A review of the current status of flood modelling for urban flood risk management in the developing countries

STFS-urban: Spatio-temporal flood simulation model for urban areas

Amidst escalating urbanization and increasing extreme climatic events, strengthening flood resilience strategies in global cities has become imperative. This study introduces an innovative spatiotemporal urban flood simulation model that seamlessly integrates diverse refined and multi-spatiotemporal scales, ranging from 7.5 to 60 min and 100-2000 m, respectively. The model comprises multi-scale radar rainfall inversion (MRI), fine-grained coupled flood simulation model (FGCFS), and transformer-CNN flood prediction (TCFP) modules. Employing the Nanjing urban area as a case study, the model's efficacy is subjected to rigorous assessment. The advantages derived from integrated refinement coupling and boundary conditions through FGCFS and TCFP are accentuated. Impressively, the results underscore the robust performance of radar rainfall inversion across most scales, revealing a correlation coefficient surpassing 0.8 and a root-mean-square error of under 5.2 mm. FGCFS achieves optimal simulated water depth changes at 7.5 min × 500 m resolution, with the Nash efficiency coefficient exceeding 0.69 (0.94 at YS observation point and 0.89 at SXM observation point), alongside percentage deviations below 12.89 (3.59 at SXM observation point and 2.42 at XJL observation point). TCFP's learning proficiency is showcased through error convergence to 0.002 m after twenty iterations, particularly suitable for resolutions below 4 m. Notably, both FGCFS and TCFP demonstrate efficient utilization of resources, enabling streamlined simulations across varying data resolutions. Consequently, our study propels a sophisticated framework harmonizing multi-scale data integration, refinement coupling, and dynamic allocation. Our work extends beyond practical solutions, offering a glimpse into the future of flood simulation modeling, and reaffirming its pivotal role within the realm of environmental research and management.

Characterizing the level of urban Flood vulnerability using the social-ecological-technological systems framework, the case of Adama city, Ethiopia

This study characterizes the flood vulnerability of Adama City, Ethiopia, where the city faces high flood vulnerability due to its unplanned urbanization in low-lying floodplain areas surrounding deforested mountains and ridges. The study applied an interlinked Social-Ecological-Technological-Systems (SETS) vulnerability framework using a GIS-based Multi-Criteria Decision-Making and Analytical Hierarchy Process (MCDM-AHP). The framework analyzed exposure, sensitivity, and adaptive capacity to flooding for each of the three SETS domains. The study analyzed 18 variables at the city level within each SETS domain. The result revealed that clusters of flood-vulnerable areas were identified by each SETS domain showing the concentration of flood vulnerability in the study area and the need to consider prompt adaptive mechanisms to severe and recurring flooding. The finding has significant implications for holistic approaches to sustainable cities. Moreover, the reduction of complex urban flood vulnerabilities according to their priority as individual or combined solutions for decision-makers and professionals in early warning and flood management systems is the other contribution of the study.

Urban flood numerical modeling and hydraulic performance of a drainage network: A case study in Algiers, Algeria

Urban sewer system management is challenging due to its higher vulnerability to flooding caused by rapid urbanization and climate change. For local decision-makers, storm water management is essential for urban planning and development. Therefore, the main objective of this study is to develop a numerical model for the sewerage network of the central catchment area of Algiers since it has experienced frequent overflows during the winter season. For this purpose, to model the sewerage networks, the model was built by coupling ArcGIS with MIKE URBAN. Its calibration and validation were performed using real-time measurements with a time step of 15 min. The model was evaluated by several statistical indicators, such as the coefficient of determination (R2), Nash-Sutcliffe efficiency (NSE), root mean square error (RMSE), and percent bias (PBIAS). The model results showed acceptable model performance, with an NSE superior to 0.50, R2 of approximately 0.63, RMSE of 7%, and PBIAS of 10% during the validation of the model. The performance parameters prove the reliability of the developed model. The employed model can be applied in other regions and could be helpful for policymakers and managers to improve flood mitigation measures based on the model prediction of the sewerage network.

