Flood Hazard Management Research Articles

Flood Hazard Mapping Using a Multi-Criteria Decision Analysis Approach Over the Indrawati River Basin

Floods are devastating natural hazard responsible for direct mortality, deterioration of crops, and damage to infrastructure and property. So, their study is crucial for watershed management and mitigation of flood hazards. The main objective of this study was to create a scientifically valid flood hazard map of the Indrawati River Basin (IRB) through the use of the multi-criteria decision analysis approach. Topographical Wetness Index (TWI), Elevation (EL), Slope (SL), Precipitation (PPT), Land Use/Land Cover (LULC), Normalized Difference Vegetation Index (NDVI), Distance from the River (DRI), Distance from the Road (DRO), Drainage Density (DD), and Soil Type (ST) were chosen as flood triggering factors based on literature review, data availability, and catchment characteristics. The analytic hierarchy process (AHP) method was used for determining the relative weight of each flood causative factor. All these factors were resampled into a 30 m × 30 m pixel size. Based on an evaluation of satellite imagery, 30 flood points were identified in the IRB, and these points were used as the testing dataset for validating the outcome. Based on results, the IRB has been classified into five different flood susceptible zones; very low, low, moderate, high, and very high. According to the study, 13%, 26%, 30%, 23%, and 8% of the total area are in very low, low, moderate, high, and very high flood susceptible zones, respectively. The Area Under the Curve (AUC) value for the success rate was 0.792. The results of this study will be crucial for concerned parties to design early warning systems and flood risk reduction measures for flood preparedness.

INTEGRATING LIDAR DATA, 2D HEC-RAS MODELING AND REMOTE SENSING TO DEVELOP FLOOD HAZARD MAPS DOWNSTREAM OF A LARGE RESERVOIR IN THE INNER EASTERN CARPATHIANS

The use of 1D, 2D, and 1D/2D modelling techniques to identify flood prone areas is a critical component of any flood hazard management project (e.g., APDF – action plan for dam failure) in the proximity of big dams and reservoirs. In this work, we manage to computed flood hazard models using 2D HEC-RAS module based on Digital Elevation Models (DEM’s) derived from Light Detection and Ranging (LiDAR) data and pre- and post-processed using Geographic Information Systems (GIS)-based software (e.g., ArcGIS, HEC-RAS). Therefore, to produce urban flood hazard (FH) maps downstream of the Strâmtori-Firiza reservoir (S-Fr) in NW Romania, a multi-scenario approach based on LiDAR-derived DEM integration, 2D hydraulic modeling, and remote sensing (RS) data validation is provided. In this context, to assess the flood control capacity (FCC) of the S-Fr hydro-technical system, three flood tests based on S-Fr flow rate with 5% (167 m3/s), 1% (270 m3/s) and 0.1% (447 m3/s) return periods were performed. The flood impact within the urban area of Baia Mare located downstream of S-Fr was achieved for each flood scenarios using four spatial data derived from the RAS Mapper module: flood extent (FE), flood depth (FD), flood velocity (FV) and flood hazard (FH). The results indicate that a large area of Baia Mare city can be affected by a potential flood caused by a dam failure and also contribute to the APDF update of S-Fr dam.

English

Tracking the changes in the river basin system and identifying areas susceptible to flood disasters is an inclusive step in flood hazard control and management. This research analyzed the spatial variability of rainfall and physiographical attributes of the main tributaries of River Benue in Taraba with the aim of identifying areas susceptible to flooding on the basins. The spatial data subjected to WLC approach for reclassification were annual rainfall, digital elevation model, basin slope, land use, soil types, drainage density, and catchment area. The clincher to flooding in the state favors land use. The composite map of flood vulnerability revealed that vulnerable areas occupied 48.6% while highly vulnerable occupied 0.9% of the entire study area of 44,359.2 km2. In terms of the area extent, the highly vulnerable region covers 410.6 km2; the vulnerable area covers 21,576.6 km2, and the areas that are not vulnerable cover 64.9 km2. The towns that are at risk of a high vulnerability to flooding within the study area are Karim Lamido, Gassol, Ibi, Lau, Donga, Wukari, parts of Takum, Ardo Kola, Kurmi, Bali, and Jalingo. It was recommended among others that as the region’s status is vulnerable to climate change and that this has a strong association with land use and land cover change, development along the floodplain should be discontinued.   Key words: Flood vulnerability; land use change; river catchment area; rank-sum method; river Lamurde, Taraba and Donga.

