WorldWideScience

Sample records for flood hazard map

  1. Flood Risk and Flood hazard maps - Visualisation of hydrological risks

    Energy Technology Data Exchange (ETDEWEB)

    Spachinger, Karl; Dorner, Wolfgang; Metzka, Rudolf [University of Applied Sciences Deggendorf (Germany); Serrhini, Kamal [Universite de Technologie de Compiegne, Genie des Systemes Urbains, France, and Universite Francois Rabelais, Unite Mixte de Recherche, Tours (France); Fuchs, Sven [Institute of Mountain Risk Engineering, University of Natural Resources and Applied Life Sciences, Vienna (Austria)], E-mail: karl.spachinger@fhd.edu

    2008-11-01

    Hydrological models are an important basis of flood forecasting and early warning systems. They provide significant data on hydrological risks. In combination with other modelling techniques, such as hydrodynamic models, they can be used to assess the extent and impact of hydrological events. The new European Flood Directive forces all member states to evaluate flood risk on a catchment scale, to compile maps of flood hazard and flood risk for prone areas, and to inform on a local level about these risks. Flood hazard and flood risk maps are important tools to communicate flood risk to different target groups. They provide compiled information to relevant public bodies such as water management authorities, municipalities, or civil protection agencies, but also to the broader public. For almost each section of a river basin, run-off and water levels can be defined based on the likelihood of annual recurrence, using a combination of hydrological and hydrodynamic models, supplemented by an analysis of historical records and mappings. In combination with data related to the vulnerability of a region risk maps can be derived. The project RISKCATCH addressed these issues of hydrological risk and vulnerability assessment focusing on the flood risk management process. Flood hazard maps and flood risk maps were compiled for Austrian and German test sites taking into account existing national and international guidelines. These maps were evaluated by eye-tracking using experimental graphic semiology. Sets of small-scale as well as large-scale risk maps were presented to test persons in order to (1) study reading behaviour as well as understanding and (2) deduce the most attractive components that are essential for target-oriented risk communication. A cognitive survey asking for negative and positive aspects and complexity of each single map complemented the experimental graphic semiology. The results indicate how risk maps can be improved to fit the needs of different user

  2. Advances in pan-European flood hazard mapping

    Science.gov (United States)

    Bates, P. D.; Alfieri, L.; Salamon, P.; Bianchi, A.; Neal, J. C.; Feyen, L.

    2013-12-01

    Flood hazard maps at trans-national scale have potential for a large number of applications ranging from climate change studies, reinsurance products, aid to emergency operations for major flood crisis, among others. However, at continental scales, only few products are available, due to the difficulty of retrieving large consistent data sets. Moreover, these are produced at relatively coarse grid resolution, which limits their applications to qualitative assessments. At finer resolution, maps are often limited to country boundaries, due to limited data sharing at trans-national level. The creation of a European flood hazard map would currently imply a collection of scattered regional maps, often lacking mutual consistency due to the variety of adopted approaches and quality of the underlying input data. In this work, we derive a pan-European flood hazard map at 100m resolution. The proposed approach is based on expanding a literature cascade model through a physically based approach. A combination of distributed hydrological and hydraulic models was set up for the European domain. Then, an observed meteorological data set is used to derive a long-term streamflow simulation and subsequently coherent design flood hydrographs for a return period of 100years along the pan-European river network. Flood hydrographs are used to simulate areas at risk of flooding and output maps are merged into a pan-European flood hazard map. The quality of this map is evaluated for selected areas in Germany and United Kingdom against national/regional hazard maps. Despite inherent limitations and model resolution issues, simulated maps are in good agreement with reference maps (hit rate between 59% and 78%, critical success index between 43% and 65%), suggesting strong potential for a number of applications at the European scale

  3. Beyond Flood Hazard Maps: Detailed Flood Characterization with Remote Sensing, GIS and 2d Modelling

    Science.gov (United States)

    Santillan, J. R.; Marqueso, J. T.; Makinano-Santillan, M.; Serviano, J. L.

    2016-09-01

    Flooding is considered to be one of the most destructive among many natural disasters such that understanding floods and assessing the risks associated to it are becoming more important nowadays. In the Philippines, Remote Sensing (RS) and Geographic Information System (GIS) are two main technologies used in the nationwide modelling and mapping of flood hazards. Although the currently available high resolution flood hazard maps have become very valuable, their use for flood preparedness and mitigation can be maximized by enhancing the layers of information these maps portrays. In this paper, we present an approach based on RS, GIS and two-dimensional (2D) flood modelling to generate new flood layers (in addition to the usual flood depths and hazard layers) that are also very useful in flood disaster management such as flood arrival times, flood velocities, flood duration, flood recession times, and the percentage within a given flood event period a particular location is inundated. The availability of these new layers of flood information are crucial for better decision making before, during, and after occurrence of a flood disaster. The generation of these new flood characteristic layers is illustrated using the Cabadbaran River Basin in Mindanao, Philippines as case study area. It is envisioned that these detailed maps can be considered as additional inputs in flood disaster risk reduction and management in the Philippines.

  4. BEYOND FLOOD HAZARD MAPS: DETAILED FLOOD CHARACTERIZATION WITH REMOTE SENSING, GIS AND 2D MODELLING

    Directory of Open Access Journals (Sweden)

    J. R. Santillan

    2016-09-01

    Full Text Available Flooding is considered to be one of the most destructive among many natural disasters such that understanding floods and assessing the risks associated to it are becoming more important nowadays. In the Philippines, Remote Sensing (RS and Geographic Information System (GIS are two main technologies used in the nationwide modelling and mapping of flood hazards. Although the currently available high resolution flood hazard maps have become very valuable, their use for flood preparedness and mitigation can be maximized by enhancing the layers of information these maps portrays. In this paper, we present an approach based on RS, GIS and two-dimensional (2D flood modelling to generate new flood layers (in addition to the usual flood depths and hazard layers that are also very useful in flood disaster management such as flood arrival times, flood velocities, flood duration, flood recession times, and the percentage within a given flood event period a particular location is inundated. The availability of these new layers of flood information are crucial for better decision making before, during, and after occurrence of a flood disaster. The generation of these new flood characteristic layers is illustrated using the Cabadbaran River Basin in Mindanao, Philippines as case study area. It is envisioned that these detailed maps can be considered as additional inputs in flood disaster risk reduction and management in the Philippines.

  5. A method for mapping flood hazard along roads.

    Science.gov (United States)

    Kalantari, Zahra; Nickman, Alireza; Lyon, Steve W; Olofsson, Bo; Folkeson, Lennart

    2014-01-15

    A method was developed for estimating and mapping flood hazard probability along roads using road and catchment characteristics as physical catchment descriptors (PCDs). The method uses a Geographic Information System (GIS) to derive candidate PCDs and then identifies those PCDs that significantly predict road flooding using a statistical modelling approach. The method thus allows flood hazards to be estimated and also provides insights into the relative roles of landscape characteristics in determining road-related flood hazards. The method was applied to an area in western Sweden where severe road flooding had occurred during an intense rain event as a case study to demonstrate its utility. The results suggest that for this case study area three categories of PCDs are useful for prediction of critical spots prone to flooding along roads: i) topography, ii) soil type, and iii) land use. The main drivers among the PCDs considered were a topographical wetness index, road density in the catchment, soil properties in the catchment (mainly the amount of gravel substrate) and local channel slope at the site of a road-stream intersection. These can be proposed as strong indicators for predicting the flood probability in ungauged river basins in this region, but some care is needed in generalising the case study results other potential factors are also likely to influence the flood hazard probability. Overall, the method proposed represents a straightforward and consistent way to estimate flooding hazards to inform both the planning of future roadways and the maintenance of existing roadways.

  6. Scoping of flood hazard mapping needs for Somerset County, Maine

    Science.gov (United States)

    Dudley, Robert W.; Schalk, Charles W.

    2006-01-01

    This report was prepared by the U.S. Geological Survey (USGS) Maine Water Science Center as the deliverable for scoping of flood hazard mapping needs for Somerset County, Maine, under Federal Emergency Management Agency (FEMA) Inter-Agency Agreement Number HSFE01-05-X-0018. This section of the report explains the objective of the task and the purpose of the report. The Federal Emergency Management Agency (FEMA) developed a plan in 1997 to modernize the FEMA flood mapping program. FEMA flood maps delineate flood hazard areas in support of the National Flood Insurance Program (NFIP). FEMA's plan outlined the steps necessary to update FEMA's flood maps for the nation to a seamless digital format and streamline FEMA's operations in raising public awareness of the importance of the maps and responding to requests to revise them. The modernization of flood maps involves conversion of existing information to digital format and integration of improved flood hazard data as needed. To determine flood mapping modernization needs, FEMA has established specific scoping activities to be done on a county-by-county basis for identifying and prioritizing requisite flood-mapping activities for map modernization. The U.S. Geological Survey (USGS), in cooperation with FEMA and the Maine State Planning Office Floodplain Management Program, began scoping work in 2005 for Somerset County. Scoping activities included assembling existing data and map needs information for communities in Somerset County (efforts were made to not duplicate those of pre-scoping completed in March 2005), documentation of data, contacts, community meetings, and prioritized mapping needs in a final scoping report (this document), and updating the Mapping Needs Update Support System (MNUSS) Database or its successor with information gathered during the scoping process. The average age of the FEMA floodplain maps in Somerset County, Maine is 18.1 years. Most of these studies were in the late 1970's to the mid 1980

  7. Deriving global flood hazard maps of fluvial floods through a physical model cascade

    OpenAIRE

    Pappenberger, F.; E. Dutra; Wetterhall, F.; Cloke, H

    2012-01-01

    Global flood hazard maps can be used in the assessment of flood risk in a number of different applications, including (re)insurance and large scale flood preparedness. Such global hazard maps can be generated using large scale physically based models of rainfall-runoff and river routing, when used in conjunction with a number of post-processing methods. In this study, the European Centre for Medium Range Weather Forecasts (ECMWF) land surface model is coupled to ERA-Interim reanalysis meteoro...

  8. Developments in large-scale coastal flood hazard mapping

    Science.gov (United States)

    Vousdoukas, Michalis I.; Voukouvalas, Evangelos; Mentaschi, Lorenzo; Dottori, Francesco; Giardino, Alessio; Bouziotas, Dimitrios; Bianchi, Alessandra; Salamon, Peter; Feyen, Luc

    2016-08-01

    Coastal flooding related to marine extreme events has severe socioeconomic impacts, and even though the latter are projected to increase under the changing climate, there is a clear deficit of information and predictive capacity related to coastal flood mapping. The present contribution reports on efforts towards a new methodology for mapping coastal flood hazard at European scale, combining (i) the contribution of waves to the total water level; (ii) improved inundation modeling; and (iii) an open, physics-based framework which can be constantly upgraded, whenever new and more accurate data become available. Four inundation approaches of gradually increasing complexity and computational costs were evaluated in terms of their applicability to large-scale coastal flooding mapping: static inundation (SM); a semi-dynamic method, considering the water volume discharge over the dykes (VD); the flood intensity index approach (Iw); and the model LISFLOOD-FP (LFP). A validation test performed against observed flood extents during the Xynthia storm event showed that SM and VD can lead to an overestimation of flood extents by 232 and 209 %, while Iw and LFP showed satisfactory predictive skill. Application at pan-European scale for the present-day 100-year event confirmed that static approaches can overestimate flood extents by 56 % compared to LFP; however, Iw can deliver results of reasonable accuracy in cases when reduced computational costs are a priority. Moreover, omitting the wave contribution in the extreme total water level (TWL) can result in a ˜ 60 % underestimation of the flooded area. The present findings have implications for impact assessment studies, since combination of the estimated inundation maps with population exposure maps revealed differences in the estimated number of people affected within the 20-70 % range.

  9. Deriving global flood hazard maps of fluvial floods through a physical model cascade

    Science.gov (United States)

    Pappenberger, Florian; Dutra, Emanuel; Wetterhall, Fredrik; Cloke, Hannah L.

    2013-04-01

    Global flood hazard maps can be used in the assessment of flood risk in a number of different applications, including (re)insurance and large scale flood preparedness. Such global hazard maps can be generated using large scale physically based models of rainfall-runoff and river routing, when used in conjunction with a number of post-processing methods. In this study, the European Centre for Medium Range Weather Forecasts (ECMWF) land surface model is coupled to ERA-Interim reanalysis meteorological forcing data, and resultant runoff is passed to a river routing algorithm which simulates floodplains and flood flow across the global land area. The global hazard map is based on a 30 yr (1979-2010) simulation period. A Gumbel distribution is fitted to the annual maxima flows to derive a number of flood return periods. The return periods are calculated initially for a 25 × 25 km grid, which is then reprojected onto a 1 × 1 km grid to derive maps of higher resolution and estimate flooded fractional area for the individual 25 × 25 km cells. Several global and regional maps of flood return periods ranging from 2 to 500 yr are presented. The results compare reasonably to a benchmark data set of global flood hazard. The developed methodology can be applied to other datasets on a global or regional scale.

  10. Deriving global flood hazard maps of fluvial floods through a physical model cascade

    Directory of Open Access Journals (Sweden)

    F. Pappenberger

    2012-11-01

    Full Text Available Global flood hazard maps can be used in the assessment of flood risk in a number of different applications, including (reinsurance and large scale flood preparedness. Such global hazard maps can be generated using large scale physically based models of rainfall-runoff and river routing, when used in conjunction with a number of post-processing methods. In this study, the European Centre for Medium Range Weather Forecasts (ECMWF land surface model is coupled to ERA-Interim reanalysis meteorological forcing data, and resultant runoff is passed to a river routing algorithm which simulates floodplains and flood flow across the global land area. The global hazard map is based on a 30 yr (1979–2010 simulation period. A Gumbel distribution is fitted to the annual maxima flows to derive a number of flood return periods. The return periods are calculated initially for a 25 × 25 km grid, which is then reprojected onto a 1 × 1 km grid to derive maps of higher resolution and estimate flooded fractional area for the individual 25 × 25 km cells. Several global and regional maps of flood return periods ranging from 2 to 500 yr are presented. The results compare reasonably to a benchmark data set of global flood hazard. The developed methodology can be applied to other datasets on a global or regional scale.

  11. Flood Hazard Mapping over Large Regions using Geomorphic Approaches

    Science.gov (United States)

    Samela, Caterina; Troy, Tara J.; Manfreda, Salvatore

    2016-04-01

    Historically, man has always preferred to settle and live near the water. This tendency has not changed throughout time, and today nineteen of the twenty most populated agglomerations of the world (Demographia World Urban Areas, 2015) are located along watercourses or at the mouth of a river. On one hand, these locations are advantageous from many points of view. On the other hand, they expose significant populations and economic assets to a certain degree of flood hazard. Knowing the location and the extent of the areas exposed to flood hazards is essential to any strategy for minimizing the risk. Unfortunately, in data-scarce regions the use of traditional floodplain mapping techniques is prevented by the lack of the extensive data required, and this scarcity is generally most pronounced in developing countries. The present work aims to overcome this limitation by defining an alternative simplified procedure for a preliminary, but efficient, floodplain delineation. To validate the method in a data-rich environment, eleven flood-related morphological descriptors derived from DEMs have been used as linear binary classifiers over the Ohio River basin and its sub-catchments, measuring their performances in identifying the floodplains at the change of the topography and the size of the calibration area. The best performing classifiers among those analysed have been applied and validated across the continental U.S. The results suggest that the classifier based on the index ln(hr/H), named the Geomorphic Flood Index (GFI), is the most suitable to detect the flood-prone areas in data-scarce environments and for large-scale applications, providing good accuracy with low requirements in terms of data and computational costs. Keywords: flood hazard, data-scarce regions, large-scale studies, binary classifiers, DEM, USA.

  12. Integrated Modeling for Flood Hazard Mapping Using Watershed Modeling System

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    Seyedeh S. Sadrolashrafi

    2008-01-01

    Full Text Available In this stduy, a new framework which integrates the Geographic Information System (GIS with the Watershed Modeling System (WMS for flood modeling is developed. It also interconnects the terrain models and the GIS software, with commercial standard hydrological and hydraulic models, including HEC-1, HEC-RAS, etc. The Dez River Basin (about 16213 km2 in Khuzestan province, IRAN, is domain of study because of occuring frequent severe flash flooding. As a case of study, a major flood in autumn of 2001 is chosen to examine the modeling framework. The model consists of a rainfall-runoff model (HEC-1 that converts excess precipitation to overland flow and channel runoff and a hydraulic model (HEC-RAS that simulates steady state flow through the river channel network based on the HEC-1, peak hydrographs. In addition, it delineates the maps of potential flood zonation for the Dez River Basin. These are achieved based on the state of the art GIS with using WMS software. Watershed parameters are calibrated manually to perform a good simulation of discharge at three sub-basins. With the calibrated discharge, WMS is capable of producing flood hazard map. The modeling framework presented in this study demonstrates the accuracy and usefulness of the WMS software for flash flooding control. The results of this research will benefit future modeling efforts by providing validate hydrological software to forecast flooding on a regional scale. This model designed for the Dez River Basin, while this regional scale model may be used as a prototype for model applications in other areas.

  13. Flood Hazard Area

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — The National Flood Hazard Layer (NFHL) data incorporates all Digital Flood Insurance Rate Map(DFIRM) databases published by FEMA, and any Letters Of Map Revision...

  14. Flood Hazard Boundaries

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — The National Flood Hazard Layer (NFHL) data incorporates all Digital Flood Insurance Rate Map(DFIRM) databases published by FEMA, and any Letters Of Map Revision...

  15. Evaluation of flood hazard maps in print and web mapping services as information tools in flood risk communication

    Science.gov (United States)

    Hagemeier-Klose, M.; Wagner, K.

    2009-04-01

    Flood risk communication with the general public and the population at risk is getting increasingly important for flood risk management, especially as a precautionary measure. This is also underlined by the EU Flood Directive. The flood related authorities therefore have to develop adjusted information tools which meet the demands of different user groups. This article presents the formative evaluation of flood hazard maps and web mapping services according to the specific requirements and needs of the general public using the dynamic-transactional approach as a theoretical framework. The evaluation was done by a mixture of different methods; an analysis of existing tools, a creative workshop with experts and laymen and an online survey. The currently existing flood hazard maps or web mapping services or web GIS still lack a good balance between simplicity and complexity with adequate readability and usability for the public. Well designed and associative maps (e.g. using blue colours for water depths) which can be compared with past local flood events and which can create empathy in viewers, can help to raise awareness, to heighten the activity and knowledge level or can lead to further information seeking. Concerning web mapping services, a linkage between general flood information like flood extents of different scenarios and corresponding water depths and real time information like gauge levels is an important demand by users. Gauge levels of these scenarios are easier to understand than the scientifically correct return periods or annualities. The recently developed Bavarian web mapping service tries to integrate these requirements.

  16. ON EFFECT OF HAZARD MAP ON CONS CIOUSNESS OF FLOOD DISASTER PREVENSION OF RESIDENTS WHO EXPERIENCED FLOOD RECENTLY

    Science.gov (United States)

    Asai, Koji; Koga, Syota; Sakakibara, Hiroyuki

    In this paper, the effect of the flood hazard map distributed to the residents who experienced flood disasters recently and an effective method for improving consciousness of flood di saster prevention are discussed. The questionnaire surveys were conducted on the residents living in the middle basin of the Nishiki River, Iwakuni city, Yamaguchi Prefecture, before and after the distribution of the hazard map. It is found from this investigation that "knowledge", "att achment", and "crisis", are the main factors in the psychological process related to the flood prevention behavior. The effect of the distribution of the hazard map is judged from the probability of the flood prevention behavior. In addition, it is also found that "knowledge", "flood experiment of T0514", "crisis", "eff ectiveness", "load", and "easy reading of the hazard map", are keys to improve the cons ciousness of flood di saster prevention.

  17. Modelling Inland Flood Events for Hazard Maps in Taiwan

    Science.gov (United States)

    Ghosh, S.; Nzerem, K.; Sassi, M.; Hilberts, A.; Assteerawatt, A.; Tillmanns, S.; Mathur, P.; Mitas, C.; Rafique, F.

    2015-12-01

    Taiwan experiences significant inland flooding, driven by torrential rainfall from plum rain storms and typhoons during summer and fall. From last 13 to 16 years data, 3,000 buildings were damaged by such floods annually with a loss US$0.41 billion (Water Resources Agency). This long, narrow island nation with mostly hilly/mountainous topography is located at tropical-subtropical zone with annual average typhoon-hit-frequency of 3-4 (Central Weather Bureau) and annual average precipitation of 2502mm (WRA) - 2.5 times of the world's average. Spatial and temporal distributions of countrywide precipitation are uneven, with very high local extreme rainfall intensities. Annual average precipitation is 3000-5000mm in the mountainous regions, 78% of it falls in May-October, and the 1-hour to 3-day maximum rainfall are about 85 to 93% of the world records (WRA). Rivers in Taiwan are short with small upstream areas and high runoff coefficients of watersheds. These rivers have the steepest slopes, the shortest response time with rapid flows, and the largest peak flows as well as specific flood peak discharge (WRA) in the world. RMS has recently developed a countrywide inland flood model for Taiwan, producing hazard return period maps at 1arcsec grid resolution. These can be the basis for evaluating and managing flood risk, its economic impacts, and insured flood losses. The model is initiated with sub-daily historical meteorological forcings and calibrated to daily discharge observations at about 50 river gauges over the period 2003-2013. Simulations of hydrologic processes, via rainfall-runoff and routing models, are subsequently performed based on a 10000 year set of stochastic forcing. The rainfall-runoff model is physically based continuous, semi-distributed model for catchment hydrology. The 1-D wave propagation hydraulic model considers catchment runoff in routing and describes large-scale transport processes along the river. It also accounts for reservoir storage

  18. Flood hazard mapping by integrating airborne laser scanning data, high resolution images and large scale maps: a case study

    OpenAIRE

    Fernandez, P.; G. Gonçalves; Gomes Pereira, L.; Moreira, M.

    2012-01-01

    The assessment and management of flood risks framework impose the mapping of flood hazard in potential flood risks areas. Floods in urban environments may happen due to rainfall extreme events and be exacerbated by saturated or impervious surfaces. Flood risk is greater in urban areas. (...)

  19. Evaluation of flood hazard maps in print and web mapping services as information tools in flood risk communication

    Directory of Open Access Journals (Sweden)

    M. Hagemeier-Klose

    2009-04-01

    Full Text Available Flood risk communication with the general public and the population at risk is getting increasingly important for flood risk management, especially as a precautionary measure. This is also underlined by the EU Flood Directive. The flood related authorities therefore have to develop adjusted information tools which meet the demands of different user groups. This article presents the formative evaluation of flood hazard maps and web mapping services according to the specific requirements and needs of the general public using the dynamic-transactional approach as a theoretical framework. The evaluation was done by a mixture of different methods; an analysis of existing tools, a creative workshop with experts and laymen and an online survey.

    The currently existing flood hazard maps or web mapping services or web GIS still lack a good balance between simplicity and complexity with adequate readability and usability for the public. Well designed and associative maps (e.g. using blue colours for water depths which can be compared with past local flood events and which can create empathy in viewers, can help to raise awareness, to heighten the activity and knowledge level or can lead to further information seeking. Concerning web mapping services, a linkage between general flood information like flood extents of different scenarios and corresponding water depths and real time information like gauge levels is an important demand by users. Gauge levels of these scenarios are easier to understand than the scientifically correct return periods or annualities. The recently developed Bavarian web mapping service tries to integrate these requirements.

  20. Flash flood hazard mapping: a pilot case study in Xiapu River Basin, China

    Directory of Open Access Journals (Sweden)

    Da-wei Zhang

    2015-07-01

    Full Text Available Flash flood hazard mapping is a supporting component of non-structural measures for flash flood prevention. Pilot case studies are necessary to develop more practicable methods for the technical support systems of flash flood hazard mapping. In this study, the headwater catchment of the Xiapu River Basin in central China was selected as a pilot study area for flash flood hazard mapping. A conceptual distributed hydrological model was developed for flood calculation based on the framework of the Xinanjiang model, which is widely used in humid and semi-humid regions in China. The developed model employs the geomorphological unit hydrograph method, which is extremely valuable when simulating the overland flow process in ungauged catchments, as compared with the original Xinanjiang model. The model was tested in the pilot study area, and the results agree with the measured data on the whole. After calibration and validation, the model is shown to be a useful tool for flash flood calculation. A practicable method for flash flood hazard mapping using the calculated peak discharge and digital elevation model data was presented, and three levels of flood hazards were classified. The resulting flash flood hazard maps indicate that the method successfully predicts the spatial distribution of flash flood hazards, and it can meet the current requirements in China.

  1. Values of Flood Hazard Mapping for Disaster Risk Assessment and Communication

    Science.gov (United States)

    Sayama, T.; Takara, K. T.