Urban stormwater management at the meso-level: A review of trends, challenges and approaches

Cities worldwide are facing a significant threat of stormwater hazards caused by the increase in extreme downpours and urbanization. Meso-level urban stormwater management focuses on alleviating the detrimental impacts of urban flooding and enhancing water resource utilization at the block or community scale, typically through 1) specific policies and management rules; 2) catchment-scale scenario simulation, optimization and evaluation; 3) the group of stormwater control measures implementation. It may effectively coordinate macro-level urban stormwater management planning and micro-level distributed stormwater control facilities. This study conducts a review of Urban Stormwater Management at Meso-level (USM-M) with a view to research publication trends, citation analysis, geographic spread and subject category, as well as content analysis, including temporal progression and research gaps. The Web of Science database and CiteSpace are used for the bibliometric analysis of 66 articles from 2006 to 2021. The results show that the number of USM-M topic articles generally has an upward trend over the years. Whilst the United States and China are leading research on this topic, the European countries have diverse local research and dense cooperation. Research foci have generally shifted from theoretical frameworks to multi-element subdivided topics and specific technical roadmaps. Moreover, the spatial layout optimization and multi-functional integration are, or will be, potential research directions in terms of enhancing stormwater utilization and co-benefits of USM-M. This systematic review concludes trends, challenges and potential approaches of USM-M, and aims to provide recommendations for researchers and policymakers on the development of a more advanced and comprehensive USM-M.

Hydrodynamic Modelling and Flood Risk Analysis of Urban Catchments under Multiple Scenarios: A Case Study of Dongfeng Canal District, Zhengzhou

In recent years, urban flooding has become an increasingly serious problem, posing a serious threat to socio-economic development and personal safety. In this paper, we consider the Dongfeng Canal area in Zhengzhou City as an example and build a 1D/2D coupled urban flood model using the InfoWorks ICM. This study area uses six scenarios with rainfall return periods of 5 a, 20 a, and 50 a, corresponding to rainfall ephemeris of 1 h and 2 h to assess the flood risk. The results of the study show that (1) The flood depth, inundation duration, and extent of inundation in the study area vary with the return period and rainfall history. Generally, most of the water accumulation is concentrated in the low-lying areas adjacent to the river and near the roadbed. (2) As the rainfall recurrence period and rainfall duration increase, the proportion of overflow at the nodes becomes more pronounced and the overload from the pipe network flows mainly to the overload. (3) The high-risk areas under the different scenarios are mainly distributed on both sides of the river, and most of the low-risk areas transform into medium- and high-risk areas as the rainfall recurrence period and rainfall duration increase. This study analyses the flood risk situation under different scenarios, as well as the elements and areas that should be monitored in case of flooding, with the aim of providing a reference for flood prevention and control in the study area and formulating corresponding countermeasures. It also serves as a reference for flood risk analysis in other areas with similar situations.

The 21st August 2020 Flood in Douala (Cameroon): A Major Urban Flood Investigated with 2D HEC-RAS Modeling

A major flood event occurred on 21 August 2020 in the densely populated Makèpè Missokè neighborhood in the city of Douala (Cameroon, Africa). Nearly 2210 buildings and 12,376 victims spread over 82 hectares were affected. A 2D HEC-RAS model is applied to simulate and characterize this event. A cross analysis of flood depth and flow velocity is used to classify the flood risk and identify areas exposed from low to high hazard. The simulations provide detailed information on the flood characteristics (extent, depth, velocity, arrival time, and duration). The simulated maximum water surface profiles are consistent with the floods marks with differences ranging from 0.02 m to 0.44 m, indicating a good agreement between the observed and simulated water levels at the peak flow (NSE = 0.94, Erel = 0.92, RMSE = 0.21 m). The maximum inundation level is 4.48 m and the flow velocity is globally low at less than 1 m/s. The average flood arrival time and duration are 5 h and 26 h, respectively, for a threshold height of 0.5 m. These results indicate a fast mobilization of the major river channel for the evacuation of this flood. The level of accuracy of the developed model of the 21 August 2020 flood event is appropriate for flood hazard assessment in the city of Douala and is designed to find operational application in future events.

