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This paper develops a fine-scaled analysis in order to determine the cost and benefit of flood protection using hardened coastal structures within a large coastal segment. The probability distribution of surges and the relative rate of sea level rise are estimated from local tidal data and combined with detailed GIS data of all buildings to compute flood damage. Examining a heterogeneous suburban coastline of 110 km length (Branford, Connecticut), the paper defines a complete set of small segments along the coast between high elevation points. For each segment, the study determines whether the benefit of seawalls exceeds the cost and the optimal height for each wall. The analysis compares a uniform wall across the entire town, a uniform wall across only the low lying parts of the coastline, and a unique wall in each micro segment that maximizes net benefits. The uniform wall across the entire town fails a benefit cost analysis. By simply restricting the wall to the 30% of the coastline that is low lying, the flood benefits begin to exceed the cost of the walls. By carefully identifying just the low lying segments where the benefit exceeds the cost, the overall benefit to cost ratio can be increased to 3 to 1. The optimal flood protection program builds walls along only 10% of the coastline. These optimal micro segments are dispersed throughout the entire town including inland along a coastal river. The optimal elevation of the top of the walls is 2.3 m which is well below the 1/100 year storm height of 3.2 m. The benefit versus cost does not justify protection against rare but locally catastrophic storms such as hurricanes. Sea level rise increases the benefits of protection but plays only a small role in current protection decisions.

The misinterpreted statistics of extreme events’ probability in drainage design codes can lead to an insufficient capacity of drainage systems. This is one of the main factors of frequent floods, especially in coastal cities. Heavy rainfalls, the so-called “1-in-50-year” or “1-in-100-year” events followed by floods, may occur there every 2–3 years. We hope to contribute to the correct understanding of extreme events’ statistics. To achieve this goal, we first recall the technique of calculating the exceedance probability of the extreme event over a period of years. Then, to illustrate the theoretical results (among which is the well-known 63.2% minimal exceedance probability over a period of N years for an event with a 1/N annual exceedance probability), we use the rainfall intensity data archived at the Israel Meteorological Service for 1938–2021. We estimate their exceedance probabilities over periods of 50 and 100 years and offer the latter as convenient measures for comparative analysis of the intensity of extreme events. These illustrations highlight the importance of considering return periods that are significantly longer than the 50- or 100-year periods currently used in drainage design, to reduce the risk of flooding and damage caused by heavy rainfalls.

The exposure of populations to sea-level rise (SLR) is a leading indicator assessing the impact of future climate change on coastal regions. SLR exposes coastal populations to a spectrum of impacts with broad spatial and temporal heterogeneity, but exposure assessments often narrowly define the spatial zone of flooding. Here we show how choice of zone results in differential exposure estimates across space and time. Further, we apply a spatio-temporal flood-modeling approach that integrates across these spatial zones to assess the annual probability of population exposure. We apply our model to the coastal United States to demonstrate a more robust assessment of population exposure to flooding from SLR in any given year. Our results suggest that more explicit decisions regarding spatial zone (and associated temporal implication) will improve adaptation planning and policies by indicating the relative chance and magnitude of coastal populations to be affected by future SLR., The exposure of populations to sea-level rise is a leading indicator assessing the impact of future climate change on coastal regions. The authors identify three spatial zones of flooding such as mean higher water, the 100 year floodplain and the low-elevation coastal zone and show population exposure can differ between those zones.

Mitigating the disaster risk of transportation infrastructure networks along the Belt and Road is crucial to realizing the area’s high trade potential in the future. This study assessed the exposure and risk of existing and planned railway assets to river flooding and earthquakes. We found that about 9.3% of these railway assets are exposed to a one in 100 year flood event, and 22.3% are exposed to a one in 475 year earthquake event. The combined flood and earthquake risk of physical damage to railway assets, expressed by expected annual damage (EAD), is estimated at USD 1438 (between 966 and 2026) million. Floods contribute the majority of the risk (96%). China has the highest EAD for both floods and earthquakes (between USD 240 and 525 million in total). Laos and Cambodia are the countries with the highest EAD per km from flooding (USD 66,125–112,154 and USD 31,954–56,844 per km, respectively), while Italy and Myanmar have the highest EAD per km from earthquakes (USD 1000–3057 and USD 893–3019 per km, respectively). For the newly built and planned projects along the Belt and Road, the EAD is estimated at USD 271 (between 205 and 357) million. The China–Indochina Peninsula Economic Corridor and China–Pakistan Economic Corridor have the highest absolute EAD and EAD per km, with EADs reaching USD 95 and USD 67 million, and USD 18 and USD 17 thousand per km, on average, respectively. For railway segments with high risks, we found that if the required adaptation cost within 20 years to realize a 10% increase of the railway quality is below 8.4% of the replacement cost, the benefits are positive.

