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Societal awareness is a crucial factor driving floodplain dynamics. When modelling these dynamics, flood awareness decay is considered constant. However, empirical studies have shown that the intensity of an experienced event can influence awareness decay. Here we explore and model the influence of variable flood awareness decay on flood losses for two types of societies that cope with flooding by adopting structural (techno society) or nonstructural (green society) protection measures. We modified an established socio-hydrological model and performed three synthetic experiments with multiple scenarios of flood awareness decay, flood intensity, and frequency. We found that, when modelling techno societies, assuming a constant awareness decay leads to underestimating societal flood awareness after severe flood events. In contrast, overestimation of flood awareness occurs when using constant awareness decay for green societies. This might lead to overestimating the effects of human–flood dynamics, such as the levee effect and adaptation effects. QC 20230420

Economic inequality is rising within many countries globally, and this can significantly influence the social vulnerability to natural hazards. We analysed income inequality and flood disasters in 67 middle- and high-income countries between 1990 and 2018 and found that unequal countries tend to suffer more flood fatalities. This study integrates geocoded mortality records from 573 major flood disasters with population and economic data to perform generalized linear mixed regression modelling. Our results show that the significant association between income inequality and flood mortality persists after accounting for the per-capita real gross domestic product, population size in flood-affected regions and other potentially confounding variables. The protective effect of increasing gross domestic product disappeared when accounting for income inequality and population size in flood-affected regions. On the basis of our results, we argue that the increasingly uneven distribution of wealth deserves more attention within international disaster-risk research and policy arenas.

Two-way interactions and feedback between hydrological and social processes in settled floodplains determine the complex human-flood system and change vulnerability over time. To focus on the dynamic role of individual and governmental decision making on flood-risk management, we developed and implemented a coupled agent-based and hydraulic modelling framework. Within this framework, household agents are located in a floodplain protected by a levee system. Individual behaviour is based on Protection Motivation Theory and includes the options to (1) not react to flood risk; (2) implement individual flood protection measures; or (3) file a complaint to the government. The government decides about reinforcing the levee system, compromising between a cost-benefit analysis and filed complaints from households. We found that individual decision making can significantly influence flood risk. In addition, the coupled agent-based and hydraulic modelling framework approach captures commonly observed socio-hydrological dynamics, namely levee and adaptation effects. It provides an explanatory tool for assessing spatial and temporal dynamics of flood risk in a socio-hydrological system.

The past few years have seen the raise of unmanned aerial vehicles (UAV) in geosciences for generating highly accurate digital elevation models (DEM) at low costs, which promises to be an interesting alternative to satellite data for small river basins. The reliability of UAV-derived topography as input to hydraulic modelling is still under investigation: here, we analyse potentialities and highlight challenges of employing UAV-derived topography in hydraulic modelling in a tropical environment, where weather conditions and remoteness of the study area might affect the quality of the retrieved data. We focused on a stretch of the Limpopo River in Mozambique, where detailed ground survey and airborne data were available. First, we tested and compared topographic data derived by UAV (25 cm), RTK-GPS (50 cm DEM), LiDAR (1 m DEM) and SRTM (30 m DEM); then, we used each DEM as input data to a hydraulic model and compared the performance of each DEM-based model against the LiDAR based model, currently used as benchmark by practitioners in the area. Despite the challenges experienced during the field campaign—and described here—, the degree of accuracy in terrain modelling produced errors in water depth calculations within the tolerances adopted in this typology of studies and comparable in magnitude to the ones obtained from high-precision topography models. This suggests that UAV is a promising source of geometric data even in natural environments with extreme weather conditions.

The effects of land use alteration, migration and urbanization are key aspects in flood management, as human activities can strongly influence the capacity of ecosystems to provide flood regulating ecosystem services and determine their demand. This study analyzes spatio–temporal dynamics of flood regulating ecosystem services to support watershed management planning. A methodology for mapping the supply and demand of flood regulation is proposed and applied to the Arno River basin, in central Italy. The spatial explicit analysis of flood regulating ecosystem services supply is carried out with SWAT - Soil and Water Assessment Tool, whose outputs are synthetized by two indicators to evaluate the retention capacity of each land use class originating from CORINE data sets. Quantification of demand for flood regulating ecosystem services is based on flood hazard classes derived from the existing local flood management plans (i.e., PAI-Piano per l’Assetto Idrogeologico and PGRA-Piano di Gestione del Rischio Alluvioni). Supply and demand data are then combined to obtain budget maps of flood regulating ecosystem services and their evolution, between 1990 and 2018. The results show how both demand and supply of ecosystem services have changed in the last decades, highlighting the main hotspots at the catchment and sub-catchment scales. With the increasing urbanization, the demand values have grown in the Arno floodplains, where residential, industrial and commercial zones are located. At the same time, land use changes have altered the water regulation supply, resulting in a generalized decrease of the basin capacity to provide flood regulation services. The maps and tables obtained show the fundamental role of forest and other vegetated areas whose protection is a priority to assure future flood regulation and associated co-benefits (e.g., regulation of air quality, reduction of erosion, improvement of water quality, wood fuel). The assessment of flood regulating here proposed is a powerful tool for decision makers to improve flood regulation and provides a sound base of knowledge to identify and locate flood prevention and mitigation measures.

