A national scale flood hazard mapping methodology: The case of Greece - Protection and adaptation policy approaches

Mapping flood susceptibility with PROMETHEE multi-criteria analysis method

On a global scale, flooding is the most devastating natural hazard with an increasingly negative impact on humans. It is necessary to accurately detect flood-prone areas. This research introduces and evaluates the Preference Ranking Organization METHod for Enrichment Evaluation (PROMETHEE) integrated with GIS in the field of flood susceptibility in comparison with two conventional multi-criteria decision analysis (MCDA) methods: analytical hierarchy process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The Spercheios river basin in Greece, which is a highly susceptible area, was selected as a case study. The application of these approaches and the completion of the study requires the creation of a geospatial database consisting of eight flood conditioning factors (elevation, slope, NDVI, TWI, geology, LULC, distance to river network, rainfall) and a flood inventory of flood (564 sites) and non-flood locations for validation. The weighting of the factors is based on the AHP method. The output values were imported into GIS and interpolated to map the flood susceptibility zones. The models were evaluated by area under the curve (AUC) and the statistical metrics of accuracy, root mean squared error (RMSE), and frequency ratio (FR). The PROMETHEE model is proven to be the most efficient with AUC = 97.21%. Statistical metrics confirm the superiority of PROMETHEE with 87.54% accuracy and 0.12 RMSE. The output maps revealed that the regions most prone to flooding are arable land in lowland areas with low gradients and quaternary formations. Very high susceptible zone covers approximately 15.00-19.50% of the total area and have the greatest FR values. The susceptibility maps need to be considered in the preparation of a flood risk management plan and utilized as a tool to mitigate the adverse impacts of floods.

Effects analysis and probability forecast (EAPF) of real-time management on urban flooding: A novel bidirectional verification framework

Government departments usually prepare and implement contingency plans to address frequent urban flooding caused by short-term heavy rainfall. Previous studies focused on the evaluation of the static impact of the policies on urban floods, while there is a lack of research on the effect of off-design conditions, real-time feedback and treatments of the flood events on urban flood mitigation, which is detrimental to the optimization of management strategies of the cities. To quantify the effects of real-time management on flood mitigation in Fuzhou City, China, this study proposed a framework (EAPF) for evaluation and risk prediction. First, we collected data on the locations, rainfall intensity, inundation time, and the triggers of the waterlogging events from 2017 to 2021. Second, based on the vigilance analyses, a structural equation model (SEM) was constructed to quantitatively evaluate the mitigation effects of management on waterlogging. Finally, a probability prediction model of dynamic drainage capacity was proposed for flood simulation caused by the rainwater grate blockage. The results indicate that the environmental factors were the decisive triggers affecting the severity of waterlogging, and increasing the frequency of management events effectively reduced the probability of blocking. The correlation between the number of management events and blocking flood events was -0.42, while a decrease in vigilance increased the possibility of flooding caused by overdue treatment. The proposed hydrological waterlogging model, which considered blockages, exhibited a Nash-Sutcliffe efficiency (NSE) coefficient exceeding 0.9 under deterministic conditions. The probability prediction model verified the mitigating effect of management on the blockages and urban flooding, and its results were consistent with those of the SEM. Our study contributes to improving the reliability of waterlogging prediction and optimizing the management flow in the developing cities.

Impact of climate change on formation of nitrogenous disinfection by products. Part I: Sea level rise and flooding events

Climate change causes heavy rainfall incidents and sea level rise, which have serious impact on the availability and quality of water resources. These extreme phenomena lead to the rise of external and internal precursors in water reservoirs, and consequently affect the formation of disinfection by-products (DBPs). The aim of this study was to investigate the formation of nitrogenous_DBPs (N-DBPs) under extreme conditions caused by climate change. For this reason, two scenarios were adapted: a) sea level rise leading to increase of water salinity and b) heavy rainfall incidents leading to flooding events. The target-compounds were haloacetonitriles (HANs), haloacetamides (HAcAms) and halonitromethane (TCNM). Chlorination and chloramination were employed as disinfection processes under different doses (5 and 10 mg/L) and contact times (24 and 72 h). The results showed enhancement on the formation of N-DBPs and changes in their profile. Sea level rise scenario led to elevated concentrations of brominated species (maximum concentration of dibromoacetonitrile 23 μg/L and maximum concentration of bromoacetamide 57 μg/L), while flooding events scenario led to extended formation of chloroacetamide and bromochloroacetonitrile up to 58 μg/L and 40 μg/L, respectively. At the same time, changes in cytotoxicity and genotoxicity of the samples were observed.

Multiparameter flood hazard, socioeconomic vulnerability and flood risk assessment for densely populated coastal city

In the current study, flood risk assessment of densely populated coastal urban Surat City, on the bank of the lower Tapi River in India, was conducted by combining the hydrodynamic model-based flood hazard and often neglected socioeconomic vulnerability. A two-dimensional (2D) hydrodynamic (HD) model was developed using physically surveyed topographic data and the existing land use land cover (LULC) of the study area (5248 km2). The satisfactory performance of the developed model was ascertained by comparing the observed and simulated water levels/depths across the river and floodplain. The 2D HD model outputs with geographic information system (GIS) applications were further used to develop probabilistic multiparameter flood hazard maps for coastal urban city. During a 100-year return period flood (Peak discharge = 34,459 m3/s), 86.5% of Surat City and its outskirt area was submerged, with 37% under the high hazard category. The north and west zones are the worst affected areas in Surat City. The socioeconomic sensitivity and adaptive capacity indicators were selected at the city's lowest administrative (ward) level. The socioeconomic vulnerability was evaluated by employing the robust data envelopment analysis (DEA) technique. Fifty-five of 89 wards in Surat City, covering 60% of the area under the jurisdiction of the Municipal Corporation, are highly vulnerable. Finally, the flood risk assessment of the city was conducted using a bivariate technique describing the distinctive contribution of flood hazard and socioeconomic vulnerability to risk. The wards adjoining the river and creek are at high flood risk, with an equal contribution of hazard and vulnerability. The ward-level hazard, vulnerability, and risk assessment of the city will help local and disaster management authorities to priorities high risk areas while planning flood management and mitigation strategies.