While it is well known that the vast majority of the time only a portion of any watershed contributes runoff to the outlet, this extent is rarely documented. The power-law form of the streamflow and contributing area (Q-Ac) relationship has been known for a half century, but it is uncommon for it to be quantified or its controls evaluated. In this study a semi-distributed hydrological model (MESH-PDMROF) that can simulate contributing area and streamflow was employed to compare contributing area and flood frequency distributions in a southern Manitoba, Canada catchment and test the hypothesis that the relationship between a catchment’s floods and contributing area is a power function that influences the form of regional flood-area relationships. The model simulated streamflow reasonably well (Nash Sutcliffe values = 0.62). Modelled estimates of the area contributing to the mean annual flood were much lower (0.3) than those derived from independent topographic analysis (0.9) described in earlier literature, even after bias and error corrections. Estimates of the coefficient and exponent of the Q-Ac power law function ranged from 0.08–0.14 and 0.9–1.12, respectively. Lower exponent values of regional flood frequency curves suggest they are a construct of Q-Ac curves from individual basins. The non-linear nature of this relationship implies any contributing area change will have a profound impact on flood magnitude. The mean annual flood of the major river in this region, the Red, has increased 33 % since 1987. Applying the coefficient and exponent ranges above suggests this is associated with an expansion in contributing areas of 29–38 %. There are implications for the attribution of causes and mitigation of nutrient transport from regional watersheds. However, how physiography and land and water management could change Q-Ac power law exponents is poorly known and MESH-PDMROF does not provide explicit estimates of the spatial distribution of contributing area. These are areas encouraged for future research.

Our understanding of ecosystem responses to exceedingly large rare flood events is currently limited. We report the resilience of aquatic community to a 100-year record-high flood, and how it varies depending on levels of water pollution, in a fourth-order gravel-bed river in northern Japan. We used data on riparian landscape structure, channel morphology, and community structure of aquatic fauna, which were collected in sites with and without effluent before (1 month–3 years) and after (10 months) the flood. Carbon and nitrogen stable isotope ratios of consumers and basal resources were measured only before (1 year) the flood. We observed aquatic food web with introduced rainbow trout (Oncorhynchus mykiss) as the top predator, with variable relative contributions of basal resources and their pathways to the rainbow trout, under the effects of water pollution. Biofilm-originating dietary carbon became the more dominant resource, with a slightly shorter food-chain length in the polluted sites. The flood led to a loss of riparian forest and a substantial increase in the proportion of exposed gravel bars (5–24%). While the average river-bed elevation changed a little, the localized scours of river bed down to > 2 m were observed with lateral shifts of channel thalweg. Despite the landscape-level physical and structural changes of ecosystem, aquatic community showed a remarkably high resilience exhibiting negligible changes in abundance, except in the polluted site where only fish abundance showed a slight decrease. This study suggests that the abundance of aquatic organisms in gravel-bed rivers is resilient to a flood of unprecedented magnitude in recent history.

Subtropical systems experience occasional severe floods, dramatically altering the phytoplankton community structure, in response to changes in salinity, nutrients, and light. This study examined the effects of a 1:100 year summer flood on the phytoplankton community in an Australian subtropical bay – Moreton Bay – over 48 weeks, from January to December 2011. Immediately after maximum flood levels were reached on the rivers flowing into the bay, the lowest salinity, and highest turbidity values, in more than a decade, were measured in the Bay and the areal extent of the flood-related parameters was also far greater than previous flood events. Changes in these parameters together with changes in Colored Dissolved Organic Matter (CDOM) and sediment concentrations significantly reduced the light availability within the water column. Despite the reduced light availability, the phytoplankton community responded rapidly (1–2 weeks) to the nutrients from flood inputs, as measured using pigment concentrations and cell counts and observed in ocean color satellite imagery. Initially, the phytoplankton community was totally dominated by micro-phytoplankton, particularly diatoms; however, in the subsequent weeks (up to 48-weeks post flood) the community changed to one of nano- and pico-plankton in all areas of the Bay not usually affected by river flow. This trend is consistent with many other studies that show the ability of micro-phytoplankton to respond rapidly to increased nutrient availability, stimulating their growth rates. The results of this study suggest that one-off extreme floods have immediate, but short-lived effects, on phytoplankton species composition and biomass as a result of the interacting and dynamic effects of changes in nutrient and light availability.