    2015-12-01

    Flood plains provide tremendous benefits for human settlements. Since olden days people have lived with floods and attempted to control them if necessary. Modern engineering works such as building embankment have enabled people to live even in flood prone areas, and over time population and economic assets have concentrated in these areas. In developing countries also, rapid land use change alters exposure and vulnerability to floods and consequently increases disaster risk. Flood hazard mapping is an essential step for any counter measures. It has various objectives including raising awareness of residents, finding effective evacuation routes and estimating potential damages through flood risk mapping. Depending on the objectives and data availability, there are also many possible approaches for hazard mapping including simulation basis, community basis and remote sensing basis. In addition to traditional paper-based hazard maps, Information and Communication Technology (ICT) promotes more interactive hazard mapping such as movable hazard map to demonstrate scenario simulations for risk communications and real-time hazard mapping for effective disaster responses and safe evacuations. This presentation first summarizes recent advancement of flood hazard mapping by focusing on Japanese experiences and other examples from Asian countries. Then it introduces a flood simulation tool suitable for hazard mapping at the river basin scale even in data limited regions. In the past few years, the tool has been practiced by local officers responsible for disaster management in Asian countries. Through the training activities of hazard mapping and risk assessment, we conduct comparative analysis to identify similarity and uniqueness of estimated economic damages depending on topographic and land use conditions.

  2. The Use of Geospatial Technologies in Flood Hazard Mapping and Assessment: Case Study from River Evros

    Science.gov (United States)

    Mentzafou, Angeliki; Markogianni, Vasiliki; Dimitriou, Elias

    2016-11-01

    Many scientists link climate change to the increase of the extreme weather phenomena frequency, which combined with land use changes often lead to disasters with severe social and economic effects. Especially floods as a consequence of heavy rainfall can put vulnerable human and natural systems such as transboundary wetlands at risk. In order to meet the European Directive 2007/60/EC requirements for the development of flood risk management plans, the flood hazard map of Evros transboundary watershed was produced after a grid-based GIS modelling method that aggregates the main factors related to the development of floods: topography, land use, geology, slope, flow accumulation and rainfall intensity. The verification of this tool was achieved through the comparison between the produced hazard map and the inundation maps derived from the supervised classification of Landsat 5 and 7 satellite imageries of four flood events that took place at Evros delta proximity, a wetland of international importance. The comparison of the modelled output (high and very high flood hazard areas) with the extent of the inundated areas as mapped from the satellite data indicated the satisfactory performance of the model. Furthermore, the vulnerability of each land use against the flood events was examined. Geographically Weighted Regression has also been applied between the final flood hazard map and the major factors in order to ascertain their contribution to flood events. The results accredited the existence of a strong relationship between land uses and flood hazard indicating the flood susceptibility of the lowlands and agricultural land. A dynamic transboundary flood hazard management plan should be developed in order to meet the Flood Directive requirements for adequate and coordinated mitigation practices to reduce flood risk.

  3. The Use of Geospatial Technologies in Flood Hazard Mapping and Assessment: Case Study from River Evros

    Science.gov (United States)

    Mentzafou, Angeliki; Markogianni, Vasiliki; Dimitriou, Elias

    2017-02-01

    Many scientists link climate change to the increase of the extreme weather phenomena frequency, which combined with land use changes often lead to disasters with severe social and economic effects. Especially floods as a consequence of heavy rainfall can put vulnerable human and natural systems such as transboundary wetlands at risk. In order to meet the European Directive 2007/60/EC requirements for the development of flood risk management plans, the flood hazard map of Evros transboundary watershed was produced after a grid-based GIS modelling method that aggregates the main factors related to the development of floods: topography, land use, geology, slope, flow accumulation and rainfall intensity. The verification of this tool was achieved through the comparison between the produced hazard map and the inundation maps derived from the supervised classification of Landsat 5 and 7 satellite imageries of four flood events that took place at Evros delta proximity, a wetland of international importance. The comparison of the modelled output (high and very high flood hazard areas) with the extent of the inundated areas as mapped from the satellite data indicated the satisfactory performance of the model. Furthermore, the vulnerability of each land use against the flood events was examined. Geographically Weighted Regression has also been applied between the final flood hazard map and the major factors in order to ascertain their contribution to flood events. The results accredited the existence of a strong relationship between land uses and flood hazard indicating the flood susceptibility of the lowlands and agricultural land. A dynamic transboundary flood hazard management plan should be developed in order to meet the Flood Directive requirements for adequate and coordinated mitigation practices to reduce flood risk.

  4. Development and evaluation of a framework for global flood hazard mapping

    Science.gov (United States)

    Dottori, Francesco; Salamon, Peter; Bianchi, Alessandra; Alfieri, Lorenzo; Hirpa, Feyera Aga; Feyen, Luc

    2016-08-01

    Nowadays, the development of high-resolution flood hazard models have become feasible at continental and global scale, and their application in developing countries and data-scarce regions can be extremely helpful to increase preparedness of population and reduce catastrophic impacts. The present work describes the development of a novel procedure for global flood hazard mapping, based on the most recent advances in large scale flood modelling. We derive a long-term dataset of daily river discharges from the hydrological simulations of the Global Flood Awareness System (GloFAS). Streamflow data is downscaled on a high resolution river network and processed to provide the input for local flood inundation simulations, performed with a two-dimensional hydrodynamic model. All flood-prone areas identified along the river network are then merged to create continental flood hazard maps for different return periods at 30‧‧ resolution. We evaluate the performance of our methodology in several river basins across the globe by comparing simulated flood maps with both official hazard maps and a mosaic of flooded areas detected from satellite images. The evaluation procedure also includes comparisons with the results of other large scale flood models. We further investigate the sensitivity of the flood modelling framework to several parameters and modelling approaches and identify strengths, limitations and possible improvements of the methodology.

  5. Flood Hazard Mapping by Using Geographic Information System and Hydraulic Model: Mert River, Samsun, Turkey

    Directory of Open Access Journals (Sweden)

    Vahdettin Demir

    2016-01-01

    Full Text Available In this study, flood hazard maps were prepared for the Mert River Basin, Samsun, Turkey, by using GIS and Hydrologic Engineering Centers River Analysis System (HEC-RAS. In this river basin, human life losses and a significant amount of property damages were experienced in 2012 flood. The preparation of flood risk maps employed in the study includes the following steps: (1 digitization of topographical data and preparation of digital elevation model using ArcGIS, (2 simulation of flood lows of different return periods using a hydraulic model (HEC-RAS, and (3 preparation of flood risk maps by integrating the results of (1 and (2.

  6. Making Coastal Flood Hazard Maps to Support Decision-Making - What End Users Want

    Science.gov (United States)

    Schubert, J.; Cheung, W. H.; Luke, A.; Gallien, T.; Aghakouchak, A.; Feldman, D.; Matthew, R.; Sanders, B. F.

    2015-12-01

    Growing awareness about accelerating Sea Level Rise (SLR) is contributing to coastal resilience initiatives around the world, with an emphasis on coastal planning, infrastructure adaptation, and emergency preparedness. Maps are the primary tool for communicating flood hazard, and their design raises two fundamental challenges: (1) representing the flood hazard in a scientifically defensible manner considering complexity associated with multiple drivers of flooding (e.g., rainfall, streamflow, storm surge, high tides, waves), urban infrastructure, and human interventions (e.g. pumping, sand bags) and (2) effectively communicating hazard information considering the specific needs of decision-makers. In this research we rely on a hydrodynamic model of coastal flooding that can be forced by multiple drivers of flooding (rainfall, high water levels, and waves) to simulate extreme flooding scenarios at street-level resolution. Model scenarios include 20%, 10%, 5%, 2% and 1% annual exceedance probability (AEP) scenarios for each possible driver of flooding and for both present and future sea levels. The resulting flood zones and related flood depths are aggregated using GIS techniques and transformed into a set of maps depicting annual exceedance probability, multi-year flood probability, 1% AEP flooding depth, uncertainty associated with model forcing data, and road network accessibility. The usability of each map is assessed through focus group discussions with local stakeholders who have distinct decision-making needs, such as homeowners, planners, and emergency response managers. Findings from this research reveal the mapped flood risk information and visualizations preferred by different decision-makers.

  7. 2013 FEMA Flood Hazard Boundaries

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — The National Flood Hazard Layer (NFHL) data incorporates all Digital Flood Insurance Rate Map(DFIRM) databases published by FEMA, and any Letters Of Map Revision...

  8. Analysis and GIS Mapping of Flooding Hazards on 10 May 2016, Guangzhou, China

    Directory of Open Access Journals (Sweden)

    Hai-Min Lyu

    2016-10-01

    Full Text Available On 10 May 2016, Guangdong Province, China, suffered a heavy rainstorm. This rainstorm flooded the whole city of Guangzhou. More than 100,000 people were affected by the flooding, in which eight people lost their lives. Subway stations, cars, and buses were submerged. In order to analyse the influential factors of this flooding, topographical characteristics were mapped using Digital Elevation Model (DEM by the Geographical Information System (GIS and meteorological conditions were statistically summarised at both the whole city level and the district level. To analyse the relationship between flood risk and urbanization, GIS was also adopted to map the effect of the subway system using the Multiple Buffer operator over the flooding distribution area. Based on the analyses, one of the significant influential factors of flooding was identified as the urbanization degree, e.g., construction of a subway system, which forms along flood-prone areas. The total economic loss due to flooding in city centers with high urbanization has become very serious. Based on the analyses, the traditional standard of severity of flooding hazards (rainfall intensity grade was modified. Rainfall intensity for severity flooding was decreased from 50 mm to 30 mm in urbanized city centers. In order to protect cities from flooding, a “Sponge City” planning approach is recommended to increase the temporary water storage capacity during heavy rainstorms. In addition, for future city management, the combined use of GIS and Building Information Modelling (BIM is recommended to evaluate flooding hazards.

  9. Flood Hazard Mapping using Hydraulic Model and GIS: A Case Study in Mandalay City, Myanmar

    Directory of Open Access Journals (Sweden)

    Kyu Kyu Sein

    2016-01-01

    Full Text Available This paper presents the use of flood frequency analysis integrating with 1D Hydraulic model (HECRAS and Geographic Information System (GIS to prepare flood hazard maps of different return periods in Ayeyarwady River at Mandalay City in Myanmar. Gumbel’s distribution was used to calculate the flood peak of different return periods, namely, 10 years, 20 years, 50 years, and 100 years. The flood peak from frequency analysis were input into HEC-RAS model to find the corresponding flood level and extents in the study area. The model results were used in integrating with ArcGIS to generate flood plain maps. Flood depths and extents have been identified through flood plain maps. Analysis of 100 years return period flood plain map indicated that 157.88 km2 with the percentage of 17.54% is likely to be inundated. The predicted flood depth ranges varies from greater than 0 to 24 m in the flood plains and on the river. The range between 3 to 5 m were identified in the urban area of Chanayetharzan, Patheingyi, and Amarapua Townships. The highest inundated area was 85 km2 in the Amarapura Township.

  10. Importance of Integrating High-Resoultion 2D Flood Hazard Maps in the Flood Disaster Management of Marikina City, Philippines

    Science.gov (United States)

    Tapales, Ben Joseph; Mendoza, Jerico; Uichanco, Christopher; Mahar Francisco Amante Lagmay, Alfredo; Moises, Mark Anthony; Delmendo, Patricia; Eneri Tingin, Neil

    2015-04-01

    Flooding has been a perennial problem in the city of Marikina. These incidences result in human and economic losses. In response to this, the city has been investing in their flood disaster mitigation program in the past years. As a result, flooding in Marikina was reduced by 31% from 1992 to 2004. [1] However, these measures need to be improved so as to mitigate the effects of floods with more than 100 year return period, such as the flooding brought by tropical storm Ketsana in 2009 which generated 455mm of rains over a 24-hour period. Heavy rainfall caused the streets to be completely submerged in water, leaving at least 70 people dead in the area. In 2012, the Southwest monsoon, enhanced by a typhoon, brought massive rains with an accumulated rainfall of 472mm for 22-hours, a number greater than that which was experienced during Ketsana. At this time, the local government units were much more prepared in mitigating the risk with the use of early warning and evacuation measures, resulting to zero casualty in the area. Their urban disaster management program, however, can be further improved through the integration of high-resolution 2D flood hazard maps in the city's flood disaster management. The use of these maps in flood disaster management is essential in reducing flood-related risks. This paper discusses the importance and advantages of integrating flood maps in structural and non-structural mitigation measures in the case of Marikina City. Flood hazard maps are essential tools in predicting the frequency and magnitude of floods in an area. An information that may be determined with the use of these maps is the locations of evacuation areas, which may be accurately positioned using high-resolution 2D flood hazard maps. Evacuation of people in areas that are not vulnerable of being inundated is one of the unnecessary measures that may be prevented and thus optimizing mitigation efforts by local government units. This paper also discusses proposals for a more

  11. Exposure, hazard and risk mapping during a flood event using open source geospatial technology

    Directory of Open Access Journals (Sweden)

    Arpit Aggarwal

    2016-07-01

    Full Text Available After a flood event there is a need to delineate the hazard footprint as quickly as possible in order to assess the magnitude of losses and to plan for the relief operations. Delineation of such hazard footprint is generally hindered by the lack of geospatial data, technology and related software packages. This paper demonstrates the use of open source data and software packages which can be used to implement most recent technology available for flood hazard footprint delineation. This study utilizes open source software packages and web applications like Geographic Resource Analysis Support System, Quantum geographic information system and Google Earth to implement a complete process of hazard mapping using remotely sensed data which include pre-processing, mapping (both hazard and exposure and accuracy assessment. In this study, Brisbane flood event of 2011 has been taken as a case study. For built-up extraction, the Landsat 7-band image has been transformed to a stack of 3-band image using vegetation, water and built-up indices. It has been observed by scattergram analysis that these transformations make vegetation, water and built-up classes more separable. Built-up area has been delineated using supervised maximum likelihood classification on the new 3-band image. For flood hazard mapping, thresholding of near-infrared band has been utilized along with the assistance of mid-infrared band to discriminate water from built-up classes. After delineating both exposure and hazard map, final risk map due to flood event has been generated to assess the urban exposure under the flood hazard impact.

  12. New version of 1 km global river flood hazard maps for the next generation of Aqueduct Global Flood Analyzer

    Science.gov (United States)

    Sutanudjaja, Edwin; van Beek, Rens; Winsemius, Hessel; Ward, Philip; Bierkens, Marc

    2017-04-01

    The Aqueduct Global Flood Analyzer, launched in 2015, is an open-access and free-of-charge web-based interactive platform which assesses and visualises current and future projections of river flood impacts across the globe. One of the key components in the Analyzer is a set of river flood inundation hazard maps derived from the global hydrological model simulation of PCR-GLOBWB. For the current version of the Analyzer, accessible on http://floods.wri.org/#/, the early generation of PCR-GLOBWB 1.0 was used and simulated at 30 arc-minute ( 50 km at the equator) resolution. In this presentation, we will show the new version of these hazard maps. This new version is based on the latest version of PCR-GLOBWB 2.0 (https://github.com/UU-Hydro/PCR-GLOBWB_model, Sutanudjaja et al., 2016, doi:10.5281/zenodo.60764) simulated at 5 arc-minute ( 10 km at the equator) resolution. The model simulates daily hydrological and water resource fluxes and storages, including the simulation of overbank volume that ends up on the floodplain (if flooding occurs). The simulation was performed for the present day situation (from 1960) and future climate projections (until 2099) using the climate forcing created in the ISI-MIP project. From the simulated flood inundation volume time series, we then extract annual maxima for each cell, and fit these maxima to a Gumbel extreme value distribution. This allows us to derive flood volume maps of any hazard magnitude (ranging from 2-year to 1000-year flood events) and for any time period (e.g. 1960-1999, 2010-2049, 2030-2069, and 2060-2099). The derived flood volumes (at 5 arc-minute resolution) are then spread over the high resolution terrain model using an updated GLOFRIS downscaling module (Winsemius et al., 2013, doi:10.5194/hess-17-1871-2013). The updated version performs a volume spreading sequentially from more upstream basins to downstream basins, hence enabling a better inclusion of smaller streams, and takes into account spreading of water

  13. National Flood Hazard Layer (NFHL)

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The National Flood Hazard Layer (NFHL) is a compilation of GIS data that comprises a nationwide digital Flood Insurance Rate Map. The GIS data and services are...

  14. Integrated Modeling for Flood Hazard Mapping Using Watershed Modeling System

    National Research Council Canada - National Science Library

    Seyedeh S. Sadrolashrafi; Thamer A. Mohamed; Ahmad R.B. Mahmud; Majid K. Kholghi; Amir Samadi

    2008-01-01

    ...) with the Watershed Modeling System (WMS) for flood modeling is developed. It also interconnects the terrain models and the GIS software, with commercial standard hydrological and hydraulic models, including HEC-1, HEC-RAS, etc...

  15. Global river flood hazard maps: hydraulic modelling methods and appropriate uses

    Science.gov (United States)

    Townend, Samuel; Smith, Helen; Molloy, James

    2014-05-01

    Flood hazard is not well understood or documented in many parts of the world. Consequently, the (re-)insurance sector now needs to better understand where the potential for considerable river flooding aligns with significant exposure. For example, international manufacturing companies are often attracted to countries with emerging economies, meaning that events such as the 2011 Thailand floods have resulted in many multinational businesses with assets in these regions incurring large, unexpected losses. This contribution addresses and critically evaluates the hydraulic methods employed to develop a consistent global scale set of river flood hazard maps, used to fill the knowledge gap outlined above. The basis of the modelling approach is an innovative, bespoke 1D/2D hydraulic model (RFlow) which has been used to model a global river network of over 5.3 million kilometres. Estimated flood peaks at each of these model nodes are determined using an empirically based rainfall-runoff approach linking design rainfall to design river flood magnitudes. The hydraulic model is used to determine extents and depths of floodplain inundation following river bank overflow. From this, deterministic flood hazard maps are calculated for several design return periods between 20-years and 1,500-years. Firstly, we will discuss the rationale behind the appropriate hydraulic modelling methods and inputs chosen to produce a consistent global scaled river flood hazard map. This will highlight how a model designed to work with global datasets can be more favourable for hydraulic modelling at the global scale and why using innovative techniques customised for broad scale use are preferable to modifying existing hydraulic models. Similarly, the advantages and disadvantages of both 1D and 2D modelling will be explored and balanced against the time, computer and human resources available, particularly when using a Digital Surface Model at 30m resolution. Finally, we will suggest some

  16. Flash Flood Hazard Susceptibility Mapping Using Frequency Ratio and Statistical Index Methods in Coalmine Subsidence Areas

    Directory of Open Access Journals (Sweden)

    Chen Cao

    2016-09-01

    Full Text Available This study focused on producing flash flood hazard susceptibility maps (FFHSM using frequency ratio (FR and statistical index (SI models in the Xiqu Gully (XQG of Beijing, China. First, a total of 85 flash flood hazard locations (n = 85 were surveyed in the field and plotted using geographic information system (GIS software. Based on the flash flood hazard locations, a flood hazard inventory map was built. Seventy percent (n = 60 of the flooding hazard locations were randomly selected for building the models. The remaining 30% (n = 25 of the flooded hazard locations were used for validation. Considering that the XQG used to be a coal mining area, coalmine caves and subsidence caused by coal mining exist in this catchment, as well as many ground fissures. Thus, this study took the subsidence risk level into consideration for FFHSM. The ten conditioning parameters were elevation, slope, curvature, land use, geology, soil texture, subsidence risk area, stream power index (SPI, topographic wetness index (TWI, and short-term heavy rain. This study also tested different classification schemes for the values for each conditional parameter and checked their impacts on the results. The accuracy of the FFHSM was validated using area under the curve (AUC analysis. Classification accuracies were 86.61%, 83.35%, and 78.52% using frequency ratio (FR-natural breaks, statistical index (SI-natural breaks and FR-manual classification schemes, respectively. Associated prediction accuracies were 83.69%, 81.22%, and 74.23%, respectively. It was found that FR modeling using a natural breaks classification method was more appropriate for generating FFHSM for the Xiqu Gully.

  17. A high-resolution physically-based global flood hazard map

    Science.gov (United States)

    Kaheil, Y.; Begnudelli, L.; McCollum, J.

    2016-12-01

    We present the results from a physically-based global flood hazard model. The model uses a physically-based hydrologic model to simulate river discharges, and 2D hydrodynamic model to simulate inundation. The model is set up such that it allows the application of large-scale flood hazard through efficient use of parallel computing. For hydrology, we use the Hillslope River Routing (HRR) model. HRR accounts for surface hydrology using Green-Ampt parameterization. The model is calibrated against observed discharge data from the Global Runoff Data Centre (GRDC) network, among other publicly-available datasets. The parallel-computing framework takes advantage of the river network structure to minimize cross-processor messages, and thus significantly increases computational efficiency. For inundation, we implemented a computationally-efficient 2D finite-volume model with wetting/drying. The approach consists of simulating flood along the river network by forcing the hydraulic model with the streamflow hydrographs simulated by HRR, and scaled up to certain return levels, e.g. 100 years. The model is distributed such that each available processor takes the next simulation. Given an approximate criterion, the simulations are ordered from most-demanding to least-demanding to ensure that all processors finalize almost simultaneously. Upon completing all simulations, the maximum envelope of flood depth is taken to generate the final map. The model is applied globally, with selected results shown from different continents and regions. The maps shown depict flood depth and extent at different return periods. These maps, which are currently available at 3 arc-sec resolution ( 90m) can be made available at higher resolutions where high resolution DEMs are available. The maps can be utilized by flood risk managers at the national, regional, and even local levels to further understand their flood risk exposure, exercise certain measures of mitigation, and/or transfer the residual

  18. Flood-hazard mapping in Honduras in response to Hurricane Mitch

    Science.gov (United States)

    Mastin, M.C.

    2002-01-01

    The devastation in Honduras due to flooding from Hurricane Mitch in 1998 prompted the U.S. Agency for International Development, through the U.S. Geological Survey, to develop a country-wide systematic approach of flood-hazard mapping and a demonstration of the method at selected sites as part of a reconstruction effort. The design discharge chosen for flood-hazard mapping was the flood with an average return interval of 50 years, and this selection was based on discussions with the U.S. Agency for International Development and the Honduran Public Works and Transportation Ministry. A regression equation for estimating the 50-year flood discharge using drainage area and annual precipitation as the explanatory variables was developed, based on data from 34 long-term gaging sites. This equation, which has a standard error of prediction of 71.3 percent, was used in a geographic information system to estimate the 50-year flood discharge at any location for any river in the country. The flood-hazard mapping method was demonstrated at 15 selected municipalities. High-resolution digital-elevation models of the floodplain were obtained using an airborne laser-terrain mapping system. Field verification of the digital elevation models showed that the digital-elevation models had mean absolute errors ranging from -0.57 to 0.14 meter in the vertical dimension. From these models, water-surface elevation cross sections were obtained and used in a numerical, one-dimensional, steady-flow stepbackwater model to estimate water-surface profiles corresponding to the 50-year flood discharge. From these water-surface profiles, maps of area and depth of inundation were created at the 13 of the 15 selected municipalities. At La Lima only, the area and depth of inundation of the channel capacity in the city was mapped. At Santa Rose de Aguan, no numerical model was created. The 50-year flood and the maps of area and depth of inundation are based on the estimated 50-year storm tide.

  19. Sept 2013 NFHL Flood Hazard Areas

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — The National Flood Hazard Layer (NFHL) data incorporates all Digital Flood Insurance Rate Map(DFIRM) databases published by FEMA, and any Letters Of Map Revision...

  20. Sept 2013 NFHL Flood Hazard Boundaries

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — The National Flood Hazard Layer (NFHL) data incorporates all Digital Flood Insurance Rate Map(DFIRM) databases published by FEMA, and any Letters Of Map Revision...

  1. Flood Hazard Mapping Assessment for El-Awali River Catchment-Lebanon

    Science.gov (United States)

    Hdeib, Rouya; Abdallah, Chadi; Moussa, Roger; Hijazi, Samar

    2016-04-01

    River flooding prediction and flood forecasting has become an essential stage in the major flood mitigation plans worldwide. Delineation of floodplains resulting from a river flooding event requires coupling between a Hydrological rainfall-runoff model to calculate the resulting outflows of the catchment and a hydraulic model to calculate the corresponding water surface profiles along the river main course. In this study several methods were applied to predict the flood discharge of El-Awali River using the available historical data and gauging records and by conducting several site visits. The HEC-HMS Rainfall-Runoff model was built and applied to calculate the flood hydrographs along several outlets on El-Awali River and calibrated using the storm that took place on January 2013 and caused flooding of the major Lebanese rivers and by conducting additional site visits to calculate proper river sections and record witnesses of the locals. The Hydraulic HEC-RAS model was then applied to calculate the corresponding water surface profiles along El-Awali River main reach. Floodplain delineation and Hazard mapping for 10,50 and 100 years return periods was performed using the Watershed Modeling System WMS. The results first show an underestimation of the flood discharge recorded by the operating gauge stations on El-Awali River, whereas, the discharge of the 100 years flood may reach up to 506 m3/s compared by lower values calculated using the traditional discharge estimation methods. Second any flooding of El-Awali River may be catastrophic especially to the coastal part of the catchment and can cause tragic losses in agricultural lands and properties. Last a major floodplain was noticed in Marj Bisri village this floodplain can reach more than 200 meters in width. Overall, performance was good and the Rainfall-Runoff model can provide valuable information about flows especially on ungauged points and can perform a great aid for the floodplain delineation and flood

  2. Exploring local risk managers' use of flood hazard maps for risk communication purposes in Baden-Württemberg

    Science.gov (United States)

    Kjellgren, S.