Linking Urban Floods to Citizen Science and Low Impact Development in Poorly Gauged Basins under Climate Changes for Dynamic Resilience Evaluation

Cities must develop actions that reduce flood risk in the face of extreme rainfall events. In this study, the dynamic resilience of the Gregorio catchment (São Carlos, Brazil) was assessed. The catchment lacks environmental monitoring and suffers from recurrent floods. The resilience curves were made considering the water depth in the drainage system as the performance index, obtained by simulations with SWMM and HEC-RAS. The calibration of the flood extension was performed using citizen science data. The contribution to increasing the dynamic resilience by implementing decentralized low impact development (LID) practices was also evaluated. For this purpose, bioretention cells were added to the SWMM simulations. The resilience curves were then calculated for the current and future climate scenario, with and without LID, for return periods of 5, 10, 50, and 100 years and duration of 30, 60, and 120 min. Intensity–duration–frequency curves (IDFs) updated by the regional climate model MIROC5 for 2050 and 2100 were used. The results showed a significant improvement in the system’s resilience for light storms and the current period due to LID practice interventions. Efficiencies were reduced for moderate and heavy storms with no significant drops in floodwater depth and resilience regardless of the scenario.

Assessing the Performance of WRF Model in Simulating Heavy Precipitation Events over East Africa Using Satellite-Based Precipitation Product

This study investigated the capability of the Weather Research and Forecasting (WRF) model to simulate seven different heavy precipitation (PRE) events that occurred across East Africa in the summer of 2020. The WRF model outputs were evaluated against high-resolution satellite-based observations, which were obtained from prior evaluations of several satellite observations with 30 stations’ data. The synoptic conditions accompanying the events were also investigated to determine the conditions that are conducive to heavy PRE. The verification of the WRF output was carried out using the area-related root mean square error (RMSE)-based fuzzy method. This method quantifies the similarity of PRE intensity distribution between forecast and observation at different spatial scales. The results showed that the WRF model reproduced the heavy PRE with PRE magnitudes ranging from 6 to >30 mm/day. The spatial pattern from the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification-Climate Data Record (PERSIANN-CCS-CDR) was close to that of the WRF output. The area-related RMSE with respect to observation showed that the error in the model tended to reduce as the spatial scale increased for all the events. The WRF and high-resolution satellite data had an obvious advantage when validating the heavy PRE events in 2020. This study demonstrated that WRF may be used for forecasting heavy PRE events over East Africa when high resolutions and subsequent simulation setups are used.

Coupled hydrodynamic modelling approach to assess land use change induced flood characteristics

MIKE 11 rainfall-runoff model and MIKE 21 overland flow model were successfully coupled in the MIKE FLOOD platform for flood simulation in Gin catchment (932 km²) of Sri Lanka to assess land use change induced changes in flood discharge. MIKE 11 Nedbør-Afstrømings-Model (NAM) rainfall-runoff simulation depicted a good agreement with the observed discharge at Thawalama and Baddegama gauging stations. MIKE FLOOD, validated against the two major flood events that occurred in May 2003 and in May 2017, showed a reasonable agreement with the observed water depths and peak discharge values displaying more than 70% goodness of fit between the observed and simulated inundated extents. Dominant land use change processes in the catchment between 1999 and 2016 were identified as the forest area and built-up land expansion at the expense of agricultural land and bare land which possessed contradictory impacts on flood generation. The impact of a single factor, 17-year land use change, on flood formation was differentiated. In the upstream sub-catchment having 490 km², despite a 0.74% increase in the built-up land, 2.85% increase in the forest area had significantly contributed to mitigate the overall flood formation with 34% and 40% reduction in the peak discharge and the flood volume, respectively. Overall reduction of the flood discharge attributed to the forest expansion emphasised the importance of preserving forest cover and pervious area. The modelling framework presented in this typical tropical monsoon catchment study could be effectively used to quantify the land use change induced flow regime variations in similar catchments.