Anecdotal information indicates that streams in the Mid-Atlantic region of the United States experience more extreme flood events than might be expected. This leads to the question of whether this is an unfounded perception or if these extreme events are actually occurring more than should be expected. If the latter is true, is this due solely to randomness, or alternately to characteristics that make certain watersheds more prone to repeated events that may be defined as 100-year or greater floods? These questions are investigated through analysis of flood events based on standard flood frequency analysis. 100-year streamflow rates for stream gages were estimated using Bulletin 17B flood frequency analysis methods, and the probability of the annual peak flow record for each gage was calculated. These probabilities were compared to a set of synthetic probabilities to evaluate their distribution. This comparison indicates that for the Mid-Atlantic region as a whole, the Bulletin 17B method does not systematically over or underestimate flood frequency. A Random Forest model of probability of actual flood record (PAFR) versus watershed and stream gage characteristics was developed and used to understand if certain characteristics are associated with PAFR. This analysis indicated that unexpected numbers of large flood events in a stream gage period of record can be attributed primarily to randomness, but there is some correlation with watershed and gage characteristics including weighted skew, drainage area, and mean annual peak discharge. The results indicate that watersheds with high values of these characteristics may warrant advanced flood frequency methods.

The US National Flood Insurance Program maps and classifies flood risk based on observed data for the frequency of flood events, using terms such as '100-year flood' and '500-year flood.' The purpose of these classifications is to convey information about the likelihood of a flood event to property owners and to inform the decision or mandate to purchase flood insurance. The flooding that followed Hurricane Harvey in 2017 brought heightened use of 100-year-flood terminology in the media. Often, the term was incorrectly used, misrepresenting the risk that is intended to be conveyed by the technical term. Misuse of flood terminology and related misperception of risk has important implications for society, including over- or under-insuring property owners and inadequate individual and collective preparedness. This paper presents the findings of a survey conducted to gauge understanding of Federal Emergency Management Agency flood risk terminology and to examine links between estimating flood risk and proximity to flooding, knowledge of independent probabilities, and demographics. We found that the driving force behind correctly interpreting technical flood terminology was understanding of independent probabilities, while education and recent exposure or proximity to a severe flood event did not have a statistically significant influence.

Estimation of flood events at ungauged sites is often performed through regional flood frequency analysis (RFFA). RFFA uses the available information at gauged sites to estimate the desired design events at the ungauged site. These regional methods are based on a prior aggregation of the hydrological information at the gauged sites, which implies loss of information. In the present study, a different approach or path for conducting RFFA is presented. First, the daily streamflow series at the ungauged site is regionally estimated from daily information at the gauged sites through a regional flow duration curve approach. Then, a local flood frequency analysis is performed on the extracted maximum peak flow series. The proposed approach, referred to as regional streamflow-based frequency analysis (RSBFA), is applied to a case study in the province of Quebec, Canada. Results indicate that the performance of the RSBFA approach is comparable to traditional methods. However, the proposed method has the a...

Floodplain mapping using hydrodynamic models is difficult in data scarce regions. Additionally, using hydrodynamic models to map floodplain over large stream network can be computationally challenging. Some of these limitations of floodplain mapping using hydrodynamic modeling can be overcome by developing computationally efficient statistical methods to identify floodplains in large and ungauged watersheds using publicly available data. This paper proposes a geomorphic model to generate probabilistic 100-year floodplain maps for the Conterminous United States (CONUS). The proposed model first categorizes the watersheds in the CONUS into three classes based on the height of the water surface corresponding to the 100-year flood from the streambed. Next, the probability that any watershed in the CONUS belongs to one of these three classes is computed through supervised classification using watershed characteristics related to topography, hydrography, land use and climate. The result of this classification is then fed into a probabilistic threshold binary classifier (PTBC) to generate the probabilistic 100-year floodplain maps. The supervised classification algorithm is trained by using the 100-year Flood Insurance Rated Maps (FIRM) from the U.S. Federal Emergency Management Agency (FEMA). FEMA FIRMs are also used to validate the performance of the proposed model in areas not included in the training. Additionally, HEC-RAS model generated flood inundation extents are used to validate the model performance at fifteen sites that lack FEMA maps. Validation results show that the probabilistic 100-year floodplain maps, generated by proposed model, match well with both FEMA and HEC-RAS generated maps. On average, the error of predicted flood extents is around 14% across the CONUS. The high accuracy of the validation results shows the reliability of the geomorphic model as an alternative approach for fast and cost effective delineation of 100-year floodplains for the CONUS.

