The VLAGG-project proved that a novel web-based modelling approach is able to generate high- class results outperforming classical flood results in multiple ways. Firstly, via the re-use of an existing high-performance modelling infrastructure it was possible to simulate in 2D the entire Flemish region (13000km²) at least 3 times, in less than one year while classical 1D simulations take efforts over many years to build models and keep them up to date. Secondly, this infrastructure allowed for running the simulations on a resolution of 2m (being at least 6 times higher than classical maps) finally delivering results on a parcel scale, and thus in line with the use of these maps for advisory purposes to the permit issuing authorities and the legal information duty to the general public. Thirdly, by presenting interim simulated flood contours via a dedicated project-website, www.vlagg.be, it was possible to engage over 150 Flemish municipalities and dozens of river-, sewer- and road managers. Due to their evaluation of the draft maps and especially due to the additional information on 7250 hydraulic structures, it was possible to enrich the models with further detailed local terrain knowledge. The resulting draft pluvial maps show an astonishing agreement with both pictures taken during multiple flood events and detailed upstream fluvial flood maps .
Flood hazard and risk data produced by the insurance sec- tor is usually kept confidential. The available data on ex- posure and flood risk could, however, also be very valuable to emergency planners and water managers. These authori- ties in turn administer the hydrological data which is poten- tially of use to the insurers, providing a potential for mutually beneficial cooperation. Cooperation between the insurance industry and governments is also desirable when it comes to creating the flood maps themselves, as communication of flood hazards and risks is done by the government whilst in- surance premiums are determined and collected by the insur- ers using their own information. As both actions concern the general public they should ideally be based on the same data. There are some examples where governments and the in- surance industry cooperate. In Austria the central govern- ment and the insurance sector explicitly worked together to create flood maps that are used for both awareness raising and premium determination. Furthermore, in both France and the UK the government disseminates its flood hazard information explicitly to the insurance companies, often ad- justed to serve their specific needs. In France this is part of a flood insurance system whereby compulsory fees on all car and house insurances are collected to cover flood losses. This fund is administered by the insurance companies for which the state acts as a reinsurer in case of a large disas- ter (Fleischhauer, 2005).
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ince of Eskişehir, was investigated using GIS. In the study area, Porsuk dam was built for flood prevention after past flood events. However, this part of the basin has the potential for flooding due to sudden and continuous rains, as well as due to potential scenarios such as the collapse of the dam. Possible floods that may occur can severely affect settlement areas, industrial zones and fertile agricultur- al lands. In the hydrological and hydraulic modeling performed for flood map- ping studies, flood rates the year of 50, 100 and 1000 were calculated using maximum flood flow rates of 64 years and possible flood maps were made by hydraulic modeling according to the obtained values. TIN model was used for the geometric data produced in the hydraulic model application and the stream center line, flow paths and cross sections were used; and then they were digitized via TIN model.
The starting point for any such effort, however, is a ful- some understanding of the strengths and limitations of exist- ing flood maps in order to identify opportunities for improve- ment. A preliminary scan commissioned by the government of Canada found that most flood maps in Canada are dated – with a median age of 18 years – and that their availabil- ity is grossly uneven across the 10 provinces (MMM Group Limited, 2014). This article seeks to extend this analysis by evaluating the quality of publicly available flood maps in Canada using internationally recognized principles of good practice. The focus here is on flood maps that are freely and publicly accessible online, given that “the dissemination of flood maps via the Internet is a very important way of bring- ing flood information to the public” especially as more peo- ple become accustomed to digital technologies (Hagemeier- Klose and Wagner, 2009, p. 572). The next section outlines the methods and analytical framework used to undertake the quality evaluation.