    2013-07-01

    In response to the EU Floods Directive (2007/60/EC), flood hazard maps are currently produced all over Europe, reflecting a wider shift in focus from "flood protection" to "risk management", for which not only public authorities but also populations at risk are seen as responsible. By providing a visual image of the foreseen consequences of flooding, flood hazard maps can enhance people's knowledge about flood risk, making them more capable of an adequate response. Current literature, however, questions the maps' awareness raising capacity, arguing that their content and design are rarely adjusted to laypeople's needs. This paper wants to complement this perspective with a focus on risk communication by studying how these tools are disseminated and marketed to the public in the first place. Judging from communication theory, simply making hazard maps publicly available is unlikely to lead to attitudinal or behavioral effects, since this typically requires two-way communication and material or symbolic incentives. Consequently, it is relevant to investigate whether and how local risk managers, who are well positioned to interact with the local population, make use of flood hazard maps for risk communication purposes. A qualitative case study of this issue in the German state of Baden-Württemberg suggests that many municipalities lack a clear strategy for using this new information tool for hazard and risk communication. Four barriers in this regard are identified: perceived disinterest/sufficient awareness on behalf of the population at risk; unwillingness to cause worry or distress; lack of skills and resources; and insufficient support. These barriers are important to address - in research as well as in practice - since it is only if flood hazard maps are used to enhance local knowledge resources that they can be expected to contribute to social capacity building.

  3. Exploring local risk managers' use of flood hazard maps for risk communication purposes in Baden-Württemberg

    Directory of Open Access Journals (Sweden)

    S. Kjellgren

    2013-07-01

    Full Text Available In response to the EU Floods Directive (2007/60/EC, flood hazard maps are currently produced all over Europe, reflecting a wider shift in focus from "flood protection" to "risk management", for which not only public authorities but also populations at risk are seen as responsible. By providing a visual image of the foreseen consequences of flooding, flood hazard maps can enhance people's knowledge about flood risk, making them more capable of an adequate response. Current literature, however, questions the maps' awareness raising capacity, arguing that their content and design are rarely adjusted to laypeople's needs. This paper wants to complement this perspective with a focus on risk communication by studying how these tools are disseminated and marketed to the public in the first place. Judging from communication theory, simply making hazard maps publicly available is unlikely to lead to attitudinal or behavioral effects, since this typically requires two-way communication and material or symbolic incentives. Consequently, it is relevant to investigate whether and how local risk managers, who are well positioned to interact with the local population, make use of flood hazard maps for risk communication purposes. A qualitative case study of this issue in the German state of Baden-Württemberg suggests that many municipalities lack a clear strategy for using this new information tool for hazard and risk communication. Four barriers in this regard are identified: perceived disinterest/sufficient awareness on behalf of the population at risk; unwillingness to cause worry or distress; lack of skills and resources; and insufficient support. These barriers are important to address – in research as well as in practice – since it is only if flood hazard maps are used to enhance local knowledge resources that they can be expected to contribute to social capacity building.

  4. Mapping Urban Risk: Flood Hazards, Race, & Environmental Justice In New York"

    Science.gov (United States)

    Maantay, Juliana; Maroko, Andrew

    2009-01-01

    This paper demonstrates the importance of disaggregating population data aggregated by census tracts or other units, for more realistic population distribution/location. A newly-developed mapping method, the Cadastral-based Expert Dasymetric System (CEDS), calculates population in hyper-heterogeneous urban areas better than traditional mapping techniques. A case study estimating population potentially impacted by flood hazard in New York City compares the impacted population determined by CEDS with that derived by centroid-containment method and filtered areal weighting interpolation. Compared to CEDS, 37 percent and 72 percent fewer people are estimated to be at risk from floods city-wide, using conventional areal weighting of census data, and centroid-containment selection, respectively. Undercounting of impacted population could have serious implications for emergency management and disaster planning. Ethnic/racial populations are also spatially disaggregated to determine any environmental justice impacts with flood risk. Minorities are disproportionately undercounted using traditional methods. Underestimating more vulnerable sub-populations impairs preparedness and relief efforts.

  5. Flood Insurance Rate Maps and Base Flood Elevations, FIRM, DFIRM, BFE - MO 2010 Greene County Special Flood Hazard Area Lines (SHP)

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This polyline layer represents the approximate effective Special Flood Hazard Area (SFHA) boundary for Greene County Missouri. This boundary became effective in...

  6. Flood Insurance Rate Maps and Base Flood Elevations, FIRM, DFIRM, BFE - MO 2014 Greene County Special Flood Hazard Area Lines (SHP)

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This polyline layer represents the approximate effective Special Flood Hazard Area (SFHA) boundary for Greene County Missouri. This boundary became effective in...

  7. Automating Flood Hazard Mapping Methods for Near Real-time Storm Surge Inundation and Vulnerability Assessment

    Science.gov (United States)

    Weigel, A. M.; Griffin, R.; Gallagher, D.

    2015-12-01

    Storm surge has enough destructive power to damage buildings and infrastructure, erode beaches, and threaten human life across large geographic areas, hence posing the greatest threat of all the hurricane hazards. The United States Gulf of Mexico has proven vulnerable to hurricanes as it has been hit by some of the most destructive hurricanes on record. With projected rises in sea level and increases in hurricane activity, there is a need to better understand the associated risks for disaster mitigation, preparedness, and response. GIS has become a critical tool in enhancing disaster planning, risk assessment, and emergency response by communicating spatial information through a multi-layer approach. However, there is a need for a near real-time method of identifying areas with a high risk of being impacted by storm surge. Research was conducted alongside Baron, a private industry weather enterprise, to facilitate automated modeling and visualization of storm surge inundation and vulnerability on a near real-time basis. This research successfully automated current flood hazard mapping techniques using a GIS framework written in a Python programming environment, and displayed resulting data through an Application Program Interface (API). Data used for this methodology included high resolution topography, NOAA Probabilistic Surge model outputs parsed from Rich Site Summary (RSS) feeds, and the NOAA Census tract level Social Vulnerability Index (SoVI). The development process required extensive data processing and management to provide high resolution visualizations of potential flooding and population vulnerability in a timely manner. The accuracy of the developed methodology was assessed using Hurricane Isaac as a case study, which through a USGS and NOAA partnership, contained ample data for statistical analysis. This research successfully created a fully automated, near real-time method for mapping high resolution storm surge inundation and vulnerability for the

  8. The Study of Insurance Premium Rate GIS Mapping Considering the Storm and Flood Hazard Risks

    Science.gov (United States)

    Lee, J. S.; Lee, I. S.

    2016-06-01

    Recently, the number of natural disaster occurrence is increasing because of abnormal changes of weather in Korea. In Korea the storm and flood insurance system is in effect to prevent these natural disasters. The national storm and flood insurance Premium rate is very low and the risk of adverse selection resides because of choosing by who lives in high risk area. To solve these problems, the storm and flood insurance rate map are required. In this study, the prototype of storm and flood insurance premium rate map of the Ulsan, Korea was made and the method of GIS analysis for the insurance premium rate calculating and the procedure of the Ulsan storm and flood insurance rate map were researched.

  9. THE STUDY OF INSURANCE PREMIUM RATE GIS MAPPING CONSIDERING THE STORM AND FLOOD HAZARD RISKS

    Directory of Open Access Journals (Sweden)

    J. S. Lee

    2016-06-01

    Full Text Available Recently, the number of natural disaster occurrence is increasing because of abnormal changes of weather in Korea. In Korea the storm and flood insurance system is in effect to prevent these natural disasters. The national storm and flood insurance Premium rate is very low and the risk of adverse selection resides because of choosing by who lives in high risk area. To solve these problems, the storm and flood insurance rate map are required. In this study, the prototype of storm and flood insurance premium rate map of the Ulsan, Korea was made and the method of GIS analysis for the insurance premium rate calculating and the procedure of the Ulsan storm and flood insurance rate map were researched.

  10. THE STUDY OF INSURANCE PREMIUM RATE GIS MAPPING CONSIDERING THE STORM AND FLOOD HAZARD RISKS

    OpenAIRE

    2016-01-01

    Recently, the number of natural disaster occurrence is increasing because of abnormal changes of weather in Korea. In Korea the storm and flood insurance system is in effect to prevent these natural disasters. The national storm and flood insurance Premium rate is very low and the risk of adverse selection resides because of choosing by who lives in high risk area. To solve these problems, the storm and flood insurance rate map are required. In this study, the prototype of storm and flood ins...

  11. Hazard Maps in the Classroom.

    Science.gov (United States)

    Cross, John A.

    1988-01-01

    Emphasizes the use of geophysical hazard maps and illustrates how they can be used in the classroom from kindergarten to college level. Depicts ways that hazard maps of floods, landslides, earthquakes, volcanoes, and multi-hazards can be integrated into classroom instruction. Tells how maps may be obtained. (SLM)

  12. Mapping the Historical Probability of Increased Flood Hazard During ENSO Events Using a New 20th Century River Flow Reanalysis

    Science.gov (United States)

    Emerton, R.; Cloke, H. L.; Stephens, L.; Woolnough, S. J.; Zsoter, E.; Pappenberger, F.

    2016-12-01

    El Niño Southern Oscillation (ENSO), a mode of variability which sees fluctuations between anomalously high or low sea surface temperatures in the Pacific, is known to influence river flow and flooding at the global scale. The anticipation and forecasting of floods is crucial for flood preparedness, and this link, alongside the predictive skill of ENSO up to seasons ahead, may provide an early indication of upcoming severe flood events. Information is readily available indicating the likely impacts of El Niño and La Niña on precipitation across the globe, which is often used as a proxy for flood hazard. However, the nonlinearity between precipitation and flood magnitude and frequency means that it is important to assess the impact of ENSO events not only on precipitation, but also on river flow and flooding. Historical probabilities provide key information regarding the likely impacts of ENSO events. We estimate, for the first time, the historical probability of increased flood hazard during El Niño and La Niña through a global hydrological analysis, using a new 20thCentury ensemble river flow reanalysis for the global river network. This dataset was produced by running the ECMWF ERA-20CM atmospheric reanalysis through a research set-up of the Global Flood Awareness System (GloFAS) using the CaMa-Flood hydrodynamic model, to produce a 110-year global reanalysis of river flow. We further evaluate the added benefit of the hydrological analysis over the use of precipitation as a proxy for flood hazard. For example, providing information regarding regions that are likely to experience a lagged influence on river flow compared to the influence on precipitation. Our results map, at the global scale, the probability of abnormally high river flow during any given month during an El Niño or La Niña; information such as this is key for organisations that work at the global scale, such as humanitarian aid organisations, providing a seasons-ahead indicator of potential

  13. Flash Flood Hazard Mapping Using Satellite Images and GIS Tools: A case study of Najran City, Kingdom of Saudi Arabia (KSA

    Directory of Open Access Journals (Sweden)

    Ismail Elkhrachy

    2015-12-01

    Full Text Available Flash flood in the cities led to high levels of water in the streets and roads, causing many problems such as bridge collapse, building damage and traffic problems. It is impossible to avoid risks of floods or prevent their occurrence, however it is plausible to work on the reduction of their effects and to reduce the losses which they may cause. Flash flood mapping to identify sites in high risk flood zones is one of the powerful tools for this purpose. Mapping flash flood will be beneficial to urban and infrastructure planners, risk managers and disaster response or emergency services during extreme and intense rainfall events. The objective of this paper is to generate flash flood map for Najran city, Saudi Arabia, using satellite images and GIS tools. To do so, we use SPOT and SRTM DEMs data for which accuracy assessment is achieved by using check points, obtained by GPS observations. Analytical Hierarchical Process (AHP is used to determine relative impact weight of flood causative factors to get a composite flood hazard index (FHI. The causative factors in this study are runoff, soil type, surface slope, surface roughness, drainage density, distance to main channel and land use. All used data are finally integrated in an ArcMap to prepare a final flood hazard map for study area. The areas in high risk flood zones are obtained by overlaying the flood hazard index map with the zone boundaries layer. The affected population number and land area are determined and compared.

  14. Building a flood hazard map due to magma effusion into the caldera lake of the Baekdusan Volcano

    Science.gov (United States)

    Lee, K.; Kim, S.; Yun, S.; Yu, S.; Kim, I.

    2013-12-01

    Many volcanic craters and calderas are filled with large amounts of water that can pose significant flood hazards to downstream communities due to their high elevation and the potential for catastrophic releases of water. Recent reports pointed out the Baekdusan volcano that is located between the border of China and North Korea as a potential active volcano. Since Millennium Eruption around 1000 AD, smaller eruptions have occurred at roughly 100-year intervals, with the last one in 1903. The volcano is showing signs of waking from a century-long slumber recently and the volcanic ash may spread up to the northeastern of Japan. The development of various forecasting techniques to prevent and minimize economic and social damage is in urgent need. Floods from lake-filled calderas may be particularly large and high. Volcanic flood may cause significant hydrologic hazards for this reason. This study focuses on constructing a flood hazard map triggered by the uplift of lake bottom due to magma effusion in the Baekdusan volcano. A physically-based uplift model was developed to compute the amount of water and time to peak flow. The ordinary differential equation was numerically solved using the finite difference method and Newton-Raphson iteration method was used to solve nonlinear equation. The magma effusion rate into the caldera lake is followed by the past record from other volcanic activities. As a result, the hydrograph serves as an upper boundary condition when hydrodynamic model (Flo-2D) runs to simulate channel routing downstream. The final goal of the study stresses the potential flood hazard represented by the huge volume of water in the caldera lake, the unique geography, and the limited control capability. he study will contribute to build a geohazard map for the decision-makers and practitioners. Keywords: Effusion rate, Volcanic flood, Caldera lake, Uplift, Flood hazard map Acknowledgement This research was supported by a grant [NEMA-BAEKDUSAN-2012-1-2] from

  15. FEMA DFIRM Flood Hazard Areas

    Data.gov (United States)

    Minnesota Department of Natural Resources — FEMA flood hazard delineations are used by the Federal Emergency Management Agency (FEMA) to designate the Special Flood Hazard Area (SFHA) and for insurance rating...

  16. 44 CFR 65.11 - Evaluation of sand dunes in mapping coastal flood hazard areas.

    Science.gov (United States)

    2010-10-01

    ... 44 Emergency Management and Assistance 1 2010-10-01 2010-10-01 false Evaluation of sand dunes in... Insurance Program IDENTIFICATION AND MAPPING OF SPECIAL HAZARD AREAS § 65.11 Evaluation of sand dunes in...-established with long-standing vegetative cover, such as the placement of sand materials in a...

  17. Morphometric analyze for flood hazard map using DTM built with LIDAR and Echo-sounder data in Danube Delta

    Science.gov (United States)

    Constantinescu, A.; Nichersu, I.; Trifanov, C.; Nichersu, I.; Mierla, M.

    2012-04-01

    will be merged with high quality LIDAR data available for the whole area and the accurate DTM result will help in better understanding of the morphology of the area, with acurate models and flooding scenarios. It is well known that is difficult to determine and delineate on the topographic maps, the floods limit, which is essential in the preparation of hazard maps. To perform a morphometric analysis for real floods is needed to be defined precisely on the 3D model. In this paper, we wish to present an analysis of flooding phenomenon in the Danube Delta, based on the study of digital models.

  18. Flooding hazards from sea extremes and subsidence

    DEFF Research Database (Denmark)

    Sørensen, Carlo; Vognsen, Karsten; Broge, Niels

    2015-01-01

    If we do not understand the effects of climate change and sea level rise (SLR) we cannot live in low-lying coastal areas in the future. Permanent inundation may become a prevalent issue but more often floods related to extreme events have the largest damage potential, and the management of flooding...... hazards needs to integrate the water loading from various sources. Furthermore, local subsidence must be accounted for in order to evaluate current and future flooding hazards and management options. We present the methodology (Figure) and preliminary results from the research project “Coastal Flooding...... Hazards due to Storm Surges and Subsidence” (2014-2017) with the objective to develop and test a practice oriented methodology for combining extreme water level statistics and land movement in coastal flooding hazard mapping and in climate change adaptation schemes in Denmark. From extreme value analysis...

  19. Disseminating near-real-time hazards information and flood maps in the Philippines through Web-GIS.

    Science.gov (United States)

    A Lagmay, Alfredo Mahar Francisco; Racoma, Bernard Alan; Aracan, Ken Adrian; Alconis-Ayco, Jenalyn; Saddi, Ivan Lester

    2017-09-01

    The Philippines being a locus of tropical cyclones, tsunamis, earthquakes and volcanic eruptions, is a hotbed of disasters. These natural hazards inflict loss of lives and costly damage to property. Situated in a region where climate and geophysical tempest is common, the Philippines will inevitably suffer from calamities similar to those experienced recently. With continued development and population growth in hazard prone areas, it is expected that damage to infrastructure and human losses would persist and even rise unless appropriate measures are immediately implemented by government. In 2012, the Philippines launched a responsive program for disaster prevention and mitigation called the Nationwide Operational Assessment of Hazards (Project NOAH), specifically for government warning agencies to be able to provide a 6hr lead-time warning to vulnerable communities against impending floods and to use advanced technology to enhance current geo-hazard vulnerability maps. To disseminate such critical information to as wide an audience as possible, a Web-GIS using mashups of freely available source codes and application program interface (APIs) was developed and can be found in the URLs http://noah.dost.gov.ph and http://noah.up.edu.ph/. This Web-GIS tool is now heavily used by local government units in the Philippines in their disaster prevention and mitigation efforts and can be replicated in countries that have a proactive approach to address the impacts of natural hazards but lack sufficient funds. Copyright © 2017. Published by Elsevier B.V.

  20. Hazard Map in Huaraz-Peru due to a Glacial Lake Outburst Flood from Palcacocha Lake

    Science.gov (United States)

    Somos-Valenzuela, M. A.; Chisolm, R. E.; McKinney, D. C.; Rivas, D.

    2013-12-01

    Palcacocha lake is located in the Ancash Region in the Cordillera Blanca at an elevation of 4,567 m in the Quilcay sub-basin, province of Huaraz, Peru. The lake drains into the Quebrada Cojup, which subsequently drains into the Quilcay River. The Quilcay River passes through the City of Huaraz emptying its water into the Santa River, which is the primary river of the basin. This location has a special interest since the city of Huaraz, which is located at the bottom of the Quilcay sub-basin, was devastated by a glacial lake outburst flood (GLOF) released from Lake Palcacocha on December 13, 1941. In that event, many lost their lives. In recent years Palcacocha has grown to the point where the lake is once again dangerous. Ice/rock avalanches from the steep surrounding slopes can now directly reach the lake. A process chain of debris flow and hyper-concentrated flow from Lake Palcacocha could easily reach the city of Huaraz with the current lake volume. Local authorities and people living in Huaraz are concerned about the threat posed by Lake Palcacocha, and consequently they have requested technical support in order to investigate the impacts that a GLOF could have in the city of Huaraz. To assess the hazard for the city of Huaraz a holistic approach is used that considers a chain of processes that could interact in a GLOF event from Lake Palcacocha. We assume that an avalanche from Palcaraju glacier, located directly above the lake, could be a GLOF trigger, followed by the formation of waves in the lake that can overtop the damming moraine starting an erosive process. The wave and avalanche simulations are described in another work, and here we use those results to simulate the propagation of the inundation downstream using FLO-2D, a model that allows us to include debris flow. GLOF hydrographs are generated using a dam break module in Mike 11. Empirical equations are used to calculate the hydrograph peaks and calibrate the inundation model. In order to quantify

  1. Stochastic Urban Pluvial Flood Hazard Maps Based upon a Spatial-Temporal Rainfall Generator

    OpenAIRE

    Nuno Eduardo Simões; Susana Ochoa-Rodríguez; Li-Pen Wang; Rui Daniel Pina; Alfeu Sá Marques; Christian Onof; Leitão, João P.

    2015-01-01

    It is a common practice to assign the return period of a given storm event to the urban pluvial flood event that such storm generates. However, this approach may be inappropriate as rainfall events with the same return period can produce different urban pluvial flooding events, i.e., with different associated flood extent, water levels and return periods. This depends on the characteristics of the rainfall events, such as spatial variability, and on other characteristics of the sewer system a...

  2. Mapping hazards from glacier lake outburst floods based on modelling of process cascades at Lake 513, Carhuaz, Peru

    OpenAIRE

    Schneider, Demian; Huggel, Christian; Cochachin, Alejo; Guillén, Sebastiàn; García, Javier

    2014-01-01

    Recent warming has had enormous impacts on glaciers and high-mountain environments. Hazards have changed or new ones have emerged, including those from glacier lakes that form as glaciers retreat. The Andes of Peru have repeatedly been severely impacted by glacier lake outburst floods in the past. An important recent event occurred in the Cordillera Blanca in 2010 when an ice avalanche impacted a glacier lake and triggered an outburst flood that affected the downstream communities and city of...

  3. Mapping hazards from glacier lake outburst floods based on modelling of process cascades at Lake 513, Carhuaz, Peru

    OpenAIRE

    Schneider, D; C. Huggel; Cochachin, A.; Guillén, S.; García, J.

    2014-01-01

    Recent warming has had enormous impacts on glaciers and high-mountain environments. Hazards have changed or new ones have emerged, including those from glacier lakes that form as glaciers retreat. The Andes of Peru have repeatedly been severely impacted by glacier lake outburst floods in the past. An important recent event occurred in the Cordillera Blanca in 2010 when an ice avalanche impacted a glacier lake and triggered an outburst flood that affected the downstream commu...

  4. Coastal Flood Hazard Composite Layer for the Coastal Flood Exposure Mapper

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This is a map service for the Coastal Flood Hazard Composite dataset. This dataset was created by combining hazard zones from the following datasets: FEMA V zones,...

  5. Geomorphological method in the elaboration of hazard maps for flash-floods in the municipality of Jucuarán (El Salvador

    Directory of Open Access Journals (Sweden)

    C. Fernández-Lavado

    2007-07-01

    Full Text Available This work deals with the elaboration of flood hazard maps. These maps reflect the areas prone to floods based on the effects of Hurricane Mitch in the Municipality of Jucuarán of El Salvador. Stream channels located in the coastal range in the SE of El Salvador flow into the Pacific Ocean and generate alluvial fans. Communities often inhabit these fans can be affected by floods. The geomorphology of these stream basins is associated with small areas, steep slopes, well developed regolite and extensive deforestation. These features play a key role in the generation of flash-floods. This zone lacks comprehensive rainfall data and gauging stations. The most detailed topographic maps are on a scale of 1:25 000. Given that the scale was not sufficiently detailed, we used aerial photographs enlarged to the scale of 1:8000. The effects of Hurricane Mitch mapped on these photographs were regarded as the reference event. Flood maps have a dual purpose (1 community emergency plans, (2 regional land use planning carried out by local authorities. The geomorphological method is based on mapping the geomorphological evidence (alluvial fans, preferential stream channels, erosion and sedimentation, man-made terraces. Following the interpretation of the photographs this information was validated on the field and complemented by eyewitness reports such as the height of water and flow typology. In addition, community workshops were organized to obtain information about the evolution and the impact of the phenomena. The superimposition of this information enables us to obtain a comprehensive geomorphological map. Another aim of the study was the calculation of the peak discharge using the Manning and the paleohydraulic methods and estimates based on geomorphologic criterion. The results were compared with those obtained using the rational method. Significant differences in the order of magnitude of the calculated discharges were noted. The rational method

  6. Estancia Special Flood Hazard Areas (SFHA)

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — This vector dataset depicts the 1% annual flood boundary (otherwise known as special flood hazard area or 100 year flood boundary) for its specified area. The data...

  7. Elephant Butte Special Flood Hazard Areas (SFHA)

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — This vector dataset depicts the 1% annual flood boundary (otherwise known as special flood hazard area or 100 year flood boundary) for its specified area. The data...

  8. Sierra County Special Flood Hazard Areas (SFHA)

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — This vector dataset depicts the 1% annual flood boundary (otherwise known as special flood hazard area or 100 year flood boundary) for its specified area. The data...

  9. 44 CFR 64.3 - Flood Insurance Maps.

    Science.gov (United States)

    2010-10-01

    ... 44 Emergency Management and Assistance 1 2010-10-01 2010-10-01 false Flood Insurance Maps. 64.3... HOMELAND SECURITY INSURANCE AND HAZARD MITIGATION National Flood Insurance Program COMMUNITIES ELIGIBLE FOR THE SALE OF INSURANCE § 64.3 Flood Insurance Maps. (a) The following maps may be prepared by...