GIS-based risk assessment of flood disaster in the Lijiang River Basin

This study is designed to provide a scientific reference for the establishment of rainstorm and flood disaster prevention system in Guilin region and improve the risk assessment of rainstorm and flood disasters. To realize the goal, a flood risk evaluation model is established by weight analysis methods including the entropy weight method and the analytic hierarchy process from 3 aspects, i.e., risk of disaster causing factors, sensitivity of disaster-pregnant environment and vulnerability of disaster bearing body. For the model, the daily precipitation 1980-2020 of 6 representative national meteorological stations in the Lijiang River Basin was used as reference data of disaster causing factors; six indicators, i.e., NDVI, river network density, geological hazard, slope, slope aspect and terrain undulation were selected as the sensitivity of disaster-pregnant environment; NPP, potential of farmland production, and population density were taken as the criteria for determining the vulnerability of disaster bearing capacity. Meanwhile, ArcGIS was used for analysis and calculation to complete the risk assessment of flood disaster in Lijiang River Basin, Guangxi. The results indicate that: (1) the hazard level of flood disaster causing factors in Lijiang River Basin shows a decreasing distribution pattern from north to south, and high-risk areas cover 3108.47 km², accounting for 21.29%; (2) the stability grade of disaster-pregnant environment shows a decreasing trend from the surrounding mountains to the plains, and the low-stability and lower-stability areas are mostly found in the low-lying areas around Lijiang River, with an area of 4218.63 km², accounting for 28.69%; (3) the vulnerability of the disaster bearing body is generally at a low level, and the areas with high level cover 246.96 km², accounting for only 1.69%; (4) under the combined effect of the above factors, the northern part of Guilin City in the Lijiang River Basin has a high risk of flood disaster.

Flood sensitivity assessment of super cities

In the context of global urbanization, more and more people are attracted to these cities with superior geographical conditions and strategic positions, resulting in the emergence of world super cities. However, with the increasing of urban development, the underlying surface of the city has changed, the soil originally covered with vegetation has been substituted by hardened pavement such as asphalt and cement roads. Therefore, the infiltration capacity of urban rainwater is greatly limited, and waterlogging is becoming more and more serious. In addition, the suburbs of the main urban areas of super cities are usually villages and mountains, and frequent flash floods seriously threaten the life and property safety of people in there. Flood sensitivity assessment is an effective method to predict and mitigate flood disasters. Accordingly, this study aimed at identifying the areas vulnerable to flood by using Geographic Information System (GIS) and Remote Sensing (RS) and apply Logistic Regression (LR) model to create a flood sensitivity map of Beijing. 260 flood points in history and 12 predictors [elevation, slope, aspect, distance to rivers, Topographic Wetness Index (TWI), Stream Power Index (SPI), Sediment Transport Index (STI), curvature, plan curvature, Land Use/Land Cover (LULC), soil, and rainfall] were used in this study. Even more noteworthy is that most of the previous studies discussed flash flood and waterlogging separately. However, flash flood points and waterlogging points were included together in this study. We evaluated the sensitivity of flash flood and waterlogging as a whole and obtained different results from previous studies. In addition, most of the previous studies focused on a certain river basin or small towns as the study area. Beijing is the world's ninth largest super cities, which was unusual in previous studies and has important reference significance for the flood sensitivity analysis of other super cities. The flood inventory data were randomly subdivided into training (70%) and test (30%) sets for model construction and testing using the Area Under Curve (AUC), respectively. The results turn out that: (1) elevation, slope, rainfall, LULC, soil and TWI were highly important among these elements, and were the most influential variables in the assessment of flood sensitivity. (2) The AUC of the test dataset revealed a prediction rate of 81.0%. The AUC was greater than 0.8, indicating that the model assessment accuracy was high. (3) The proportion of high risk and extremely high risk areas was 27.44%, including 69.26% of the flood events in this study, indicating that the flood distribution in these areas was relatively dense and the susceptibility was high. Super cities have a high population density, and once flood disasters occur, the losses brought by them are immeasurable. Thus, flood sensitivity map can provide meaningful information for policy makers to enact appropriate policies to reduce future damage.