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In this study flood maps have been generated for the years 2010, 2013, and 2014 for highest water level of each year. The developed flood maps showed comparatively high inundation of the floodplain of Dharla River which mainly covers the kurigram District. From flood pattern analysis, the peak of 2010 has been found much higher than the peaks for 2013 and 2014. From the observa- tion of satellite images of Dharla River, it has been found that the right banks at the upstream and the left banks at downstream have suffered severe erosion. As a consequence, the course of the Dharla River has been shifted vastly since 1987 to 2017. These findings can be useful for taking necessary steps to improve and maintain the embankment to minimize erosion loses in upcoming years. For ca- libration and validation, the water level data of the upstream station (Ta- luk-Simulbari) has been used as no intermediate station was available. Hence, for better and more accurate results an intermediate station is recommended for future study.
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A factor that is likely to cause only minor differences in the uncertainty in the maps created by the Monte Carlo analysis, is including more observations. For both cases it was found that the uncertainty maps created using Monte Carlo analysis were quite insensitive to the density of observations, with some areas containing only very few observations but having a large probability of flooding, and others containing multiple observations but having a low probability of flooding. Although it is not reflected in the Monte Carlo analysis results, the presence of clustered observations makes it more probable an area is flooded and combining the water level information in these observations can give a better estimation of the actual water levels. This last element is actually accurately reflected when purely doing a Monte Carlo simulation of water level errors. In case many observations with water level errors are in close vicinity of each other, the interpolation method will average their values, meaning that errors are more or less filtered. However, the locational errors of many observations which are originally in close vicinity will not cancel each other out, since the locational errors added to them cause them to shift. Especially this last characteristic of the Monte Carlo analysis causes the calculated uncertainties to not accurately reflect the density of observations. This should be included however, to accurately represent flood extent uncertainty. This can for example be done by reducing the locational errors of observations which are in close vicinity to other observations. Alternatively, the results of the Monte Carlo analysis can be post-processed to reflect the presence or absence of observations.
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Abstract. Flood hazard mapping in the United States (US) is deeply tied to the National Flood Insurance Program (NFIP). Consequently, publicly available flood maps provide essen- tial information for insurance purposes, but they do not nec- essarily provide relevant information for non-insurance as- pects of flood risk management (FRM) such as public educa- tion and emergency planning. Recent calls for flood hazard maps that support a wider variety of FRM tasks highlight the need to deepen our understanding about the factors that make flood maps useful and understandable for local end users. In this study, social scientists and engineers explore opportuni- ties for improving the utility and relevance of flood hazard maps through the co-production of maps responsive to end users’ FRM needs. Specifically, two-dimensional flood mod- eling produced a set of baseline hazard maps for stakeholders of the Tijuana River valley, US, and Los Laureles Canyon in Tijuana, Mexico. Focus groups with natural resource man- agers, city planners, emergency managers, academia, non- profit, and community leaders refined the baseline hazard maps by triggering additional modeling scenarios and map revisions. Several important end user preferences emerged, such as (1) legends that frame flood intensity both qualita- tively and quantitatively, and (2) flood scenario descriptions that report flood magnitude in terms of rainfall, streamflow, and its relation to an historic event. Regarding desired haz- ard map content, end users’ requests revealed general consis- tency with mapping needs reported in European studies and
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social media content, since platforms such as Twitter, Face- book and Flickr produce large amounts of real-time data. In coarse-scale applications for example, these data can be used to detect the occurrence of a natural disaster (Earle et al., 2011). On a more detailed level these data have also been used to assess the geographic extent of a disaster. In the con- text of flood mapping, some investigations used these data as auxiliary data. Examples include the assessment of the accuracy of remote-sensing-derived flood maps using Flickr data (Sun et al., 2015) and the selection of the most realis- tic result of a series of hydraulic model runs based on Twit- ter data (Smith et al., 2015). Others actually created flood maps directly from the data. Schnebele et al. (2014) used the density of flood-related Twitter messages (tweets) to get an indication of flood extent; in the PetaJakarta project, the number of tweets in an area is used to indicate flood sever- ity (Holderness and Turpin, 2015). Fohringer et al. (2015) created flood maps by interpolating water levels which were manually derived from photographs on Flickr and Twitter. Eilander et al. (2016), in contrast, used an automatic method to derive water depths and locations from tweets and created flood maps using a flood fill algorithm. To our knowledge, no flood-related studies have used data from Facebook until now, which is likely due to Facebook being a more closed network. Flickr and Twitter allow for all public data to be found and extracted using their “application programming interfaces” (APIs; interfaces to extract data from online plat- forms). The Facebook API, however, is much more restric- tive and cannot be used to retrieve large amounts of public data.