Know to Predict, Forecast to Warn: A Review of Flood Risk Prediction Tools

Flood prediction has advanced significantly in terms of technique and capacity to achieve policymakers’ objectives of accurate forecast and identification of flood-prone and impacted areas. Flood prediction tools are critical for flood hazard and risk management. However, numerous reviews on flood modelling have focused on individual models. This study presents a state-of-the-art review of flood prediction tools with a focus on analyzing the chronological growth of the research in the field of flood prediction, the evolutionary trends in flood prediction, analysing the strengths and weaknesses of each tool, and finally identifying the significant gaps for future studies. The article conducted a review and meta-analysis of 1101 research articles indexed by the Scopus database in the last five years (2017–2022) using Biblioshiny in r. The study drew an up-to-date picture of the recent developments, emerging topical trends, and gaps for future studies. The finding shows that machine learning models are widely used in flood prediction, while Probabilistic models like Copula and Bayesian Network (B.N.) play significant roles in the uncertainty assessment of flood risk, and should be explored since these events are uncertain. It was also found that the advancement of the remote sensing, geographic information system (GIS) and cloud computing provides the best platform to integrate data and tools for flood prediction. However, more research should be conducted in Africa, South Africa and Australia, where less work is done and the potential of the probabilistic models in flood prediction should be explored.

A Review of the Flood Hazard and Risk Management in The South Iran Region, Particularly Konarak, Makoran in Balochestan

Flood preparedness involves building capacities that enable minimizing losses through effective response and recovery. While there are many preparedness assessments at household and community level, very few have been conducted at institutional level. This study assessed the households' perspectives of the preparedness of civil protection institutions in Iran during the 2021 Balochestan flood disaster and identified the capacity building needs of the civil protection institutions. It used a 5-point Likert scale to measure six preparedness indicators: emergency plans, early warning systems, evacuation, resources, disaster knowledge, and relocation camp management. Data came from a cross-sectional survey involving 1456 household-heads who were randomly selected among the affected villagers of Konarak.The findings revealed various gaps in preparedness both at indicator and at variable level. While some indicators showed incipient levels of development in all their variables, others had a combination of low and developed variables. The preparedness elements that critically need immediate attention include the development of emergency plans, evacuation of populations at risk and the prepositioning of resources. However, it is pertinent to mention that civil protection Systems in Iran need to strengthen most of their preparedness elements. As such, this study recommends strengthening preparedness activities in civil protection systems.

Geospatial assessment of effect of urbanization on natural drainage in a medium-sized town of Akure, Nigeria

This study examined the state of some urban streams in a tropical environment of Nigeria. It analysed satellite images which include Landsat 5 TM (1986), Landsat 7 ETM (2002), Landsat 8 (2018) imageries with 30 m resolution using ENVI 5.3. The images were classified using a supervised classification algorithm to obtain land use/land cover themes for spatial analysis in the ArcMap 10.3 environment while drainage systems were extracted from the topographical map of 1966 of the study area. Result showed a total drainage network of about 90, covering a total distance of 307 km in 1986, which decreased to 226.87 km in 2018. Within the study period (32 years), more streams experienced decrease in lengths (231.44 km) than those whose lengths increased (129.85 km). While 136.92 km stream lengths were lost within the study period, about 25.45 km stream length were rejuvenated. Urban landuse correspondingly increased from 18.8% in 1986 to 52.2% in 2018, suggesting that a converse relationship between increase in urban land cover and dearth of streams in the area. The study provides information regarding the current status of the drainage system for effective planning and management of flood hazards in the study area. It also canvasses for adequate provision and maintenance of setbacks to streams and rivers under the supervision of the Development Control Department (DCD) of the Ondo State Ministry of Urban Development and Regional Planning (OSMUD&RP).