  10. Flood hazard and management: a UK perspective.

    Science.gov (United States)

    Wheater, Howard S

    2006-08-15

    This paper discusses whether flood hazard in the UK is increasing and considers issues of flood risk management. Urban development is known to increase fluvial flood frequency, hence design measures are routinely implemented to minimize the impact. Studies suggest that historical effects, while potentially large at small scale, are not significant for large river basins. Storm water flooding within the urban environment is an area where flood hazard is inadequately defined; new methods are needed to assess and manage flood risk. Development on flood plains has led to major capital expenditure on flood protection, but government is attempting to strengthen the planning role of the environmental regulator to prevent this. Rural land use management has intensified significantly over the past 30 years, leading to concerns that flood risk has increased, at least at local scale; the implications for catchment-scale flooding are unclear. New research is addressing this issue, and more broadly, the role of land management in reducing flood risk. Climate change impacts on flooding and current guidelines for UK practice are reviewed. Large uncertainties remain, not least for the occurrence of extreme precipitation, but precautionary guidance is in place. Finally, current levels of flood protection are discussed. Reassessment of flood hazard has led to targets for increased flood protection, but despite important developments to communicate flood risk to the public, much remains to be done to increase public awareness of flood hazard.

  11. 78 FR 52956 - Proposed Flood Hazard Determinations

    Science.gov (United States)

    2013-08-27

    ... 77662. City of Pinehurst Pinehurst City Hall, 2497 Martin Luther King Jr. Drive, Orange, TX 77630. City... SECURITY Federal Emergency Management Agency Proposed Flood Hazard Determinations AGENCY: Federal Emergency... Register (78 FR 36220-36222) a proposed flood hazard determination notice that contained an erroneous...

  12. Flash Flood Hazard Mapping Using Satellite Images and GIS Tools: A case study of Najran City, Kingdom of Saudi Arabia (KSA)

    OpenAIRE

    Ismail Elkhrachy

    2015-01-01

    Flash flood in the cities led to high levels of water in the streets and roads, causing many problems such as bridge collapse, building damage and traffic problems. It is impossible to avoid risks of floods or prevent their occurrence, however it is plausible to work on the reduction of their effects and to reduce the losses which they may cause. Flash flood mapping to identify sites in high risk flood zones is one of the powerful tools for this purpose. Mapping flash flood will be beneficial...

  13. Flood insurance in Canada: implications for flood management and residential vulnerability to flood hazards.

    Science.gov (United States)

    Oulahen, Greg

    2015-03-01

    Insurance coverage of damage caused by overland flooding is currently not available to Canadian homeowners. As flood disaster losses and water damage claims both trend upward, insurers in Canada are considering offering residential flood coverage in order to properly underwrite the risk and extend their business. If private flood insurance is introduced in Canada, it will have implications for the current regime of public flood management and for residential vulnerability to flood hazards. This paper engages many of the competing issues surrounding the privatization of flood risk by addressing questions about whether flood insurance can be an effective tool in limiting exposure to the hazard and how it would exacerbate already unequal vulnerability. A case study investigates willingness to pay for flood insurance among residents in Metro Vancouver and how attitudes about insurance relate to other factors that determine residential vulnerability to flood hazards. Findings indicate that demand for flood insurance is part of a complex, dialectical set of determinants of vulnerability.

  14. Flood Insurance in Canada: Implications for Flood Management and Residential Vulnerability to Flood Hazards

    Science.gov (United States)

    Oulahen, Greg

    2015-03-01

    Insurance coverage of damage caused by overland flooding is currently not available to Canadian homeowners. As flood disaster losses and water damage claims both trend upward, insurers in Canada are considering offering residential flood coverage in order to properly underwrite the risk and extend their business. If private flood insurance is introduced in Canada, it will have implications for the current regime of public flood management and for residential vulnerability to flood hazards. This paper engages many of the competing issues surrounding the privatization of flood risk by addressing questions about whether flood insurance can be an effective tool in limiting exposure to the hazard and how it would exacerbate already unequal vulnerability. A case study investigates willingness to pay for flood insurance among residents in Metro Vancouver and how attitudes about insurance relate to other factors that determine residential vulnerability to flood hazards. Findings indicate that demand for flood insurance is part of a complex, dialectical set of determinants of vulnerability.

  15. A high-resolution global flood hazard model

    Science.gov (United States)

    Sampson, Christopher C.; Smith, Andrew M.; Bates, Paul B.; Neal, Jeffrey C.; Alfieri, Lorenzo; Freer, Jim E.

    2015-09-01

    Floods are a natural hazard that affect communities worldwide, but to date the vast majority of flood hazard research and mapping has been undertaken by wealthy developed nations. As populations and economies have grown across the developing world, so too has demand from governments, businesses, and NGOs for modeled flood hazard data in these data-scarce regions. We identify six key challenges faced when developing a flood hazard model that can be applied globally and present a framework methodology that leverages recent cross-disciplinary advances to tackle each challenge. The model produces return period flood hazard maps at ˜90 m resolution for the whole terrestrial land surface between 56°S and 60°N, and results are validated against high-resolution government flood hazard data sets from the UK and Canada. The global model is shown to capture between two thirds and three quarters of the area determined to be at risk in the benchmark data without generating excessive false positive predictions. When aggregated to ˜1 km, mean absolute error in flooded fraction falls to ˜5%. The full complexity global model contains an automatically parameterized subgrid channel network, and comparison to both a simplified 2-D only variant and an independently developed pan-European model shows the explicit inclusion of channels to be a critical contributor to improved model performance. While careful processing of existing global terrain data sets enables reasonable model performance in urban areas, adoption of forthcoming next-generation global terrain data sets will offer the best prospect for a step-change improvement in model performance.

  16. Flood hazard zoning in Yasooj region, Iran, using GIS and multi-criteria decision analysis

    Directory of Open Access Journals (Sweden)

    Omid Rahmati

    2016-05-01

    Full Text Available Flood is considered to be the most common natural disaster worldwide during the last decades. Flood hazard potential mapping is required for management and mitigation of flood. The present research was aimed to assess the efficiency of analytical hierarchical process (AHP to identify potential flood hazard zones by comparing with the results of a hydraulic model. Initially, four parameters via distance to river, land use, elevation and land slope were used in some part of the Yasooj River, Iran. In order to determine the weight of each effective factor, questionnaires of comparison ratings on the Saaty's scale were prepared and distributed to eight experts. The normalized weights of criteria/parameters were determined based on Saaty's nine-point scale and its importance in specifying flood hazard potential zones using the AHP and eigenvector methods. The set of criteria were integrated by weighted linear combination method using ArcGIS 10.2 software to generate flood hazard prediction map. The inundation simulation (extent and depth of flood was conducted using hydrodynamic program HEC-RAS for 50- and 100-year interval floods. The validation of the flood hazard prediction map was conducted based on flood extent and depth maps. The results showed that the AHP technique is promising of making accurate and reliable prediction for flood extent. Therefore, the AHP and geographic information system (GIS techniques are suggested for assessment of the flood hazard potential, specifically in no-data regions.

  17. Flood hazard and flood risk assessment using a time series of satellite images: a case study in Namibia.

    Science.gov (United States)

    Skakun, Sergii; Kussul, Nataliia; Shelestov, Andrii; Kussul, Olga

    2014-08-01

    In this article, the use of time series of satellite imagery to flood hazard mapping and flood risk assessment is presented. Flooded areas are extracted from satellite images for the flood-prone territory, and a maximum flood extent image for each flood event is produced. These maps are further fused to determine relative frequency of inundation (RFI). The study shows that RFI values and relative water depth exhibit the same probabilistic distribution, which is confirmed by Kolmogorov-Smirnov test. The produced RFI map can be used as a flood hazard map, especially in cases when flood modeling is complicated by lack of available data and high uncertainties. The derived RFI map is further used for flood risk assessment. Efficiency of the presented approach is demonstrated for the Katima Mulilo region (Namibia). A time series of Landsat-5/7 satellite images acquired from 1989 to 2012 is processed to derive RFI map using the presented approach. The following direct damage categories are considered in the study for flood risk assessment: dwelling units, roads, health facilities, and schools. The produced flood risk map shows that the risk is distributed uniformly all over the region. The cities and villages with the highest risk are identified. The proposed approach has minimum data requirements, and RFI maps can be generated rapidly to assist rescuers and decisionmakers in case of emergencies. On the other hand, limitations include: strong dependence on the available data sets, and limitations in simulations with extrapolated water depth values.

  18. Coastal Flooding Hazards due to storm surges and subsidence

    DEFF Research Database (Denmark)

    Sørensen, Carlo; Knudsen, Per; Andersen, Ole B.

    Flooding hazard and risk mapping are major topics in low-lying coastal areas before even considering the adverse effects of sea level rise (SLR) due to climate change. While permanent inundation may be a prevalent issue, more often floods related to extreme events (storm surges) have the largest...... damage potential.Challenges are amplified in some areas due to subsidence from natural and/or anthropogenic causes. Subsidence of even a few mm/y may over time greatly impair the safety against flooding of coastal communities and must be accounted for in order to accomplish the economically most viable...

  19. 44 CFR 65.12 - Revision of flood insurance rate maps to reflect base flood elevations caused by proposed...

    Science.gov (United States)

    2010-10-01

    ... 44 Emergency Management and Assistance 1 2010-10-01 2010-10-01 false Revision of flood insurance rate maps to reflect base flood elevations caused by proposed encroachments. 65.12 Section 65.12... INSURANCE AND HAZARD MITIGATION National Flood Insurance Program IDENTIFICATION AND MAPPING OF...

  20. Flooding scenarios, hazard mapping and damages estimation: what if the 2011 Cinque Terre event had happened in Genoa?

    Science.gov (United States)

    Silvestro, Francesco; Rebora, Nicola; Rossi, Lauro; Dolia, Daniele; Gabellani, Simone; Pignone, Flavio; Masciulli, Cristiano

    2016-04-01

    During the autumn of 2011 two catastrophic very intense rainfall events affected two different parts of the Liguria Region of Italy causing various flash floods, the first occurred in October and the second at the beginning of November. Various studies demonstrated that the two events had a similar genesis and similar triggering elements. In this work we did the exercise of putting the rainfall field of the first event (Cinque Terre area) on the main catchment, stroke by the second event, that has its mouth in correspondence of the biggest city of the Liguria Region: Genoa. A flood forecast framework and a hydraulic model were used as tools to quantitatively carry out a "what if" experiment, a proper methodology for damages estimation is then used to estimate the potential losses and the people affected. The results are interesting, surprising and in such a way worrying: a peak flow with return period larger than 200 years would have occurred with an estimated damage between 120 and 220 million of euros for the city of Genoa, Italy.

  1. Seismic hazard maps for Haiti

    Science.gov (United States)

    Frankel, Arthur; Harmsen, Stephen; Mueller, Charles; Calais, Eric; Haase, Jennifer

    2011-01-01

    We have produced probabilistic seismic hazard maps of Haiti for peak ground acceleration and response spectral accelerations that include the hazard from the major crustal faults, subduction zones, and background earthquakes. The hazard from the Enriquillo-Plantain Garden, Septentrional, and Matheux-Neiba fault zones was estimated using fault slip rates determined from GPS measurements. The hazard from the subduction zones along the northern and southeastern coasts of Hispaniola was calculated from slip rates derived from GPS data and the overall plate motion. Hazard maps were made for a firm-rock site condition and for a grid of shallow shear-wave velocities estimated from topographic slope. The maps show substantial hazard throughout Haiti, with the highest hazard in Haiti along the Enriquillo-Plantain Garden and Septentrional fault zones. The Matheux-Neiba Fault exhibits high hazard in the maps for 2% probability of exceedance in 50 years, although its slip rate is poorly constrained.

  2. Coproduction of flood hazard assessment with public participation geographic information system

    Science.gov (United States)

    Cheung, W. H.; Houston, D.; Schubert, J.; Basolo, V.; Feldman, D.; Matthew, R.; Sanders, B. F.; Karlin, B.; Goodrich, K.; Contreras, S.; Reyes, A.; Serrano, K.; Luke, A.

    2015-12-01

    While advances in computing have enabled the development of more precise and accurate flood models, there is growing interest in the role of crowdsourced local knowledge in flood modeling and flood hazard assessment. In an effort to incorporate the "wisdom of the crowd" in the identification and mitigation of flood hazard, this public participation geographic information system (PPGIS) study leveraged tablet computers and cloud computing to collect mental maps of flooding from 166 households in Newport Beach, California. The mental maps were analyzed using GIS techniques and compared with professional hydrodynamic model of coastal flooding. The results revealed varying levels of agreement between residents' mental maps and professional model of flood risk in regions with different personal and contextual characteristics. The quantification of agreement using composite indices can help validate professional models, and can also alert planners and decisionmakers of the need to increase flood awareness among specific populations.

  3. Truth or Consequences Special Flood Hazard Areas (SFHA)

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — This vector dataset depicts the 1% annual flood boundary (otherwise known as special flood hazard area or 100 year flood boundary) for its specified area. The data...

  4. Rethinking flood hazard at the global scale

    Science.gov (United States)

    Schumann, Guy J.-P.; Stampoulis, Dimitrios; Smith, Andrew M.; Sampson, Christopher C.; Andreadis, Konstantinos M.; Neal, Jeffrey C.; Bates, Paul D.

    2016-10-01

    Flooding is governed by the amount and timing of water spilling out of channels and moving across adjacent land, often with little warning. At global scales, flood hazard is typically inferred from streamflow, precipitation or from satellite images, yielding a largely incomplete picture. Thus, at present, the floodplain inundation variables, which define hazard, cannot be accurately predicted nor can they be measured at large scales. Here we present, for the first time, a complete continuous long-term simulation of floodplain water depths at continental scale. Simulations of floodplain inundation were performed with a hydrodynamic model based on gauged streamflow for the Australian continent from 1973 to 2012. We found the magnitude and timing of floodplain storage to differ significantly from streamflow in terms of their distribution. Furthermore, floodplain volume gave a much sharper discrimination of high hazard and low hazard periods than discharge. These discrepancies have implications for characterizing flood hazard at the global scale from precipitation and streamflow records alone, suggesting that simulations and observations of inundation are also needed.

  5. Geographic Information System and Remote Sensing Applications in Flood Hazards Management: A Review

    Directory of Open Access Journals (Sweden)

    Dano Umar Lawal

    2011-09-01

    Full Text Available The purpose of this study is to examine and review the various applications of GIS and remote sensing tools in flood disaster management as opposed to the conventional means of recording the hydrological parameters, which in many cases failed to capture an extreme event. In the recent years, GIS along with remote sensing has become the key tools in flood disaster monitoring and management. Advancement particularly in the area of remote sensing application has developed gradually from optical remote sensing to microwave or radar remote sensing, which has proved a profound capability of penetrating a clouded sky and provided all weather capabilities compared to the later (optical remote sensing in flood monitoring, mapping, and management. The main concern here is delineation of flood prone areas and development of flood hazard maps indicating the risk areas likely to be inundated by significant flooding along with the damageable objects maps for the flood susceptible areas. Actually, flood depth is always considered to be the basic aspect in flood hazard mapping, and therefore in determining or estimating the flood depth, a Digital Elevation Model data (DEM is considered to be the most appropriate means of determining the flood depth from a remotely sensed data or hydrological data. Accuracy of flood depth estimation depends mainly on the resolution of the DEM data in a flat terrain and in the regions that experiences monsoon seasons such as the developing countries of Asia where there is a high dependence on agriculture, which made any effort for flood estimation or flood hazard mapping difficult due to poor availability of high resolution DEM. More so the idea of Web-based GIS is gradually becoming a reality, which plays an important role in the flood hazard management. Therefore, this paper provides a review of applications of GIS and remote sensing technology in flood disaster monitoring and management.

  6. 32 CFR 643.31 - Policy-Flood hazards.

    Science.gov (United States)

    2010-07-01

    ... 32 National Defense 4 2010-07-01 2010-07-01 true Policy-Flood hazards. 643.31 Section 643.31... ESTATE Policy § 643.31 Policy—Flood hazards. Each Determination of Availability Report will include an evaluation of the flood hazards, if any, relative to the property involved in the proposed outgrant...

  7. 34 CFR 75.611 - Avoidance of flood hazards.

    Science.gov (United States)

    2010-07-01

    ... 34 Education 1 2010-07-01 2010-07-01 false Avoidance of flood hazards. 75.611 Section 75.611... by a Grantee? Construction § 75.611 Avoidance of flood hazards. In planning the construction, a...) Evaluate flood hazards in connection with the construction; and (b) As far as practicable, avoid...

  8. Hazard Map for Autonomous Navigation

    DEFF Research Database (Denmark)

    Riis, Troels

    This dissertation describes the work performed in the area of using image analysis in the process of landing a spacecraft autonomously and safely on the surface of the Moon. This is suggested to be done using a Hazard Map. The correspondence problem between several Hazard Maps are investigated fu...

  9. F4100320001.TIF - FEMA Flood Insurance Rate Maps for the Seaside-Gearhart, Oregon, Area: Seaside 1

    Data.gov (United States)

    U.S. Geological Survey, Department of the Interior — FEMA's Flood Insurance Rate Map (FIRM) depicts the spatial extent of Special Flood Hazard Areas (SFHAs) and other thematic features related to flood risk assessment....

  10. F4100300001D.TIF - FEMA Flood Insurance Rate Maps for the Seaside-Gearhart, Oregon, Area: Gearhart

    Data.gov (United States)

    U.S. Geological Survey, Department of the Interior — FEMA's Flood Insurance Rate Map (FIRM) depicts the spatial extent of Special Flood Hazard Areas (SFHAs) and other thematic features related to flood risk assessment....

  11. F4100320001.TIF - FEMA Flood Insurance Rate Maps for the Seaside-Gearhart, Oregon, Area: Seaside 1

    Data.gov (United States)

    U.S. Geological Survey, Department of the Interior — FEMA's Flood Insurance Rate Map (FIRM) depicts the spatial extent of Special Flood Hazard Areas (SFHAs) and other thematic features related to flood risk assessment....

  12. F4100320002C.TIF - FEMA Flood Insurance Rate Maps for the Seaside-Gearhart, Oregon, Area: Seaside 2

    Data.gov (United States)

    U.S. Geological Survey, Department of the Interior — FEMA's Flood Insurance Rate Map (FIRM) depicts the spatial extent of Special Flood Hazard Areas (SFHAs) and other thematic features related to flood risk assessment....

  13. F4100300001D.TIF - FEMA Flood Insurance Rate Maps for the Seaside-Gearhart, Oregon, Area: Gearhart

    Data.gov (United States)

    U.S. Geological Survey, Department of the Interior — FEMA's Flood Insurance Rate Map (FIRM) depicts the spatial extent of Special Flood Hazard Areas (SFHAs) and other thematic features related to flood risk...

  14. F4100300001D.TIF - FEMA Flood Insurance Rate Maps for the Seaside-Gearhart, Oregon, Area: Gearhart

    Data.gov (United States)

    U.S. Geological Survey, Department of the Interior — FEMA's Flood Insurance Rate Map (FIRM) depicts the spatial extent of Special Flood Hazard Areas (SFHAs) and other thematic features related to flood risk assessment....

  15. F4100320002C.TIF - FEMA Flood Insurance Rate Maps for the Seaside-Gearhart, Oregon, Area: Seaside 2

    Data.gov (United States)

    U.S. Geological Survey, Department of the Interior — FEMA's Flood Insurance Rate Map (FIRM) depicts the spatial extent of Special Flood Hazard Areas (SFHAs) and other thematic features related to flood risk assessment....

  16. SEERISK concept: Dealing with climate change related hazards in southeast Europe: A common methodology for risk assessment and mapping focusing on floods, drought, winds, heat wave and wildfire.

    Science.gov (United States)

    Papathoma-Koehle, Maria; Promper, Catrin; Glade, Thomas

    2014-05-01

    Southeast Europe is a region that suffers often from natural hazards and has experienced significant losses in the recent past due to extreme weather conditions and their side-effects (cold and heat waves, extreme precipitation leading to floods / flash floods, thunderstorms, extreme winds, drought and wildfires). SEERISK ("Joint Disaster Management Risk Assessment and Preparedness in the Danube macro-region") is a European funded SEE (Southeast Europe) project that aims at the harmonisation and consistency among risk assessment practices undertaken by the partner countries at various levels regarding climate change related disasters. A common methodology for risk assessment has been developed that offers alternatives in order to tackle the problem of limited data. The methodology proposes alternative steps for hazard and vulnerability assessment that, according to the data availability, range from detailed modelling to expert judgement. In the present study the common methodology has been adapted for five hazard types (floods, drought, winds, heat wave and wildfire) that are expected to be affected by climate change in the future and are relevant for the specific study areas. The last step will be the application of the methodology in six different case studies in Hungary, Romania, Bosnia, Bulgaria, Slovakia and Serbia followed by field exercises.

  17. Uncertainty in flood risk mapping

    Science.gov (United States)

    Gonçalves, Luisa M. S.; Fonte, Cidália C.; Gomes, Ricardo

    2014-05-01

    A flood refers to a sharp increase of water level or volume in rivers and seas caused by sudden rainstorms or melting ice due to natural factors. In this paper, the flooding of riverside urban areas caused by sudden rainstorms will be studied. In this context, flooding occurs when the water runs above the level of the minor river bed and enters the major river bed. The level of the major bed determines the magnitude and risk of the flooding. The prediction of the flooding extent is usually deterministic, and corresponds to the expected limit of the flooded area. However, there are many sources of uncertainty in the process of obtaining these limits, which influence the obtained flood maps used for watershed management or as instruments for territorial and emergency planning. In addition, small variations in the delineation of the flooded area can be translated into erroneous risk prediction. Therefore, maps that reflect the uncertainty associated with the flood modeling process have started to be developed, associating a degree of likelihood with the boundaries of the flooded areas. In this paper an approach is presented that enables the influence of the parameters uncertainty to be evaluated, dependent on the type of Land Cover Map (LCM) and Digital Elevation Model (DEM), on the estimated values of the peak flow and the delineation of flooded areas (different peak flows correspond to different flood areas). The approach requires modeling the DEM uncertainty and its propagation to the catchment delineation. The results obtained in this step enable a catchment with fuzzy geographical extent to be generated, where a degree of possibility of belonging to the basin is assigned to each elementary spatial unit. Since the fuzzy basin may be considered as a fuzzy set, the fuzzy area of the basin may be computed, generating a fuzzy number. The catchment peak flow is then evaluated using fuzzy arithmetic. With this methodology a fuzzy number is obtained for the peak flow

  18. Visualising interactive flood risk maps in a dynamic Geobrowser

    Science.gov (United States)

    Yaw Manful, Desmond; He, Yi; Cloke, Hannah; Pappenberger, Florian; Li, Zhijia; Wetterhall, Fredrik; Huang, Yingchun; Hu, Yuzhong

    2010-05-01

    Communicating flood forecast products effectively to end-users is the final step in the flood event simulation process. A prototype of the Novel Flood Early Warning System (NEWS) based on the TIGGE (THORPEX Interactive Grand Global Ensemble) database explores new avenues to visualise flood forecast products in a dynamic and interactive manner. One of the possibilities NEWS is currently assessing is Google Maps. Google Maps is a basic web mapping service application and technology provided by Google, free (for non-commercial use). It powers many map-based services including maps embedded on third-party websites via the Google Maps API. Creating a customized map interface requires adding the Google JavaScript code to a page, and then using Javascript functions to add points to the map. Flood maps allow end-users to visualise and navigate a world that is too large and complex to be seen directly. The NEWS software will attempt to deal with the following issues: • Uncertainty visualization in hazards maps • Visualizing uncertainty for sector specific risk managers • Uncertainty representation of point and linear data The objective is improve the information content of flood risk maps making them more useful to specific end-users.

  19. DIGITAL FLOOD INSURANCE RATE MAP DATABASE,

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...

  20. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, , USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...

  1. Flood Insurance Rate Map, Scott County, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  2. Flood hazard and flood risk assessment at the local spatial scale: a case study

    Directory of Open Access Journals (Sweden)

    Matej Vojtek

    2016-11-01

    Full Text Available With regard to minimizing flood damage, there are measures of different character each of which has its justification and plays an important role in flood protection. Implementation of traditional flood protection measures is still very important; however, an increasing role should be played particularly by flood prevention and flood risk management. The paper presents a case study on flood hazard and flood risk assessment at the local spatial scale using geographic information systems, remote sensing, and hydraulic modelling. As for determining flood hazard in the model area, which has 3.23 km2, the estimation of maximum flood discharges and hydraulic modelling were important steps. The results of one-dimensional hydraulic modelling, which are water depth and flow velocity rasters, were the basis for determining flood hazard and flood risk. In order to define flood risk, the following steps were applied: determining flood intensity on the basis of water depth and flow velocity rasters, determining flood hazard using three categories (low, medium, and high based on flood intensity, defining vulnerability for the classes of functional areas using three categories of acceptable risk (low, medium, and high, and lastly determination of flood risk which represents a synthesis of flood hazard and vulnerability of the model area.

  3. USGS National Seismic Hazard Maps

    Science.gov (United States)

    Frankel, A.D.; Mueller, C.S.; Barnhard, T.P.; Leyendecker, E.V.; Wesson, R.L.; Harmsen, S.C.; Klein, F.W.; Perkins, D.M.; Dickman, N.C.; Hanson, S.L.; Hopper, M.G.