Inundation Resilience Analysis of Metro-Network from a Complex System Perspective Using the Grid Hydrodynamic Model and FBWM Approach: A Case Study of Wuhan

The upward trend of metro flooding disasters inevitably brings new challenges to urban underground flood management. It is essential to evaluate the resilience of metro systems so that efficient flood disaster plans for preparation, emergency response, and timely mitigation may be developed. Traditional response solutions merged multiple sources of data and knowledge to support decision-making. An obvious drawback is that original data sources for evaluations are often stationary, inaccurate, and subjective, owing to the complexity and uncertainty of the metro station’s actual physical environment. Meanwhile, the flood propagation path inside the whole metro station network was prone to be neglected. This paper presents a comprehensive approach to analyzing the resilience of metro networks to solve these problems. Firstly, we designed a simplified weighted and directed metro network module containing six characteristics by a topological approach while considering the slope direction between sites. Subsequently, to estimate the devastating effects and details of the flood hazard on the metro system, a 100-year rainfall–flood scenario simulation was conducted using high-precision DEM and a grid hydrodynamic model to identify the initially above-ground inundated stations (nodes). We developed a dynamic node breakdown algorithm to calculate the inundation sequence of the nodes in the weighted and directed network of the metro. Finally, we analyzed the resilience of the metro network in terms of toughness strength and organization recovery capacity, respectively. The fuzzy best–worst method (FBWM) was developed to obtain the weight of each assessment metric and determine the toughness strength of each node and the entire network. The results were as follows. (1) A simplified three-dimensional metro network based on a complex system perspective was established through a topological approach to explore the resilience of urban subways. (2) A grid hydrodynamic model was developed to accurately and efficiently identify the initially flooded nodes, and a dynamic breakdown algorithm realistically performed the flooding process of the subway network. (3) The node toughness strength was obtained automatically by a nonlinear FBWM method under the constraint of the minimum error to sustain the resilience assessment of the metro network. The research has considerable implications for managing underground flooding and enhancing the resilience of the metro network.

An Analysis of Agricultural Systems Modelling Approaches and Examples to Support Future Policy Development under Disruptive Changes in New Zealand

Agricultural systems have entered a period of significant disruption due to impacts from change drivers, increasingly stringent environmental regulations and the need to reduce unwanted discharges, and emerging technologies and biotechnologies. Governments and industries are developing strategies to respond to the risks and opportunities associated with these disruptors. Modelling is a useful tool for system conceptualisation, understanding, and scenario testing. Today, New Zealand and other nations need integrated modelling tools at the national scale to help industries and stakeholders plan for future disruptive changes. In this paper, following a scoping review process, we analyse modelling approaches and available agricultural systems’ model examples per thematic applications at the regional to national scale to define the best options for the national policy development. Each modelling approach has specificities, such as stakeholder engagement capacity, complex systems reproduction, predictive or prospective scenario testing, and users should consider coupling approaches for greater added value. The efficiency of spatial decision support tools working with a system dynamics approach can help holistically in stakeholders’ participation and understanding, and for improving land planning and policy. This model combination appears to be the most appropriate for the New Zealand national context.

Flood impact on the Spanish Mediterranean coast since 1960 based on the prevailing synoptic patterns

In a changing climate and in social context, tools and databases with high spatiotemporal resolution are needed for increasing the knowledge on the relationship between meteorological events and flood impacts; hence, analysis of high-resolution spatiotemporal databases with detailed information on the frequency, intensity, and impact of floods is necessary. However, the methodological nature of flood databases hinders relating specific flood events to the weather events that cause them; hence, methodologies for classifying flood cases according to the synoptic patterns that generate them are also necessary. Knowing which synoptic patterns are likely to generate risk situations allows for a probabilistic approach with high spatial resolution regarding the timing of occurrence, affected area, and expected damage from floods. To achieve these objectives, we use the SMC-Flood Database, a high-resolution spatiotemporal flood database covering the 1960-2015 period for all municipalities along the Spanish Mediterranean coast. To relate floods with the synoptic conditions that generated them, we used a multivariate analysis method on the corrected daily anomalies of the surface pressure fields, 850 hPa temperature, and 500 hPa geopotential height, all of which were obtained from the 20th Century Reanalysis Project V2. Results show that 12 atmospheric synoptic patterns can statistically explain the 3608 flood cases that occurred in the study area between 1960 and 2015. These flood cases were classified into 847 atmospherically induced flood events. These results reduce the uncertainty during decision making because of the classification of potential risk situations. The Mediterranean Basin is a region where floods have serious socioeconomic impacts; hence, this work helps improving prevention measures and providing information for policymakers, mainly regarding land use planning and early warning systems.

An Optimized Weighted Naïve Bayes Method for Flood Risk Assessment

Floods occur frequently and cause considerable damage to local environments. Effectively assessing the flood risk contributes to reducing loss caused by such disasters. In this study, the weighted naïve Bayes (WNB) method was selected to evaluate flood risk, and the entropy weight method was employed to compute the weights. A sampling and verifying model was employed to generate the most accurate conditional probability table (MACPT) to calculate the probability of flooding. When using the framework integrating WNB with the sampling and verifying model, previous studies could not obtain a WNB-based MACPT and the WNB classification accuracy, for lacking WNB functions that could be called directly. Facing this issue, in this study we developed WNB functions with the MATLAB platform to directly integrate with the sampling and verifying model to generate a WNB-based MACPT, contributing to the greater interpretability and extensibility of the model. Shantou and Jieyang cities in China were selected as the study area. The results demonstrate that: (1) a WNB-based MACPT can reflect the real spatial distribution of flood risk and (2) the WNB outperform the NB when integrated with the sampling and verifying model. The resulting gridded estimation reveal a detailed spatial pattern of flood risk, which can serve as a realistic reference for decision making related to floods. Furthermore, the proposed method uses less data, which would be helpful in developing countries where long-term intensive hydrologic monitoring is limited.