Snowmelt Flood Susceptibility Assessment in Kunlun Mountains Based on the Swin Transformer Deep Learning Method

Modeling and assessing the susceptibility of snowmelt floods is critical for flood hazard management. However, the current research on snowmelt flood susceptibility lacks a valid large-scale modeling approach. In this study, a novel high-performance deep learning model called Swin Transformer was used to assess snowmelt susceptibility in the Kunlun Mountains region, where snowmelt floods occur frequently. Support vector machine (SVM), random forest (RF), deep neural network (DNN) and convolutional neural network (CNN) were also involved in the performance comparison. Eighteen potential conditioning factors were combined with a historical flood inventory to form the database. Apart from the susceptibility assessment, sensitivity analysis was also conducted to reflect the impact of the conditioning factors on the susceptibility of different types of snowmelt floods. The results showed that Swin Transformer achieved the highest score in the model performance test (AUC = 0.99) and successfully identified the relationship between conditioning factors and snowmelt flooding. Elevation and distance to rivers are the most important factors that affect snowmelt flooding in the study region, whereas rainfall and snow water equivalent are the dominant natural factors for mixed and warming types. In addition, the north-central parts of the study area have high susceptibility to snowmelt flooding. The methods and results can provide scientific support for snowmelt flood modeling and disaster management.

Local-Level Flood Hazard Management in Canada: An Assessment of Institutional Structure and Community Engagement in the Red River Valley of Manitoba

While there is a large body of literature focusing on global-level flood hazard management, including preparedness, response, and recovery, there is a lack of research examining the patterns and dynamics of community-level flood management with a focus on local engagement and institutional mechanism. The present research explores how local communities mobilize themselves, both individually and institutionally, to respond to emerging flood-related situations and recover from their impacts. A case study approach was applied to investigate two towns in the Red River Valley of Manitoba, Canada: St. Adolphe and Ste. Agathe. Data collection consisted of in-depth interviews and oral histories provided by local residents, in addition to analysis of secondary official records and documents. The findings revealed that local community-level flood preparedness, response, and recovery in the Province of Manitoba are primarily designed, governed, managed, and evaluated by the provincial government authorities using a top-down approach. The non-participatory nature of this approach makes community members reluctant to engage with precautionary and response measures, which in turn results in undesired losses and damages. It is recommended that the Government of Manitoba develop and implement a collaborative and participatory community-level flood management approach that draws upon the accumulated experiential knowledge of local stakeholders and institutions.

Flood hazard assessment in a mountainous river basin in Thessaly, Greece, based on 1D/2D numerical simulation

• Flood inundation and hazard maps, under steady and unsteady flow conditions based on GIS and 1D/2D flow model usage, were generated. • Proposed methodology was based on a comparison between different transform methods. • Flood hazard maps creation is necessary for the positioning and planning of appropriate flood protection projects. • Comparison between 1D/2D numerical results. Floods are amongst the greatest natural hazards in the world. In the last decade, many flood events have been observed and recorded in Greece, which have had a direct effect on river ecosystem consistency. Flood hazard assessment is a critical parameter for flood hazard areas identification and for taking relevant mitigation measures to prevent the hazard. Therefore, the study and evaluation of streams’ hydraulic behaviour is necessary for flood management. The aim of this research work is to present a substantial methodology for flood hazard management based on GIS and hydrological/hydraulic numerical models. Flood inundation and flood hazard maps in a stream area under steady and unsteady flow conditions, using one–dimensional and two–dimensional flow models were generated, as an extension of the European Directive 2007/60/EC. For unsteady flow simulations, a hydrograph was used, based on the available rainfall data of the flood occurrence period. The simulation under steady flow conditions was performed for return periods of 100 and 1000 years. Numerical results of one–dimensional and two–dimensional numerical models were adequately compared with available field measurements providing the reliability of the current methodology.

