disasters in China

The frequency of global floods has increased, posing significant threats to economic development and human safety. Existing flood risk zoning studies in disaster prevention lack integration of the natural–economic–social chain and urban resilience factors. This study addresses this gap by constructing flood disaster risk and intensity indices using data from 31 provinces and 295 prefectural-level cities in China from 2011 to 2022. These indices incorporate natural (rainfall), economic (GDP), and social (population, built-up area) indicators to assess the flood likelihood and loss degree, providing comprehensive risk and intensity ratings. The study also examines the impact of resilience factors—environmental (green space), infrastructural (rainwater pipeline density), and natural resource (watershed areas)—on flood intensity. Findings reveal that high-risk regions are mainly in the Yangtze River Basin and southern regions, while high-intensity regions are primarily in the middle and lower Yangtze River and certain northwestern cities. Increasing rainwater pipeline density mitigates flood impacts in high-risk, high-intensity areas, while expanding green spaces and pipelines are effective in high-risk, low-intensity regions. This paper proposes a comprehensive flood hazard zoning mechanism integrating natural, economic, and social factors with urban resilience, offering insights and a scientific basis for urban flood management.

Masjid-based Disaster Management: How Masjids in Malaysia Support the Needy

The COVID-19 pandemic and the great flood of 2021 significantly impacted society. However, masjids have proven effective institutions in disaster management efforts, particularly in social welfare. This study examines the role of masjids in managing disasters in Malaysia, specifically during the 2021 great flood and the COVID-19 pandemic. The qualitative research involves in-depth interviews based on case studies, with the results analyzed thematically. A total of 14 masjids as informants were selected from across Malaysia, including nine of the best masjids in 2021 and five with disaster management experience. The findings reveal that masjids are crucial in disaster management, encompassing services, health, placement, incentives, and infrastructure. These contributions help effectively manage the community during disasters. The study highlights two key factors contributing to the success of masjids in disaster management: the core concern of maqasid sharʿiah and the synergy of da’wah in strengthening the relationship between masjids and the community. The implications of this study contribute to both theory and practice, particularly in designing ideal masjid management, preserving community welfare, and maintaining the Sustainable Development Goals (SDGs).

SHIFT: a spatial-heterogeneity improvement in DEM-based mapping of global geomorphic floodplains

Abstract. Floodplains are a vital part of the global riverine system. Among all the global floodplain delineation strategies empowered by remote sensing, digital elevation model (DEM)-based delineation is considered to be computationally efficient with relatively low uncertainties, but the parsimonious model struggles with incorporating the basin-level spatial heterogeneity of the hydrological and geomorphic influences into the map. In this study, we propose a globally applicable thresholding scheme for DEM-based floodplain delineation to improve the representation of spatial heterogeneity. Specifically, we develop a stepwise approach to estimate the floodplain hydraulic geometry (FHG) scaling parameters for river basins worldwide at the scale of the level-3 HydroBASINS to best respect the scaling law while approximating the spatial extent of two publicly available global flood maps derived from hydrodynamic modeling. The estimated FHG exponent exhibits a significant positive relationship with the basins' hydroclimatic conditions, particularly in 33 of the world's major river basins, indicating the ability of the approach to capture fingerprints from heterogeneous hydrological and geomorphic influences. Based on the spatially varying FHG parameters, a ∼ 90 m resolution global floodplain map named the Spatial Heterogeneity Improved Floodplain by Terrain analysis (SHIFT) is delineated, which takes the hydrologically corrected MERIT Hydro dataset as the DEM inputs and the height above nearest drainage (HAND) as the terrain attribute. Our results demonstrate that SHIFT validates better with reference maps than both hydrodynamic-modeling- and DEM-based approaches with universal parameters. The improved delineation mainly includes better differentiation between main streams and tributaries in major basins and a more comprehensive representation of stream networks in aggregated river basins. SHIFT estimates the global floodplain area to be 9.91×106 km2, representing 6.6 % of the world's total land area. SHIFT data layers are available at two spatial resolutions (90 m and 1 km), along with the updated parameters, at https://doi.org/10.5281/zenodo.11835133 (Zheng et al., 2024). We anticipate that SHIFT will be used to support applications requiring boundary delineations of the global geomorphic floodplains.