Levees protect floodplain areas from frequent flooding, but they can paradoxically contribute to more severe flood losses. The construction or reinforcement of levees can attract more assets and people in flood-prone area, thereby increasing the potential flood damage when levees eventually fail. Moreover, structural protection measures can generate a sense of complacency, which can reduce preparedness, thereby increasing flood mortality rates. We explore these phenomena in the Jamuna River floodplain in Bangladesh. In this study area, different levels of flood protection have co-existed alongside each other since the 1960s, with a levee being constructed only on the right bank and its maintenance being assured only in certain places. Primary and secondary data on population density, human settlements, and flood fatalities were collected to carry out a comparative analysis of two urban areas and two rural areas with different flood protection levels. We found that the higher the level of flood protection, the higher the increase of population density over the past decades as well as the number of assets exposed to flooding. Our results also show that flood mortality rates associated with the 2017 flooding in Bangladesh were lower in the areas with lower protection level. This empirical analysis of the unintended consequences of structural flood protection is relevant for the making of sustainable policies of disaster risk reduction and adaptation to climate change in rapidly changing environments.

The reliability of a levee system is a crucial factor in flood risk management. In this study we present a probabilistic methodology to assess the effects of levee cover strength on levee failure probability, triggering time, flood propagation and consequent impacts on population and assets. A method for determining fragility curves is used in combination with the results of a one-dimensional hydrodynamic model to estimate the conditional probability of levee failure in each river section. Then, a levee breach model is applied to calculate the possible flood hydrographs, and for each breach scenario a two-dimensional hydrodynamic model is used to estimate flood hazard (flood extent and timing, maximum water depths) and flood impacts (economic damage and affected population) in the areas at risk along the river reach. We show an application for levee overtopping and different flood scenarios for a 98 km reach of the lower Po River in Italy. The results show how different design solutions for the le...

The availability of planetary-scale geospatial datasets that can support the study of water-related disasters in the Anthropocene is rapidly growing. We review 124 global and free datasets allowing spatial (and temporal) analyses of floods, droughts and their interactions with human societies. Our collection of datasets is available in a descriptive list for download at . The purpose of providing an overview of datasets across a wide range of hydrological and socioeconomic variables is to highlight research opportunities across scientific disciplines for the study of the water-society interplay. Our collection of datasets confirms that the availability of geospatial data capturing hydrological hazards and exposure is far more mature than those capturing vulnerability aspects. We do not only highlight the unprecedented opportunities associated with these global datasets for the study of water-related disasters in the Anthropocene, but also discuss the challenges associated with their exploitation. These challenges include: (a) time varying datasets advised not to be used in time series analyses; (b) fine spatial resolution datasets advised not to be used in local scale studies; (c), datasets built by a wide variety of data sources prohibiting systematic uncertainty assessments; and (d) datasets built by covariate variables preventing interaction studies.

In this study, we explore the long-term trends of floodplain population dynamics at different spatial scales in the contiguous United States (U.S.). We exploit different types of datasets from 1790&ndash, 2010&mdash, i.e., decadal spatial distribution for the population density in the US, global floodplains dataset, large-scale data of flood occurrence and damage, and structural and nonstructural flood protection measures for the US. At the national level, we found that the population initially settled down within the floodplains and then spread across its territory over time. At the state level, we observed that flood damages and national protection measures might have contributed to a learning effect, which in turn, shaped the floodplain population dynamics over time. Finally, at the county level, other socio-economic factors such as local flood insurances, economic activities, and socio-political context may predominantly influence the dynamics. Our study shows that different influencing factors affect floodplain population dynamics at different spatial scales. These facts are crucial for a reliable development and implementation of flood risk management planning.