Combining SAR images with land cover products for rapid urban flood mapping

Synthetic Aperture Radar (SAR) is an indispensable source of data for mapping and monitoring flood hazards, thanks to its ability to image the Earth’s surface in all weather conditions and at all times. Through cloud computing platforms such as Google Earth Engine (GEE), SAR imagery can be used in near-real time for rapid flood mapping. This has facilitated the disaster response community to make informed decisions in flood hazard interventions and management plans. However, rapid urban flood mapping using SAR is challenging, due to the complex land cover configuration in urban environments, coupled with complicated backscattering mechanisms. Here, we propose a novel method to utilise SAR imagery and land use-land cover (LULC) products for rapid urban flood mapping. Our approach uses a Land Cover Product to segment the study area into LULC types and differentiate each type with respect to whether double bounce is expected to occur during the flooding events. The normalised difference index was derived using a multi-temporal SAR image stack, and the threshold segmentation method was adopted for flood mapping. In addition, DEM and Surface Water datasets were employed to refine the flood extraction results using a morphological correction approach. We assessed the method quantitatively using two use cases: the 2017 Houston and 2022 Coraki flood events. Based on fine spatial resolution optical imagery, the proposed method achieved an accuracy of 92.7% for the August 2017 Houston flood mapping task and 89% for the March 2022 Coraki flood mapping task, which not only represents at least 13% in accuracy compared to non-LCP based flood extraction method, but also provides strong capability for rapid flood mapping in urban settings.

Spatio-Temporal Rainfall Variability and Concentration over Sri Lanka

Changes in precipitation patterns significantly affect flood and drought hazard management and water resources at local to regional scales. Therefore, the main motivation behind this paper is to examine the spatial and temporal rainfall variability over Sri Lanka by Standardized Rainfall Anomaly Index (SRAI) and Precipitation Concentration Index (PCI) from 1990 to 2019. The Mann–Kendall (MK) trend test and Sen’s slope (SS) were utilized to assess the trend in the precipitation concentration based on PCI. The Inverse Distance Weighting (IDW) interpolation method was incorporated to measure spatial distribution. Precipitation variability analysis showed that seasonal variations are more than those of annual variations. In addition, wet, normal, and dry years were identified over Sri Lanka using SRAI. The maximum SRAI (2.27) was observed for the year 2014 for the last 30 years (1990–2019), which shows the extremely wet year of Sri Lanka. The annual and seasonal PCI analysis showed moderate to irregular rainfall distribution except for the Jaffna and Ratnapura areas (annual scale-positive changes in Katugastota for 21.39% and Wellawaya for 17.6%; seasonal scale-Vavuniya for 33.64%, Trincomalee for 31.26%, and Batticaloa for 18.79% in SWMS). The MK test, SS-test, and percent change analyses reveal that rainfall distribution and concentration change do not show a significant positive or negative change in rainfall pattern in Sri Lanka, despite a few areas which experienced significant positive changes. Therefore, this study suggests that the rainfall in Sri Lanka follows the normal trend of precipitation with variations observed both annually and seasonally.

District flood vulnerability assessment using analytic hierarchy process (AHP) with historical flood events in Bhutan.

Flood hazards are common in Bhutan as a result of torrential rainfall. Historical flooding events also point to flooding during the main monsoon season of the year, which has had a huge impact in many parts of the country. To account for climate change patterns in flood hazards in Bhutan, 116 historical flood events between 1968 and 2020 for 20 districts were retrieved and reviewed. The preliminary review revealed that the frequency of flood occurrence has increased by three times in recent years. In this study, seven flood vulnerability (FV) indicators were considered. Five are the attributes of historical floods, classified into a number of incidents for flood events, fatalities, affected population, and infrastructure damages including economic losses. Additionally, the highest annual rainfall and existence of a flood map were other two indicators considered. Using historical data, flood hazard and impact zonation were performed. The analytic hierarchy process (AHP) method was employed to derive a multi-criteria decision model. This resulted in priority ranking of the seven FV indicators, broadly classified into social, physical/economic, and environmental. Thereafter, an indicator-based weighted method was used to develop the district flood vulnerability index (DFVI) map of Bhutan. The DFVI map should help researchers understand the flood vulnerability scenarios in Bhutan and use these to mediate flood hazard and risk management. According to the study, FVI is very high in Chhukha, Punakha, Sarpang, and Trashigang districts, and the index ranges between 0.75 to 1.0.