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Among the ten communes of Abidjan, Abobo has the sad reputation of being the most vulnerable to the risk of flooding because of its recurrence and the many victims and material damage it causes each year. The demolition of dwellings in the numerous basins is causing more and more households to settle in the Sagbé and Anonkoi Kouté watershed valleys. This makes these populations more vulnerable to floods. This study was initiated to study its drainage system in order to limit the vulnerability of the stakes. The main goal is to prevent the risk of flooding with high performing tools and high precision. For this purpose, data were first collected on the study area. Then a flood simulation model was created. Finally, the future floods of the study area were simulated using the tools HEC-GeoRAS and HEC-RAS. The results of this study made it possible to know the influence of the floods in the valleys of the Sagbé-Anonkoi Kouté watershed, and to make flood maps for the return periods of 5, 25; 50; and 100 years, to facilitate decision-making.
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The region of Dakar, which is the most affected by the floods (in terms of the recurrence, the extent of the damage and losses), accounts for the largest number of poor people in absolute terms. The national agency for statistics and demo- graphy estimates its poor household rate at 26.1 percent for a population of 3 137 196 inhabitants in 2013, nearly a quarter (23.2 percent) of the national pop- ulation . It is therefore legitimate to question a possible relationship between poverty and the floods noted in Dakar or whether poor people are more vulner- able to flooding. This paper presents an analysis of the multidimensional poverty suffered by the population of Dakar, particularly those exposed to floods. In- deed, we combine flood maps, representing the main flooded areas of Dakar in 2009 and 2011, with detailed data sets on health, education and the standard of living of individuals in order to detect a possible correlation between poverty le- vels and floods. And, unlike the studies that preceded it and for which poverty is measured by monetary indices, we focus on the multiple deprivations affecting the health, education and standard of living of the populations as well as on the geographical distribution of the poor population in Dakar. Thus, this work is ar- ticulated around three sections: the first is dedicated to the review of the litera- ture, the second exposes the methodology used and the third discusses the em- pirical results.
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ern distributary of Danube Delta, St. George, for flood events having return periods between 20 and 1000 years. For this purpose, a 2D hydraulic model was set up with the help of CCHE-2D finite difference code (University of Mississippi, USA). In this context, the objectives of present study are: 1) to extend the sparsely available measured values of hydrodynamic parameters over the entire study reach for discharge values in the (800-1600) m 3 /s range; 2) to draw the flood maps and compare them with corresponding satellite images ; 3) to analyze the flooding behavior of St. George village and assess the risk factors under various scenarios such as: river flood events, sea level raise or sea storms (which lead to high waves and raised sea level at the downstream boundary of the study reach). The findings are helpful for local authorities in order to inform the population and take the appropriate defense measures in the future.
A further web-based tool included in the CM approach was a separately developed e-learning platform, where struc- tured learning material was provided on various FRM top- ics. Knowledge from the project as well as from outside was collected, organised and made available online to a variety of stakeholder groups in the form of short courses. The plat- form currently allows access to education and training mate- rial collected and/or developed in the project, in an organised fashion, designed to allow for collaboration between “train- ers“ and “trainees” through a variety of online environments including forums, chats and e-classes (Makropoulos et al., 2009). The structuring of the courses targets four different groups: (a) planners (b) modellers (c) real-time operators and emergency managers and (d) general public. A separate short course with different material for each target group was de- veloped for this purpose: the course for planners focused on planning guidelines, the link between planning and risk mapping and available FRM measures. The course for mod- ellers introduced advanced data management topics, innova- tive flood simulation approaches and risk map creation, while the course for real-time operators focused on issues of real- time rainfall prediction, flood control as well as methods and tools for emergency planning. Finally, the course targeting the general public exposed the trainees to actions that can be taken before, during and after a flood, as well to issues and approaches for community engagement and active par- ticipation in FRM. The e-learning platform was developed using the open source Moodle content management system (www.moodle.org).
