In 2022, there were at least half a million retail, office, and multi-unit residential buildings facing risk of flood damage in the United States. California recorded the highest number of buildings at risk, with over 75 thousand units. Florida also recorded more than 70 thousand buildings at risk of flood damage at the time. The states with a higher number of buildings at risk are also some of the leading states in terms of population.

In 2022, more than 30 thousand retail, office, and multi-unit residential properties were facing risk of flood damage in the New York metropolitan area. Meanwhile, Miami's metro area had some 26 thousand buildings at risk of flooding. That year, the structural damage to buildings at risk of flooding in the U.S. was estimated at 13.5 billion U.S. dollars.

In 2022, nearly 13 thousand multi-unit residential properties were at risk of flood damage in Florida. The state of New York also recorded more than ten thousand buildings of the type at risk of flood damage. At the time, there were at least half a million buildings at risk of flood damage in the U.S..

This statistic illustrates the share of water damage claims out of the total housing claims in France between 2008 and 2018. In 2018, 40 percent of home-related claims in France were water damage claims.

There are a number of factors which influence the direct consequence of flooding. The most important are depth of inundation, velocity, duration of inundation and water quality. Though computer modelling techniques exist that can provide an estimate of these variables, this information is seldom used to estimate the impact of flooding on a community. This work describes the first step to improve this situation using data collected for the Swan River system in Perth, Western Australia. Here, it is shown that residential losses are underestimated when stage-damage functions or the velocity-stage-damage functions are used in isolation. This is because the functions are either limited to assessing partial damage or structural failure resulting from the movement of a house from its foundations. This demonstrates the need to use a combination of techniques to assess the direct economic impact of flooding.

The Swan River is the main river through Perth, the capital city of Western Australia. Direct tangible economic losses to residential dwellings in Perth was based on hydraulic modelling using the one dimensional unsteady flow model HEC-RAS, geographical information systems, a building exposure database and synthetic stage-damage curves. Eight flood scenarios ranging from the 10 year average recurrence interval (ARI) to the 2000 year ARI event were examined. The combined structure and contents flood losses ranged from A$17 million to A$659 million for insured structures and A$14 million to A$583 million for uninsured structures. This equates to an average annual damage of A$9.6 million and A$7.9 million respectively. The results reinforce the need to consider a wide range of varying magnitude flood events when assessing losses due to the temporal and spatial variation between flood scenarios.

Please note that the inundation area as displayed in this web map is only an estimate. The estimated inundation area was compiled from various data inputs including:911 calls for serviceBaton Rouge Fire Department search and rescue data points311 citizen requests for serviceStreet-level damage assessmentsDebris collection routesRoad closure informationNOAA imageryCivil Air Patrol imageryFEMA DFIRM flood hazard areasTo complement these datasets, the City-Parish also received input from the general public to identify areas that were or were not inundated, and modifications to the layer were made based on this crowdsource input. Please note that not all structures in the estimated inundated area were impacted by floodwaters, as some structures are elevated above the water line or were otherwise spared from flooding.The residential sales data was provided courtesy of the Greater Baton Rouge Association of Realtors for the period of February 12 - August 12, 2016.

Even though water damage represented the leading cause of damage claims in home insurance in France in 2019, it was fire damage that had the highest average cost that year among all types of claims, at around 10 thousand euros.