The viability of a renewable electricity system depends on a relatively small share of hydropower storage resources to regulate climate variations and the spatially uneven distribution of renewable energy. By spatio-temporal coordination of hydropower production over larger regions, the energy storage demand will be reduced and contribute to a “virtual” energy storage gain that in Europe was found to be almost twice the actual energy storage capacity of hydropower reservoirs. In an attempt to quantify this gain, hydropower availability was simulated for most parts of the European continent for a 35-year period based on historical hydrometeorological data. The most significant benefits from spatio-temporal management arise at distances between 1200 and 3000 km, i.e., on the continental scale, which can have implications for a future renewable energy system at large. Furthermore, we discuss a condition termed “energy-domain-specific drought”, which is a risk that can be reduced by the spatio-temporal management of power production. Virtual energy storage gain is not explicitly considered in the management models of hydropower production systems but could in principle complement existing management incentives.

Over the past decades, a variety of valuable research studies has helped to advance our understanding of the advantages and limitations of satellite derived precipitation datasets as a forcing to hydrological models, in combination with or as an alternative to gauge data. However, most studies have assessed the performance of only one single dataset (or a few), have used global precipitation datasets to force lumped models on regional/large-scale basins or have tested more complex distributed models only at small-scale basins. In addition, only few studies have re-calibrated the model for each precipitation dataset or have investigated reanalysis-based precipitation datasets. We aimed at addressing these gaps in the literature: in particular, we compared the performance of 18 different precipitation datasets when used as main forcing in a grid-based distributed hydrological model to assess streamflow in medium to large-scale river basins. These datasets are classified as Uncorrected Satellites (Class 1), Corrected Satellites (Class 2) and Reanalysis - Gauges based datasets (Class 3). To provide a broad-based analysis, 8 large-scale river basins (Amazon, Brahmaputra, Congo, Danube, Godavari, Mississippi, Rhine and Volga) having different sizes, hydrometeorological characteristics, and human influence were selected. The distributed hydrological model was recalibrated for each precipitation dataset individually. We found that there is not a unique best performing precipitation dataset for all basins and that results are very sensitive to the basin characteristics. However, a few datasets persistently outperform the others: SM2RAIN-ASCAT for Class 1, CHIRPS V2.0, MSWEP V2.1, and CMORPH-CRTV1.0 for Class 2, GPCC and WFEDEI GPCC for Class 3. Surprisingly, precipitation datasets showing the highest model accuracy at basin outlets do not show the same high performance in internal locations, supporting the use of distributed modelling approach rather than lumped.

This paper offers a conceptual approach to explore the complex dynamics of floodplains as fully coupled human-water systems. A number of hydrologists have recently investigated the impact of human activities (such as flood control measures, land-use changes, and settlement patterns) on the frequency and severity of floods. Meanwhile, social scientists have shown how interactions between society and waters in deltas and floodplain areas, including the frequency and severity of floods, have an impact on the ways in which social relations unfold (in terms of governance processes, policies, and institutions) and societies are organised (spatially, politically, and socially). However, we argue that the interactions and associated feedback mechanisms between hydrological and social processes remain largely unexplored and poorly understood. Thus, there is a need to better understand how the institutions and governance processes interact with hydrological processes in deltas and floodplains to influence the frequency and severity of floods, while (in turn) hydrological processes co-constitute the social realm and make a difference for how social relations unfold to shape governance processes and institutions. Our research goal, therefore, is not in identifying one or the other side of the cycle (hydrological or social), but in explaining the relationship between them: how, when, where, and why they interact, and to what result for both social relations and hydrological processes? We argue that long time series of hydrological and social data, along with remote sensing data, can be used to observe floodplain dynamics from unconventional approaches, and understand the complex interactions between water and human systems taking place in floodplain areas, across scales and levels of human impacts, and within different hydro-climatic conditions, socio-cultural settings, and modes of governance.

In flood risk assessment, there remains a lack of analytical frameworks capturing the dynamics emerging from two-way feedbacks between physical and social processes, such as adaptation and levee effect. The former, “adaptation effect”, relates to the observation that the occurrence of more frequent flooding is often associated with decreasing vulnerability. The latter, “levee effect”, relates to the observation that the non-occurrence of frequent flooding (possibly caused by flood protection structures, e.g. levees) is often associated to increasing vulnerability. As current analytical frameworks do not capture these dynamics, projections of future flood risk are not realistic. In this paper, we develop a new approach whereby the mutual interactions and continuous feedbacks between floods and societies are explicitly accounted for. Moreover, we show an application of this approach by using a socio-hydrological model to simulate the behavior of two main prototypes of societies: green societies, which cope with flooding by resettling out of flood-prone areas; and technological societies, which deal with flooding also by building levees or dikes. This application shows that the proposed approach is able to capture and explain the aforementioned dynamics (i.e. adaptation and levee effect) and therefore contribute to a better understanding of changes in flood risk, within an iterative process of theory development and empirical research.