Flood risks across the Pearl River basin, China, were evaluated using a peak flood flow dataset covering a period of 1951–2014 from 78 stations and historical flood records of the past 1000 years. The generalized extreme value (GEV) model and the kernel estimation method were used to evaluate frequencies and risks of hazardous flood events. Results indicated that (1) no abrupt changes or significant trends could be detected in peak flood flow series at most of the stations, and only 16 out of 78 stations exhibited significant peak flood flow changes with change points around 1990. Peak flood flow in the West River basin increased and significant increasing trends were identified during 1981–2010; decreasing peak flood flow was found in coastal regions and significant trends were observed during 1951–2014 and 1966–2014. (2) The largest three flood events were found to cluster in both space and time. Generally, basin-scale flood hazards can be expected in the West and North River basins. (3) The occurrence rate of floods increased in the middle Pearl River basin but decreased in the lower Pearl River basin. However, hazardous flood events were observed in the middle and lower Pearl River basin, and this is particularly true for the past 100 years. However, precipitation extremes were subject to moderate variations and human activities, such as building of levees, channelization of river systems, and rapid urbanization; these were the factors behind the amplification of floods in the middle and lower Pearl River basin, posing serious challenges for developing measures of mitigation of flood hazards in the lower Pearl River basin, particularly the Pearl River Delta (PRD) region.

In this work, we use the 2D model XBeach to dynamically simulate coastal erosion due to a synthetic 100-year storm impacting a typical North Atlantic barrier beach located in southern RI. This storm was extracted from the North Atlantic Coast Comprehensive Study (NACCS) database, based on results of an extreme value analysis of more than 1000 NACCS storms. XBeach parameters are first calibrated/validated by simulating Hurricane Irene (August 2011), for which both nearshore wave data and pre- and post-storm beach profiles were available in the study area. Comparing results to observations allowed calibration of the wave asymmetry and skewness parameter ( γ u a = 0.3 ) in the model, resulting in a 6% mean relative error between the simulated and measured subaerial eroded volumes along 4 transects. In the 100-year storm XBeach simulations that include overwash, effects of land cover on beach erosion, in particular vegetation, are assessed by specifying a spatially varying bed friction function of high-resolution land cover. Results show that healthy back-dune vegetation is essential to prevent the dune crest from being fully eroded down to its toe level. The predicted median 100-year eroded volume is 46 m3/m for the entire barrier beach, in good agreement with FEMA's empirical value of 50 m3/m at the two “official 1D transects” within the study area; mean post-storm reductions in dune crest elevations are in similar agreement. The model, however, predicts very large alongshore variations of these parameters, with eroded volumes over 1000 m3/m where breaching and the opening of surge channels occurs. Overall, dune segmentation simulated in the model for the 100-year storm appears to be realistic and consistent with dune topography and land cover. XBeach thus provides an improved 2D methodology for assessing the impact of extreme storms on Atlantic barrier beaches, predicting changes in dune morphology, and quantifying the protective role of vegetation, and effects of land cover in general.

This paper presents quantitative criteria for flash flood warning that can be used to rapidly assess flash flood occurrence based on only rainfall estimates. This study was conducted for 200 small mountainous sub-catchments of the Han River basin in South Korea because South Korea has recently suffered many flash flood events. The quantitative criteria are calculated based on flash flood guidance (FFG), which is defined as the depth of rainfall of a given duration required to cause frequent flooding (1–2-year return period) at the outlet of a small stream basin and is estimated using threshold runoff (TR) and antecedent soil moisture conditions in all sub-basins. The soil moisture conditions were estimated during the flooding season, i.e., July, August and September, over 7 years (2002–2009) using the Sejong University Rainfall Runoff (SURR) model. A ROC (receiver operating characteristic) analysis was used to obtain optimum rainfall values and a generalized precipitation–area (P–A) curve was developed for flash flood warning thresholds. The threshold function was derived as a P–A curve because the precipitation threshold with a short duration is more closely related to basin area than any other variables. For a brief description of the P–A curve, generalized thresholds for flash flood warnings can be suggested for rainfall rates of 42, 32 and 20 mm h−1 in sub-basins with areas of 22–40, 40–100 and > 100 km2, respectively. The proposed P–A curve was validated based on observed flash flood events in different sub-basins. Flash flood occurrences were captured for 9 out of 12 events. This result can be used instead of FFG to identify brief flash flood (less than 1 h), and it can provide warning information to decision-makers or citizens that is relatively simple, clear and immediate.