Channel Conveyance Variability can Influence Flood Risk as Much as Streamflow Variability in Western Washington State

AbstractChanges in the severity and likelihood of flooding events are typically associated with changes in the intensity and frequency of streamflows, but temporal adjustments in a river's conveyance capacity can also contribute to shifts in flood hazard. To assess the relative importance of channel conveyance to flood hazard, we compare variations in channel conveyance to variations in the flow magnitude of moderate (1.2 years) floods at 50 river gauges in western Washington State between 1930 and 2020. In unregulated rivers, moderate floods have increased across the region, but in regulated rivers this trend is suppressed and in some cases reversed. Variations in channel conveyance are ubiquitous, but the magnitude and timing of adjustments are not regionally uniform. At 40% of gages, conveyance changes steadily and gradually. More often, however, conveyance variability is nonlinear, consisting of multidecadal oscillations (36% of gages), rapid changes due to unusually large sediment‐supply events (14% of gages), and increases or decreases to conveyance following flow regulation (10% of gages). The relative importance of conveyance variability for flood risk depends on the mode of adjustment; in certain locations with historic landslides, extreme floods, and flow regulation, the influence of conveyance changes on flood risk matches or exceeds that of streamflow at the same site. Flood hazard management would benefit from incorporating historic long‐term and short‐term conveyance changes in predictions of future flood hazard variability.

Improved Process Management of Glacial Lake Outburst Flood Hazards by Integrating Modular Monitoring, Assessment, and Simulation

Improved Process Management of Glacial Lake Outburst Flood Hazards by Integrating Modular Monitoring, Assessment, and Simulation

Flood Hazard Mapping and Flood Preparedness Literacy of the Elderly Population Residing in Bangkok, Thailand

This research aimed at assessing flood hazard areas and flood literacy of the elderly population in Bangkok, Thailand and analyzing their flood preparedness through SWOT analysis. Expert interviews and a community survey were conducted. Using the analytic hierarchy process (AHP) and GIS technique, the results indicated that land-use, drainage density, and annual maximum rainfall were the most heavily weighted factors in flood hazard mapping in Bangkok. About half (50.32%) of Bangkok’s total area was defined as high flood hazard area. A total of 736 questionnaires were distributed in flood-prone areas and in the areas with the highest percentage of elderly population. The results of both SWOT and survey analysis found that many senior citizens have low digital and media literacy and limited experience in using information technology for flood preparedness. Lack of integration of disaster risk reduction and aging population policy, ineffective warning system, and lack of access to disaster preparedness training were the key barriers in reducing vulnerability to flood hazard. The survey revealed that the majority of elderly respondents (75%) have neither used online applications for their flood hazard management both before and during flood disaster nor shared/communicated information via online platforms. Some respondents (13%) used Facebook and Line applications to obtain information before a flood event. Very few of the elderly respondents (<2%) accessed the national/provincial web-based platform to find out flood-related information. Almost all respondents, especially who are living in high-risk flood zones, had never participated in the community training of flood preparedness and management. Therefore, effective strategies in enhancing social engagement of the elderly and their literacy skills in flood risk preparedness and management are urgently needed.

Spatial targeting of nature‐based solutions for flood risk management within river catchments

AbstractA wide range of nature‐based solutions for flood hazard management work by storing and slowing flow within catchments, and therefore, there is a need to identify the optimal locations for implementing these solutions. This paper presents a relative scoring‐based mapping of the likely locations that contribute to the flood peak. Targeting flow reduction and attenuating mitigation actions in these locations can be an effective way to reduce flood damages at impact points downstream. The presented tool, SCIMAP‐Flood, uses information on land cover, hydrological connectivity, flood generating rainfall patterns and hydrological travel time distributions to impacted communities to find the potential source areas of flood waters. The importance of each location in the catchment is weighted based on its contribution to the flood hazard at each of the downstream impact points. In the example application, SCIMAP‐Flood is applied at a 5‐m grid resolution for the River Eden catchment, Cumbria, England, to provide sub‐field scale information at the landscape extent. Therefore, the tool can identify sub‐catchments where more detailed work can test different mitigation measures.