General Abstract/Purpose (70 words): Data were collected to assist in cost-benefit analysis of flood mitigation actions that could be taken by the U.S. and Canada to prevent structural damage and associated costs and losses in future flood conditions, including conditions worse than the historical record flooding in spring of 2011. Data were commissioned to revise or fill gaps in estimates from structural damage modeling software commonly used for depth-damage economic assessments of flood impacts. The Summary text that immediately follows this introductory sentence offers overview information, but also includes context and detail that is not present in the Word document ("Principal Indicator Combo SET - REVIEW FINAL v2.docx") that constitutes the main body of this data release, supported by Excel files (that are copied without formatting in csv files for each Excel tab). Lake Champlain is a relatively large lake bordered by New York on the western side and Vermont on the eastern side, whose uppermost region spans the U.S.-Canadian border. The 436 mi^2 (1,130 km^2) lake sits within a 9,277 mi^2 (23,900 km^2) basin, and Champlain’s only drainage point is north into Canada via the Richelieu River into the province of Quebec. About 75% of the Lake Champlain shoreline of New York is within Adirondack State Park, covering all or part of Clinton, Essex, and Washington counties. Of Vermont’s 14 counties, Franklin, Chittenden, and Addison Counties border Lake Champlain, while Grand Isle is surrounded by Champlain and at its northern edge the Canadian border. Development and anthropogenic modifications, especially over the last 50 years, have converted wetlands, changed the timing and flows of water, and increased impervious surface area including new residences in floodplains on both sides of the border. Occasionally there is damaging flooding, with significant economic damages in New York, Vermont, and Quebec. With flood stage at 99.57’ (30.35m) and major flooding from 101.07’ (30.81m) over sea level, a 101.4’ (30.91m) flood in 1993 broke the previous recorded high flood in 1869. Following the third heaviest recorded snow, almost no seasonal snowmelt, then heavy rains, the spring of 2011 brought record flooding more than one foot over the 1993 record to 102.77’ (31.32m), expanding the lake’s area by 66 mi^2 (106.2 km^2, or about 5.8%). From reaching flood stage to peak and then returning to a lake level below flood stage took around six weeks. Wind-to-wave-driven erosion was up to 5 feet (1.5m) above static lake elevation in some areas. The record flood height (102.77’) is often reported as 103.07’ or 103.27’ in Burlington, owing to different vertical and horizontal datums and digital elevation models (DEMs), and some wave action. In a 1976 flood the U.S. side incurred more than 50% of the economic damages, but in 2011, Quebec experienced some 80% of structural and economic damages estimated at $82 million. Tropical Storm Irene hit the area in August of 2011 and did far more damage on the American side, for example spurring $29 million in home and business repair loans for damage across 12 of Vermont’s 14 counties. Co-reporting across the two events for 2011 confounded some data, making it impossible to separately identify spring flooding numbers. Following the Boundary Waters Treaty between the U.S. and Canada in 1909, from 1912 the International Joint Commission (IJC) handles boundary water issues between the two countries. The IJC Lake Champlain Richelieu River (LCRR) Study Project is a bi-national (U.S., Canada) multi-agency effort to assess flood risk and flood mitigation options as they affect potential structural damages and wider non-structural damages that include secondary economic, community, and psychological effects. Key economic parts of the report to the IJC LCRR Study Board are calculated using a new tool developed for the study project, an Integrated Socio-Economic-Environmental (ISEE) model, with forecasting for damages up to 105.57’ flood (105.9’, or 106’ [32.3m] for short, by alternative datum and DEMs, as apply in some of the modeling and estimations herein). There is also a Collaborative Decision Support Tool (CDST) that also processes non-structural economic damages, costs, or losses as inputs. CDST is a pared-down version of ISEE that applies historical estimates but does not project outcomes for higher floods in the future. Outputs from this data release are inputs to the ISEE or the CDST for calculations of the benefit-to-cost ratios projected to follow different structural interventions. For example adding a weir in the Richelieu River yielded a greater-than-one benefit-to-cost ratio in late-stage modeling, whereas a dam on either side, or an entirely new canal on the Canadian side, were never entertained as cost feasible or even appropriate. USGS economists were contracted to supply economic “principal indicators” for potential U.S.-side depth-damage effects from lake-rise flooding. The scope of this analysis is limited by several factors associated with the objectives of the IJC LCRR Study Board. Damages from tributary flooding were defined out of a project focused on joint-management options for mitigating flood effects, as tributary flows would be managed only by the U.S. Uncommonly low Lake Champlain levels were also ultimately considered as a stakeholder concern (the weir option also addressed this concern). It is standard to model economic damages to structures and related economic costs due to flooding using the FEMA-designed Hazus®-MH (Multi-Hazard) Flood Model of structural damages (https://www.fema.gov/flood-maps/products-tools/hazus; the Hazus-MH Technical Manual, 2011, 569pp, which explains definitions and parameterization of the tool rather than use of the tool itself, is a frequently referred source here). “Hazus” (tool) modeling is used in the LCRR Study Board research to estimate structural damages at different flood depths, and the primary work presented in this data release estimates depth-damage values for “Principal Indicators” (PIs) that were defined to supplement or alternatively estimate results from applying Hazus, where gaps exist or where straight Hazus values may be questionable in the LCRR context. A number of Principal Indicators were estimated on the Canadian and U.S. sides, where no PIs include any estimates for repair of structural damage, as those calculations are done separately using the Hazus tool (or the ISEE model application with Hazus outputs as inputs). In the final list, the USGS team produced estimates for six PIs: temporary lodging costs, residential debris clean-up and disposal, damage to roads and bridges, damage to water treatment facilities, income loss from industrial or commercial properties, and separately and specifically recreation sector income loss. So associated with residential damage, the costs of securing emergency and longer-term lodging when a household is displaced by lake-rise flooding are estimated, and the costs of cleaning up and removing and disposing of debris from residential property damage are estimated. In the public sector, costs of clean up and repair of damages to roads and bridges from lake-rise flooding are calculated, as are damages and potential revenue losses from flood mitigation measures and service reductions where public or private water utilities are inundated by lake-rise flooding. In the commercial sector, revenue losses from being closed for business due to flooding are calculated outside of the recreation sector, and then also for the recreation sector as lakeside campgrounds, marinas, and ferry services (where the last is also used for local commercial traffic). All of these PIs are characterized by being little-discussed in the literature. To derive information necessary to bound economic estimates for each of the 6 PIs, consultation with subject-matter experts in New York and Vermont (or at agencies covering these areas) was employed more often than anything in peer-reviewed literature specifically applied. Depth-damage functions that result are not formal mathematical functions, and across the six PIs calculations and results tend to be in increments of one foot or more. Results thus suggest magnitudes of costs that comply with reasonable scenario assumptions for a small but fairly consistent set of flood depths from 99.57’ to 105.57’, where the latter value is almost three feet (1m) above the historic maximum flood. Nothing reported in these estimates is empirically deterministic, or capable of including probabilistic error margins. Simplifying assumptions serve first to actually simplify the calculations and legibility of estimated results, and second to avoid the impression that specifically calibrated empirical estimations are being conducted. This effort offers plausible, logical, reliable, and reproducible magnitudes for estimates, using a method that can be easily modified if better information becomes available for future estimations. Certain worksheets and specific results are withheld to avoid the outright identification of specific businesses (or homes). Facts in this abstract generally attribute to: International Lake Champlain-Richelieu River Study Board, 2019. The Causes and Impacts of Past Floods in the Lake Champlain-Richelieu River Basin – Historical Information on Flooding, A Report to the International Joint Commission, 108pp (https://ijc.org/en/lcrr). Some supplemental factual support is from: Lake Champlain Basin Program, 2013. Flood Resilience in the Lake Champlain Basin and Upper Richelieu River, 93 pp (https://ijc.org/en/lcrr).