    2000-01-01

    The U.S. Geological Survey (USGS) recently completed new probabilistic seismic hazard maps for the United States, including Alaska and Hawaii. These hazard maps form the basis of the probabilistic component of the design maps used in the 1997 edition of the NEHRP Recommended Provisions for Seismic Regulations for New Buildings and Other Structures, prepared by the Building Seismic Safety Council arid published by FEMA. The hazard maps depict peak horizontal ground acceleration and spectral response at 0.2, 0.3, and 1.0 sec periods, with 10%, 5%, and 2% probabilities of exceedance in 50 years, corresponding to return times of about 500, 1000, and 2500 years, respectively. In this paper we outline the methodology used to construct the hazard maps. There are three basic components to the maps. First, we use spatially smoothed historic seismicity as one portion of the hazard calculation. In this model, we apply the general observation that moderate and large earthquakes tend to occur near areas of previous small or moderate events, with some notable exceptions. Second, we consider large background source zones based on broad geologic criteria to quantify hazard in areas with little or no historic seismicity, but with the potential for generating large events. Third, we include the hazard from specific fault sources. We use about 450 faults in the western United States (WUS) and derive recurrence times from either geologic slip rates or the dating of pre-historic earthquakes from trenching of faults or other paleoseismic methods. Recurrence estimates for large earthquakes in New Madrid and Charleston, South Carolina, were taken from recent paleoliquefaction studies. We used logic trees to incorporate different seismicity models, fault recurrence models, Cascadia great earthquake scenarios, and ground-motion attenuation relations. We present disaggregation plots showing the contribution to hazard at four cities from potential earthquakes with various magnitudes and

  4. Smoky River coal flood risk mapping study

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    2004-06-01

    The Canada-Alberta Flood Damage Reduction Program (FDRP) is designed to reduce flood damage by identifying areas susceptible to flooding and by encouraging application of suitable land use planning, zoning, and flood preparedness and proofing. The purpose of this study is to define flood risk and floodway limits along the Smoky River near the former Smoky River Coal (SRC) plant. Alberta Energy has been responsible for the site since the mine and plant closed in 2000. The study describes flooding history, available data, features of the river and valley, calculation of flood levels, and floodway determination, and includes flood risk maps. The HEC-RAS program is used for the calculations. The flood risk area was calculated using the 1:100 year return period flood as the hydrological event. 7 refs., 11 figs., 7 tabs., 3 apps.

  5. Benchmarking an operational procedure for rapid flood mapping and risk assessment in Europe

    Science.gov (United States)

    Dottori, Francesco; Salamon, Peter; Kalas, Milan; Bianchi, Alessandra; Feyen, Luc

    2016-04-01

    The development of real-time methods for rapid flood mapping and risk assessment is crucial to improve emergency response and mitigate flood impacts. This work describes the benchmarking of an operational procedure for rapid flood risk assessment based on the flood predictions issued by the European Flood Awareness System (EFAS). The daily forecasts produced for the major European river networks are translated into event-based flood hazard maps using a large map catalogue derived from high-resolution hydrodynamic simulations, based on the hydro-meteorological dataset of EFAS. Flood hazard maps are then combined with exposure and vulnerability information, and the impacts of the forecasted flood events are evaluated in near real-time in terms of flood prone areas, potential economic damage, affected population, infrastructures and cities. An extensive testing of the operational procedure is carried out using the catastrophic floods of May 2014 in Bosnia-Herzegovina, Croatia and Serbia. The reliability of the flood mapping methodology is tested against satellite-derived flood footprints, while ground-based estimations of economic damage and affected population is compared against modelled estimates. We evaluated the skill of flood hazard and risk estimations derived from EFAS flood forecasts with different lead times and combinations. The assessment includes a comparison of several alternative approaches to produce and present the information content, in order to meet the requests of EFAS users. The tests provided good results and showed the potential of the developed real-time operational procedure in helping emergency response and management.

  6. FEMA Hazard Mitigation Assistance Flood Mitigation Assistance (FMA) Data

    Data.gov (United States)

    Department of Homeland Security — This dataset contains closed and obligated projects funded under the following Hazard Mitigation Assistance (HMA) grant programs: Flood Mitigation Assistance (FMA)....

  7. FEMA Hazard Mitigation Assistance Repetitive Flood Claims (RFC) Data

    Data.gov (United States)

    Department of Homeland Security — This dataset contains closed and obligated projects funded under the following Hazard Mitigation Assistance (HMA) grant programs: Repetitive Flood Claims (RFC). The...

  8. Hydrologic versus geomorphic drivers of trends in flood hazard

    Science.gov (United States)

    Slater, Louise J.; Bliss Singer, Michael; Kirchner, James W.

    2016-04-01

    Flooding is a major threat to lives and infrastructure, yet trends in flood hazard are poorly understood. The capacity of river channels to convey flood flows is typically assumed to be stationary, so changes in flood frequency are thought to be driven primarily by trends in streamflow. However, changes in channel capacity will also modify flood hazard, even if the flow frequency distribution does not change. We developed new methods for separately quantifying how trends in both streamflow and channel capacity have affected flood frequency at gauging sites across the United States. Using daily discharge records and manual field measurements of channel cross-sectional geometry for USGS gauging stations that have defined flood stages (water levels), we present novel methods for measuring long-term trends in channel capacity of gauged rivers, and for quantifying how they affect overbank flood frequency. We apply these methods to 401 U.S. rivers and detect measurable trends in flood hazard linked to changes in channel capacity and/or the frequency of high flows. Flood frequency is generally nonstationary across these 401 U.S. rivers, with increasing flood hazard at a statistically significant majority of sites. Changes in flood hazard driven by channel capacity are smaller, but more numerous, than those driven by streamflow, with a slight tendency to compensate for streamflow changes. Our results demonstrate that accurately quantifying changes in flood hazard requires accounting separately for trends in both streamflow and channel capacity, or using water levels directly. They also show that channel capacity trends may have unforeseen consequences for flood management and for estimating flood insurance costs. Slater, L. J., M. B. Singer, and J. W. Kirchner (2015), Hydrologic versus geomorphic drivers of trends in flood hazard, Geophys. Res. Lett., 42, 370-376, doi:10.1002/2014GL062482.

  9. NASA Global Flood Mapping System

    Science.gov (United States)

    Policelli, Fritz; Slayback, Dan; Brakenridge, Bob; Nigro, Joe; Hubbard, Alfred

    2017-01-01

    Product utility key factors: Near real time, automated production; Flood spatial extent Cloudiness Pixel resolution: 250m; Flood temporal extent; Flash floods short duration on ground?; Landcover--Water under vegetation cover vs open water

  10. Dynamic Flood Vulnerability Mapping with Google Earth Engine

    Science.gov (United States)

    Tellman, B.; Kuhn, C.; Max, S. A.; Sullivan, J.

    2015-12-01

    Satellites capture the rate and character of environmental change from local to global levels, yet integrating these changes into flood exposure models can be cost or time prohibitive. We explore an approach to global flood modeling by leveraging satellite data with computing power in Google Earth Engine to dynamically map flood hazards. Our research harnesses satellite imagery in two main ways: first to generate a globally consistent flood inundation layer and second to dynamically model flood vulnerability. Accurate and relevant hazard maps rely on high quality observation data. Advances in publicly available spatial, spectral, and radar data together with cloud computing allow us to improve existing efforts to develop a comprehensive flood extent database to support model training and calibration. This talk will demonstrate the classification results of algorithms developed in Earth Engine designed to detect flood events by combining observations from MODIS, Landsat 8, and Sentinel-1. Our method to derive flood footprints increases the number, resolution, and precision of spatial observations for flood events both in the US, recorded in the NCDC (National Climatic Data Center) storm events database, and globally, as recorded events from the Colorado Flood Observatory database. This improved dataset can then be used to train machine learning models that relate spatial temporal flood observations to satellite derived spatial temporal predictor variables such as precipitation, antecedent soil moisture, and impervious surface. This modeling approach allows us to rapidly update models with each new flood observation, providing near real time vulnerability maps. We will share the water detection algorithms used with each satellite and discuss flood detection results with examples from Bihar, India and the state of New York. We will also demonstrate how these flood observations are used to train machine learning models and estimate flood exposure. The final stage of

  11. Rapid Response Flood Water Mapping

    Science.gov (United States)

    Policelli, Fritz; Brakenridge, G. R.; Coplin, A.; Bunnell, M.; Wu, L.; Habib, Shahid; Farah, H.

    2010-01-01

    Since the beginning of operation of the MODIS instrument on the NASA Terra satellite at the end of 1999, an exceptionally useful sensor and public data stream have been available for many applications including the rapid and precise characterization of terrestrial surface water changes. One practical application of such capability is the near-real time mapping of river flood inundation. We have developed a surface water mapping methodology based on using only bands 1 (620-672 nm) and 2 (841-890 nm). These are the two bands at 250 m, and the use of only these bands maximizes the resulting map detail. In this regard, most water bodies are strong absorbers of incoming solar radiation at the band 2 wavelength: it could be used alone, via a thresholding procedure, to separate water (dark, low radiance or reflectance pixels) from land (much brighter pixels) (1, 2). Some previous water mapping procedures have in fact used such single band data from this and other sensors that include similar wavelength channels. Adding the second channel of data (band 1), however, allows a band ratio approach which permits sediment-laden water, often relatively light at band 2 wavelengths, to still be discriminated, and, as well, provides some removal of error by reducing the number of cloud shadow pixels that would otherwise be misclassified as water.

  12. Flood hazards in the Seattle-Tacoma urban complex and adjacent areas, Washington

    Science.gov (United States)

    Foxworthy, B.L.; Nassar, E.G.

    1975-01-01

    Floods are natural hazards that have complicated man's land-use planning for as long as we have had a history. Although flood hzards are a continuing danger, the year-to-year threat cannot be accurately predicted. Also, on any one stream, the time since the last destructive flood might be so long that most people now living near the stream have not experienced such a flood. Because of the unpredictability and common infrequency of disastrous flooding, or out of ignorance about the danger, or perhaps because of an urge to gamble, man tends to focus his attention on only the advantages of the flood-prone areas, rather than the risk due to the occasional major flood. The purposes of this report are to: (1) briefly describe flood hazards in this region, including some that may be unique to the Puget Sound basin, (2) indicate the parts of the area for which flood-hazard data are available, and (3) list the main sources of hydrologic information that is useful for flood-hazard analysis in conjuction with long-range planning. This map-type report is one of a series being prepared by the U.S. Geological Survey to present basic environmental information and interpretations to assist land-use planning in the Puget Sound region.

  13. Determining the Optimum Post Spacing of LIDAR-Derived Elevation Data in Varying Terrain for Flood Hazard Mapping Purposes in North Carolina and Texas

    Science.gov (United States)

    Berglund, Judith; Davis, Bruce; Estep, Lee

    2004-01-01

    The major flood events in the United States in the past few years have made it apparent that many floodplain maps being used by State governments are outdated and inaccurate. In response, many Stated have begun to update their Federal Emergency Management Agency (FEMA) Digital Flood Insurance Rate Maps. Accurate topographic data is one of the most critical inputs for floodplain analysis and delineation. Light detection and ranging (LIDAR) altimetry is one of the primary remote sensing technologies that can be used to obtain high-resolution and high-accuracy digital elevation data suitable for hydrologic and hydraulic (H&H) modeling, in part because of its ability to "penetrate" various cover types and to record geospatial data from the Earth's surface. However, the posting density or spacing at which LIDAR collects the data will affect the resulting accuracies of the derived bare Earth surface, depending on terrain type and land cover type. For example, flat areas are thought to require higher or denser postings than hilly areas to capture subtle changes in the topography that could have a significant effect on flooding extent. Likewise, if an area has dense understory and overstory, it may be difficult to receive LIDAR returns from the Earth's surface, which would affect the accuracy of that bare Earth surface and thus would affect flood model results. For these reasons, NASA and FEMA have partnered with the State of North Carolina and with the U.S./Mexico Foundation in Texas to assess the effect of LIDAR point density on the characterization of topographic variation and on H&H modeling results for improved floodplain mapping. Research for this project is being conducted in two areas of North Carolina and in the City of Brownsville, Texas, each with a different type of terrain and varying land cover/land use. Because of various project constraints, LIDAR data were acquired once at a high posting density and then decimated to coarser postings or densities. Quality

  14. 76 FR 37893 - Loans in Areas Having Special Flood Hazards

    Science.gov (United States)

    2011-06-28

    ... From the Federal Register Online via the Government Publishing Office DEPARTMENT OF THE TREASURY Office of Thrift Supervision Loans in Areas Having Special Flood Hazards AGENCY: Office of Thrift... collection. Title of Proposal: Loans in Areas Having Special Flood Hazards. OMB Number: 1550-0088....

  15. Combined fluvial and pluvial urban flood hazard analysis: method development and application to Can Tho City, Mekong Delta, Vietnam

    Directory of Open Access Journals (Sweden)

    H. Apel

    2015-08-01

    Full Text Available Many urban areas experience both fluvial and pluvial floods, because locations next to rivers are preferred settlement areas, and the predominantly sealed urban surface prevents infiltration and facilitates surface inundation. The latter problem is enhanced in cities with insufficient or non-existent sewer systems. While there are a number of approaches to analyse either fluvial or pluvial flood hazard, studies of combined fluvial and pluvial flood hazard are hardly available. Thus this study aims at the analysis of fluvial and pluvial flood hazard individually, but also at developing a method for the analysis of combined pluvial and fluvial flood hazard. This combined fluvial-pluvial flood hazard analysis is performed taking Can Tho city, the largest city in the Vietnamese part of the Mekong Delta, as example. In this tropical environment the annual monsoon triggered floods of the Mekong River can coincide with heavy local convective precipitation events causing both fluvial and pluvial flooding at the same time. Fluvial flood hazard was estimated with a copula based bivariate extreme value statistic for the gauge Kratie at the upper boundary of the Mekong Delta and a large-scale hydrodynamic model of the Mekong Delta. This provided the boundaries for 2-dimensional hydrodynamic inundation simulation for Can Tho city. Pluvial hazard was estimated by a peak-over-threshold frequency estimation based on local rain gauge data, and a stochastic rain storm generator. Inundation was simulated by a 2-dimensional hydrodynamic model implemented on a Graphical Processor Unit (GPU for time-efficient flood propagation modelling. All hazards – fluvial, pluvial and combined – were accompanied by an uncertainty estimation considering the natural variability of the flood events. This resulted in probabilistic flood hazard maps showing the maximum inundation depths for a selected set of probabilities of occurrence, with maps showing the expectation (median

  16. Flood Insurance Rate Maps and Base Flood Elevations, FIRM, DFIRM, BFE - MO 2014 Springfield FEMA Base Flood Elevations (SHP)

    Data.gov (United States)

    NSGIC State | GIS Inventory — This polyline layer indicates the approximate effective FEMA Base Flood Elevation (BFE) associated with the corresponding Special Flood Hazard Area (SFHA). Each line...

  17. Flood Insurance Rate Maps and Base Flood Elevations, FIRM, DFIRM, BFE - MO 2010 Springfield FEMA Base Flood Elevations (SHP)

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This polyline layer indicates the approximate effective FEMA Base Flood Elevations (BFE) associated with the corresponding Special Flood Hazard Area (SFHA). Each...

  18. Delivering integrated HAZUS-MH flood loss analyses and flood inundation maps over the Web

    Science.gov (United States)

    Hearn,, Paul P.; Longenecker, Herbert E.; Aguinaldo, John J.; Rahav, Ami N.

    2013-01-01

    Catastrophic flooding is responsible for more loss of life and damages to property than any other natural hazard. Recently developed flood inundation mapping technologies make it possible to view the extent and depth of flooding on the land surface over the Internet; however, by themselves these technologies are unable to provide estimates of losses to property and infrastructure. The Federal Emergency Management Agency’s (FEMA's) HAZUS-MH software is extensively used to conduct flood loss analyses in the United States, providing a nationwide database of population and infrastructure at risk. Unfortunately, HAZUS-MH requires a dedicated Geographic Information System (GIS) workstation and a trained operator, and analyses are not adapted for convenient delivery over the Web. This article describes a cooperative effort by the US Geological Survey (USGS) and FEMA to make HAZUS-MH output GIS and Web compatible and to integrate these data with digital flood inundation maps in USGS’s newly developed Inundation Mapping Web Portal. By running the computationally intensive HAZUS-MH flood analyses offline and converting the output to a Web-GIS compatible format, detailed estimates of flood losses can now be delivered to anyone with Internet access, thus dramatically increasing the availability of these forecasts to local emergency planners and first responders.

  19. Increasing resilience through participative flood risk map design

    Science.gov (United States)

    Fuchs, Sven; Spira, Yvonne; Stickler, Therese

    2013-04-01

    In recent years, an increasing number of flood hazards has shown to the European Commission and the Member States of the European Union the importance of flood risk management strategies in order to reduce losses and to protect the environment and the citizens. Exposure to floods as well as flood vulnerability might increase across Europe due to the ongoing economic development in many EU countries. Thus even without taking climate change into account an increase of flood disasters in Europe might be foreseeable. These circumstances have produced a reaction in the European Commission, and a Directive on the Assessment and Management of Flood Risks was issued as one of the three components of the European Action Programme on Flood Risk Management. Floods have the potential to jeopardise economic development, above all due to an increase of human activities in floodplains and the reduction of natural water retention by land use activities. As a result, an increase in the likelihood and adverse impacts of flood events is expected. Therefore, concentrated action is needed at the European level to avoid severe impacts on human life and property. In order to have an effective tool available for gathering information, as well as a valuable basis for priority setting and further technical, financial and political decisions regarding flood risk mitigation and management, it is necessary to provide for the establishment of flood risk maps which show the potential adverse consequences associated with different flood scenarios. So far, hazard and risk maps are compiled in terms of a top-down linear approach: planning authorities take the responsibility to create and implement these maps on different national and local scales, and the general public will only be informed about the outcomes (EU Floods Directive, Article 10). For the flood risk management plans, however, an "active involvement of interested parties" is required, which means at least some kind of multilateral

  20. Influence of dem in Watershed Management as Flood Zonation Mapping

    Science.gov (United States)

    Alrajhi, Muhamad; Khan, Mudasir; Afroz Khan, Mohammad; Alobeid, Abdalla

    2016-06-01

    Despite of valuable efforts from working groups and research organizations towards flood hazard reduction through its program, still minimal diminution from these hazards has been realized. This is mainly due to the fact that with rapid increase in population and urbanization coupled with climate change, flood hazards are becoming increasingly catastrophic. Therefore there is a need to understand and access flood hazards and develop means to deal with it through proper preparations, and preventive measures. To achieve this aim, Geographical Information System (GIS), geospatial and hydrological models were used as tools to tackle with influence of flash floods in the Kingdom of Saudi Arabia due to existence of large valleys (Wadis) which is a matter of great concern. In this research paper, Digital Elevation Models (DEMs) of different resolution (30m, 20m,10m and 5m) have been used, which have proven to be valuable tool for the topographic parameterization of hydrological models which are the basis for any flood modelling process. The DEM was used as input for performing spatial analysis and obtaining derivative products and delineate watershed characteristics of the study area using ArcGIS desktop and its Arc Hydro extension tools to check comparability of different elevation models for flood Zonation mapping. The derived drainage patterns have been overlaid over aerial imagery of study area, to check influence of greater amount of precipitation which can turn into massive destructions. The flow accumulation maps derived provide zones of highest accumulation and possible flow directions. This approach provide simplified means of predicting extent of inundation during flood events for emergency action especially for large areas because of large coverage area of the remotely sensed data.

  1. Accumulation risk assessment for the flooding hazard

    Science.gov (United States)

    Roth, Giorgio; Ghizzoni, Tatiana; Rudari, Roberto

    2010-05-01

    One of the main consequences of the demographic and economic development and of markets and trades globalization is represented by risks cumulus. In most cases, the cumulus of risks intuitively arises from the geographic concentration of a number of vulnerable elements in a single place. For natural events, risks cumulus can be associated, in addition to intensity, also to event's extension. In this case, the magnitude can be such that large areas, that may include many regions or even large portions of different countries, are stroked by single, catastrophic, events. Among natural risks, the impact of the flooding hazard cannot be understated. To cope with, a variety of mitigation actions can be put in place: from the improvement of monitoring and alert systems to the development of hydraulic structures, throughout land use restrictions, civil protection, financial and insurance plans. All of those viable options present social and economic impacts, either positive or negative, whose proper estimate should rely on the assumption of appropriate - present and future - flood risk scenarios. It is therefore necessary to identify proper statistical methodologies, able to describe the multivariate aspects of the involved physical processes and their spatial dependence. In hydrology and meteorology, but also in finance and insurance practice, it has early been recognized that classical statistical theory distributions (e.g., the normal and gamma families) are of restricted use for modeling multivariate spatial data. Recent research efforts have been therefore directed towards developing statistical models capable of describing the forms of asymmetry manifest in data sets. This, in particular, for the quite frequent case of phenomena whose empirical outcome behaves in a non-normal fashion, but still maintains some broad similarity with the multivariate normal distribution. Fruitful approaches were recognized in the use of flexible models, which include the normal

  2. Development of flood risk mapping in Kota Tinggi, Malaysia

    Science.gov (United States)

    Tam, T. H.

    2014-02-01

    Flood risk maps provide valuable information for development of flood risk management. Geospatial technology and modeling enable us to monitor natural disasters around the world. Flooding is the most severe natural disaster that causing huge economic losses every year. Flood risk maps are an essential tool for assessing the consequences of flooding. The main aim of this study is to initiate a framework to develop a local-based flood risk map. Flood risk maps can be produced by using integration of geospatial technology and hydrodynamic modeling. Results show that a flood risk map for Kota Tinggi is produced with unsatisfactory information in term of flood damage.

  3. 76 FR 72961 - Flood Hazard Determinations (Including Flood Elevation Determinations)-Change in Notification and...

    Science.gov (United States)

    2011-11-28

    ..., etc.) and provide both a physical address and an internet address where the specific flood elevations... both a physical address and an internet address where the specific flood hazards (as shown in a Flood... published in the Federal Register on or after December 1, 2011. ADDRESSES: The docket for this notice...

  4. Groundwater flood hazards in lowland karst terrains

    Science.gov (United States)

    Naughton, Owen; McCormack, Ted

    2016-04-01

    The spatial and temporal complexity of flooding in karst terrains pose unique flood risk management challenges. Lowland karst landscapes can be particularly susceptible to groundwater flooding due to a combination of limited drainage capacity, shallow depth to groundwater and a high level of groundwater-surface water interactions. Historically the worst groundwater flooding to have occurred in the Rep. of Ireland has been centred on the Gort Lowlands, a karst catchment on the western coast of Ireland. Numerous notable flood events have been recorded throughout the 20th century, but flooding during the winters of 2009 and 2015 were the most severe on record, inundating an area in excess of 20km2 and causing widespread and prolonged disruption and damage to property and infrastructure. Effective flood risk management requires an understanding of the recharge, storage and transport mechanisms during flood conditions, but is often hampered by a lack of adequate data. Using information gathered from the 2009 and 2015 events, the main hydrological and geomorphological factors which influence flooding in this complex lowland karst groundwater system under are elucidated. Observed flood mechanisms included backwater flooding of sinks, overland flow caused by the overtopping of sink depressions, high water levels in turlough basins, and surface ponding in local epikarst watersheds. While targeted small-scale flood measures can locally reduce the flood risk associated with some mechanisms, they also have the potential to exacerbate flooding down-catchment and must be assessed in the context of overall catchment hydrology. This study addresses the need to improve our understanding of groundwater flooding in karst terrains, in order to ensure efficient flood prevention and mitigation in future and thus help achieve the aims of the EU Floods Directive.

  5. Flood inundation map library, Fort Kent, Maine

    Science.gov (United States)

    Lombard, Pamela J.

    2012-01-01

    Severe flooding occurred in northern Maine from April 28 to May 1, 2008, and damage was extensive in the town of Fort Kent (Lombard, 2010). Aroostook County was declared a Federal disaster area on May 9, 2008. The extent of flooding on both the Fish and St. John Rivers during this event showed that the current Federal Emergency Management Agency (FEMA) Flood Insurance Study (FIS) and Flood Insurance Rate Map (FIRM) (Federal Emergency Management Agency, 1979) were out of date. The U.S. Geological Survey (USGS) conducted a study to develop a flood inundation map library showing the areas and depths for a range of flood stages from bankfull to the flood of record for Fort Kent to complement an updated FIS (Federal Emergency Management Agency, in press). Hydrologic analyses that support the maps include computer models with and without the levee and with various depths of backwater on the Fish River. This fact sheet describes the methods used to develop the maps and describes how the maps can be accessed.

  6. Flood Mapping Using InSAR Coherence Map

    Science.gov (United States)

    Selmi, S.; Ben Abdallah, W.; Abdelfatteh, R.