Schedel, J.R., Jr. and Schedel, A.L., 2018. Analysis of variance of flood events on the U.S. East Coast: The impact of sea-level rise on flood event severity and frequency. This is an investigation to quantify the severity and frequency of coastal flood events on the U.S. East Coast. Flood events are defined as instances during which the water level exceeds a predetermined threshold. An examination of monthly water-level data from 13 locations on the U.S. East Coast shows an increasing mean sea-level trend. Analysis of variance was used to compare the frequency of flood events. At all 13 locations, flood events were compared to established moderate and minor flood stages in the pre-1990 and post-1990 time frames. Flood stages are defined as water-level heights associated with various levels of flooding. For all locations, compared to absolute water heights based on a fixed datum, these flood stages were exceeded more frequently today than in the past. However, when the effects of sea-level rise w...

During preliminary flood risk assessment in Lithuania 54 significant flood areas (SFA) were identified. The detailed flood hazard and risk maps were prepared for these areas in 2014. European Union Floods Directive does not indicate the concrete criteria for SFA delineation. The uncertainty analysis shows that the total length of SFA is not very sensitive to used methodology. In some rivers the uncertainties of 100 year flood peek discharge (Q1%) were large, but the variation of SFA boundary location was relatively small due to properties of hydrological network. The catchment area and Q1% change rapidly near the junction with large tributaries, so the boundaries of SFA are usually attached to these junctions. The formal criteria are mostly used to evaluate the possibility of significant floods, but the delineation of SFA is usually based on subjective decision.

There is a chronic disconnection among purely probabilistic flood frequency analysis of flood hazards, flood risks, and hydrological flood mechanisms, which hamper our ability to assess future flood impacts. We present a vulnerability-based approach to estimating riverine flood risk that accommodates a more direct linkage between decision-relevant metrics of risk and the dominant mechanisms that cause riverine flooding. We adapt the conventional peaks-over-threshold (POT) framework to be used with extreme precipitation from different climate processes and rainfall-runoff-based model output. We quantify the probability that at least one adverse hydrologic threshold, potentially defined by stakeholders, will be exceeded within the next N years. This approach allows us to consider flood risk as the summation of risk from separate atmospheric mechanisms, and supports a more direct mapping between hazards and societal outcomes. We perform this analysis within a bottom-up framework to consider the relevance and consequences of information, with varying levels of credibility, on changes to atmospheric patterns driving extreme precipitation events. We demonstrate our proposed approach using a case study for Fall Creek in Ithaca, NY, USA, where we estimate the risk of stakeholder-defined flood metrics from three dominant mechanisms: summer convection, tropical cyclones, and spring rain and snowmelt. Using downscaled climate projections, we determine how flood risk associated with a subset of mechanisms may change in the future, and the resultant shift to annual flood risk. The flood risk approach we propose can provide powerful new insights into future flood threats.

Pluvial flooding is a problem in many cities and for city planning purpose the mechanisms behind pluvial flooding are of interest. Previous studies seldom use insurance claim data to analyse city scale characteristics that lead to flooding. In the present study, two long time series (∼20 years) of flood claims from property owners have been collected and analysed in detail to investigate the mechanisms and characteristics leading to urban flooding. The flood claim data come from the municipal water utility company and property owners with insurance that covers property loss from overland flooding, groundwater intrusion through basement walls and flooding from the drainage system. These data are used as a proxy for flood severity for several events in the Swedish city of Malmo. It is discussed which rainfall characteristics give most flooding and why some rainfall events do not lead to severe flooding, how city scale topography and sewerage system type influence spatial distribution of flood claims, and which impact high sea level has on flooding in Malmo. Three severe flood events are described in detail and compared with a number of smaller flood events. It was found that the main mechanisms and characteristics of flood extent and its spatial distribution in Malmo are intensity and spatial distribution of rainfall, distance to the main sewer system as well as overland flow paths, and type of drainage system, while high sea level has little impact on the flood extent. Finally, measures that could be taken to lower the flood risk in Malmo, and other cities with similar characteristics, are discussed.