Data-driven flood hazard zonation of Italy

We present Flood-SHE, a data-driven, statistically-based procedure for the delineation of areas expected to be inundated by river floods. We applied Flood-SHE in the 23 River Basin Authorities (RBAs) in Italy using information on the presence or absence of inundations obtained from existing flood zonings as the dependent variable, and six hydro-morphometric variables computed from a 10 m × 10 m DEM as covariates. We trained 96 models for each RBA using 32 combinations of the hydro-morphometric covariates for the three return periods, for a total of 2208 models, which we validated using 32 model sets for each of the covariate combinations and return periods, for a total of 3072 validation models. In all the RBAs, Flood-SHE delineated accurately potentially inundated areas that matched closely the corresponding flood zonings defined by physically-based hydro-dynamic flood routing and inundation models. Flood-SHE delineated larger to much larger areas as potentially subject of being inundated than the physically-based models, depending on the quality of the flood information. Analysis of the sites with flood human consequences revealed that the new data-driven inundation zones are good predictors of flood risk to the population of Italy. Our experiment confirmed that a small number of hydro-morphometric terrain variables is sufficient to delineate accurate inundation zonings in a variety of physiographical settings, opening to the possibility of using Flood-SHE in other areas. We expect the new data-driven inundation zonings to be useful where flood zonings built on hydrological modelling are not available, and to decide where improved flood hazard zoning is needed.

GIS-Based Spatial and Multi-Criteria Assessment of Riverine Flood Potential: A Case Study of the Nitra River Basin, Slovakia

The aim of this study was to identify the areas with different levels of riverine flood potential (RFP) in the Nitra river basin, Slovakia, using multi-criteria evaluation (MCE)-analytical hierarchical process (AHP), geographic information systems (GIS), and seven flood conditioning factors. The RFP in the Nitra river basin had not yet been assessed through MCE-AHP. Therefore, the methodology used can be useful, especially in terms of the preliminary flood risk assessment required by the EU Floods Directive. The results showed that classification techniques of natural breaks (Jenks), equal interval, quantile, and geometric interval classified 32.03%, 29.90%, 41.84%, and 53.52% of the basin, respectively, into high and very high RFP while 87.38%, 87.38%, 96.21%, and 98.73% of flood validation events, respectively, corresponded to high and very high RFP. A single-parameter sensitivity analysis of factor weights was performed in order to derive the effective weights, which were used to calculate the revised riverine flood potential (RRFP). In general, the differences between the RFP and RRFP can be interpreted as an underestimation of the share of high and very high RFP as well as the share of flood events in these classes within the RFP assessment. Therefore, the RRFP is recommended for the assessment of riverine flood potential in the Nitra river basin.

Use of Factor Analysis (FA), Artificial Neural Networks (ANNs), and Multiple Linear Regression (MLR) for Electrical Conductivity Prediction in Aquifers in the Gallikos River Basin, Northern Greece

Due to the fact of water resource deterioration from human activities and increased demand over the last few decades, optimization of management practices and policies is required, for which more reliable data are necessary. Cost and time are always of importance; therefore, methods that can provide low-cost data in a short period of time have been developed. In this study, the ability of an artificial neural network (ANN) and a multiple linear regression (MLR) model to predict the electrical conductivity of groundwater samples in the GallikosRiver basin, northern Greece, was examined. A total of 233 samples were collected over the years 2004–2005 from 89 sampling points. Descriptive statistics, Pearson correlation matrix, and factor analysis were applied to select the inputs of the water quality parameters. Input data to the ANN and MLR were Ca, Mg, Na, and Cl. The best results regarding the ANN were provided by a model that included one hidden layer of three neurons. The mean absolute percentage error, modeling efficiency, and root mean square error were used to evaluate the performances of the methods and to compare the prediction capabilities of the ANN and MLR. We concluded that the ANN and MLR models were valid and had similar accuracy (using the same inputs) with a large number of samples, but in the case of a smaller data set, the MLR showed a better performance.

A critical review of water resources in Greece: The key role of agricultural adaptation to climate-water effects

Τhe management and protection of a country's water resources is a matter of high priority, ensuring the development and socio-economic stability of a country. Unquestionably, Greece is a characteristic example of this, as water distribution is highly spatially and temporally unequally distributed, while irrigation and tourist consumers as well as the pollution load are expected to be increased in the near future. Water resources in Greece are particularly affected by climate extremes, with droughts, floods and soil erosion by water being the utmost consequences. Greece consumes the greatest amount of its available water resources in the agricultural sector. Also, there is much evidence of water shortage and bad/poor chemical status of some water bodies, mainly due to saltwater intrusion in coastal agricultural areas and intensively agricultural activities. Therefore, this review provides a literature update on the quantity and quality aspects of water resources in Greece for each water body, focusing on water relation effects (aridity/drought, floods and soil erosion by water). This paper, based on different sources of information and an extensive database of water related data, collects, evaluates and groups data from a quantity and quality point of view for all the different water bodies of Greece. Specific water districts such as the Aegean islands, the eastern part of Crete, Attica and Thessaly are expected to be the most affected by climate-water impacts with reduced crops yields, a greater risk of droughts and/or floods, a loss of agricultural land, declining water availability, and degraded water resources (surface and groundwater). Based on these findings, the proposed review highlights agricultural adaptation practices for increasing water use efficiency, ensuring the sustainability of water resources in Greece.