    2014-09-01

    Classic approaches for the detection of flooded areas are based on a static analysis of optical images and/or SAR data during and after the event. In this paper, we aim to extract the flooded zones by using the SAR image coupled with the InSAR coherence. A new formulation of the ratio approach for flood detection is given considering InSAR coherence. Our contribution is to take advantage from the coherence map provided using the InSAR pairs (one before and one after the event) to enhance the detection of flooded areas. We explore the fact that the coherence values during and after the flood are mainly differents on the flooded zones and we give a more suitable flood decision rule using this assumption. The proposed approach is tested and validated in the case of the flood taken place in 2005 in the region of Kef in Tunisia.

  7. Updated Colombian Seismic Hazard Map

    Science.gov (United States)

    Eraso, J.; Arcila, M.; Romero, J.; Dimate, C.; Bermúdez, M. L.; Alvarado, C.

    2013-05-01

    The Colombian seismic hazard map used by the National Building Code (NSR-98) in effect until 2009 was developed in 1996. Since then, the National Seismological Network of Colombia has improved in both coverage and technology providing fifteen years of additional seismic records. These improvements have allowed a better understanding of the regional geology and tectonics which in addition to the seismic activity in Colombia with destructive effects has motivated the interest and the need to develop a new seismic hazard assessment in this country. Taking advantage of new instrumental information sources such as new broad band stations of the National Seismological Network, new historical seismicity data, standardized global databases availability, and in general, of advances in models and techniques, a new Colombian seismic hazard map was developed. A PSHA model was applied. The use of the PSHA model is because it incorporates the effects of all seismic sources that may affect a particular site solving the uncertainties caused by the parameters and assumptions defined in this kind of studies. First, the seismic sources geometry and a complete and homogeneous seismic catalog were defined; the parameters of seismic rate of each one of the seismic sources occurrence were calculated establishing a national seismotectonic model. Several of attenuation-distance relationships were selected depending on the type of seismicity considered. The seismic hazard was estimated using the CRISIS2007 software created by the Engineering Institute of the Universidad Nacional Autónoma de México -UNAM (National Autonomous University of Mexico). A uniformly spaced grid each 0.1° was used to calculate the peak ground acceleration (PGA) and response spectral values at 0.1, 0.2, 0.3, 0.5, 0.75, 1, 1.5, 2, 2.5 and 3.0 seconds with return periods of 75, 225, 475, 975 and 2475 years. For each site, a uniform hazard spectrum and exceedance rate curves were calculated. With the results, it is

  8. Overcoming complexities for consistent, continental-scale flood mapping

    Science.gov (United States)

    Smith, Helen; Zaidman, Maxine; Davison, Charlotte

    2013-04-01

    The EU Floods Directive requires all member states to produce flood hazard maps by 2013. Although flood mapping practices are well developed in Europe, there are huge variations in the scale and resolution of the maps between individual countries. Since extreme flood events are rarely confined to a single country, this is problematic, particularly for the re/insurance industry whose exposures often extend beyond country boundaries. Here, we discuss the challenges of large-scale hydrological and hydraulic modelling, using our experience of developing a 12-country model and set of maps, to illustrate how consistent, high-resolution river flood maps across Europe can be produced. The main challenges addressed include: data acquisition; manipulating the vast quantities of high-resolution data; and computational resources. Our starting point was to develop robust flood-frequency models that are suitable for estimating peak flows for a range of design flood return periods. We used the index flood approach, based on a statistical analysis of historic river flow data pooled on the basis of catchment characteristics. Historical flow data were therefore sourced for each country and collated into a large pan-European database. After a lengthy validation these data were collated into 21 separate analysis zones or regions, grouping smaller river basins according to their physical and climatic characteristics. The very large continental scale basins were each modelled separately on account of their size (e.g. Danube, Elbe, Drava and Rhine). Our methodology allows the design flood hydrograph to be predicted at any point on the river network for a range of return periods. Using JFlow+, JBA's proprietary 2D hydraulic hydrodynamic model, the calculated out-of-bank flows for all watercourses with an upstream drainage area exceeding 50km2 were routed across two different Digital Terrain Models in order to map the extent and depth of floodplain inundation. This generated modelling for

  9. Debris flow hazard mapping, Hobart, Tasmania, Australia

    Science.gov (United States)

    Mazengarb, Colin; Rigby, Ted; Stevenson, Michael

    2015-04-01

    constrained by aerial photographs to decade precision and many predate regional photography (pre 1940's). We have performed runout modelling, using 2D hydraulic modelling software (RiverFlow2D with Mud and Debris module), in order to calibrate our model against real events and gain confidence in the choice of parameters. Runout modelling was undertaken in valley systems with volumes calibrated to existing flood model likelihoods for each catchment. The hazard outputs from our models require developing a translation to hazard models used in Australia. By linking to flood mapping we aim to demonstrate to emergency managers where existing mitigation measures may be inadequate and how they can be adapted to address multiple hazards.

  10. A framework for the case-specific assessment of Green Infrastructure in mitigating urban flood hazards

    Science.gov (United States)

    Schubert, Jochen E.; Burns, Matthew J.; Fletcher, Tim D.; Sanders, Brett F.

    2017-10-01

    This research outlines a framework for the case-specific assessment of Green Infrastructure (GI) performance in mitigating flood hazard in small urban catchments. The urban hydrologic modeling tool (MUSIC) is coupled with a fine resolution 2D hydrodynamic model (BreZo) to test to what extent retrofitting an urban watershed with GI, rainwater tanks and infiltration trenches in particular, can propagate flood management benefits downstream and support intuitive flood hazard maps useful for communicating and planning with communities. The hydrologic and hydraulic models are calibrated based on current catchment conditions, then modified to represent alternative GI scenarios including a complete lack of GI versus a full implementation of GI. Flow in the hydrologic/hydraulic models is forced using a range of synthetic rainfall events with annual exceedance probabilities (AEPs) between 1-63% and durations from 10 min to 24 h. Flood hazard benefits mapped by the framework include maximum flood depths and extents, flow intensity (m2/s), flood duration, and critical storm duration leading to maximum flood conditions. Application of the system to the Little Stringybark Creek (LSC) catchment shows that across the range of AEPs tested and for storm durations equal or less than 3 h, presently implemented GI reduces downstream flooded area on average by 29%, while a full implementation of GI would reduce downstream flooded area on average by 91%. A full implementation of GI could also lower maximum flow intensities by 83% on average, reducing the drowning hazard posed by urban streams and improving the potential for access by emergency responders. For storm durations longer than 3 h, a full implementation of GI lacks the capacity to retain the resulting rainfall depths and only reduces flooded area by 8% and flow intensity by 5.5%.

  11. 77 FR 76499 - Changes in Flood Hazard Determinations

    Science.gov (United States)

    2012-12-28

    ... Flood Insurance Rate Maps (FIRMs), and in some cases the Flood Insurance Study (FIS) reports, currently... officer Community map Community State and county Location and case No. of community repository Effective... Honorable Gay Laramie County September 27, 2012 560029 1268). of Laramie County Woodhouse,...

  12. Efficient pan-European river flood hazard modelling through a combination of statistical and physical models

    Science.gov (United States)

    Paprotny, Dominik; Morales-Nápoles, Oswaldo; Jonkman, Sebastiaan N.

    2017-07-01

    Flood hazard is currently being researched on continental and global scales, using models of increasing complexity. In this paper we investigate a different, simplified approach, which combines statistical and physical models in place of conventional rainfall-run-off models to carry out flood mapping for Europe. A Bayesian-network-based model built in a previous study is employed to generate return-period flow rates in European rivers with a catchment area larger than 100 km2. The simulations are performed using a one-dimensional steady-state hydraulic model and the results are post-processed using Geographical Information System (GIS) software in order to derive flood zones. This approach is validated by comparison with Joint Research Centre's (JRC) pan-European map and five local flood studies from different countries. Overall, the two approaches show a similar performance in recreating flood zones of local maps. The simplified approach achieved a similar level of accuracy, while substantially reducing the computational time. The paper also presents the aggregated results on the flood hazard in Europe, including future projections. We find relatively small changes in flood hazard, i.e. an increase of flood zones area by 2-4 % by the end of the century compared to the historical scenario. However, when current flood protection standards are taken into account, the flood-prone area increases substantially in the future (28-38 % for a 100-year return period). This is because in many parts of Europe river discharge with the same return period is projected to increase in the future, thus making the protection standards insufficient.

  13. Probabilistic Flood Mapping using Volunteered Geographical Information

    Science.gov (United States)

    Rivera, S. J.; Girons Lopez, M.; Seibert, J.; Minsker, B. S.

    2016-12-01

    Flood extent maps are widely used by decision makers and first responders to provide critical information that prevents economic impacts and the loss of human lives. These maps are usually obtained from sensory data and/or hydrologic models, which often have limited coverage in space and time. Recent developments in social media and communication technology have created a wealth of near-real-time, user-generated content during flood events in many urban areas, such as flooded locations, pictures of flooding extent and height, etc. These data could improve decision-making and response operations as events unfold. However, the integration of these data sources has been limited due to the need for methods that can extract and translate the data into useful information for decision-making. This study presents an approach that uses volunteer geographic information (VGI) and non-traditional data sources (i.e., Twitter, Flicker, YouTube, and 911 and 311 calls) to generate/update the flood extent maps in areas where no models and/or gauge data are operational. The approach combines Web-crawling and computer vision techniques to gather information about the location, extent, and water height of the flood from unstructured textual data, images, and videos. These estimates are then used to provide an updated flood extent map for areas surrounding the geo-coordinate of the VGI through the application of a Hydro Growing Region Algorithm (HGRA). HGRA combines hydrologic and image segmentation concepts to estimate a probabilistic flooding extent along the corresponding creeks. Results obtained for a case study in Austin, TX (i.e., 2015 Memorial Day flood) were comparable to those obtained by a calibrated hydrologic model and had good spatial correlation with flooding extents estimated by the Federal Emergency Management Agency (FEMA).

  14. Evaluation of various modelling approaches in flood routing simulation and flood area mapping

    Science.gov (United States)

    Papaioannou, George; Loukas, Athanasios; Vasiliades, Lampros; Aronica, Giuseppe

    2016-04-01

    An essential process of flood hazard analysis and mapping is the floodplain modelling. The selection of the modelling approach, especially, in complex riverine topographies such as urban and suburban areas, and ungauged watersheds may affect the accuracy of the outcomes in terms of flood depths and flood inundation area. In this study, a sensitivity analysis implemented using several hydraulic-hydrodynamic modelling approaches (1D, 2D, 1D/2D) and the effect of modelling approach on flood modelling and flood mapping was investigated. The digital terrain model (DTMs) used in this study was generated from Terrestrial Laser Scanning (TLS) point cloud data. The modelling approaches included 1-dimensional hydraulic-hydrodynamic models (1D), 2-dimensional hydraulic-hydrodynamic models (2D) and the coupled 1D/2D. The 1D hydraulic-hydrodynamic models used were: HECRAS, MIKE11, LISFLOOD, XPSTORM. The 2D hydraulic-hydrodynamic models used were: MIKE21, MIKE21FM, HECRAS (2D), XPSTORM, LISFLOOD and FLO2d. The coupled 1D/2D models employed were: HECRAS(1D/2D), MIKE11/MIKE21(MIKE FLOOD platform), MIKE11/MIKE21 FM(MIKE FLOOD platform), XPSTORM(1D/2D). The validation process of flood extent achieved with the use of 2x2 contingency tables between simulated and observed flooded area for an extreme historical flash flood event. The skill score Critical Success Index was used in the validation process. The modelling approaches have also been evaluated for simulation time and requested computing power. The methodology has been implemented in a suburban ungauged watershed of Xerias river at Volos-Greece. The results of the analysis indicate the necessity of sensitivity analysis application with the use of different hydraulic-hydrodynamic modelling approaches especially for areas with complex terrain.

  15. 78 FR 14571 - Changes in Flood Hazard Determinations

    Science.gov (United States)

    2013-03-06

    ... Insurance Study (FIS) reports, currently in effect for the listed communities. The flood hazard... effect in order to remain qualified for participation in the National Flood Insurance Program (NFIP... Docket No.: B- Town of Camp Verde The Honorable Bob Town Clerk's Office, December 31, 2012 040131...

  16. 24 CFR 3285.302 - Flood hazard areas.

    Science.gov (United States)

    2010-04-01

    ... 24 Housing and Urban Development 5 2010-04-01 2010-04-01 false Flood hazard areas. 3285.302 Section 3285.302 Housing and Urban Development Regulations Relating to Housing and Urban Development... URBAN DEVELOPMENT MODEL MANUFACTURED HOME INSTALLATION STANDARDS Foundations § 3285.302 Flood...

  17. Flood risk assessment and mapping for the Lebanese watersheds

    Science.gov (United States)

    Abdallah, Chadi; Hdeib, Rouya

    2016-04-01

    Of all natural disasters, floods affect the greatest number of people worldwide and have the greatest potential to cause damage. Nowadays, with the emerging global warming phenomenon, this number is expected to increase. The Eastern Mediterranean area, including Lebanon (10452 Km2, 4.5 M habitant), has witnessed in the past few decades an increase frequency of flooding events. This study profoundly assess the flood risk over Lebanon covering all the 17 major watersheds and a number of small sub-catchments. It evaluate the physical direct tangible damages caused by floods. The risk assessment and evaluation process was carried out over three stages; i) Evaluating Assets at Risk, where the areas and assets vulnerable to flooding are identified, ii) Vulnerability Assessment, where the causes of vulnerability are assessed and the value of the assets are provided, iii) Risk Assessment, where damage functions are established and the consequent damages of flooding are estimated. A detailed Land CoverUse map was prepared at a scale of 1/ 1 000 using 0.4 m resolution satellite images within the flood hazard zones. The detailed field verification enabled to allocate and characterize all elements at risk, identify hotspots, interview local witnesses, and to correlate and calibrate previous flood damages with the utilized models. All filed gathered information was collected through Mobile Application and transformed to be standardized and classified under GIS environment. Consequently; the general damage evaluation and risk maps at different flood recurrence periods (10, 50, 100 years) were established. Major results showed that floods in a winter season (December, January, and February) of 10 year recurrence and of water retention ranging from 1 to 3 days can cause total damages (losses) that reach 1.14 M for crop lands and 2.30 M for green houses. Whereas, it may cause 0.2 M to losses in fruit trees for a flood retention ranging from 3 to 5 days. These numbers differs

  18. 7 CFR 1980.318 - Flood or mudslide hazard area precautions.

    Science.gov (United States)

    2010-01-01

    ... 7 Agriculture 14 2010-01-01 2009-01-01 true Flood or mudslide hazard area precautions. 1980.318... Flood or mudslide hazard area precautions. RHS policy is to discourage lending in designated flood and mudslide hazard areas. Loan guarantees shall not be issued in designated flood/mudslide hazard areas...

  19. Improving flood risk mapping in Italy: the FloodRisk open-source software

    Science.gov (United States)

    Albano, Raffaele; Mancusi, Leonardo; Craciun, Iulia; Sole, Aurelia; Ozunu, Alexandru

    2017-04-01

    management process, enhancing their awareness. This FOSS approach can promotes transparency and accountability through a process of "guided discovery". Moreover, the immediacy with which information is presented by the qualitative flood risk map, can facilitate and speed up the process of knowledge acquisition. An application of FloodRisk model is showed on a pilot case in "Serio" Valley, (North Italy), and its strengths and limits, in terms of additional efforts required in its application compared with EDQ procedure, have been highlighted focusing on the utility of the results provided for the development of FRMPs. Although they still present limits which prevent the FloodRisk application without critically consider the peculiarities of the investigated area in terms of available knowledge on hazard, exposure and vulnerability, the proposed approach surely produces an increase in available knowledge of flood risk and its drivers. This further information cannot be neglected for defining risk mitigation objectives and strategies. Hence, considering the ongoing efforts in the improvement of data availability and quality, FloodRisk could be a suitable tool for the next revision of flood risk maps due by December 2019, supporting effectively Italian and EU practitioners in the delineation of FRMPs (and for flood risk management in general).

  20. 78 FR 9406 - Final Flood Hazard Determinations

    Science.gov (United States)

    2013-02-08

    ... Georgetown......... Georgetown Township Office, 1515 Baldwin Street, Jenison, MI 49428. Charter Township of... Domestic Assistance No. 97.022, ``Flood Insurance.'') James A. Walke, Acting Deputy Associate...

  1. Flash flood area mapping utilising SENTINEL-1 radar data

    Science.gov (United States)

    Psomiadis, Emmanouil

    2016-10-01

    The new European Observatory radar data of polar orbiting satellite system Sentinel-1 provide a continuous and systematic data acquisition, enabling flood events monitoring and mapping. The study area is the basin of Sperchios River in Fthiotida Prefecture, Central Greece, having an increased ecological, environmental and socio-economic interest. The catchment area and especially the river delta, faces several problems and threats caused by anthropogenic activities and natural processes. The geomorphology of Sperchios catchment area and the drainage network formation provoke the creation of floods. A large flash flood event took place in late January early February 2015 following an intense and heavy rainfall that occurred in the area. Two space born radar images, obtained from Sentinel-1 covering the same area, one before and another one during the flood event, were processed. Two different methods were utilized so as to produce flood hazard maps, which demonstrate the inundated areas. The results of the two methods were similar and the flooded area was detected and delineated ideally.

  2. THE USAGE OF TECHNOLOGIES IN TERRESTRIAL MEASUREMENTS FOR HAZARD MAPS

    Directory of Open Access Journals (Sweden)

    VELE Dan

    2015-06-01

    Full Text Available In the context of natural phenomena (earthquakes, floods, landslides etc. bring economical and social prejudices year by year, watching on them and taking decisions becomes mandatory for reducing the material and human lives loss. Making hazard maps represents a tool used on wide global scale but also particularly in our country. This paper work has the purpose to reveal the interests of certain authors related to the usage of the new technologies of terrestrial measurements (GPS technologies, photogrammetry, cartography and of remote sensing in order to make these hazard maps.

  3. 78 FR 78995 - Proposed Flood Hazard Determinations

    Science.gov (United States)

    2013-12-27

    ... determinations, which may include additions or modifications of any Base Flood Elevation (BFE), base flood depth... Beverly Shores Town Hall, 500 South Broadway, Beverly Shores, IN 46301. Town of Burns Harbor Building Department, 1240 North Boo Road, Burns Harbor, IN 46304. Town of Chesterton Building Department, 726 Broadway...

  4. Combined fluvial and pluvial urban flood hazard analysis: concept development and application to Can Tho city, Mekong Delta, Vietnam

    Science.gov (United States)

    Apel, Heiko; Martínez Trepat, Oriol; Nghia Hung, Nguyen; Thi Chinh, Do; Merz, Bruno; Viet Dung, Nguyen

    2016-04-01

    coincidence into account. All hazards - fluvial, pluvial and combined - were accompanied by an uncertainty estimation taking into account the natural variability of the flood events. This resulted in probabilistic flood hazard maps showing the maximum inundation depths for a selected set of probabilities of occurrence, with maps showing the expectation (median) and the uncertainty by percentile maps. The results are critically discussed and their usage in flood risk management are outlined.

  5. Potential flood hazard assessment by integration of ALOS PALSAR and ASTER GDEM: a case study for the Hoa Chau commune, Hoa Vang district, in central Vietnam

    Science.gov (United States)

    Huong, Do Thi Viet; Nagasawa, Ryota

    2014-01-01

    The potential flood hazard was assessed for the Hoa Chau commune in central Vietnam in order to identify the high flood hazard zones for the decision makers who will execute future rural planning. A new approach for deriving the potential flood hazard based on integration of inundation and flow direction maps is described. Areas inundated in the historical flood event of 2007 were extracted from Advanced Land Observing Satellite (ALOS) phased array L-band synthetic aperture data radar (PALSAR) images, while flow direction characteristics were derived from the ASTER GDEM to extract the depressed surfaces. Past flood experience and the flow direction were then integrated to analyze and rank the potential flood hazard zones. The land use/cover map extracted from LANDSAT TM and flood depth point records from field surveys were utilized to check the possibility of susceptible inundated areas, extracting data from ALOS PALSAR and ranking the potential flood hazard. The estimation of potential flood hazard areas revealed that 17.43% and 17.36% of Hoa Chau had high and medium potential flood hazards, respectively. The flow direction and ALOS PALSAR data were effectively integrated for determining the potential flood hazard when hydrological and meteorological data were inadequate and remote sensing images taken during flood times were not available or were insufficient.

  6. Concept of Flood Risk Map and Its Application

    Institute of Scientific and Technical Information of China (English)

    ZHENG Yuanchang

    2001-01-01

    @@ 1 The concept of flood risk map and its mapping procedure Flood risk management is the process for analysis and assessment of flood risks as well as to form nd implement the disaster mitigation plans. Flood risk analysis is the basic work of flood risk assessment and management that can provide people with the possibilities of flood occurrence and its risk in specific areas and consequently raise the public awareness of flood help to form a reasonable flood prevention plan. However, flood risk mapping is a popular measure adopted by many countries and it provides possible flood areas and water levels as well as possible losses in a friendly way. To form a flood risk map, it is neccessary to allocate the historical information,compute the flood risk and analyze the data.

  7. Flood Hazards: Communicating Hydrology and Complexity to the Public

    Science.gov (United States)

    Holmes, R. R.; Blanchard, S. F.; Mason, R. R.

    2010-12-01

    Floods have a major impact on society and the environment. Since 1952, approximately 1,233 of 1,931 (64%) Federal disaster declarations were due directly to flooding, with an additional 297 due to hurricanes which had associated flooding. Although the overall average annual number of deaths due to flooding has decreased in the United States, the average annual flood damage is rising. According to the Munich Reinsurance Company in their publication “Schadenspiegel 3/2005”, during 1990s the world experienced as much as $500 billion in economic losses due to floods, highlighting the serious need for continued emphasis on flood-loss prevention measures. Flood-loss prevention has two major elements: mitigation (including structural flood-control measures and land-use planning and regulation) and risk awareness. Of the two, increasing risk awareness likely offers the most potential for protecting lives over the near-term and long-term sustainability in the coming years. Flood-risk awareness and risk-aware behavior is dependent on communication, involving both prescriptive and educational measures. Prescriptive measures (for example, flood warnings and stormwater ordinances) are and have been effective, but there is room for improvement. New communications technologies, particularly social media utilizing mobile, smart phones and text devices, for example, could play a significant role in increasing public awareness of long-term risk and near-term flood conditions. The U.S. Geological Survey (USGS), for example, the Federal agency that monitors the Nation’s rivers, recently released a new service that can better connect the to the public to information about flood hazards. The new service, WaterAlert (URL: http://water.usgs.gov/wateralert/), allows users to set flood notification thresholds of their own choosing for any USGS real-time streamgage. The system then sends emails or text messages to subscribers whenever the threshold conditions are met, as often as the

  8. 77 FR 74856 - Changes in Flood Hazard Determinations

    Science.gov (United States)

    2012-12-18

    ... Community map repository modification No. Alabama: Baldwin (FEMA Docket No.: City of Gulf Shores The.... ] (Catalog of Federal Domestic Assistance No. 97.022, ``Flood Insurance.'') Dated: November 28, 2012. James...

  9. 24 CFR 201.28 - Flood and hazard insurance, and Coastal Barriers properties.

    Science.gov (United States)

    2010-04-01

    ... 24 Housing and Urban Development 2 2010-04-01 2010-04-01 false Flood and hazard insurance, and... Disbursement Requirements § 201.28 Flood and hazard insurance, and Coastal Barriers properties. (a) Flood... part if the property securing repayment of the loan is located in a special flood hazard...

  10. A GIS Flood Tool for Rapid Inundation Mapping

    Science.gov (United States)

    Verdin, K. L.; Verdin, J. P.; Gadain, H.; Mathis, M.; Woodbury, M.; Rusack, E.; Brakenridge, G. R.

    2013-12-01

    Many developing countries lack objectively produced flood inundation mapping around which to build scenarios for mitigation and response planning. Conventional methods, involving costly field surveys and hydraulic modeling, are often beyond the means of sub-national and national units of government. At the same time, organizations with a global perspective on flooding (UN agencies, donor disaster assistance agencies, non-governmental organizations, and insurance companies) lack a globally consistent approach for flood hazard characterization. In order to meet these needs, tools were developed for flood mapping with globally available or locally produced topographic data. The tools were tested in a variety of settings with the assistance of a number of partner organizations. This work was done in cooperation with the U.S. Agency for International Development Office of U.S. Foreign Disaster Assistance. The resulting GIS Flood Tool (GFT) was developed to operate on digital elevation model (DEM) data to produce patterns of flood inundation corresponding to a river discharge or stage value specified by the user. The GFT uses the DEM to derive stream networks and stream cross-sections. The Manning equation is then used to construct a stage-discharge relationship for each cross-section. Use of the stage-discharge relationship along with a specially processed Relative DEM allows for rapid mapping of the inundated extent. The resulting inundation patterns can be used in conjunction with additional geographic information describing settlement patterns, transportation networks, land use and land cover to assess vulnerability to flood events and support preparedness planning. Comparison of the GFT results with inundation patterns produced using conventional hydraulic modeling approaches (HEC-RAS) and satellite remote sensing shows good correlation in many settings. Training sessions have been conducted in East Africa, where the tool is in use by national and regional

  11. Integrated flood risk assessment for the Mekong Delta through the combined assessment of flood hazard change and social vulnerability

    Science.gov (United States)

    Apel, Heiko; Garschagen, Matthias; Delgado, José Miguel; Viet Dung, Nguyen; Van Tuan, Vo; Thanh Binh, Nguyen; Birkmann, Joern; Merz, Bruno

    2013-04-01

    agro-ecological zones and socio-economic population profiles. The focus herein is particularly on understanding the causal constellations and trajectories of vulnerability patterns. Secondly, key vulnerability parameters identified in step one are translated into quantitative indicators and aggregated into a vulnerability index, allowing for spatial analysis. Thirdly, ways to assess future vulnerability trajectories in the context of the ongoing socio-economic transformation in the Mekong Delta are explored. In effect, this analysis generates an integrated risk assessment that is based not only on an advancement of current flood hazard assessments but also on a detailed vulnerability assessment that goes beyond the mapping of exposure. The study thereby contributes knowledge of great relevance for informing disaster risk management and adaptation policies. In addition, the analysis allows for a dynamic perspective and the examination of key trends in the flood risk of the Mekong Delta.