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5.1 Geographical distribution of landslides and floods We use the catalogue of landslide and flood events obtained from the AVI database to study the geographical distribution of sites damaged by landslide and flood events in Italy, in the period from 1900 to 2002 (Fig. 2). The catalogue lists the exact or the approximate location of 21 159 sites affected by 32 162 landslide events and of 15 904 sites affected by 29 233 flooding events. Comparison of the geographical distribu- tion of the damaged sites with the administrative boundaries reveals that all the 103 Italian provinces experienced recur- sively landslides or floods. Of the total number of 8103 Ital- ian municipalities (in 1998), 4846 (59.5%, covering 75% of the territory) have experienced at least once a landslide, 4492 (55.1%, covering 71% of the territory) have experienced at list once a flood, and 6475 (79.9%, covering 91% of the territory) have experienced both landslides and floods. The 1638 municipalities (20.1%, covering 9% of the territory) for which information on historical landslides or floods is not available in the AVI database are mostly small in size, or are located in remote and inhabited mountain areas or in the hinterland of large cities (e.g. Milan). In these municipal- ities, which cover about 9.5% of the Italian territory, land- slides and floods may have occurred but they may have not been noticed. Alternatively, they may have been observed but quickly removed, or they may have not been reported be- cause they did not cause damage. Figure 15 shows the den- sity of damaging events in each municipality, i.e. the number of landslide (Fig. 15A) and inundation (Fig. 15B) events per 10 km 2 . These maps are available on line from the SICI map server (http://sicimaps.irpi.cnr.it). Similar maps can be pre- pared for the number of sites affected by landslides, floods or both.
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An additive model has been adopted for generation of flood hazard map. It is recognized that the principle of assigning rank to the variables is very crucial in this entire process of hazard mapping. The variable ‘Near_Dist’ which is the proximity to the river has been attached high importance because where the risk of inundation is very low other variables do not contribute anything to the element of flood hazard. Districts have been assigned rank for each of the 5 hazard indicators. These ranking scheme clearly displays that very low or 2 ranking have been applied at very high ‘Near_Dist’ value to prevent the far districts from getting a higher flood hazard index on the basis of other factors. On the other hand, ranking have been increased at rate with higher risk of flood occurrence.
Five of the seven reviewed PERC reports act as case stud- ies supporting the concern about exposure growth empha- sised by the UNISDR (2013) and reflected in the focus of the Sendai Framework for Disaster Risk Reduction (United Nations, 2015). In the central European, Xaver, Balkan, Kar- nali, and Morocco flood PERCs, increasing population and asset density in high-risk areas is identified as a key driver of increasing risk and a vital concern for the future. In Germany and the UK, this build-up is occurring despite the existence of robust government institutions. In the Balkans, Morocco, and Nepal, increased urbanisation is often unofficial or ille- gal. The PERC analysis of the Morocco floods emphasises the changing nature of floods in the region. While tradition- ally floods were considered to be beneficial for crop irriga- tion, changing demographic, and socio-economic dynamics, particularly urbanisation, has seen them turn from benefit to disaster. This insight is a prime example of a lesson running through many of the PERCs – that our traditional or histori- cal understanding of flood dynamics is inadequate for future planning. The PERC analyses emphasise that it is not enough to attempt to correct risk after it has built up, but that prospec- tive risk reduction is the only way to truly arrest growth in disaster risk. This is particularly compelling considering the problems with corrective risk reduction – namely the levee effect – described below.