Geographical information systems (GIS) have been used to model building flood damage in South East Queensland. The research shows that if a flood with a 1% annual exceedence probability (AEP) occurred simultaneously in all rivers in the region, 47 000 properties would be inundated, with about half of the properties likely to experience overfloor flooding.

90% of affected properties are located in the Brisbane-Bremer River system and the Gold Coast catchment. 89% of properties affected by flooding are residential. Nearly 60% of the residential flood damage is located in the Brisbane-Bremer River system, with damage estimated to be highest in those areas which historically have suffered high flood losses. Equivalent average damage per residential building is highest in the Gold Coast catchment.

If the cost of the actual damages were to be spread among all residential buildings in South East Queensland, than the equivalent flood damage would be 1.09% damage from a flood with a 1% AEP.

Bangladesh is one of the most flood affected country in the world. The frequency, intensity and duration of floods has been increased during last few decades. Due to increased population settlements in floodplains and irregular development damage of infrastructure, crop and property has increased creating severe impact on lives and livelihood. Understanding the severity and identification of extent and types of flood damage is highly important to plan effective response. The aim of this study was to develop appropriate methodology to determine extent of flood and damaged areas in near real time basis to support operational response. We have used Sentinel-1 synthetic aperture radar (SAR) images to generate flood extend data for the year 2017.

In 2022, Florida was the U.S. state with the highest structural damage costs associated with flood risk across office, retail, and multi-residential buildings, at nearly two billion U.S. dollars. Repair or replacement costs of buildings at risk of flooding in Pennsylvania and California could also add up to over one billion dollars in each state. That year, there were at least half a million buildings at risk of flood damage in the U.S..

Flooding occurs regularly in many parts of New Zealand, often resulting in damage and financial loss for building owners. Significant damage occurs upon flood waters entering buildings. An effective method of mitigating flood damage is to elevate building floor levels above design flood levels. This measure can reduce or eliminate the potential damage caused by more frequent (i.e. below design level) flood events. In this Nelson City case study, we analyse how elevating the building finished floor levels can reduce direct economic loss in a modelled Maitai River 1% annual exceedance probability (AEP) flood scenario. RiskScape software is used to model direct economic loss for three impact scenarios; present-day, +0.5m floor levels and +1m floor levels. First finished floor levels are only raised for residential timber frame buildings constructed on pile foundations. For more information about RiskScape software please contact the NIWA project lead, Ryan Paulik (Ryan.Paulik@niwa.co.nz ) or the GNS project lead, Richard Woods (r.woods@gns.cri.nz).