  12. Has land subsidence changed the flood hazard potential? A case example from the Kujukuri Plain, Chiba Prefecture, Japan

    Science.gov (United States)

    Chen, H. L.; Ito, Y.; Sawamukai, M.; Su, T.; Tokunaga, T.

    2015-11-01

    Coastal areas are subject to flood hazards because of their topographic features, social development and related human activities. The Kujukuri Plain, Chiba Prefecture, Japan, is located nearby the Tokyo metropolitan area and it faces to the Pacific Ocean. In the Kujukuri Plain, widespread occurrence of land subsidence has been caused by exploitation of groundwater, extraction of natural gas dissolved in brine, and natural consolidation of the Holocene and landfill deposits. The locations of land subsidence include areas near the coast, and it may increase the flood hazard potential. Hence, it is very important to evaluate flood hazard potential by taking into account the temporal change of land elevation caused by land subsidence, and to prepare hazard maps for protecting the surface environment and for developing an appropriate land-use plan. In this study, flood hazard assessments at three different times, i.e., 1970, 2004, and 2013 are implemented by using a flood hazard model based on Multicriteria Decision Analysis with Geographical Information System techniques. The model incorporates six factors: elevation, depression area, river system, ratio of impermeable area, detention ponds, and precipitation. Main data sources used are 10 m resolution topography data, airborne laser scanning data, leveling data, Landsat-TM data, two 1:30 000 scale river watershed maps, and precipitation data from observation stations around the study area and Radar data. The hazard assessment maps for each time are obtained by using an algorithm that combines factors with weighted linear combinations. The assignment of the weight/rank values and their analysis are realized by the application of the Analytic Hierarchy Process method. This study is a preliminary work to investigate flood hazards on the Kujukuri Plain. A flood model will be developed to simulate more detailed change of the flood hazard influenced by land subsidence.

  13. Adige river in Trento flooding map, 1892: private or public risk transfer?

    Science.gov (United States)

    Ranzi, Roberto

    2016-04-01

    For the determination of the flood risk hydrologist and hydraulic engineers focuse their attention mainly to the estimation of physical factors determining the flood hazard, while economists and experts of social sciences deal mainly with the estimation of vulnerability and exposure. The fact that flood zoning involves both hydrological and socio-economic aspects, however, was clear already in the XIX century when the impact of floods on inundated areas started to appear in flood maps, for instance in the UK and in Italy. A pioneering 'flood risk' map for the Adige river in Trento, Italy, was already published in 1892, taking into account in detail both hazard intensity in terms of velocity and depth, frequency of occurrence, vulnerability and economic costs for flood protection with river embankments. This map is likely to be the reinterpreted certainly as a pioneering, and possibly as the first flood risk map for an Italian river and worldwide. Risk levels were divided in three categories and seven sub-categories, depending on flood water depth, velocity, frequency and damage costs. It is interesting to notice the fact that at that time the map was used to share the cost of levees' reparation and enhancement after the severe September 1882 flood as a function of the estimated level of protection of the respective areas against the flood risk. The sharing of costs between public bodies, the railway company and private owners was debated for about 20 years and at the end the public sustained the major costs. This shows how already at that time the economic assessment of structural flood protections was based on objective and rational cost-benefit criteria, that hydraulic risk mapping was perceived by the society as fundamental for the design of flood protection systems and that a balanced cost sharing between public and private was an accepted approach although some protests arose at that time.

  14. Flood Hazard and Risk Analysis in Urban Area

    Science.gov (United States)

    Huang, Chen-Jia; Hsu, Ming-hsi; Teng, Wei-Hsien; Lin, Tsung-Hsien

    2017-04-01

    Typhoons always induce heavy rainfall during summer and autumn seasons in Taiwan. Extreme weather in recent years often causes severe flooding which result in serious losses of life and property. With the rapid industrial and commercial development, people care about not only the quality of life, but also the safety of life and property. So the impact of life and property due to disaster is the most serious problem concerned by the residents. For the mitigation of the disaster impact, the flood hazard and risk analysis play an important role for the disaster prevention and mitigation. In this study, the vulnerability of Kaohsiung city was evaluated by statistics of social development factor. The hazard factors of Kaohsiung city was calculated by simulated flood depth of six different return periods and four typhoon events which result in serious flooding in Kaohsiung city. The flood risk can be obtained by means of the flood hazard and social vulnerability. The analysis results provide authority to strengthen disaster preparedness and to set up more resources in high risk areas.

  15. 32 CFR 644.352 - Evaluation and reporting of flood hazards.

    Science.gov (United States)

    2010-07-01

    ... 32 National Defense 4 2010-07-01 2010-07-01 true Evaluation and reporting of flood hazards. 644... Property to General Services Administration (gsa) § 644.352 Evaluation and reporting of flood hazards... presence of flood hazards. If such hazards are found, a report will be forwarded to HQDA...

  16. Flood Insurance Rate Maps and Base Flood Elevations, FIRM, DFIRM, BFE - MO 2014 Springfield FEMA Base Flood Elevations (SHP)

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This polyline layer indicates the approximate effective FEMA Base Flood Elevation (BFE) associated with the corresponding Special Flood Hazard Area (SFHA). Each line...

  17. Open Source Web-Based Solutions for Disseminating and Analyzing Flood Hazard Information at the Community Level

    Science.gov (United States)

    -Santillan, M. M.-M.; Santillan, J. R.; Morales, E. M. O.

    2017-09-01

    We discuss in this paper the development, including the features and functionalities, of an open source web-based flood hazard information dissemination and analytical system called "Flood EViDEns". Flood EViDEns is short for "Flood Event Visualization and Damage Estimations", an application that was developed by the Caraga State University to address the needs of local disaster managers in the Caraga Region in Mindanao, Philippines in accessing timely and relevant flood hazard information before, during and after the occurrence of flood disasters at the community (i.e., barangay and household) level. The web application made use of various free/open source web mapping and visualization technologies (GeoServer, GeoDjango, OpenLayers, Bootstrap), various geospatial datasets including LiDAR-derived elevation and information products, hydro-meteorological data, and flood simulation models to visualize various scenarios of flooding and its associated damages to infrastructures. The Flood EViDEns application facilitates the release and utilization of this flood-related information through a user-friendly front end interface consisting of web map and tables. A public version of the application can be accessed at http://121.97.192.11:8082/"target="_blank">http://121.97.192.11:8082/. The application is currently expanded to cover additional sites in Mindanao, Philippines through the "Geo-informatics for the Systematic Assessment of Flood Effects and Risks for a Resilient Mindanao" or the "Geo-SAFER Mindanao" Program.

  18. Utilising social media contents for flood inundation mapping

    Science.gov (United States)

    Schröter, Kai; Dransch, Doris; Fohringer, Joachim; Kreibich, Heidi

    2016-04-01

    Data about the hazard and its consequences are scarce and not readily available during and shortly after a disaster. An information source which should be explored in a more efficient way is eyewitness accounts via social media. This research presents a methodology that leverages social media content to support rapid inundation mapping, including inundation extent and water depth in the case of floods. It uses quantitative data that are estimated from photos extracted from social media posts and their integration with established data. Due to the rapid availability of these posts compared to traditional data sources such as remote sensing data, areas affected by a flood, for example, can be determined quickly. Key challenges are to filter the large number of posts to a manageable amount of potentially useful inundation-related information, and to interpret and integrate the posts into mapping procedures in a timely manner. We present a methodology and a tool ("PostDistiller") to filter geo-located posts from social media services which include links to photos and to further explore this spatial distributed contextualized in situ information for inundation mapping. The June 2013 flood in Dresden is used as an application case study in which we evaluate the utilization of this approach and compare the resulting spatial flood patterns and inundation depths to 'traditional' data sources and mapping approaches like water level observations and remote sensing flood masks. The outcomes of the application case are encouraging. Strengths of the proposed procedure are that information for the estimation of inundation depth is rapidly available, particularly in urban areas where it is of high interest and of great value because alternative information sources like remote sensing data analysis do not perform very well. The uncertainty of derived inundation depth data and the uncontrollable availability of the information sources are major threats to the utility of the approach.

  19. Toward economic flood loss characterization via hazard simulation

    Science.gov (United States)

    Czajkowski, Jeffrey; Cunha, Luciana K.; Michel-Kerjan, Erwann; Smith, James A.

    2016-08-01

    Among all natural disasters, floods have historically been the primary cause of human and economic losses around the world. Improving flood risk management requires a multi-scale characterization of the hazard and associated losses—the flood loss footprint. But this is typically not available in a precise and timely manner, yet. To overcome this challenge, we propose a novel and multidisciplinary approach which relies on a computationally efficient hydrological model that simulates streamflow for scales ranging from small creeks to large rivers. We adopt a normalized index, the flood peak ratio (FPR), to characterize flood magnitude across multiple spatial scales. The simulated FPR is then shown to be a key statistical driver for associated economic flood losses represented by the number of insurance claims. Importantly, because it is based on a simulation procedure that utilizes generally readily available physically-based data, our flood simulation approach has the potential to be broadly utilized, even for ungauged and poorly gauged basins, thus providing the necessary information for public and private sector actors to effectively reduce flood losses and save lives.

  20. Suitability estimation for urban development using multi-hazard assessment map.

    Science.gov (United States)

    Bathrellos, George D; Skilodimou, Hariklia D; Chousianitis, Konstantinos; Youssef, Ahmed M; Pradhan, Biswajeet

    2017-01-01

    Preparation of natural hazards maps are vital and essential for urban development. The main scope of this study is to synthesize natural hazard maps in a single multi-hazard map and thus to identify suitable areas for the urban development. The study area is the drainage basin of Xerias stream (Northeastern Peloponnesus, Greece) that has frequently suffered damages from landslides, floods and earthquakes. Landslide, flood and seismic hazard assessment maps were separately generated and further combined by applying the Analytical Hierarchy Process (AHP) and utilizing a Geographical Information System (GIS) to produce a multi-hazard map. This map represents the potential suitability map for urban development in the study area and was evaluated by means of uncertainty analysis. The outcome revealed that the most suitable areas are distributed in the southern part of the study area, where the landslide, flood and seismic hazards are at low and very low level. The uncertainty analysis shows small differences on the spatial distribution of the suitability zones. The produced suitability map for urban development proves a satisfactory agreement between the suitability zones and the landslide and flood phenomena that have affected the study area. Finally, 40% of the existing urban pattern boundaries and 60% of the current road network are located within the limits of low and very low suitability zones.

  1. Prompt Proxy Mapping of Flood Damaged Rice Fields Using MODIS-Derived Indices

    Directory of Open Access Journals (Sweden)

    Youngjoo Kwak

    2015-11-01

    Full Text Available Flood mapping, particularly hazard and risk mapping, is an imperative process and a fundamental part of emergency response and risk management. This paper aims to produce a flood risk proxy map of damaged rice fields over the whole of Bangladesh, where monsoon river floods are dominant and frequent, affecting over 80% of the total population. This proxy risk map was developed to meet the request of the government on a national level. This study represents a rapid, straightforward methodology for estimating rice-crop damage in flood areas of Bangladesh during the large flood from July to September 2007, despite the lack of primary data. We improved a water detection algorithm to achieve a better discrimination capacity to discern flood areas by using a modified land surface water index (MLSWI. Then, rice fields were estimated utilizing a hybrid rice field map from land-cover classification and MODIS-derived indices, such as the normalized difference vegetation index (NDVI and enhanced vegetation index (EVI. The results showed that the developed method is capable of providing instant, comprehensive, nationwide mapping of flood risks, such as rice field damage. The detected flood areas and damaged rice fields during the 2007 flood were verified by comparing them with the Advanced Land Observing Satellite (ALOS AVNIR-2 images (a 10 m spatial resolution and in situ field survey data with moderate agreement (K = 0.57.

  2. 24 CFR 3285.406 - Flood hazard areas.

    Science.gov (United States)

    2010-04-01

    ... 24 Housing and Urban Development 5 2010-04-01 2010-04-01 false Flood hazard areas. 3285.406 Section 3285.406 Housing and Urban Development Regulations Relating to Housing and Urban Development... URBAN DEVELOPMENT MODEL MANUFACTURED HOME INSTALLATION STANDARDS Anchorage Against Wind § 3285.406...

  3. 77 FR 77085 - Changes in Flood Hazard Determinations

    Science.gov (United States)

    2012-12-31

    ... Insurance Study (FIS) reports, currently in effect for the listed communities. The flood hazard... The Honorable Sean 5291 East 60th April 11, 2012 080006 (11-08-0367P). Ford, Sr., Mayor, Avenue...-0747P). Ford, Sr., Mayor, Avenue, Commerce City of Commerce City, CO 80022. City, 7887 East 60th...

  4. Behavior of the Rambla Nogalte (Murcia) during floods. Implications for the mapping of flood hazard risk; Funcionamiento de la rambla de Nogalte (Murcia) durante avenidas. Implicaciones para la cartografia de peligrosidad por riesgo de avenidas

    Energy Technology Data Exchange (ETDEWEB)

    Ortega Becerril, J. A.; Garzon Heydt, M. g.; Garcia Lopez-Davalillo, J. C.; Rodriguez Franco, A.

    2009-07-01

    Discharge data obtained for the Rambla de Nogalte ephemeral stream by means of hydrologic modelling are low in relation to the discharge that was estimated for the 1973 catastrophic flood event. Watershed characteristics such as an elongated morphology, long stream and moderate slope defining a high concentration time do not favour either large discharge. The reason for it might be the high load attained on the 1973 flood not taken in account for the ordinary hydrologic modelling and the lack of flow evacuation capacity of the aggrading alluvial fan system at the stream mouth. (Author) 8 refs.

  5. Real-time forecasts of flood hazard and impact: some UK experiences

    Directory of Open Access Journals (Sweden)

    Cole Steven J.

    2016-01-01

    Full Text Available Major UK floods over the last decade have motivated significant technological and scientific advances in operational flood forecasting and warning. New joint forecasting centres between the national hydrological and meteorological operating agencies have been formed that issue a daily, national Flood Guidance Statement (FGS to the emergency response community. The FGS is based on a Flood Risk Matrix approach that is a function of potential impact severity and likelihood. It has driven an increased demand for robust, accurate and timely forecast and alert information on fluvial and surface water flooding along with impact assessments. The Grid-to-Grid (G2G distributed hydrological model has been employed across Britain at a 1km resolution to support the FGS. Novel methods for linking dynamic gridded estimates of river flow and surface runoff with more detailed offline flood risk maps have been developed to obtain real-time probabilistic forecasts of potential impacts, leading to operational trials. Examples of the national-scale G2G application are provided along with case studies of forecast flood impact from (i an operational Surface Water Flooding (SWF trial during the Glasgow 2014 Commonwealth Games, (ii SWF developments under the Natural Hazards Partnership over England & Wales, and (iii fluvial applications in Scotland.

  6. Landslide Hazard Zonation Mapping and Comparative Analysis of Hazard Zonation Maps

    Institute of Scientific and Technical Information of China (English)

    S. Sarkar; R. Anbalagan

    2008-01-01

    Landslide hazard zonation mapping at regional level of a large area provides a broad trend of landslide potential zones. A macro level landslide hazard zonation for a small area may provide a better insight into the landslide hazards. The main objective of the present work was to carry out macro landslide hazard zonation mapping on 1:50,000 scale in an area where regional level zonation mapping was conducted earlier. In the previous work the regional landslide hazard zonation maps of Srinagar-Rudraprayag area of Garhwal Himalaya in the state of Uttarakhand were prepared using subjective and objective approaches. In the present work the landslide hazard zonation mapping at macro level was carded out in a small area using a Landslide Hazard Evaluation Factor rating scheme. The hazard zonation map produced by using this technique classifies the area into relative hazard classes in which the high hazard zones well correspond with high frequency of landslides. The results of this map when compared with the regional zonation maps prepared earlier show that application of the present technique identified more details of the hazard zones, which are broadly shown in the earlier zonation maps.

  7. Climate change impact on flood hazard

    Directory of Open Access Journals (Sweden)

    M. Brilly

    2014-09-01

    Full Text Available Climate changes have a high impact on river discharges and therefore on floods. There are a few different methods we can use to predict discharge changes in the future. In this paper we used the complex HBV model for the Vipava River and simple correlation between discharge and precipitation data for the Soča River. The discharge prediction is based on the E-OBS precipitation data for three future time periods (2011–2040, 2041–2070 and 2071–2100. Estimated discharges for those three future periods are presented for both rivers. But a special situation occurs at the confluence where the two rivers with rather different catchments unite, and this requires an additional probability analysis.

  8. Use of Geologic and Paleoflood Information for INL Probabilistic Flood Hazard Decisions

    Science.gov (United States)

    Ostenaa, D.; O'Connell, D.; Creed, B.

    2009-05-01

    The Big Lost River is a western U.S., closed basin stream which flows through and terminates on the Idaho National Laboratory. Historic flows are highly regulated, and peak flows decline downstream through natural and anthropomorphic influences. Glaciated headwater regions were the source of Pleistocene outburst floods which traversed the site. A wide range of DOE facilities (including a nuclear research reactor) require flood stage estimates for flow exceedance probabilities over a range from 1/100/yr to 1/100,000/yr per DOE risk based standards. These risk management objectives required the integration of geologic and geomorphic paleoflood data into Bayesian non parametric flood frequency analyses that incorporated measurement uncertainties in gaged, historical, and paleoflood discharges and non exceedance bounds to produce fully probabilistic flood frequency estimates for annual exceedance probabilities of specific discharges of interest. Two-dimensional hydraulic flow modeling with scenarios for varied hydraulic parameters, infiltration, and culvert blockages on the site was conducted for a range of discharges from 13-700 m3/s. High-resolution topographic grids and two-dimensional flow modeling allowed detailed evaluation of the potential impacts of numerous secondary channels and flow paths resulting from flooding in extreme events. These results were used to construct stage probability curves for 15 key locations on the site consistent with DOE standards. These probability curves resulted from the systematic inclusion of contributions of uncertainty from flood sources, hydraulic modeling, and flood-frequency analyses. These products also provided a basis to develop weights for logic tree branches associated with infiltration and culvert performance scenarios to produce probabilistic inundation maps. The flood evaluation process was structured using Senior Seismic Hazard Analysis Committee processes (NRC-NUREG/CR-6372) concepts, evaluating and integrating the

  9. geomorphological mapping and geophysical profiling for the evaluation of natural hazards in an alpine catchment

    NARCIS (Netherlands)

    Seijmonsbergen, A.C.; de Graaff, L.W.S.

    2006-01-01

    Liechtenstein has faced an increasing number of natural hazards over recent decades: debris flows, slides, snow avalanches and floods repeatedly endanger the local infrastructure. Geomorphological field mapping and geo-electrical profiling was used to assess hazards near Malbun, a village potentiall

  10. geomorphological mapping and geophysical profiling for the evaluation of natural hazards in an alpine catchment

    NARCIS (Netherlands)

    Seijmonsbergen, A.C.; de Graaff, L.W.S.

    2006-01-01

    Liechtenstein has faced an increasing number of natural hazards over recent decades: debris flows, slides, snow avalanches and floods repeatedly endanger the local infrastructure. Geomorphological field mapping and geo-electrical profiling was used to assess hazards near Malbun, a village

  11. 77 FR 67016 - Proposed Flood Hazard Determinations

    Science.gov (United States)

    2012-11-08

    ... change any existing ordinances that are more stringent in their floodplain management requirements. The... Osage County, Oklahoma, and Incorporated Areas Maps Available for Inspection Online at: http://riskmap6... 39232. City of Jackson Department of Public Works, 200 South President Street, Jackson, MS 39205....

  12. Panchromatic Satellite Image Classification for Flood Hazard Assessment

    Directory of Open Access Journals (Sweden)

    Ahmed Shaker

    2012-11-01

    Full Text Available The study aims to investigate the use of panchromatic (PAN satellite image data for flood hazard assessment with anaid of various digital image processing techniques. Two SPOT PAN satellite images covering part of the Nile River inEgypt were used to delineate the flood extent during the years 1997 and 1998 (before and after a high flood. Threeclassification techniques, including the contextual classifier, maximum likelihood classifier and minimum distanceclassifier, were applied to the following: 1 the original PAN image data, 2 the original PAN image data and grey-levelco-occurrence matrix texture created from the PAN data, and 3 the enhanced PAN image data using an edgesharpeningfilter. The classification results were assessed with reference to the results derived from manualdigitization and random checkpoints. Generally, the results showed improvement of the calculation of flood area whenan edge-sharpening filter was used. In addition, the maximum likelihood classifier yielded the best classificationaccuracy (up to 97% compared to the other two classifiers. The research demonstrates the benefits of using PANsatellite imagery as a potential data source for flood hazard assessment.

  13. Safety of Italian dams in the face of flood hazard

    Science.gov (United States)

    Bocchiola, Daniele; Rosso, Renzo

    2014-09-01

    Most rivers in Italy are segmented by dams that need rehabilitation because of (1) safety requirements by increasingly risk-averse societies, (2) changes in the downstream river and riparian system after dams building, (3) poor initial design at the time of completion and (4) modified priorities of watershed management. Safe design of flood spillways is a major concern, and requires to cope with low frequency flood hazard. One must estimate flood figures with high return periods (R ⩾ 1000-10,000 years) but statistical methods involve large uncertainties because of the short length of the available records. This paper investigates the return period of the design flood of existing spillways RS of large dams in Italy. We used re-normalized flood frequency approach and regionalization using the Generalized Extreme Value distribution. The estimation of the site specific index flood is carried out by simple scaling with basin area at the regional level. The result show that 55% (245) of the 448 examined dams are equipped by spillway with RS > 10,000; and 71% (315) of the dams have RS > 1000. Conversely, 29% (130) of the dams display RS routing may dampen the outflow hydrograph, but one should carefully account for the need of achieving accurate dam safety assessment of these dams based on site specific investigations, also accounting for global change forcing.

  14. Mapping Near-Earth Hazards

    Science.gov (United States)

    Kohler, Susanna

    2016-06-01

    How can we hunt down all the near-Earth asteroids that are capable of posing a threat to us? A new study looks at whether the upcoming Large Synoptic Survey Telescope (LSST) is up to the job.Charting Nearby ThreatsLSST is an 8.4-m wide-survey telescope currently being built in Chile. When it goes online in 2022, it will spend the next ten years surveying our sky, mapping tens of billions of stars and galaxies, searching for signatures of dark energy and dark matter, and hunting for transient optical events like novae and supernovae. But in its scanning, LSST will also be looking for asteroids that approach near Earth.Cumulative number of near-Earth asteroids discovered over time, as of June 16, 2016. [NASA/JPL/Chamberlin]Near-Earth objects (NEOs) have the potential to be hazardous if they cross Earths path and are large enough to do significant damage when they impact Earth. Earths history is riddled with dangerous asteroid encounters, including the recent Chelyabinsk airburst in 2013, the encounter that caused the kilometer-sized Meteor Crater in Arizona, and the impact thought to contribute to the extinction of the dinosaurs.Recognizing the potential danger that NEOs can pose to Earth, Congress has tasked NASA with tracking down 90% of NEOs larger than 140 meters in diameter. With our current survey capabilities, we believe weve discovered roughly 25% of these NEOs thus far. Now a new study led by Tommy Grav (Planetary Science Institute) examines whether LSST will be able to complete this task.Absolute magnitude, H, of asynthetic NEO population. Though these NEOs are all larger than 140 m, they have a large spread in albedos. [Grav et al. 2016]Can LSST Help?Based on previous observations of NEOs and resulting predictions for NEO properties and orbits, Grav and collaborators simulate a synthetic population of NEOs all above 140 m in size. With these improved population models, they demonstrate that the common tactic of using an asteroids absolute magnitude as a

  15. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, FRANKLIN COUNTY, VA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  16. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SIMPSON COUNTY, KY

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  17. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, FRANKLIN COUNTY, PA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  18. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, FRANKLIN COUNTY, KANSAS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  19. Digital Flood Insurance Rate Map Database, Bradford County, Pennsylvania, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  20. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Franklin COUNTY, Kentucky

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  1. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, FRANKLIN COUNTY, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  2. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MACON COUNTY, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  3. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, FRANKLIN COUNTY, ILLINOIS USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  4. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, FRANKLIN COUNTY, AL

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  5. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, FRANKLIN COUNTY, IOWA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...