In Bangladesh, socio-economic and hydrological processes are both extremely dynamic and inter-related. Human population patterns are often explained as a response, or adaptation strategy, to physical events, e.g. flooding, salt-water intrusion, and erosion. Meanwhile, these physical processes are exacerbated, or mitigated, by diverse human interventions, e.g. river diversion, levees and polders. In this context, this paper describes an attempt to explore the complex interplay between floods and societies in Bangladeshi floodplains. In particular, we performed a spatially-distributed analysis of the interactions between the dynamics of human settlements and flood inundation patterns. To this end, we used flooding simulation results from inundation modelling, LISFLOOD-FP, as well as global datasets of population distribution data, such as the Gridded Population of the World (20 years, from 1990 to 2010) and HYDE datasets (310 years, from 1700 to 2010). The outcomes of this work highlight the behaviour of Bangladeshi floodplains as complex human–water systems and indicate the need to go beyond the traditional narratives based on one-way cause–effects, e.g. climate change leading to migrations.

This study compares model averaging and model selection methods to estimate design floods, while accounting for the observation error that is typically associated with annual maximum flow data. Model selection refers to methods where a single distribution function is chosen based on prior knowledge or by means of selection criteria. Model averaging refers to methods where the results of multiple distribution functions are combined. Numerical experiments were carried out by generating synthetic data using the Wakeby distribution function as the parent distribution. For this study, comparisons were made in terms of relative error and root mean square error (RMSE) referring to the 1-in-100 year flood. The experiments show that model averaging and model selection methods lead to similar results, especially when short samples are drawn from a highly asymmetric parent. Also, taking an arithmetic average of all design flood estimates gives estimated variances similar to those obtained with more complex weighted model averaging.

The risk coping culture of a community plays a major role in the development of urban floodplains. In this paper we analyse, in a conceptual way, the interplay of community risk coping culture, flooding damage and economic growth. We particularly focus on three aspects: (i) collective memory, i.e., the capacity of the community to keep risk awareness high; (ii) risk-taking attitude, i.e., the amount of risk the community is collectively willing to be exposed to; and (iii) trust of the community in risk reduction measures. To this end, we use a dynamic model that represents the feedback between the hydrological and social system components. Model results indicate that, on the one hand, by under perceiving the risk of flooding (because of short collective memory and too much trust in flood protection structures) in combination with a high risk-taking attitude, community development is severely limited because of high damages caused by flooding. On the other hand, overestimation of risk (long memory and lack of trust in flood protection structures) leads to lost economic opportunities and recession. There are many scenarios of favourable development resulting from a trade-off between collective memory and trust in risk reduction measures combined with a low to moderate risk-taking attitude. Interestingly, the model gives rise to situations in which the development of the community in the floodplain is path dependent, i.e., the history of flooding may lead to community growth or recession.

Isla Hispaniola, the island shared by Haiti and the Dominican Republic, has repeatedly witnessed catastrophic disasters that caused dramatic economic losses and killed thousands of people over the last decades. One striking example is the disastrous flood event that hit the transnational basin of River Soliette on 24 May 2004. The event resulted from a severe tropical storm and killed over 1000 Haitian and 400 Dominican people. In the context of an International Cooperation Initiative, the University of Bologna led several activities in the fields of flood risk assessment, management and mitigation, and sustainable development planning for the Soliette river basin. This paper presents new techniques for a timely assessment of flood risk in data-scarce and remote areas in developing countries showing in particular: (1) Global Navigation Satellite System (GNSS)-based topographic survey of the Soliette riverbed, carried out under urgent temporal, logistical and technical constraints, and (2) some possible utilisations of the collected topographic data for the identification of structural and non-structural flood risk mitigation measures. The analysis enabled us to set and test a GNSS-based technique for fast, reliable and detailed topographic surveys in remote regions, which represents a valid option for similar areas with limited accessibility in emerging and developing countries.