Performance evaluation of artificial intelligence paradigms-artificial neural networks, fuzzy logic, and adaptive neuro-fuzzy inference system for flood prediction

Flood prediction has gained prominence world over due to the calamitous socio-economic impacts this hazard has and the anticipated increase of its incidence in the near future. Artificial intelligence (AI) models have contributed significantly over the last few decades by providing improved accuracy and economical solutions to simulate physical flood processes. This study explores the potential of the AI computing paradigm to model the stream flow. Artificial neural network (ANN), fuzzy logic, and adaptive neuro-fuzzy inference system (ANFIS) algorithms are used to develop nine different flood prediction models using all the available training algorithms. The performance of the developed models is evaluated using multiple statistical performance evaluators. The predictability and robustness of the models are tested through the simulation of a major flood event in the study area. A total of 12 inputs were used in the development of the models. Five training algorithms were used to develop the ANN models (Bayesian regularization, Levenberg Marquardt, conjugate gradient, scaled conjugate gradient, and resilient backpropagation), two fuzzy inference systems to develop fuzzy models (Mamdani and Sugeno), and two training algorithms to develop the ANFIS models (hybrid and backpropagation). The ANFIS model developed using hybrid training algorithm gave the best performance metrics with Nash-Sutcliffe Model Efficiency (NSE) of 0.968, coefficient of correlation (R²) of 97.066%, mean square error (MSE) of 0.00034, root mean square error (RMSE) of 0.018, mean absolute error (MAE) of 0.0073, and combined accuracy (CA) of 0.018, implying the potential of using the developed models for flood forecasting. The significance of this research lies in the fact that a combination of multiple inputs and AI algorithms has been used to develop the flood models. In summary, this research revealed the potential of AI algorithm-based models in predicting floods and also developed some useful techniques that can be used by the Flood Control Departments of various states/regions/countries for flood prognosis.

The Impact of Flood Risk on the Activity of the Residential Land Market in a Polish Cultural Heritage Town

The article attempts to determine the effect of perceived flood risk, based on identified flood hazard zones, on the level of activity in the market of land property designated for housing developments in the historical town of Sandomierz, Poland. The study employed graphical, analytical, quantitative methods, and spatial analyses with GIS tools. The proposed methodology, involving spatial interpolation of the phenomenon (Kernel Density Estimation (KDE) and Inverse Distance Weighting (IDW)) and an expert opinion survey, facilitates the assessment of the market activity in towns where transactions are scarce. Trade in property is lower in areas at risk of flooding than for the remaining parts of the town. The potential flood hazard zone affects both the activity of the property market and the average prices of land. The study demonstrated that both a flood and flood risk affect the levels of market activity and the prices of residential land. However, this impact differs at various times and locations and is greater immediately after a flood. Properties located in the most attractive location within an area are characterised by a greater sensitivity to this risk.

Capturing transformation of flood hazard over a large River Basin under changing climate using a top-down approach

Existing flood modeling studies over coastal catchments involving different combinations of model chain setup imparting complex information fails to entail the needs of policy or decision-makers. Thus, a comprehensive framework that pertains to the requirements of practitioners and provides more perspicuous flood hazard information is required. In this paper, a novel approach translating complex flood hazard information in the form of decision priority maps derived using a rational combination of models (physical and statistical) is elucidated at the finest administrative scale. The proposed methodology is illustrated over a highly flood-prone deltaic region in Mahanadi River Basin, India, to characterize impacts of climate change for a 1:100 years return period flood event under future conditions (2026-2055). The modeled flood events are further analyzed to capture the transformation dynamics of flood hazard classes (FHCs) in near-future, for prioritizing areas with greater hazard potential. Interestingly, the results capture a high transformation characteristic from low to high FHCs in agriculture-dominated areas, which are significantly greater than the areas experiencing flood hazard reduction. The results show a significant increase of 12.5% and 27.35% in areas with high FHCs under RCP4.5 and RCP8.5 scenarios, respectively. Moreover, a notable climate change response is indicated under both climate change scenarios, with approximately 22% (RCP4.5) and 25% (RCP8.5) in villages showing a drastic increment in flood hazard magnitude. The results thus highlight the importance of identifying and prioritizing the areas for flood adaptation where a relative change in flood hazard potential is higher due to climate change. Therefore, we conclude that this study can provide an insight into the implication of new approaches for effective communication of flood information by bridging the gaps between scientific communities and decision-makers in appraisal for better flood adaptation measures.

The Impact of Reforestation Induced Land Cover Change (1990–2017) on Flood Peak Discharge Using HEC-HMS Hydrological Model and Satellite Observations: A Study in Two Mountain Basins, China

Understanding the effect of land use and land cover (LULC) type change on watershed hydrological response is essential for adopting applicable measures to control floods. In China, the Grain to Green Program (GTGP) and the Natural Forest Conservation Program (NFCP) have had a substantial impact on LULC. We investigate the effect of these conservation efforts on flood peak discharge in two mountainous catchments. We used a series of Landsat images ranging from 1990 to 2016/2017 to evaluate the LULC changes. Further to this, the hydrological responses at the basin and sub-basin scale were generated by the Hydrologic Modeling System (HEC-HMS) under four LULC scenarios. Between 1990 and 2016/2017, both catchments experienced an increase in forest and urban land by 18% and 2% in Yanhe and by 16% and 8% in Guangyuan, respectively. In contrast, the agricultural land decreased by approximately 30% in Yanhe and 24% in Guangyuan, respectively. The changes in land cover resulted in decrease in flood peak discharge ranging from 14% in Yanhe to 6% in Guangyuan. These findings provide a better understanding on the impact of reforestation induced LULC change on spatial patterns of typical hydrological responses of mountainous catchment and could help to mitigate flash flood hazards in other mountainous regions.