Sustainable development goals (SDGs) associated with flash flood hazard mapping and management measures through morphometric evaluation

This study is the first to demonstrate the interlinkages between several flash flood mitigation strategies and sustainable development goals (SDGs) by understanding the basins' morphometric characteristics. The basins' features were described by eighteen morphometric parameters, covering the watershed geometry, drainage network, and relief. The flash flood hazard degree (FFHD) was significantly (p < 0.05) correlated with texture ratio (R 2=0.712), surface slope (R 2=0.705), relief (R 2=0.690), basin area (R 2=0.666), ruggedness number (R 2=0.615), and sinuosity index (R 2=0.551). These parameters were used to define the sustainability of flash flood management actions, viz., protecting water bodies and livestock, harvesting freshwater from rainfall, restoring water-related ecosystems, implementing safe working areas, and applying hydro-electrical facilities for energy generation. Essential non-structural and structural measures were suggested to mitigate flood damages. The proposed mitigation measures showed high relationships with the environmental, economic, and social pillars of sustainability, reducing the negative impacts of flash floods on the fulfilment of SDGs.

Managing Flood Hazard in a Complex Cross-Border Region Using Sentinel-1 SAR and Sentinel-2 Optical Data: A Case Study from Prut River Basin (NE Romania)

In this study, an alternative solution for flood risk management in complex cross-border regions is presented. In these cases, due to different flood risk management legislative approaches, there is a lack of joint cooperation between the involved countries. As a main consequence, LiDAR-derived digital elevation models and accurate flood hazard maps obtained by means of hydrological and hydraulic modeling are missing or are incomplete. This is also the case for the Prut River, which acts as a natural boundary between European Union (EU) member Romania and non-EU countries Ukraine and Republic of Moldova. Here, flood hazard maps were developed under the European Floods Directive (2007/60/EC) only for the Romanian territory and only for the 1% exceeding probability (respectively floods that can occur once every 100 years). For this reason, in order to improve the flood hazard management in the area and consider all cross-border territories, a fully remote sensing approach was considered. Using open-source SAR Sentinel-1 and Sentinel-2 data characterized by an improved temporal resolution, we managed to capture the maximum spatial extent of a flood event that took place in the aforementioned river sector (middle Prut River course) during the 24 and 27 June 2020. Moreover, by means of flood frequency analysis, the development of a transboundary flood hazard map with an assigned probability, specific to the maximum flow rate recorded during the event, was realized.

Hydrological sentinels and the relative emergence of climate change signals in New Zealand river flows

ABSTRACT Hydrological impacts of climate change are prompting water resource and flood hazard management to adapt to non-stationary conditions. Among the factors influencing these policy decisions is the question of timing: when are we likely to see climate change effects? Using a national climate-hydrology model cascade over the 21st century, times and extents of emergence of six hydrological metrics are evaluated across New Zealand rivers. While the stringency of the emergence criteria has a significant effect on timing and extent, emergence for all metrics generally occurs after mid-century, if at all, and in the country’s South Island. Looking at the first emerging metrics, in contrast, allows us to sidestep the high uncertainties of the time of emergence, revealing mean winter flows to be the most extensive sentinel of climate change in New Zealand rivers among the metrics considered. This has implications for hydrological monitoring to inform timely climate change adaptation.

Sensitivity Analysis of Flash Flood Hazard on Sediment Load Characteristics

Changing climate has raised attention toward weather-driven natural hazards, such as rain-induced flash floods. The flooding model is an efficient tool used in flash flood warning and hazard management. More and more evidence showed significant impacts of sediment on hydrodynamics and flooding hazard of flash flood. But little information is available regarding flooding hazard sensitivity to sediment characteristics, which hampers the inclusion of sediment characteristics into the flash flood warning system and hazard management. This study used a 1D model to simulate flood hazards. After calibrating and validating the hydrodynamic model, we carried out simulations to test the sensitivity of flood hazard to sediment characteristics like inflow point, size distribution, and concentration. Our results showed that sediment from highly erosive slopes affects the flooding hazard more than sediment from watershed. This is particularly true when sediment particles are fine particles with a medium size of 0.06 mm. When medium particle size of sediment increased above 1 mm, most of the sediment particles are deposited in the river and we see little effect on flooding hazard downstream. Sediment concentration significantly influenced the flooding hazard but was less important than sediment inflow point and medium particle size. Our study suggested considering more characteristics than concentration when including sediment particles into the flash flood warning system.