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Forecasting rainfall depths and flood risk assessment are important input components of many hydro-meteorological models, especially for flash-flood forecasting models (Hall et al., 2005; Taramasso et al., 2005). Inundation is one of the natural disasters, which causes many damages worldwide. Therefore, its prediction is of great concern for preventing economical and human losses, in particular, in vulnerable ar- eas. Iran is located in a semi-arid region where is mainly gov- erned by Subtropical High Pressure (SHP) systems (Sabzi- parvar, 2008). Due to the presence of the SHP systems, ac- cording to K¨oppen climate classification, Iran is known as semi-arid and arid region (Sabziparvar and Ghafouri, 2008). The mean annual rainfall of Iran is about 242 mm (Din- pashoh et al., 2004). Unfortunately, due to high variability of precipitation, the spatial and temporal distribution.of this little rainfall are not uniform throughout the country. Based on the improper topography, soil conditions and plant cove- rage, most of the heavy rainfalls could not penetrate into the ground. This leads to sudden run-off flows in the affected ar- eas. The official reports indicate that many people loss their lives, animals, and agricultural crops as a result of distractive floods, especially in south-west of Iran. In this regard, the fountainhead of Dalaki watershed basin causes many dam- aging floods.
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1.5 Extreme value theory to estimating return periods The return period estimation is based on the annual maxima of the river water storage produced by CaMa-Flood. There are many different statistical distributions which can be used in the estimation of flood frequency, ranging from general logistics distributions in countries such as the UK (Reed et al., 2002) to the log Pearson Type III in the USA (IACWD, 1982). In this study, the aim was to apply the same modelling method across the globe constrained by data availability (e.g. time series of only 30 yr available from ERA-Interim) and computational resources (e.g. requirement for dynamic dis- tribution fitting in every cell across the global land area). Therefore the Gumbel distribution (EV1), estimated using L- moments, was chosen whose two parameters can be easily estimated by the method of moments and which allows the cheap computation of confidence limits for the fitted data. The method is described in detail in Shaw et al. (2011). The EV1 distribution was computed for the river water storage annual maxima on the model grid and 2, 5, 10, 20, 50, 75, 100, 200, 500 yr return periods calculated. The respective river water storages (S) were converted to river water lev- els (D) using the river network parameters, river length (L) and width (W ):
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The land use map demonstrates that the distance separating the Kand River and buildings is negligible. HEC-RAS software is used to calculate surface water profiles for 100-year return period. Flood discharges are calculated with the values of 451.86 m3/s. The mean depth of water is 1.4m. The vulnerability analysis of buildings shows that if the flood with the depth of less than 70 cm occurs, most equipment of the buildings will be damaged. As a result, the flood risk is high in the areas that have less distance with river. Therefore, flood risks maps are useful in making more precise decisions and actions relative to risk reduction management and mitigation.
Abstract Possible effects of climate change on floods magnitude and effects are discussed in this document based on existing data and projected changes in precipitation until 2099. This methodology is applied to Matucana Village, which suffers the effects of floods and debris flows. First, historical peak precipitation, fitted to Gumbel distribution, was used, After that, percentage projected changes of precipitation were used to obtain the new mean precipitation to each period 2010–2039, 2040–2069 and 2070–2099; these mean precipitations define a new Gumbel distribution for every time period. Then, projected maximal precipitations to 100 years of return period are estimated and the corresponding peak flow hydrographs were built. Finally, hazard maps are plotted. This application is possible because Matucana is located in a climatologically homogeneous basin. The final results suggest an important increase in magnitude and affected area by floods in the next 90 years under the A1FI emission scenario.
In conclusion, the research analyzes physical and functional as well as environmental characteristics of three areas of Mokpo that were selected for design adaptations. The fundamental adaptation strategies include selec- tive protection of major infrastructure and multi-tiered terraces for flood protection; designing wetlands and parks to accommodate temporary inundation and thereby function as vegetated buffers for coastal areas; and phased relocation of urban development in combination with raising the ground level. In addition, the research shows that reclaimed land is more susceptible to future inundation, due to its low elevation and ground subsi- dence.