Water balance and flashiness for a large floodplain system: A case study of Poyang Lake, China

Lakes and river-related floodplains are hydrologically complex and dynamic systems that exhibit frequent wetting and drying. Poyang Lake and its extremely productive wetland constitute the largest lake-floodplain system of the Yangtze River basin. This study aims to use a daily water balance model in combination with a physically based hydrodynamic model to investigate the overall hydrological regime of the lake-floodplain system. Water balance analysis shows that 79.0% and 12.2% of yearly inflows are from river discharges from the upstream gauged and downstream ungauged catchments, respectively. The direct precipitation contributes around 3.0% on the lake surface, while the balance of 1.2% is sourced from floodplain runoff (0.5%) and lake's backflow (0.7%). Around 86.9% of the total lake outflow is discharged into the Yangtze River, while 1.5% evaporates for the lake water surface. Net groundwater discharge (11.6%) has greater impacts on the water balance than the net groundwater recharge (4.6%). Water balance results highlight that the catchment rivers and the associated groundwater system are important parts of Poyang Lake. In general, the catchment rivers exhibit higher flashiness during the rising and flood periods than the other periods, and the flashiness in the lake downstream and floodplains is higher than in the lake upstream regions and the main lake, respectively, demonstrating spatiotemporal variability in the flood pulse in the lake-floodplain system. This study contributes to provide more detailed information regarding hydrological components and their relative effects to decision-makers for both Poyang Lake and other similar floodplains, given proposals to cope with the climate and human interventions and the accelerating pace of water resources and water safety management.

A new bivariate risk classifier for flood management considering hazard and socio-economic dimensions

Identification of flood-risk dynamics is pivotal for refurbishing the existing and future flood-management options. The present study quantifies the marginal and compound contributions of hazard and vulnerability to flood-risk through an innovative concept of Risk-classifier, designed in the form of a 5 × 5 choropleth. The proposed framework is demonstrated at the finest administrative scale of village-level over Jagatsinghpur district in Mahanadi River basin, Odisha (India) for two-time frames: Scenario-I (1970-2011) and Scenario-II (1970-2001). An increase in high and very high hazard and vulnerable villages is noticed in Scenario-I, the majority of them lying in the coastal stretches (S-E region) and adjoining flood plains of Mahanadi River (N-W region). Scenario-I is characterized by the majority of hazard-driven and compound (both hazard and vulnerability) risk villages, while Scenario II is characterized by a majority of vulnerability driven-risk villages. For the vulnerability-driven risk villages, rigorous enforcement of policies and mitigation schemes are recommended, while for hazard-driven risk villages, enhancement of structural measures and flood-plain zoning should be exercised. Such exhaustive flood-risk information may serve as a valuable cartographic product for the civic authorities and stakeholders and help in prioritizing flood mitigation actions for improved environmental planning and management.

Flood Risk Evaluation in the Middle Reaches of the Yangtze River Based on Eigenvector Spatial Filtering Poisson Regression

A Poisson regression based on eigenvector spatial filtering (ESF) is proposed to evaluate the flood risk in the middle reaches of the Yangtze River in China. Regression analysis is employed to model the relationship between the frequency of flood alarming events observed by hydrological stations and hazard-causing factors from 2005 to 2012. Eight factors, including elevation (ELE), slope (SLO), elevation standard deviation (ESD), river density (DEN), distance to mainstream (DIST), NDVI, annual mean rainfall (RAIN), mean annual maximum of three-day accumulated precipitation (ACC) and frequency of extreme rainfall (EXE) are selected and integrated into a GIS environment for the identification of flood-prone basins. ESF-based Poisson regression (ESFPS) can filter out the spatial autocorrelation. The methodology includes construction of a spatial weight matrix, testing of spatial autocorrelation, decomposition of eigenvectors, stepwise selection of eigenvectors and calculation of regression coefficients. Compared with the pseudo R squared obtained by PS (0.56), ESFPS exhibits better fitness with a value of 0.78, which increases by approximately 39.3%. ESFPS identifies six significant factors including ELE, DEN, EXE, DIST, ACC and NDVI, in which ACC and NDVI are the first two main factors. The method can provide decision support for flood risk relief and hydrologic station planning.

Flash flood susceptibility modeling using an optimized fuzzy rule based feature selection technique and tree based ensemble methods