The present work introduces a national scale flood hazard assessment methodology, using multi-criteria analysis and artificial neural networks (ANNs) techniques in a GIS environment. The proposed methodology was applied in Greece, where flash floods are a relatively frequent phenomenon and it has become more intense over the last decades, causing significant damages in rural and urban sectors. In order the most prone flooding areas to be identified, seven factor-maps (that are directly related to flood generation) were combined in a GIS environment. These factor-maps are: a) the Flow accumulation (F), b) the Land use (L), c) the Altitude (A), b) the Slope (S), e) the soil Erodibility (E), f) the Rainfall intensity (R), and g) the available water Capacity (C). The name to the proposed method is "FLASERC". The flood hazard for each one of these factors is classified into five categories: Very low, low, moderate, high, and very high. The above factors are combined and processed using the appropriate ANN algorithm tool. For the ANN training process spatial distribution of historical flooded points in Greece within the five different flood hazard categories of the aforementioned seven factor-maps were combined. In this way, the overall flood hazard map for Greece was determined. The final results are verified using additional historical flood events that have occurred in Greece over the last 100years. In addition, an overview of flood protection measures and adaptation policy approaches were proposed for agricultural and urban areas located at very high flood hazard areas.

A comprehensive flood risk assessment should aim not only at quantifying uncertainties but also the variability of risk over time. In this study, an efficient modelling framework was proposed to perform probabilistic hazard and risk analysis in dike-protected river systems accounting for morphological variability and uncertainty. The modelling framework combined the use of: (1) continuous synthetic discharge forcing, (2) a stochastic dike breach model dynamically coupled to a stochastic unsteady one-dimensional hydraulic model (MIKE1D) describing river flows, (3) a catalogue of pre-run probabilistic inundation maps (MIKE SHE) and (4) a damage and loss model (CAPRA). The methodology was applied using continuous simulations to a 45-km reach of the Upper Koshi River, Nepal, to investigate the changes in breach and flood hazards and subsequent risks after 2 and 5 years of probable river bed aggradation. The study results indicated an increase in annual average loss of 4% per year driven by changes in loss distribution in the most frequent loss return periods (20–500 years). The use of continuous simulations and dike breach model also provided a more robust estimation of risk metrics as compared to traditional binary treatment of flood defence and/or the direct association of flow with loss return periods. The results were helpful to illustrate the potential impacts of dynamic river morphology, dike failure and continuous simulation and their significance when devising flood risk study methodologies.

Current understanding of flood response is deficient concerning the variation of flood generation at different spatial scales as a function of spatial and temporal variations in storm rainfall. This study therefore investigates the relationship between rainfall spatial variability and flood response through a multi-scale nested experiment. Hydrological data from an extensive network in the Eden catchment, UK, were collected for a range of flood events over varying scales from 1.1 km2 to 2,286 km2. The data were analysed to show the spatial scale dependency of flood peak and lag time. Peak specific discharge for winter events appears to remain constant with area up to 20–30 km2, corresponding to the main upland headwater catchments, thereafter declining as area increases. The flood response to the convective storms depends on the location of the rainfall and the downstream rates of change of runoff and peak discharge can vary significantly from the winter storm relationships. Particularly for large synoptic storms, average scaling laws for peak discharge have been quantified (exponents ranging between 0.75 and 0.86), illustrating the non-linear nature of the cross-scale variations. Such laws provide a means of linking the headwater catchments with the larger scale at which planners and decision-makers operate.

The extensive constructions of reservoirs change the hydrologic characteristics of the associated watersheds, which increases the complexity of watershed flood control decisions. By evaluating the impacts of the multi-reservoir system on the flood hydrograph, it becomes possible to improve the effectiveness of the flood control decisions. This study compares the non-reservoir flood hydrograph with the actual observed flood hydrograph using the Lutaizi upstream of Huai River in East China as a representative case, where 20 large-scale/large-sized reservoirs have been built. Based on the total impact of the multi-reservoir system, a novel strategy is presented to evaluate the contribution of each reservoir to the total impact. According their contributions, the “highly effective” reservoirs for watershed flood control are identified via hierarchical clustering. Moreover, the degree of impact of the reservoir operation rules on the flood hydrograph are estimated. We find the multi-reservoir system of Huai River has a significant impact on flooding at the Lutaizi section, on average reducing the flood volume and peak discharge by 13.92 × 108 m3 and 18.7% respectively. Under the current operation rules, the volume and peak discharge of flooding at the Lutaizi section are reduced by 13.69 × 108 m3 and 1429 m3/s respectively. Each reservoir has a different impact on the flood hydrograph at the Lutaizi section. In particular, the Meishan, Xianghongdian, Suyahu, Nanwan, Nianyushan and Foziling reservoirs exert a strong influence on the flood hydrograph, and are therefore important for flood control on the Huai River.