Leading flood loss estimation models include Federal Emergency Management Agency’s (FEMA’s) Hazus, FEMA’s Flood Assessment Structure Tool (FAST), and (U.S.) Hydrologic Engineering Center’s Flood Impact Analysis (HEC-FIA), with each requiring different data input. No research to date has compared the resulting outcomes from such models at a neighborhood scale. This research examines the building and content loss estimates by Hazus Level 2, FAST, and HEC-FIA, over a levee-protected census block in Metairie, in Jefferson Parish, Louisiana. Building attribute data in National Structure Inventory (NSI) 2.0 are compared against “best available data” (BAD) collected at the individual building scale from Google Street View, Jefferson Parish building inventory, and 2019 National Building Cost Manual, to assess the sensitivity of input building inventory selection. Results suggest that use of BAD likely enhances flood loss estimation accuracy over existing reliance on default data in the software or from a national data set that generalizes over a broad scale. Although the three models give similar mean (median) building and content loss, Hazus Level 2 results diverge from those produced by FAST and HEC-FIA at the individual building level. A statistically significant difference in mean (median) building loss exists, but no significant difference is found in mean (median) content loss, between building inventory input (i.e., NSI 2.0 vs BAD), but both the building and content loss vary at the individual building scale due to difference in building-inventory-reported foundation height, foundation type, number of stories, replacement cost, and content cost. Moreover, building loss estimation also differs significantly by depth-damage function (DDF), for flood depths corresponding with the longest return periods, with content loss differing significantly by DDF at all return periods tested, from 10 to 500 years. Knowledge of the extent of estimated differences aids in understanding the degree of uncertainty in flood loss estimation. Much like the real estate industry uses comparable home values to appraise a home, flood loss planners should use multiple models to estimate flood-related losses. Moreover, results from this study can be used as a baseline for assessing losses from other hazards, thereby enhancing protection of human life and property.