Geo-Accumulation Index of Manganese in Soils Due to Flooding in Boac and Mogpog Rivers, Marinduque, Philippines with Mining Disaster Exposure

This paper presents the effects of flooding on the accumulation of manganese (Mn) in soils within proximity of the Boac and Mogpog rivers in Marinduque of The Philippines. Marinduque, an island province in the Philippines, experienced two catastrophic tailings storage facility (TSF) failures in the 1990s that released sulfide-rich tailings into the two major rivers. The Philippines experiences 21–23 typhoons every year, 11 of which pass thru Marinduque that causing inundation of floodplain areas in the province. A flood hazard map developed using LiDAR DEM was utilized for the Boac and Mogpog rivers for an accurate representation of flooding events. A portable X-ray fluorescence spectrometer (pXRF) and a Hannah multi-parameter device were used for the on-site analyses of Mn concentration and water physico-chemical properties, respectively. Spatial grid mapping with zonal statistics was employed for a comprehensive analysis of all the data collected and processed. Correlation analysis was carried out on Mn concentrations in soil and surface water, electrical conductivity (EC), total dissolved solids (TDS), pH, temperature, curve number (CN), and flood heights. The curve number indicates the runoff response characteristic of the Mogpog-Boac River basin. The results show that 40% of the total floodplain area of Boac and Mogpog were subjected to high hazards with flood heights above 1.5 m. The Mn content of soils had a statistically significant moderate positive correlation with flood height (r = 0.458) and a moderate negative correlation with pH (r = −0.438). This condition suggested that more extensive flooding promotes Mn contamination of floodplain soils in the two rivers, the source of which includes the mobilization of Mn-bearing silt, sediments, and mine drainage from the abandoned mine pits and TSFs. There is also a strong negative correlation between pH and Mn concentrations in surface water, a relationship attributed to the solubilization of Mn-bearing precipitates based on geochemical modeling results. Using Muller’s geo-accumulation index, 77.5% of the total floodplain of the two rivers was identified as “moderately contaminated” with an average Mn soil content of 3.4% by weight (34,000 mg/kg). The Mn contamination map of floodplain soils in the Mogpog and Boac rivers described in this study could guide relevant regional, national, and local government agencies in planning appropriate intervention, mitigation, remediation, and rehabilitation strategies to limit human exposure to highly contaminated areas.

Remote Sensing Methods for Flood Prediction: A Review

Floods are a major cause of loss of lives, destruction of infrastructure, and massive damage to a country's economy. Floods, being natural disasters, cannot be prevented completely; therefore, precautionary measures must be taken by the government, concerned organizations such as the United Nations Office for Disaster Risk Reduction and Office for the coordination of Human Affairs, and the community to control its disastrous effects. To minimize hazards and to provide an emergency response at the time of natural calamity, various measures must be taken by the disaster management authorities before the flood incident. This involves the use of the latest cutting-edge technologies which predict the occurrence of disaster as early as possible such that proper response strategies can be adopted before the disaster. Floods are uncertain depending on several climatic and environmental factors, and therefore are difficult to predict. Hence, improvement in the adoption of the latest technology to move towards automated disaster prediction and forecasting is a must. This study reviews the adoption of remote sensing methods for predicting floods and thus focuses on the pre-disaster phase of the disaster management process for the past 20 years. A classification framework is presented which classifies the remote sensing technologies being used for flood prediction into three types, which are: multispectral, radar, and light detection and ranging (LIDAR). Further categorization is performed based on the method used for data analysis. The technologies are examined based on their relevance to flood prediction, flood risk assessment, and hazard analysis. Some gaps and limitations present in each of the reviewed technologies have been identified. A flood prediction and extent mapping model are then proposed to overcome the current gaps. The compiled results demonstrate the state of each technology's practice and usage in flood prediction.