Flood frequency analysis (FFA) is fundamental for providing hazard probability of flood risk assessment as well as for determining design flood. It is often the case that mega cities are located along downstream reaches of a large river basin in many areas of all over the world, and their extreme flood frequencies are assumed to be highly affected by dam operation and river overflow of its upstream areas. In particular, when upstream areas are also protected by river dike system, historical discharge samples cannot represent the impact of upstream river overflow on downstream extreme flood frequencies because it rarely occurs. FFA without this consideration, especially in large river basins which include several potential floodplains, would lead to inappropriate assessment of flood risk. To deal with this issue, FFA needs to incorporate flood-inundation modelling of upstream areas; however, previous studies on FFA have focused on smaller watersheds and combined rainfall-runoff models. Therefore, this study examined the impact of river overflow and dam operation of upstream areas on downstream extreme flood frequencies through a case study of the Yodo River basin (7280 km2). To achieve FFA in a large river basin, this study combined a flood-inundation model of upstream Kyoto City area to a rainfall-based flood frequency model (RFFM) which accounts for the probability of spatial and temporal rainfall pattern over the river basin in a practical manner. The RFFM was validated with reproduced discharge samples of historical storm events and then applied to extreme flood frequency estimation. The application clarified that upstream river overflow causes much more drastic change of downstream flood frequencies beyond the design level than dam operation, which indicates that FFA for flood risk assessment needs to consider river overflow of its upstream areas otherwise flood risk of downstream areas will be overestimated. Furthermore, the scheme also produced the cumulative distribution function of flood area, which represents flood risk of upstream areas.

Cost-benefit analysis is commonly used for engineering planning and design problems in practice. However, previous cost-benefit based design flood estimation is based on stationary assumption. This study develops a non-stationary cost-benefit based design flood estimation approach. This approach integrates a non-stationary probability distribution function into cost-benefit analysis, and influence of non-stationarity on expected total cost (including flood damage and construction costs) and design flood estimation can be quantified. To facilitate design flood selections, a ‘Risk-Cost’ analysis approach is developed, which reveals the nexus of extreme flood risk, expected total cost and design life periods. Two basins, with 54-year and 104-year flood data respectively, are utilized to illustrate the application. It is found that the developed approach can effectively reveal changes of expected total cost and extreme floods in different design life periods. In addition, trade-offs are found between extreme flood risk and expected total cost, which reflect increases in cost to mitigate risk. Comparing with stationary approaches which generate only one expected total cost curve and therefore only one design flood estimation, the proposed new approach generate design flood estimation intervals and the ‘Risk-Cost’ approach selects a design flood value from the intervals based on the trade-offs between extreme flood risk and expected total cost. This study provides a new approach towards a better understanding of the influence of non-stationarity on expected total cost and design floods, and could be beneficial to cost-benefit based non-stationary design flood estimation across the world.

Extreme flood estimation has been a key research topic in hydrological sciences. Reliable estimates of such events are necessary as structures for flood conveyance are continuously evolving in size and complexity and, as a result, their failure-associated hazards become more and more pronounced. Due to this fact, several estimation techniques intended to improve flood frequency analysis and reducing uncertainty in extreme quantile estimation have been addressed in the literature in the last decades. In this paper, we develop a Bayesian framework for the indirect estimation of extreme flood quantiles from rainfall-runoff models. In the proposed approach, an ensemble of long daily rainfall series is simulated with a stochastic generator, which models extreme rainfall amounts with an upper-bounded distribution function, namely, the 4-parameter lognormal model. The rationale behind the generation model is that physical limits for rainfall amounts, and consequently for floods, exist and, by imposing an appropriate upper bound for the probabilistic model, more plausible estimates can be obtained for those rainfall quantiles with very low exceedance probabilities. Daily rainfall time series are converted into streamflows by routing each realization of the synthetic ensemble through a conceptual hydrologic model, the Rio Grande rainfall-runoff model. Calibration of parameters is performed through a nonlinear regression model, by means of the specification of a statistical model for the residuals that is able to accommodate autocorrelation, heteroscedasticity and nonnormality. By combining the outlined steps in a Bayesian structure of analysis, one is able to properly summarize the resulting uncertainty and estimating more accurate credible intervals for a set of flood quantiles of interest. The method for extreme flood indirect estimation was applied to the American river catchment, at the Folsom dam, in the state of California, USA. Results show that most floods, including exceptionally large non-systematic events, were reasonably estimated with the proposed approach. In addition, by accounting for uncertainties in each modeling step, one is able to obtain a better understanding of the influential factors in large flood formation dynamics.