Natural catastrophic events are one of most critical aspects for health and economy all around the world. However, the impact in a poor region can impact more dramatically than in others countries. Isla Hispaniola (Haiti and the Dominican Republic), one of the poorest regions of the planet, has repeatedly been hit by catastrophic natural disasters that caused incalculable human and economic losses. After the catastrophic flood event occurred in the basin of River Soliette on May 24th, 2004, the General Direction for Development and Cooperation of the Italian Department of Foreign Affairs funded an international cooperation initiative (ICI) coordinated by the University of Bologna, that involved Haitian and Dominican institutions.Main purpose of the ICI was hydrological and hydraulic analysis of the May 2004 flood event aimed at formulating a suitable and affordable flood risk mitigation plan, consisting of structural and non-structural measures. In this contest, a topographic survey was necessary to realize the hydrological model and to improve the knowledge in some areas candidates to be site for mitigation measures.To overcome the difficulties arising from the narrowness of funds, surveyors and limited time available for the survey, only GPS technique have been used, both for framing aspects (using PPP approach), and for geometrical survey of the river by means of river cross-sections and detailed surveys in two areas (RTK technique). This allowed us to reconstruct both the river geometry and the DTM’s of two expansion areas (useful for design hydraulic solutions for mitigate flood-hazard risk).

La Plata Basin is shared by five countries (Argentina, Bolivia, Brazil, Paraguay and Uruguay), which have fast growing economies in South America. These countries need energy for their sustainable development; hence, hydropower can play a very important role as a renewable clean source of energy. This paper presents an analysis of the current hydropower production and electricity demand in La Plata Basin (LPB), and it analyses the maximum and residual hydropower potential of the basin for a horizon of 30 yr (i.e. year 2040). Current hydropower production is estimated based on historical available data, while future energy production is deduced from the available water in the catchment (estimated based on measured hydrographs of the past years), whereas electricity demand is assessed by correlating existing electricity demand with the estimated population growth and economic development. The maximum and residual hydropower potential of the basin were assessed for the mean annual flows of the present hydrological regime (1970–2000) and topographical characteristics of the area. Computations were performed using an integrated GIS environment called VAPIDRO-ASTE released by the Research on Energy System (Italy). The residual hydropower potential of the basin is computed considering first that the water supply needs for population, industry and agriculture are served, and then hydropower energy is produced. The calculated hydropower production is found to be approximately half of the estimated electricity demand, which shows that there is a need to look for other sources of energy in the future.

The scientific literature has widely shown that hydraulic modelling is affected by many sources of uncertainty (e.g. model structure, input data, model parameters). However, when hydraulic models are used for engineering purposes (e.g. flood defense design), there is still a tendency to make a deterministic use of them. More specifically, the prediction of flood design profiles is often based on the outcomes of a calibrated hydraulic model. Despite the good results in model calibration, this prediction is affected by significant uncertainty, which is commonly considered by adding a freeboard to the simulated flood profile. A more accurate approach would require an explicit analysis of the sources of uncertainty affecting hydraulic modelling and design flood estimation. This paper proposes an alternative approach, which is based on the use of uncertain flood profiles, where the most significant sources of uncertainty are explicitly analyzed. An application to the Po river reach between Cremona and Borgoforte (Italy) is used to illustrate the proposed framework and compare it to the traditional approach. This paper shows that the deterministic approach underestimates the design flood profile and questions whether the freeboard, often arbitrarily defined, might lead to a false perception of additional safety levels.

The scouring effect of the flowing water around bridge piers may undermine the stability of the structure, leading to extremely high direct and indirect costs and, in extreme cases, the loss of human lives. The use of Artificial Neural Network (ANN) models has been recently proposed in the literature for estimating the maximum scour depth around bridge piers: this study aims at further investigating the potentiality of the ANN approach and, in particular, at analysing the influence of the experimental setting (laboratory or field data) and of the sediment transport mode (clear water or live bed) on the prediction performances. A large database of both field and laboratory observations has been collected from the literature for predicting the maximum local scour depth as a function of a parsimonious set of variables characterizing the flow, the sediments and the pier. Neural networks with an increasing degree of specialization have been implemented – using different subsets of the calibration data in the training phase – and validated over an external validation dataset. The results confirm that the ANN scour depths' predictions outperform the estimates obtained by empirical formulae conventionally used in the literature and in the current engineering practice, and demonstrate the importance of taking into account the differences in the type of available data – laboratory or field data – and the sediment transport mode – clear water or live bed conditions.

[1] Flood-related fatalities in Africa, as well as associated economic losses, have increased dramatically over the past half-century. There is a growing global concern about the need to identify the causes for such increased flood damages. To this end, we analyze a large, consistent and reliable dataset of floods in Africa. Identification of causes is not easy given the diverse economic settings, demographic distribution and hydro-climatic conditions of the African continent. On the other hand, many African river basins have a relatively low level of human disturbance and, therefore, provide a unique opportunity to analyze climatic effects on floods. We find that intensive and unplanned human settlements in flood-prone areas appears to be playing a major role in increasing flood risk. Timely and economically sustainable actions, such as the discouragement of human settlements in flood-prone areas and the introduction of early warning systems are, therefore, urgently needed.