Modeling the Impact of Building-Level Flood Mitigation Measures Made Possible by Early Flood Warnings on Community-Level Flood Loss Reduction

The growing number of flood disasters worldwide and the subsequent catastrophic consequences of these events have revealed the flood vulnerability of communities. Flood impact predictions are essential for better flood risk management which can result in an improvement of flood preparedness for vulnerable communities. Early flood warnings can provide households and business owners additional time to save certain possessions or products in their buildings. This can be accomplished by elevating some of the water-sensitive components (e.g., appliances, furniture, electronics, etc.) or installing a temporary flood barrier. Although many qualitative and quantitative flood risk models have been developed and highlighted in the literature, the resolution used in these models does not allow a detailed analysis of flood mitigation at the building- and community level. Therefore, in this article, a high-fidelity flood risk model was used to provide a linkage between the outputs from a high-resolution flood hazard model integrated with a component-based probabilistic flood vulnerability model to account for the damage for each building within the community. The developed model allowed to investigate the benefits of using a precipitation forecast system that allows a lead time for the community to protect its assets and thereby decreasing the amount of flood-induced losses.

Climate Justice Planning in Global South: Applying a Coupled Nature–Human Flood Risk Assessment Framework in a Case for Ho Chi Minh City, Vietnam

Developing countries in the global south that contribute less to climate change have suffered greater from its impacts, such as extreme climatic events and disasters compared to developed countries, causing climate justice concerns globally. Ho Chi Minh City has experienced increased intensity and frequency of climate change-induced urban floods, causing socio-economic damage that disturbs their livelihoods while urban populations continue to grow. This study aims to establish a citywide flood risk map to inform risk management in the city and address climate justice locally. This study applied a flood risk assessment framework integrating a coupled nature–human approach and examined the spatial distribution of urban flood hazard and urban flood vulnerability. A flood hazard map was generated using selected morphological and hydro-meteorological indicators. A flood vulnerability map was generated based on a literature review and a social survey weighed by experts’ priorities using the Fuzzy Delphi Method and Analytic Network Process. Vulnerability indicators including demographic characteristics, infrastructure, and land use patterns were used to generate a flood vulnerability map. The results illustrate that almost the entire central and northeastern parts of the city are at high flood risk, whereas the western part is at low flood risk. The findings have implications in urban planning via identifying risk hot spots in order to prioritize resources for mitigating hazards and enhancing community resilience to urban floods.

A Flood Inundation Modeling Approach for Urban and Rural Areas in Lake and Large-Scale River Basins

Fluvial floods are one of the primary natural hazards to our society, and the associated flood risk should always be evaluated for present and future conditions. The European Union’s (EU) Floods Directive highlights the importance of flood mapping as a key stage for detecting vulnerable areas, assessing floods’ impacts, and identifying damages and compensation plans. The implementation of the EU Flood Directive in Greece is challenging because of its geophysical and climatic variability and diverse hydrologic and hydraulic conditions. This study addressed this challenge by modeling of design rainfall at the sub-watershed level and subsequent estimation of flood design hydrographs using the Natural Resources Conservation Service (NRCS) Unit Hydrograph Procedure. The HEC-RAS 2D model was used for flood routing, estimation of flood attributes (i.e., water depths and flow velocities), and mapping of inundated areas. The modeling approach was applied at two complex and ungauged representative basins: The Lake Pamvotida basin located in the Epirus Region of the wet Western Greece, and the Pinios River basin located in the Thessaly Region of the drier Central Greece, a basin with a complex dendritic hydrographic system, expanding to more than 1188 river-km. The proposed modeling approach aimed at better estimation and mapping of flood inundation areas including relative uncertainties and providing guidance to professionals and academics.