  6. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MCINTOSH COUNTY, GEORGIA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...

  7. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, JUNEAU COUNTY, WISCONSIN, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk;...

  8. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SAUK COUNTY, WISCONSIN, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk;...

  9. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Colbert COUNTY, AL

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  10. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Spalding County, GA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  11. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, GUNNISON COUNTY, COLORADO, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...

  12. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, OSAGE COUNTY, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  13. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, DODGE COUNTY, NEBRASKA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...

  14. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SHAWNEE COUNTY, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  15. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Douglas COUNTY, Nevada

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  16. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SANDUSKY COUNTY, OHIO, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  17. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CITY OF RADFORD, VIRGINIA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  18. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CLAY COUNTY, GEORGIA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  19. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WEBSTER COUNTY, KY

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  20. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, POLK COUNTY, NEBRASKA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  1. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MCCRACKEN COUNTY, KY

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  2. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, OCEANA COUNTY, MICHIGAN, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...

  3. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, JEFFERSON COUNTY, AR, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  4. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SCREVEN COUNTY, GEORGIA, USA

    Data.gov (United States)

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  5. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Barnwell COUNTY, South Carolina

    Data.gov (United States)

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  6. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, GADSDEN COUNTY, FL, USA

    Data.gov (United States)

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  7. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, FINNEY COUNTY, USA

    Data.gov (United States)

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  8. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MARLBORO COUNTY, SC

    Data.gov (United States)

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  9. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MONTGOMERY COUNTY, GEORGIA, USA

    Data.gov (United States)

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  10. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Wilcox COUNTY, AL

    Data.gov (United States)

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  11. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WILCOX COUNTY, GEORGIA, USA

    Data.gov (United States)

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  12. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, RALLS COUNTY, USA

    Data.gov (United States)

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  13. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MARION COUNTY, USA

    Data.gov (United States)

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  14. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, FOREST COUNTY, WISCONSIN, USA

    Data.gov (United States)

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  15. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CLAY COUNTY, AR

    Data.gov (United States)

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  16. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BARRY COUNTY, MI

    Data.gov (United States)

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  17. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LOGAN COUNTY, KY

    Data.gov (United States)

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  18. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BAY COUNTY, FLORIDA, USA

    Data.gov (United States)

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  19. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, POLK COUNTY, FLORIDA, USA

    Data.gov (United States)

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  20. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MADERA COUNTY, CALIFORNIA

    Data.gov (United States)

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  1. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, DUTCHESS COUNTY, NEW YORK

    Data.gov (United States)

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  2. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Cole COUNTY, USA

    Data.gov (United States)

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  3. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, EUREKA COUNTY, NEVADA

    Data.gov (United States)

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  4. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BLAINE COUNTY, IDAHO

    Data.gov (United States)

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  5. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, YANKTON, SOUTH DAKOTA, USA

    Data.gov (United States)

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  6. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ALAMEDA COUNTY, CALIFORNIA

    Data.gov (United States)

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  7. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, KAUAI COUNTY, HAWAII, USA

    Data.gov (United States)

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  8. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, DADE COUNTY, GEORGIA, USA

    Data.gov (United States)

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  9. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, RAVALLI, MONTANA, USA

    Data.gov (United States)

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  10. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ESCAMBIA COUNTY, AL

    Data.gov (United States)

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  11. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, COOS COUNTY, OREGON, USA

    Data.gov (United States)

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  12. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Washington COUNTY, NE

    Data.gov (United States)

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  13. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Lincoln COUNTY, New Mexico

    Data.gov (United States)

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  14. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LARUE COUNTY, KY

    Data.gov (United States)

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  15. DIGITAL FLOOD INSURANCE RATE MAP, CITY OF PORTLAND, OREGON

    Data.gov (United States)

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  16. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CASS COUNTY, NEBRASKA

    Data.gov (United States)

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  17. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, COLFAX COUNTY, NEBRASKA

    Data.gov (United States)

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  18. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Adair County, OK

    Data.gov (United States)

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  19. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ORANGE COUNTY, CALIFORNIA, USA

    Data.gov (United States)

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  20. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MONROE COUNTY, GEORGIA, USA

    Data.gov (United States)

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  1. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, NEVADA COUNTY, CALIFORNIA, USA

    Data.gov (United States)

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  2. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SHOSHONE COUNTY, IDAHO

    Data.gov (United States)

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  3. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, POLK COUNTY, USA

    Data.gov (United States)

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  4. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Taylor County, GA

    Data.gov (United States)

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  5. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CHENANGO COUNTY, NEW YORK

    Data.gov (United States)

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  6. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, STE. GENEVIEVE, MO, USA

    Data.gov (United States)

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  7. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BROOKS COUNTY, TEXAS

    Data.gov (United States)

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  8. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BONNER COUNTY, IDAHO

    Data.gov (United States)

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  9. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Pike COUNTY, OHIO, USA

    Data.gov (United States)

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  10. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Campbell COUNTY, Kentucky

    Data.gov (United States)

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  11. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, NAPA COUNTY, CALIFORNIA

    Data.gov (United States)

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  12. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MARION COUNTY, ILLINOIS USA

    Data.gov (United States)

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  13. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WAKULLA COUNTY, FL, USA

    Data.gov (United States)

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  14. PRELIMINARY DIGITAL FLOOD INSURANCE RATE MAP DATABASE, STEPHENS COUNTY, OK

    Data.gov (United States)

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  15. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MEEKER COUNTY, MINNESOTA, USA

    Data.gov (United States)

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  16. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MAVERICK COUNTY, TX

    Data.gov (United States)

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  17. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BANKS COUNTY, GEORGIA, USA

    Data.gov (United States)

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  18. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, PIKE COUNTY, AL

    Data.gov (United States)

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  19. DIGITAL FLOOD INSURANCE RATE MAP DATABASE ROCKLAND COUNTY, NY, USA

    Data.gov (United States)

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  20. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Kenton COUNTY, Kentucky

    Data.gov (United States)

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  1. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WAYNE COUNTY, USA

    Data.gov (United States)

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  2. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BENSON COUNTY, NORTH DAKOTA

    Data.gov (United States)

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  3. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BUTTE COUNTY, CALIFORNIA, USA

    Data.gov (United States)

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  4. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, NEWTON COUNTY, MISSOURI, USA

    Data.gov (United States)

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  5. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ELLIOTT COUNTY, KY

    Data.gov (United States)

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  6. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BRYAN COUNTY, GEORGIA, USA

    Data.gov (United States)

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  7. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, COOS COUNTY, OREGON, USA

    Data.gov (United States)

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  8. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Buckingham County, VA, USA

    Data.gov (United States)

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  9. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LEA COUNTY, NM, USA

    Data.gov (United States)

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  10. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Spartanburg County, South Carolina

    Data.gov (United States)

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  11. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WHITFIELD COUNTY, GEORGIA, USA

    Data.gov (United States)

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  12. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Douglas COUNTY, Nevada

    Data.gov (United States)

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  13. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, KLEBERG COUNTY, TX, USA

    Data.gov (United States)

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  14. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, GOLIAD COUNTY, TEXAS, USA

    Data.gov (United States)

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  15. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ATASCOSA COUNTY, TEXAS

    Data.gov (United States)

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  16. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WAYNE COUNTY, PENNSYLVANIA

    Data.gov (United States)

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  17. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BARNSTABLE COUNTY, MA

    Data.gov (United States)

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  18. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HAMILTON COUNTY, OHIO

    Data.gov (United States)

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  19. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HENRY COUNTY, GEORGIA, USA

    Data.gov (United States)

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  20. Digital Flood Insurance Rate Map Database, Westmoreland County, Virginia, USA

    Data.gov (United States)

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  1. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CITY OF RICHMOND, VIRGINIA

    Data.gov (United States)

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  2. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, YUMA COUNTY, ARIZONA, USA

    Data.gov (United States)

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  3. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, INGHAM COUNTY, MICHIGAN, USA

    Data.gov (United States)

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  4. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, TODD COUNTY, KY

    Data.gov (United States)

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  5. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WALKER COUNTY, TX

    Data.gov (United States)

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  6. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LICKING COUNTY, OHIO USA

    Data.gov (United States)

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  7. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LEE COUNTY, TEXAS

    Data.gov (United States)

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  8. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, RIVERSIDE COUNTY, CALIFORNIA, USA

    Data.gov (United States)

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  9. DIGITAL FLOOD INSURANCE RATE MAP DATABASE,CAMDEN COUNTY, GEORGIA

    Data.gov (United States)

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  10. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, JACKSON COUNTY, GEORGIA, USA

    Data.gov (United States)

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  11. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, PASCO COUNTY, FLORIDA, USA

    Data.gov (United States)

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  12. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LOWNDES COUNTY, GEORGIA, USA

    Data.gov (United States)

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  13. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, EFFINGHAM COUNTY, GEORGIA, USA

    Data.gov (United States)

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  14. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CHARLTON COUNTY, GEORGIA, USA

    Data.gov (United States)

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  15. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BRADFORD COUNTY, FL, USA

    Data.gov (United States)

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  16. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, UNION COUNTY, FLORIDA, USA

    Data.gov (United States)

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  17. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, DAKOTA COUNTY, MINNESOTA, USA

    Data.gov (United States)

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  18. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, GRANT COUNTY, WISCONSIN, USA

    Data.gov (United States)

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  19. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LIBERTY COUNTY, GEORGIA

    Data.gov (United States)

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  20. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HENDERSON COUNTY, ILLINOIS USA

    Data.gov (United States)

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  1. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, VERMILION COUNTY, ILLINOIS USA

    Data.gov (United States)

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  2. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LEE COUNTY, ILLINOIS USA

    Data.gov (United States)

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  3. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BOONE COUNTY, ILLINOIS USA

    Data.gov (United States)

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  4. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, JEFFERSON COUNTY, ILLINOIS USA

    Data.gov (United States)

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  5. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, KNOX COUNTY, ILLINOIS USA

    Data.gov (United States)

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  6. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MASON COUNTY, ILLINOIS USA

    Data.gov (United States)

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  7. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, KANE COUNTY, ILLINOIS USA

    Data.gov (United States)

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  8. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WILLIAMSON COUNTY, ILLINOIS USA

    Data.gov (United States)

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  9. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WHITESIDE COUNTY, ILLINOIS USA

    Data.gov (United States)

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  10. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, PIATT COUNTY, ILLINOIS USA

    Data.gov (United States)

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  11. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, GRUNDY COUNTY, ILLINOIS USA

    Data.gov (United States)

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  12. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LAWRENCE COUNTY, ILLINOIS USA

    Data.gov (United States)

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  13. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MACON COUNTY, ILLINOIS USA

    Data.gov (United States)

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  14. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, RANDALL COUNTY, TX, USA

    Data.gov (United States)

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  15. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SUMNER COUNTY, USA

    Data.gov (United States)

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  16. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, PEMBINA COUNTY, USA

    Data.gov (United States)

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  17. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MIDLAND COUNTY, MICHIGAN, USA

    Data.gov (United States)

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  18. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Liberty County, TX

    Data.gov (United States)

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  19. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WALKER COUNTY, AL

    Data.gov (United States)

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  20. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WALKER COUNTY, GEORGIA, USA

    Data.gov (United States)

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  1. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WASHINGTON COUNTY, USA

    Data.gov (United States)

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  2. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Tuolumne County, USA

    Data.gov (United States)

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  3. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MARSHALL, IOWA, USA

    Data.gov (United States)

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  4. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LINN COUNTY, IA, USA

    Data.gov (United States)

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  5. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MUSCATINE COUNTY, USA

    Data.gov (United States)

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  6. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LAFAYETTE COUNTY, MISSOURI, USA

    Data.gov (United States)

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  7. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BUTLER COUNTY, KY

    Data.gov (United States)

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  8. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BATH COUNTY, KY

    Data.gov (United States)

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  9. DRAFT DIGITAL FLOOD INSURANCE RATE MAP DATABASE, EDGEFIELD COUNTY, SC

    Data.gov (United States)

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  10. DRAFT DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LANCASTER COUNTY, SC

    Data.gov (United States)

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  11. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, COLES COUNTY, ILLINOIS USA

    Data.gov (United States)

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  12. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SUMTER COUNTY, AL

    Data.gov (United States)

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  13. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Smith COUNTY, Tennessee

    Data.gov (United States)

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  14. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, VAL VERDE COUNTY, TEXAS

    Data.gov (United States)

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  15. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BUTLER COUNTY, NE

    Data.gov (United States)

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  16. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WASATCH COUNTY, UTAH, USA

    Data.gov (United States)

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  17. DIGITAL FLOOD INSURANCE RATE MAP DATABASE FOR HOWARD COUNTY, NEBRASKA

    Data.gov (United States)

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  18. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CLARK COUNTY, USA

    Data.gov (United States)

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  19. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SUFFOLK COUNTY, MA

    Data.gov (United States)

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  20. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, DUKES COUNTY, MA

    Data.gov (United States)

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  1. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CRAIG COUNTY, VIRGINIA

    Data.gov (United States)

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  2. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, TALLADEGA COUNTY, AL

    Data.gov (United States)

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  3. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CAMERON COUNTY, PA, USA

    Data.gov (United States)

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  4. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CLINTON COUNTY, MISSOURI, USA

    Data.gov (United States)

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  5. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Marion County, AL

    Data.gov (United States)

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  6. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CHEROKEE COUNTY, KANSAS

    Data.gov (United States)

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  7. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SALINE COUNTY, ILLINOIS USA

    Data.gov (United States)

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  8. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CHAMPAIGN COUNTY, USA

    Data.gov (United States)

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  9. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Linn County, Oregon, USA

    Data.gov (United States)

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  10. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, COBB COUNTY, GEORGIA

    Data.gov (United States)

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  11. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, TRUMBULL COUNTY, OHIO, USA

    Data.gov (United States)

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  12. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SCOTT COUNTY, MISSOURI

    Data.gov (United States)

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  13. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CRAWFORD COUNTY, MICHIGAN, USA

    Data.gov (United States)

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  14. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ETOWAH COUNTY, AL

    Data.gov (United States)

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  15. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WORCESTER COUNTY, MASSACHUSETTS

    Data.gov (United States)

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  16. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MONROE COUNTY MICHIGAN, USA

    Data.gov (United States)

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  17. Digital Flood Insurance Rate Map Database, Sussex County, Delaware, USA

    Data.gov (United States)

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  18. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, OTTAWA COUNTY, OHIO, USA

    Data.gov (United States)

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  19. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SAN JACINTO COUNTY, TX

    Data.gov (United States)

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  20. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Letcher COUNTY, Kentucky

    Data.gov (United States)

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  1. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LAWRENCE COUNTY, AR

    Data.gov (United States)

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  2. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Accomack County, VIRGINIA

    Data.gov (United States)

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  3. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, RALEIGH COUNTY, WV, USA

    Data.gov (United States)

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  4. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SOLANO COUNTY, CALIFORNIA, USA

    Data.gov (United States)

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  5. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BATES COUNTY, MISSOURI, USA

    Data.gov (United States)

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  6. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HURON COUNTY, MICHIGAN USA

    Data.gov (United States)

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  7. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LEE COUNTY, FLORIDA

    Data.gov (United States)

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  8. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, RED WILLOW COUNTY, USA

    Data.gov (United States)

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  9. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, THURSTON COUNTY, NEBRASKA

    Data.gov (United States)

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  10. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, GWINNETT COUNTY, GEORGIA, USA

    Data.gov (United States)

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  11. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ALLAMAKEE COUNTY, IOWA

    Data.gov (United States)

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  12. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CHICKASAW COUNTY, IOWA, USA

    Data.gov (United States)

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  13. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BOONE COUNTY, IOWA, USA

    Data.gov (United States)

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  14. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Scott, Iowa, USA

    Data.gov (United States)

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  15. FINAL DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CHEROKEE COUNTY, SC

    Data.gov (United States)

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  16. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Delaware County, New York

    Data.gov (United States)

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  17. Digital Flood Insurance Rate Map for Randolph County, IN

    Data.gov (United States)

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  18. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BOYLE COUNTY, KY

    Data.gov (United States)

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  19. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HARRISON COUNTY, KY

    Data.gov (United States)

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  20. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Berks County, Pennsylvania, USA

    Data.gov (United States)

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  1. Digital Flood Insurance Rate Map Database, Randolph County, AR, USA

    Data.gov (United States)

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  2. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LAURENS COUNTY, SC

    Data.gov (United States)

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  3. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, DESOTO COUNTY, FL, USA

    Data.gov (United States)

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  4. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CULLMAN COUNTY, AL

    Data.gov (United States)

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  5. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MACOMB COUNTY, MI

    Data.gov (United States)

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  6. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Perry COUNTY, PENNSYLVANIA

    Data.gov (United States)

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  7. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, DINWIDDIE COUNTY, VIRGINIA

    Data.gov (United States)

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  8. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, POLK COUNTY, TX

    Data.gov (United States)

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  9. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Ottawa County, OK

    Data.gov (United States)

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  10. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, TAYLOR COUNTY, FL, USA

    Data.gov (United States)

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  11. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, NAPA COUNTY, CALIFORNIA, USA

    Data.gov (United States)

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  12. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WILSON COUNTY, TEXAS, USA

    Data.gov (United States)

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  13. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WALLER COUNTY, TX

    Data.gov (United States)

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  14. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ERIE COUNTY, PENNSYLVANIA

    Data.gov (United States)

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  15. Digital Flood Insurance Rate Map Database, Bradford County, Pennsylvania, USA

    Data.gov (United States)

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  16. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CRAWFORD COUNTY, AR ,USA

    Data.gov (United States)

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  17. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Lancaster County, VA, USA

    Data.gov (United States)

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  18. Digital Flood Insurance Rate Map Database, PRINCE GEORGE, VA, USA

    Data.gov (United States)

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  19. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WESTMORELAND COUNTY, VA, USA

    Data.gov (United States)

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  20. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CAMPBELL COUNTY, VA, USA

    Data.gov (United States)

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  1. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Northumberland County, VA, USA

    Data.gov (United States)

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  2. Digital Flood Insurance Rate Map Database, Charles County, Maryland, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  3. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MONTGOMERY COUNTY, AL

    Data.gov (United States)

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  4. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MADISON COUNTY, USA

    Data.gov (United States)

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  5. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SUMTER COUNTY, FLORIDA, USA

    Data.gov (United States)

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  6. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BACON COUNTY, GEORGIA, USA

    Data.gov (United States)

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  7. Digital Flood Insurance Rate Map Database, Allegheny County, Pennsylvania, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  8. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, FOREST COUNTY, PA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  9. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, GRANT COUNTY, New Mexico

    Data.gov (United States)

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  10. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WILSON COUNTY, TENNESSEE

    Data.gov (United States)

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  11. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CHRISTIAN COUNTY, KY

    Data.gov (United States)

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  12. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MENDOCINO COUNTY, CALIFORNIA

    Data.gov (United States)

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  13. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MIDDLESEX COUNTY, MASSACHUSETTS

    Data.gov (United States)

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  14. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, TRAVIS COUNTY, TEXAS

    Data.gov (United States)

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  15. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ALBEMARLE COUNTY, VIRGINIA

    Data.gov (United States)

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  16. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, NIAGARA COUNTY, NEW YORK

    Data.gov (United States)

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  17. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Oswego COUNTY, New York

    Data.gov (United States)

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  18. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BALTIMORE COUNTY, MARYLAND

    Data.gov (United States)

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  19. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WOODFORD COUNTY, ILLINOIS USA

    Data.gov (United States)

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  20. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Dooly County, GA

    Data.gov (United States)

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  1. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, COOPER COUNTY, MISSOURI, USA

    Data.gov (United States)

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  2. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BANDERA COUNTY, TEXAS

    Data.gov (United States)

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  3. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CARBON COUNTY, UTAH

    Data.gov (United States)

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  4. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MONROE COUNTY, NEW YORK

    Data.gov (United States)

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  5. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BROWN COUNTY, MN, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  6. DIGITAL FLOOD INSURANCE RATE MAP, SUTTER COUNTY, CALIFORNIA (UNINCORPORATED AREAS)

    Data.gov (United States)

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  7. DIGITAL FLOOD INSURANCE RATE MAP, CITY OF MCGRATH, ALASKA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  8. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, TAMA COUNTY, IOWA

    Data.gov (United States)

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  9. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LOGAN COUNTY, ILLINOIS USA

    Data.gov (United States)

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  10. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LABETTE COUNTY, KANSAS

    Data.gov (United States)

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  11. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SUMMERS COUNTY, WEST VIRGINIA

    Data.gov (United States)

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  12. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CALDWELL PARISH, LOUISIANA, USA

    Data.gov (United States)

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  13. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, GLOUCESTER COUNTY, NEW JERSEY

    Data.gov (United States)

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  14. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Warren County, Tennessee

    Data.gov (United States)

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  15. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, PITTSYLVANIA COUNTY, VIRGINIA

    Data.gov (United States)

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  16. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, STARR COUNTY, TEXAS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  17. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, RANDOLPH COUNTY, WV, USA

    Data.gov (United States)

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  18. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, STARK COUNTY, OHIO

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  19. FINAL DIGITAL FLOOD INSURANCE RATE MAP DATABASE, INYO COUNTY, CA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  20. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, RIVERSIDE COUNTY, CALIFORNIA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  1. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CLARK COUNTY, KY

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  2. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CODINGTON COUNTY, SOUTH DAKOTA

    Data.gov (United States)

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  3. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, PIMA COUNTY, ARIZONA, USA

    Data.gov (United States)

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  4. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MADISON COUNTY, FLORIDA

    Data.gov (United States)

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  5. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, NEWPORT COUNTY, RHODE ISLAND

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  6. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, DUBUQUE COUNTY, IA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  7. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MIAMI COUNTY, KANSAS, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  8. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Mobile COUNTY, AL

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  9. PRELIMINARY DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CHESTERFIELD COUNTY, SC

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  10. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SISKIYOU COUNTY, CALIFORNIA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  11. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Charles COUNTY, MD, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  12. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MODOC COUNTY, CALIFORNIA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  13. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, TULARE COUNTY, CALIFORNIA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  14. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, FANNIN COUNTY, GEORGIA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  15. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, TOOMBS COUNTY, GEORGIA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...

  16. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HARDIN COUNTY, TX

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  17. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, KENTON COUNTY, KENTUCKY, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  18. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Medina COUNTY, OHIO

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  19. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Pike County, GA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  20. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WASHTENAW COUNTY, MICHIGAN, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  1. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ST. FRANCOIS COUNTY, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  2. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, FRESNO COUNTY, CALIFORNIA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  3. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CLEARFIELD COUNTY, PA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  4. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SHELBY COUNTY, OHIO, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  5. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, TUSCARAWAS COUNTY, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  6. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, New London County, CT

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  7. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, JONES COUNTY, GEORGIA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...

  8. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WASHINGTON COUNTY, AL

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  9. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HARTFORD COUNTY, CONNECTICUT

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  10. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, TEHAMA COUNTY, CALIFORNIA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  11. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MERCER COUNTY, KY

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  12. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, RENO COUNTY, KANSAS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  13. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CASCADE COUNTY, MONTANA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...

  14. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, PIKE COUNTY, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  15. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SULLIVAN COUNTY, PA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  16. DIGITAL FLOOD INSURANCE RATE MAP DATABAES, LA PAZ COUNTY, AZ

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  17. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, City of Poquoson, Virginia

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  18. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MORGAN COUNTY, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  19. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ISABELLA COUNTY, MICHIGAN, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...

  20. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SEWARD COUNTY, USA

    Data.gov (United States)

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  1. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MANATEE COUNTY, FL, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  2. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, TRIGG COUNTY, KY

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  3. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BALTIMORE CITY, MARYLAND

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  4. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CHRISTIAN COUNTY, ILLINOIS USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  5. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Centre County, PENNSYLVANIA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  6. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, DANE COUNTY, WISCONSIN, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk;...

  7. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, POTTER COUNTY, TX, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  8. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, DUNN COUNTY, WISCONSIN, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk;...

  9. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SIERRA COUNTY, CALIFORNIA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  10. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MAUI COUNTY, HAWAII, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  11. Digital Flood Insurance Rate Map Database, ZAPATA COUNTY, TEXAS, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  12. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MEDINA COUNTY, OHIO

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  13. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, JESSAMINE COUNTY, KY

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  14. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, COLFAX COUNTY, New Mexico

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  15. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MURRAY COUNTY, GEORGIA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  16. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CALLOWAY COUNTY, KY

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  17. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, TAOS COUNTY, New Mexico

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  18. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Cibola COUNTY, New Mexico

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  19. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LACLEDE COUNTY, MISSOURI, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  20. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BARROW COUNTY, GEORGIA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...