The map shows the result of a procedure for pluvial flood hazard (PFH) mapping in urban areas, developed by using easy to find data, usually available from local authorities. Data were processed using a sequence of tools in a GIS environment. Two parameters have been evaluated: (1) susceptibility, defined as the probability of a flood occurring in a certain area (‘flood-prone areas’) which depends on the ground morphology (i.e. presence of depressions, fill volume of depressions) and spatial density of previously observed floods and (2) potential impact, formed from all factors influencing the damage (e.g. value of exposed heritage or number of people potentially involved), as well as the induced hazard due to damage. Susceptibility and potential impact were each divided into five classes and a score matrix was constructed; the final PFH is then defined by the summation of the scores within the matrix. The methodology used is suitable for a comprehensive, mostly automatic, first-level analysis of PFH in urban areas, and it is easily replicable. The obtained flood hazard map could provide a useful tool for civil protection purposes, that is, for hazard evaluation and emergency planning.

Flooding in urban areas during heavy rainfall, often characterized by short duration and high-intensity events, is known as "surface water flooding." Analyzing surface water flood risk is complex as it requires understanding of biophysical and human factors, such as the localized scale and nature of heavy precipitation events, characteristics of the urban area affected (including detailed topography and drainage networks), and the spatial distribution of economic and social vulnerability. Climate change is recognized as having the potential to enhance the intensity and frequency of heavy rainfall events. This study develops a methodology to link high spatial resolution probabilistic projections of hourly precipitation with detailed surface water flood depth maps and characterization of urban vulnerability to estimate surface water flood risk. It incorporates probabilistic information on the range of uncertainties in future precipitation in a changing climate. The method is applied to a case study of Greater London and highlights that both the frequency and spatial extent of surface water flood events are set to increase under future climate change. The expected annual damage from surface water flooding is estimated to be to be £171 million, £343 million, and £390 million/year under the baseline, 2030 high, and 2050 high climate change scenarios, respectively.

Author(s): Matti, B; Dahlke, HE; Dieppois, B; Lawler, DM; Lyon, SW | Abstract: Fluvial flood events have substantial impacts on humans, both socially and economically, as well as on ecosystems (e.g., hydroecology and pollutant transport). Concurrent with climate change, the seasonality of flooding in cold environments is expected to shift from a snowmelt-dominated to a rainfall-dominated flow regime. This would have profound impacts on water management strategies, that is, flood risk mitigation, drinking water supply, and hydro power. In addition, cold climate hydrological systems exhibit complex interactions with catchment properties and large-scale climate fluctuations making the manifestation of changes difficult to detect and predict. Understanding a possible change in flood seasonality and defining related key drivers therefore is essential to mitigate risk and to keep management strategies viable under a changing climate. This study explores changes in flood seasonality across near-natural catchments in Scandinavia using circular statistics and trend tests. Results indicate strong seasonality in flooding for snowmelt-dominated catchments with a single peak occurring in spring and early summer (March through June), whereas flood peaks are more equally distributed throughout the year for catchments located close to the Atlantic coast and in the south of the study area. Flood seasonality has changed over the past century seen as decreasing trends in summer maximum daily flows and increasing winter and spring maximum daily flows with 5–35% of the catchments showing significant changes at the 5% significance level. Seasonal mean daily flows corroborate those findings with higher percentages (5–60%) of the catchments showing statistically significant changes. Alterations in annual flood occurrence also point towards a shift in flow regime from snowmelt-dominated to rainfall-dominated with consistent changes towards earlier timing of the flood peak (significant for 25% of the catchments). Regionally consistent patterns suggest a first-order climate control as well as a local second-order catchment control, which causes inter-seasonal variability in the streamflow response.

To bridge the gap between 1D and 2D hydraulic models for regional scale assessment and global river routing models, we coupled the CaMa-Flood (Catchment-based Macro-scale Floodplain) model and the regional hydrological model SWIM (Soil and Water Integrated Model) as a tool for large-scale flood risk assessments. As a proof-of-concept study, we tested the coupled models in a meso-scale catchment in Germany. The Mulde River has a catchment area of ca. 6,171 km2 and is a sub-catchment of the Elbe River. The modified CaMa-Flood model routes the sub-basin-based daily runoff generated by SWIM along the river network and estimates the river discharge as well as flood inundation areas. The results show that the CaMa-Flood hydrodynamic algorithm can reproduce the daily discharges from 1991 to 2003 well. It outperforms the Muskingum flow routing method (the default routing method in the SWIM) for the 2002 extreme flood event. The simulated flood inundation area in August 2002 is comparable with the observations along the main river. However, problems may occur in upstream areas. The results presented here show the potential of the coupled models for flood risk assessments along large rivers.