The main objective of the present study was to provide a novel methodological approach for flash flood susceptibility modeling based on a feature selection method (FSM) and tree based ensemble methods. The FSM, used a fuzzy rule based algorithm FURIA, as attribute evaluator, whereas GA were used as the search method, in order to obtain optimal set of variables used in flood susceptibility modeling assessments. The novel FURIA-GA was combined with LogitBoost, Bagging and AdaBoost ensemble algorithms. The performance of the developed methodology was evaluated at the Bao Yen district and the Bac Ha district of Lao Cai Province in the Northeast region of Vietnam. For the case study, 654 floods and twelve geo-environmental variables were used. The predictive performance of each model was estimated through the calculation of the classification accuracy, the sensitivity, the specificity, the success and predictive rate curve and the area under the curves (AUC). The FURIA-GA FSM compared to a conventional rule based method gave more accurate predictive results. Also, the FURIA-GA based models, presented higher learning and predictive ability compared to the ensemble models that had not undergone a FSM. Based on the predictive classification accuracy, FURIA-GA-Bagging (93.37%) outperformed FURIA-GA-LogitBoost (92.35%) and FURIA-GA-AdaBoost (89.03%). FURIA-GA-Bagging showed also the highest sensitivity (96.94%) and specificity (89.80%). On the other hand, the FURIA-GA-LogitBoost showed the lowest percentage in very high susceptible zone and the highest relative flash-flood density, whereas the FURIA-GA-AdaBoost achieved the highest prediction AUC value (0.9740), based on the prediction rate curve, followed by FURIA-GA-Bagging (0.9566), and FURIA-GA-LogitBoost (0.8955). It can be concluded that the usage of different statistical metrics, provides different outcomes concerning the best prediction model, which mainly could be attributed to sites specific settings. The proposed models could be considered as a novel alternative investigation tools appropriate for flash flood susceptibility mapping.

A flood susceptibility model at the national scale based on multicriteria analysis

River flooding is a specific worldwide type of flooding responsible for considerable human and material losses. An extensive knowledge about flood conditioning factors and a diverse set of methodologies for flood susceptibility evaluations are available, although there is still field for improvement regarding methodologies for small-scale flood susceptibility assessment, particularly relevant in data-scarce contexts. This research applied to mainland Portugal, introduces a multicriteria methodology to assess flood susceptibility at national scale considering three flood-conditioning factors: flow accumulation, average slope angle and average relative permeability. These three factors resume other factors usually considered in literature, related to morphology and potential runoff. This work includes data from the flood conditioning factors considering the cumulative role of the entire contributive area and not only the on-site characteristics. The weight of each factor was assigned based on expert opinion and validated using available flood damages databases with >150 years of records. From the several tested flood susceptibility models, the one that best fits the historical records was chosen, which corresponds also to a more valued role of flow accumulation factor. Results provide an accurate differentiation of transboundary, regional and local rivers. The scores of stream flood susceptibility were later transformed to a single value per each of the 278 municipalities of mainland Portugal. Representing the natural susceptibility to river flooding, these results can be cross-analyzed with structural mitigation measures, spatial planning instruments, exposure and vulnerability data along the respective floodplains, in order to identify water streams that require a more detailed and concerned future intervention and an exhaustive susceptibility study at the local scale.

A comparison of statistical methods and multi-criteria decision making to map flood hazard susceptibility in Northern Iran

In north of Iran, flood is one of the most important natural hazards that annually inflict great economic damages on humankind infrastructures and natural ecosystems. The Kiasar watershed is known as one of the critical areas in north of Iran, due to numerous floods and waste of water and soil resources, as well as related economic and ecological losses. However, a comprehensive and systematic research to identify flood-prone areas, which may help to establish management and conservation measures, has not been carried out yet. Therefore, this study tested four methods: evidential belief function (EBF), frequency ratio (FR), Technique for Order Preference by Similarity To ideal Solution (TOPSIS) and Vlse Kriterijumsk Optimizacija Kompromisno Resenje (VIKOR) for flood hazard susceptibility mapping (FHSM) in this area. These were combined in two methodological frameworks involving statistical and multi-criteria decision making approaches. The efficiency of statistical and multi-criteria methods in FHSM were compared by using area under receiver operating characteristic (AUROC) curve, seed cell area index and frequency ratio. A database containing flood inventory maps and flood-related conditioning factors was established for this watershed. The flood inventory maps produced included 132 flood conditions, which were randomly classified into two groups, for training (70%) and validation (30%). Analytical hierarchy process (AHP) indicated that slope, distance to stream and land use/land cover are of key importance in flood occurrence in the study catchment. In validation results, the EBF model had a better prediction rate (0.987) and success rate (0.946) than FR, TOPSIS and VIKOR (prediction rate 0.917, 0.888, and 0.810; success rate 0.939, 0.904, and 0.735, respectively). Based on their frequency ratio and seed cell area index values, all models except VIKOR showed acceptable accuracy of classification.

Flood Susceptibility Mapping on a National Scale in Slovakia Using the Analytical Hierarchy Process

Flood susceptibility mapping and assessment is an important element of flood prevention and mitigation strategies because it identifies the most vulnerable areas based on physical characteristics that determine the propensity for flooding. This study aims to define the flood susceptibility zones for the territory of Slovakia using a multi-criteria approach, particularly the analytical hierarchy process (AHP) technique, and geographic information systems (GIS). Seven flood conditioning factors were chosen: hydrography—distance from rivers, river network density; hydrology—flow accumulation; morphometry—elevation, slope; and permeability—curve numbers, lithology. All factors were defined as raster datasets with the resolution of 50 x 50 m. The AHP technique was used to calculate the factor weights. The relative importance of the selected factors prioritized slope degree as the most important factor followed by river network density, distance from rivers, flow accumulation, elevation, curve number, and lithology. It was found that 33.1% of the territory of Slovakia is characterized by very high to high flood susceptibility. The flood susceptibility map was validated against 1513 flood historical points showing very good agreement between the computed susceptibility zones and historical flood events of which 70.9% were coincident with high and very high susceptibility levels, thus confirming the effectiveness of the methodology adopted.