A robust ensemble of hybrid and bivariate statistical models for flood prediction mapping in Lower Damodar River Basin of India

This research explores flood prediction in the Lower Damodar River Basin (LDRB) using a hybrid ensemble of a Naïve Bayes Tree (NBT) and five bivariate statistical models such as Evidential Belief Function (EBF), Index of Entropy (IOE), Frequency Ratio (FR), Statistical Index (SI), and Modified Information Value (MIV). A total of 348 flood locations and 15 conditioning factors including hydrological, topographical and land cover were considered for this analysis. To ensure the precision of model predictions, a multicollinearity assessment was executed. Receiver operating characteristic (ROC) curve, area under curve (AUC), mean absolute error (MAE), mean squared error (MSE), and root mean squared error (RMSE) were performed to compare and asses each of the models all. The results reveal that all models performed well in creating flood hazard maps with AUC >0.8 and RMSE <0.4. FR and EBF models demonstrated the highest predictive accuracy (AUC=0.85), followed by the IOE, SI, and MIV models. The ensemble of NBT with bivariate models shows promising results, showcasing reduced error metrics and improved accuracy for the IOE, SI, and MIV models. This study highlights the potential of ensemble models in flood hazard prediction, offering valuable insights for global flood risk management. The successful application of these data-driven models showcases their importance in forecasting flood risks, aiding decision-makers and planners in developing more effective flood mitigation strategies.

Frequency Bias Causes Overestimation of Climate Change Impacts on Global Flood Occurrence

AbstractThe frequency change of 100‐year flood events is often determined by fitting extreme value distributions to annual maximum discharge from a historical base period. This study demonstrates that this approach may significantly bias the computed flood frequency change. An idealized experiment shows frequency bias exceeding 100% for a 50‐year base period. Further analyses using Monte Carlo simulations, mathematical derivations, and hydrological model outputs reveal that bias magnitude inversely relates to base period length and is weakly influenced by the generalized extreme value distribution's shape parameter. The bias, persisting across different estimation methods, implies floods may exceed local defenses designed based on short historical records more often than expected, even without climate change. We introduce a frequency bias adjustment method, which significantly reduces the projected rise in global flood occurrence. This suggests a substantial part of the earlier projected increase in flood occurrence and impacts is not attributable to climate change.

A time-space varying distributed unit hydrograph (TS-DUH) for operational flash flood forecasting using publicly-available datasets

Although flash floods originate in upstream (headwater) catchments causing hazards locally, they often occur at short time scales and affect widely while relatively small size catchments respond to intense rainfall systems. Due to the heavy cost of computing and parameterization, high resolution spatially distributed hydrological models are not effectively deployed for global or large-scale flash flood forecasting. This research proposes a Time-Space varying Distributed Unit Hydrograph (TS-DUH) based on publicly-available data for efficient global scale flash flood forecasting. TS-DUH estimates the runoff travel time (and flow velocity) from each location to the catchment outlet based on topographic and hydroclimatic characteristics, while it delineates the runoff-drainage process by emphasizing the heterogeneous and dynamic runoff contribution corresponding to rainfall and soil moisture variations. The excess rainfall is estimated by two methods: the Soil Conservation Service’s (SCS) curve (CN-Runoff), and the widely used Global Flood Monitoring System (GFMS) which provides both long-term (2000-present) well-archived and real-time online global runoff (GFMS-Runoff) prediction based on a distributed hydrological model. TS-DUH is first validated based on 6,324 flash flood events from 281 small-to-medium-sized catchments broadly distributed across the CONUS, using “perfect” precipitation input (i.e., NLDAS-2 for this study). TS-DUH calibrated with GFMS-Runoff shows better performance than using CN-Runoff, with a probability of detection (POD) value of 0.9 for all the events and 71% of them having a KGE metric above 0.5. More importantly, with the real-time satellite rainfall-driven GFMS-Runoff, the long-term (2003–2020) TS-DUH simulation shows better performance than GFMS, indicating promising value in global flash flood prediction of the TS-DUH-integrated GFMS.

Flood monitoring in an Giang Province, Vietnam using global flood mapper and Sentinel-1 SAR

ABSTRACT According to the 2018 report of the United Nations Office for Disaster Risk Reduction (UNISDR), the total number of recorded flood events between 1998 and 2017 was 3,148. This means there is an average of 13 monthly floods on our Earth. Satellite monitoring using Sentinel-1 synthetic aperture radar (SAR) can be effectively used to map floods. It provides a short revisit time, a high spatial resolution of 5 m, and a swath of up to 400 km. In this study, we compare two promising flood mapping algorithms to map floods which are common phenomena in the An Giang area, and to monitor the mapping difficulty of flood peaks. The two mapping algorithms are Global Flood Mapper (GFM), an open-source Google Earth Engine (GEE) application; and the other algorithm implemented the Otsu threshold method and change detection approach. They were visually compared using Sentinel-2 optical data and a land-use land cover (LULC) map of An Giang, Vietnam. The results show that the GFM overcomes the misclassification of flooded and non-flooded areas. A good statistical agreement showed that the performance of flood maps derived from the modified GFM algorithm on Sentinel-1 VH-VV-VH RGB composite and Sentinel-2 images have better accuracy than the Otsu Sentinel-1 technique by ∼ 20 % .