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

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

Why People (Do Not) Adopt the Private Precautionary and Mitigation Measures: A Review of the Issue from the Perspective of Recent Flood Risk Research

Based on the literature review, this paper synthesizes recent state of knowledge on flood risk perception and related human behaviors. The main attention is paid to private precautionary and mitigation measures, and the reasons why these are (not) adopted by agents such as individual households. Results of a wide range of relevant studies are presented and critically examined. The findings are presented within an interpretive framework established during the review process; six key themes (responsibility, risk perception, people and social environment, geography of risk, emotions, theories and conceptual models) and several sub-themes closely related to them were identified by the content/thematic analysis. These were then utilized to overview and discuss particular factors and issues involved, as well as various relevant theoretical underpinnings and conceptual models. The review identifies, illustrates, and addresses not only the consensual views and contradictory findings of flood risk research, but also several related and essential ambiguities, uncertainties, and knowledge gaps. Based on these findings, suggestions for future research are discussed, including the terminological, semantic, methodological, theoretical, and ethical aspects. The paper thus serves two main tasks: (a) It is a useful reference/departure point for those with research interests in topics and issues such as flood risk perception, flood risk protective and mitigation behaviors and measures, or flood risk management in general; and (b) it provides suggestions and incentives for future flood risk research agenda.

Effect of Urban Development in Risk of Floods in Veracruz, Mexico

Urban floods have adverse effects on the population and the economy, and they are increasing in frequency and magnitude. The State of Veracruz is the region of Mexico with the highest number of disasters, more than 50% of the total number nationwide, in the 1970–2015 period. During the 1990s, disasters in this region increased from 5 to 10 events per year, mostly in relation to intense rains and floods. This study analyzes the factors that increase the risk of urban floods in the regions: (i) the Pánuco River, (ii) the Papaloapan River, and (iii) the Coatzacoalcos River regions, combining hazard data and estimates of vulnerability factors. The 95th percentile of daily precipitation (P95) is used as a threshold of heavy rain, i.e., the natural hazard. Vulnerability is estimated in terms of the percentage of natural vegetation loss due to changes in land cover and land use in the hydrological basins and the expansion of the urban areas in the regions under study. The risk of flood was compared with records of flood events focusing on the low-frequency variations of risks and disaster activity. The trends in urban flood activity are related to the loss of natural vegetation and deterioration of the basins leading to a loss of infiltration, i.e., larger runoffs. Even when the intensity of precipitation in recent decades remains without clear trends, or shows negative tendencies in the number of intense events, the number of floods is higher mostly because of the deterioration of hydrologic basins. Therefore, the risk of flooding in the state of Veracruz is mainly related to environmental factors that result in vulnerability rather than changes in the trends of extreme precipitation activity. This result means that disaster risk reduction actions should be mainly related to rehabilitation of the basins.

Urban Flood Modeling Using 2D Shallow-Water Equations in Ouagadougou, Burkina Faso

Appropriate methods and tools accessibility for bi-dimensional flow simulation leads to their weak use for floods assessment and forecasting in West African countries, particularly in urban areas where huge losses of life and property are recorded. To mitigate flood risks or to elaborate flood adaptation strategies, there is a need for scientific information on flood events. This paper focuses on a numerical tool developed for urban inundation extent simulation due to extreme tropical rainfall in Ouagadougou city. Two-dimensional (2D) shallow-water equations are solved using a finite volume method with a Harten, Lax, Van Leer (HLL) numerical fluxes approach. The Digital Elevation Model provided by NASA’s Shuttle Radar Topography Mission (SRTM) was used as the main input of the model. The results have shown the capability of the numerical tool developed to simulate flow depths in natural watercourses. The sensitivity of the model to rainfall intensity and soil roughness coefficient was highlighted through flood spatial extent and water depth at the outlet of the watershed. The performance of the model was assessed through the simulation of two flood events, with satisfactory values of the Nash–Sutcliffe criterion of 0.61 and 0.69. The study is expected to be useful for flood managers and decision makers in assessing flood hazard and vulnerability.