Development of a spatially complete floodplain map of the conterminous United States using random forest

Floodplains perform several important ecosystem services, including storing water during precipitation events and reducing peak flows, thus reducing flooding of downstream communities. Understanding the relationship between flood inundation and floodplains is critical for ecosystem and community health and well-being, as well as targeting floodplain and riparian restoration. Many communities in the United States, particularly those in rural areas, lack inundation maps due to the high cost of flood modeling. Only 60% of the conterminous United States has Flood Insurance Rate Maps (FIRMs) through the U.S. Federal Emergency Management Agency (FEMA). We developed a 30-meter resolution flood inundation map of the conterminous United States (CONUS) using random forest classification to fill the gaps in the FIRM. Input datasets included digital elevation model (DEM)-derived variables, flood-related soil characteristics, and land cover. The existing FIRM 100-year floodplains, called the Special Flood Hazard Area (SHFA), were used to train and test the random forests for fluvial and coastal flooding. Models were developed for each hydrologic unit code level four (HUC-4) watershed and each 30-meter pixel in the CONUS was classified as floodplain or non-floodplain. The most important variables were DEM-derivatives and flood-based soil characteristics. Models captured 79% of the SFHA in the CONUS. The overall F1 score, which balances precision and recall, was 0.78. Performance varied geographically, exceeding the CONUS scores in temperate and coastal watersheds but were less robust in the arid southwest. The models also consistently identified headwater floodplains not present in the SFHA, lowering performance measures but providing critical information missing in many low-order stream systems. The performance of the random forest models demonstrates the method's ability to successfully fill in the remaining unmapped floodplains in the CONUS, while using only publicly available data and open source software.

Flood inundation assessment for the Hanoi Central Area, Vietnam under historical and extreme rainfall conditions

Flash floods have long been common in Asian cities, with recent increases in urbanization and extreme rainfall driving increasingly severe and frequent events. Floods in urban areas cause significant damage to infrastructure, communities and the environment. Numerical modelling of flood inundation offers detailed information necessary for managing flood risk in such contexts. This study presents a calibrated flood inundation model using referenced photos, an assessment of the influence of four extreme rainfall events on water depth and inundation area in the Hanoi central area. Four types of historical and extreme rainfall were input into the inundation model. The modeled results for a 2008 flood event with 9 referenced stations resulted in an R² of 0.6 compared to observations. The water depth at the different locations was simulated under the four extreme rainfall types. The flood inundation under the Probable Maximum Precipitation presents the highest risk in terms of water depth and inundation area. These results provide insights into managing flood risk, designing flood prevention measures, and appropriately locating pump stations.

Application of fuzzy weight of evidence and data mining techniques in construction of flood susceptibility map of Poyang County, China

In China, floods are considered as the most frequent natural disaster responsible for severe economic losses and serious damages recorded in agriculture and urban infrastructure. Based on the international experience prevention of flood events may not be completely possible, however identifying susceptible and vulnerable areas through prediction models is considered as a more visible task with flood susceptibility mapping being an essential tool for flood mitigation strategies and disaster preparedness. In this context, the present study proposes a novel approach to construct a flood susceptibility map in the Poyang County, JiangXi Province, China by implementing fuzzy weight of evidence (fuzzy-WofE) and data mining methods. The novelty of the presented approach is the usage of fuzzy-WofE that had a twofold purpose. Firstly, to create an initial flood susceptibility map in order to identify non-flood areas and secondly to weight the importance of flood related variables which influence flooding. Logistic Regression (LR), Random Forest (RF) and Support Vector Machines (SVM) were implemented considering eleven flood related variables, namely: lithology, soil cover, elevation, slope angle, aspect, topographic wetness index, stream power index, sediment transport index, plan curvature, profile curvature and distance from river network. The efficiency of this new approach was evaluated using area under curve (AUC) which measured the prediction and success rates. According to the outcomes of the performed analysis, the fuzzy WofE-SVM model was the model with the highest predictive performance (AUC value, 0.9865) which also appeared to be statistical significant different from the other predictive models, fuzzy WofE-RF (AUC value, 0.9756) and fuzzy WofE-LR (AUC value, 0.9652). The proposed methodology and the produced flood susceptibility map could assist researchers and local governments in flood mitigation strategies.

Mapping flood susceptibility in mountainous areas on a national scale in China

Mountainous terrain covers nearly half of China and is susceptible to floods, which can lead to substantial losses of human life and property. Historical flooding records from government bulletins and newspapers, the only available information regarding floods that have occurred in some mountainous areas, are valuable for understanding flood disaster mechanisms in these regions. In this study, the flood susceptibility in mountainous regions in China was mapped based on historical flooding records from 1949 to 2000. A Random Forest (RF) model, which can handle large datasets through factor contribution analysis, was chosen to characterize the relationships between flooding occurrences and twelve geographic, meteorological, and hydrological explanatory factors. The results indicate that the RF model can effectively identify flood-prone areas and has advantages over artificial neural network (ANN) and support vector machine (SVM) methods. Among these explanatory factors, the geographic factors (elevation, longitude and drainage density) are the most important predictors of flooding in China's mountainous areas, whereas the hydrological factors (relative elevation and curve number) are the least important. Two independent datasets of historical flooding events from the Bulletin of Flood and Drought Disasters in China (2006-2014) alongside news reports and yearbooks (2008-2014) were collected and chosen to validate the capability of the RF model. The validation results confirm that the RF model can identify the flood susceptibility with satisfactory accuracy. This study proposes a preliminary flood susceptibility map of mountainous areas in China and provides a reference for predicting and mitigating potentially disastrous flooding events.