Deciphering the Role of Total Water Storage Anomalies in Mediating Regional Flooding

AbstractRegional floods result from various flood generation mechanisms. Traditional analyses mainly link flooding to extreme rainfall, with limited input from soil moisture. Total water storage (TWS) is a holistic measure of basin wetness, including additional storage components from surface water, snow, and groundwater. Utilizing a new 5‐day Gravity Recovery and Climate Experiment and its Follow On (GRACE(‐FO)) data set, we investigated the linkage between short‐term TWS anomaly (TWSA) and regional flooding. The 5‐day TWSA solutions revealed flood signals missed by monthly TWSA solutions. Global basins exhibit distinct storage‐discharge co‐evolution patterns, offering new insights into flood mechanisms and propensity. Our bivariate event analyses show the annual maximum river discharges co‐occur more often with the TWSA maxima than with precipitation in many basins. Further analyses revealed TWSA's time‐lagged effect on river discharge, particularly in basins susceptible to floods triggered by saturation‐excess runoff. The 5‐day TWSA provides a new source of information for enhancing global flood preparedness.

Unforgetting: The Yarri and Jacky Jacky Memorial at Gundagai

ABSTRACT There is a sculpture in the main street of Gundagai (NSW, Australia) that honours two Wiradjuri men, Yarri and Jacky Jacky, local heroes in the Great Flood of 1852. Unveiled in 2017, the bronze sculpture depicts the two men and a scribbly-bark canoe used to save settler townspeople from the floodwaters. The sculpture, unmissable in the town’s centre, reminds even the most forgetful onlooker of colonialism. Unlike many Australian monuments, this statue does not memorialise and legitimise colonialism or celebrate nationalism and White male “heroes”. Rather, it is a testament to the Wiradjuri men’s bravery, skill and compassion. Standing before the monument, I was dumbstruck. Why? Despite settler-colonial violence, land theft, oppression and dismissals of Wiradjuri warnings, the men risked their lives. The statue slowed me down, interrupted my trajectory. The Yarri and Jacky Jacky memorial invites passers-by into a space of encounter, feeling and thoughtfulness. In response, I follow a plurality of stories and the tributaries of the imagination to weave a tale of a complex shared Aboriginal–settler past and present.

Towards integrated flood management: Vulnerability and flood risk in the Ayeyarwady Delta of Myanmar

Despite the rising global flood risk, the impacts of flooding remain systematically underestimated, leading to significant consequences for particularly vulnerable river deltas. Most studies focus either on single hazards or social vulnerability while overlooking the interconnected dynamics of deltaic social-ecological systems. In response to the first priority of the Sendai Framework, which calls for an understanding of disaster risk in all its dimensions, we apply the Global Delta Risk Index in the Ayeyarwady Delta of Myanmar. We combine 55 indicators of social- and ecosystem vulnerability with 100-year, 500-year, and 1,000-year scenarios of pluvial, fluvial, and coastal flood hazards and exposure at the sub-delta scale. Using townships as units of analysis allows for bridging the gap between global and local case studies, providing insights that are meaningful for risk-informed development of the delta as a whole system. We also examine the distinctive characteristics that define the delta systems that are particularly prone to flooding. Our results reveal patterns and drivers of flood risk and vulnerability that affect at least 65 % of the delta's population and 60 % of its ecosystem, with self-reinforcing dynamics, but also those that contribute to the mutual resilience of both systems. We argue that the principles of integrated flood management should be applied to leverage scarce resources to simultaneously reduce risk, secure livelihoods and preserve ecosystem services.

Impacts of Climate Change on Logistics and Supply Chains

While the global public is struggling with Covid-19 and the subsequent inflation, war and energy crisis, climate change has turned into a forgotten crisis. However, with the increasing number of disasters caused by extreme weather events in different continents of the world, climate change has started to attract attention all over the world. Extreme weather events such as global warming, floods and hurricanes, which are the result of global climate change, not only affect the lives of societies, but also affect logistics and supply chains. The first spots to see the effects of extreme weather conditions as a result of climate change are the production centers in different parts of the world and the international logistics sector, which ensures that the goods produced in these facilities reach the markets. By this study, the qualitative effects of climate change on supply chains were investigated and this research is expected to shed light on the effects of climate change on international logistics activities.

Who is ‘stuck’ in the past? The impact of the 1998 great flood on the adoption of agricultural technology in China

ABSTRACT This study estimates the impact of the 1998 Great Flood on the adoption of agricultural technology in China and its mechanism using a cross-sectional dataset collected from the ERA5-Land, the China Labor Force Dynamic Survey, the China Rural Statistical Yearbook, and the China Population Census. The analysis reveals an inverted U-shaped correlation between farmers’ exposure to the flood event and their propensity to embrace technological advancements in agriculture. Farmers who experienced medium flood exposure tend to be risk lovers and exhibit a 4.9% higher likelihood of adopting agricultural technology compared to their counterparts who did not experience the flood. On the contrary, farmers who faced extreme flood exposure tend to be risk averse and exhibit an 11.9% diminished likelihood of adopting agricultural technology compared to those who remained unscathed by the flood.