Flood hazard assessments have been carried out for several areas in the District. These include the Poverty Bay Flats, Gisborne urban area, and the Mangatuna/ Wharekaka Area for the Hikuwai/Uawa River. The flood hazard varies across liable areas. Generally towards the edge of the flooded area depths are shallow and floodwaters move at slow speeds. Therefore the degree of hazard is low. However floodwaters are generally deep and flow swiftly in the vicinity of the main river channel and other major flood flow paths. These areas generally have a high degree of flood hazard with silt and debris deposition. The process of assessing flood hazard, firstly involves a study into flood behaviour. This involves estimating discharge for the various sized floods and the determination of water levels, velocities and depth of flooding. Then secondly a design flood standard' is selected. The determination of thatdesign flood standard' balances the social, economic and ecological considerations against the consequences of flooding. If the standard is too low development will be inundated relatively frequently with greater damage. If the standard is too high land will incur unwarranted controls. The selection of the design flood standard depends on flood behaviour, landuse and consequences of larger floods. The level of protection offered by flood mitigation works may be different from the design flood standard adopted for land use planning. That level is dictated by economics of the situation or physical limitations of the site. It is prudent to assume that floods may occur greater than the ability of protection works to contain them. The design flood standard is intended to reduce the impacts of such floods, by avoiding or limiting development which would be affected.Flood Overlay categories includea) Flood Hazard Overlay 1 (River and Floodway): These are the main routes for floodwaters. They include all watercourses and adjacent berms liable to regular flooding. Floodwaters could be deep and fast flowing. These are areas unsuitable for regular human occupation. Floodway areas are areas which even if only partially blocked would cause a significant redistribution of flood flows. Care needs be taken not to alter the level of the land in a way which could divert floodwaters and cause adverse effects. Activities which could trap sediment in a flood and build up the river berms should also be avoided. b) Flood Hazard Overlay 2A (Moderate/High Hazard Areas): Similar to Flood Hazard Overlay 2 except that: i. ii. The flood hazard varies between “moderate” and “high”; and Flood warning systems and evacuation plans provide some measure of protection to residents Within this overlay some areas are unsuitable for permanent habitation, while others may be suitable subject to the practicality of evacuation routes and the potential numbers to be evacuated. c) Flood Hazard Overlay 2 (High Hazard Areas): Flooding in high hazard areas is associated with flow over stopbanks and roads and deep overland flow confined to narrow valleys. Floodwaters could cause structural damage to buildings and in extreme cases light framed houses could be swept away. Heavy silt deposition can occur. These areas are generally unsuitable for permanent habitation. Care needs be taken not to alter the level of the land in a way which could divert floodwaters and cause adverse effects. Activities which could trap sediment in a flood and build up the river berms should be avoided. d) Flood Hazard Overlay 3 (Flood Ponding Areas): This contains low-lying areas or basins subject to occasional but relatively deep flooding. Generally floodwaters would be slow moving or stationary. For Poverty Bay these areas have been flooded in 1985 and/or 1988. Ponding areas store floodwaters during major rainfall events. Infilling of these areas may divert and raise the level of floodwaters elsewhere. e) Flood Hazard Overlay 4 (Areas Liable to Flooding): contains areas on floodplains that have previously been flooded. For Poverty Bay that is flooding from the 1985 and/or 1988 floods. For the Mangatuna/ Wharekaka area it is flooding from the 1988 flood. For the Waimata Taruheru and Turanganui Rivers and the Waikanae Creek it is flooding from the 1977 and/or 1985 flood. f) Flood Hazard Overlay 5 (Flood Fringe Areas): contains areas that have not previously flooded but are expected to be flooded under design flood standard conditions. Generally water would be shallow and slow moving. These areas are generally suitable for permanent habitation as flooding should not cause structural damage. However floor levels need to be high enough for inhabitants to remain safely in houses until effective evacuation can take place. Care needs be taken not to alter the level of the land in a way which could divert floodwaters and cause adverse effects. g) Flood Hazard Overlay 6 (Old River Loops): These areas are old river loops that can be flooded to depths exceeding 1m. They are not generally suitable for residential occupation because the depth of water could cause difficulties in evacuation. Care needs be taken not to alter the level of the land in a way which could divert floodwaters and cause adverse effects. h) Flood Hazard Overlay 7 (Urban Stormwater Flood Hazard Area): These areas are affected by flooding from local streams and drains in design flood conditions. The stormwater reticulation system within the Gisborne urban area is presently undergoing an upgrading programme and the extent of this area may be able to be reduced when this programme is complete. However, work on this has only just begun and therefore the 1977 and 1985 floodspread maps are to be used until then as the basis of this overlay area. i) Flood Hazard Overlay 8 (Urban Ponding Areas): Urban ponding areas store floodwaters during major rainfall events. Infilling of these areas would put extra stress on urban reticulation systems or require expensive upgrading of such systems. j) Flood Hazard Overlay 9 (Urban Floodways): These are main routes for floodwaters. They include all rivers, streams and watercourses and adjacent berms liable to flooding. Floodwaters could be deep and fast flowing. Floodway areas are areas which even if partially blocked would cause a significant redistribution of flood flows. Care needs to be taken not to cause adverse effects by diverting or impeding floodwaters.

This is a MD iMAP hosted service. Find more information on http://imap.maryland.gov. Coastal storm and flood events may increase the likelihood of danger or damage to life and property as a consequence of inundation and shoreline erosion. These hazards are caused by storms and exacerbated by sea level rise. The Community Flood Risk Areas represent residential areas at risk to coastal flooding where populations may be less equipped to prepare for - respond to - or recover from a coastal hazard event. Risk Areas are ranked from 1 to 5 to indicate relative risk - with 1 indicating very low risk and 5 indicating very high risk. Risk rankings incorporate population density - social parameters (i.e. age - income - language proficiency) - and probability of exposure to a flood hazard event in any one given year. 2013 US Census Bureau American Community Survey - 2010 Maryland Department of Planning land use land cover - and effective FEMA floodplain data as of December 2015 were used to identify and rank risk areas. Last Updated: 3/31/2016Feature Service Link: http://geodata.md.gov/imap/rest/services/Environment/MD_CoastalResiliencyAssessment/FeatureServer/4 ADDITIONAL LICENSE TERMS: The Spatial Data and the information therein (collectively "the Data") is provided "as is" without warranty of any kind either expressed implied or statutory. The user assumes the entire risk as to quality and performance of the Data. No guarantee of accuracy is granted nor is any responsibility for reliance thereon assumed. In no event shall the State of Maryland be liable for direct indirect incidental consequential or special damages of any kind. The State of Maryland does not accept liability for any damages or misrepresentation caused by inaccuracies in the Data or as a result to changes to the Data nor is there responsibility assumed to maintain the Data in any manner or form. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the State of Maryland in the metadata.

description: A FEMA housing inspection for renters is used to assess personal property loss and for owners to assess damage to their home as well as personal property. This inspection is done to determine eligibility for FEMA Individual Assistance. For both rental and owner inspections, if the property has flood damage the inspector measures the height of the flooding. They indicate the highest floor of the flooding (for example, Basement, 1st floor, 2nd floor, etc) and the height of the flooding in that room. In addition for the units without flooding, HUD has estimated minor/major/severe damage based on the damage inspection estimates for real property (owner) and personal property (renter). This file only presents data on block groups with 10 or more damaged housing units. The suppression to only including 10 or more damaged housing units results in an exclusion of about 6% of the total flooded units. These data are as of January 17, 2013 and reflect Hurricane Sandy damage in the states of New York, New Jersey, Connecticut, and Rhode Island. These data are incomplete, as each day there are additional registrants and inspections. This should be a viewed as a preliminary snapshot to assist with planning.; abstract: A FEMA housing inspection for renters is used to assess personal property loss and for owners to assess damage to their home as well as personal property. This inspection is done to determine eligibility for FEMA Individual Assistance. For both rental and owner inspections, if the property has flood damage the inspector measures the height of the flooding. They indicate the highest floor of the flooding (for example, Basement, 1st floor, 2nd floor, etc) and the height of the flooding in that room. In addition for the units without flooding, HUD has estimated minor/major/severe damage based on the damage inspection estimates for real property (owner) and personal property (renter). This file only presents data on block groups with 10 or more damaged housing units. The suppression to only including 10 or more damaged housing units results in an exclusion of about 6% of the total flooded units. These data are as of January 17, 2013 and reflect Hurricane Sandy damage in the states of New York, New Jersey, Connecticut, and Rhode Island. These data are incomplete, as each day there are additional registrants and inspections. This should be a viewed as a preliminary snapshot to assist with planning.

Nepal is one of the most flood affected country in the world. The frequency, intensity and duration of floods has been increased during last few decades. Due to increased population settlements in floodplains and irregular development damage of infrastructure, crop and property has increased creating severe impact on lives and livelihood. Understanding the severity and identification of extent and types of flood damage is highly important to plan effective response. The aim of this study was to develop appropriate methodology to determine extent of flood and damaged areas in near real time basis to support operational response. We have used Sentinel-1 synthetic aperture radar (SAR) images to generate flood extend data for the year 2017.

Within Chapter 2, the characteristics of the study's catchments are described. This includes land use, social profile, heritage and vegetation issues. A list of authorities and agencies who may be affected when flooding occurs in the catchment is also provided. Chapter 3 outlines the information that was available for this study including mapping and previous studies. The flood damages data base (a description of all residential and business properties in the Middle Brook, Kingdon Ponds, …Show full descriptionWithin Chapter 2, the characteristics of the study's catchments are described. This includes land use, social profile, heritage and vegetation issues. A list of authorities and agencies who may be affected when flooding occurs in the catchment is also provided. Chapter 3 outlines the information that was available for this study including mapping and previous studies. The flood damages data base (a description of all residential and business properties in the Middle Brook, Kingdon Ponds, Parsons Gully and Figtree Gully floodplains) is described, together with the community consultation strategy utilised in this study, and the results of the community questionnaire. Chapter 4 presents the assessment of flood behaviour within Figtree Gully which was carried out within the study. A description of recent flood history, flood behaviour and the impacts and potential damage caused by floods is provided in Chapter 5. Chapter 6 provides an overview of floodplain management including existing management measures, the selection of the flood planning level and funding. Chapter 6 also details the methodology used for the assessment of floodplain management options. The range of floodplain management options that was examined for the Scone district, as part of this study, is described in Chapter 7. Chapter 8 outlines the Floodplain Management Plan for Scone, summarising the recommendations of the study. A full list of references is provided in Chapter 9. An index of key words is contained in Chapter 10. A number of appendices are included. Appendix A is the largest and comprises an extensive description of town planning issues.

In 2022, structural damage costs due to flood risk across multi-unit residential buildings in Florida were estimated at almost 600 million U.S. dollars. Estimated costs to repair damages or replace buildings at flood risk in New York added up to 262.1 million dollars. At the time, the state of New York had over 11 thousand residential buildings at risk of flood damage.