Evaluation of Flood and Landslide Risk to the Population of Italy

We have compiled a database of floods and landslides that occurred in Italy between AD 1279 and 2002 and caused deaths, missing persons, injuries, and homelessness. Analysis of the database indicates that more than 50,593 people died, went missing, or were injured in 2580 flood and landslide events. Harmful events were inventoried in 26.3% of the 8103 Italian municipalities. Fatal events were most frequent in the Alpine regions of northern Italy and were caused by both floods and landslides. In southern Italy, landslides were the principal agents of fatalities and were most numerous in the Campania region. Casualties were most frequent in the autumn. Fast-moving landslides, including rock falls, rockslides, rock avalanches, and debris flows, caused the largest number of deaths. In order to assess the overall risk posed by these processes, we merged the historical catalogs and identified 2682 “hydrogeomorphological” events that triggered single or multiple landslides and floods. We estimated individual risk through the calculation of mortality rates for both floods and landslides and compared these rates to the death rates for other natural, medical, and human-induced hazards in Italy. We used the frequency distribution of events with fatalities to ascertain the magnitude and frequency of the societal risks posed by floods and landslides. We quantified these risks in a Bayesian model that describes the probabilities of fatal flood and landslide events in Italy.     

Transboundary River Floods and Institutional Capacity1

Abstract: While transboundary flood events have become more frequent on a global scale the past two decades, they appear to be overlooked in the international river basin (IRB) cooperation and management arena. The present study therefore combined geopolitical measures with biophysical and socioeconomic variables in an attempt to identify the IRBs with adequate institutional capacity for management of transboundary floods. It also classified basins that would possibly benefit from enlarging the institutional capacity related to transboundary floods. Of the 279 known IRBs, only 78 were represented by a transboundary rivers institution. A mere eight of the 153 identified institutions had transboundary flooding listed as an issue in their mandate. Overall, 43 basins, where transboundary floods were frequent during the period 1985‐2005, had no institutional capacity for IRBs. The average death and displacement tolls were found to be lower in the 37 basins with institutional capacity, even though these basins experienced twice as much transboundary floods with significant higher magnitudes than those in basins without institutional capacity. Overall, the results suggested that institutional capacity plays a role in the reduction of flood‐related casualties and affected individuals. River basins such as the Juba‐Shibeli, Han, Kura‐Araks, Ma, Maritsa, Po, Coco/Segovia, Grijalva, Artibonite, Changuinola, Coatan Achute, and Orinoco experienced more than one transboundary river flood, but have not yet set up any institutions for such events, or signed any appropriate treaties focused on floods. These basins were therefore recommended to consider focusing attention on this apparent lack of institutional capacity when it comes to managing transboundary flood events.     

Climate Change Impacts and Uncertainties on Spring Flooding of Lake Champlain and the Richelieu River

The source of the Richelieu River is Lake Champlain, located between the states of New York, Vermont, and Québec. In 2011, the lake and the Richelieu River reached historical flood levels, raising questions about the influence of climate change on the watershed. The objectives of this work are to model the hydrology of the watershed, construct a reservoir model for the lake and to analyze flooding trends using climate simulations. The basin was modeled using the HSAMI lumped conceptual model from Hydro‐Québec with a semi‐distributed approach in order to estimate the inflows into Lake Champlain. The discharge at the Richelieu River was computed by using a mass balance equation between the inputs and outputs of Lake Champlain. Future trends were estimated over the 2041‐2070 and 2071‐2100 periods using a large number of outputs from general circulation models and regional climate models downscaled with constant scaling and daily translation methods. While there is a certain amount of uncertainty as to future trends, there is a decreasing tendency in the magnitude of the mean spring flood. A flood frequency analysis showed most climate projections indicate the severity of most extreme spring floods may be reduced over the two future periods although results are subject to a much larger uncertainty than for the mean spring flood. On the other hand, results indicate summer‐fall extreme events such as caused by hurricane Irene in August 2011 may become more frequent in the future.     

HISTORICAL EVOLUTION OF FLOODING DAMAGE ON A USA/QUEBEC RIVER BASIN1

ABSTRACT: There is a general belief in the public eye that extreme events such as floods are becoming more and more common. This paper explores this hypothesis by examining the historical evolution of annual expected flooding damage on the Chateauguay River Basin, located at the border between the United States and the province of Quebec, Canada. A database of basin land use was constructed for the years 1930 and 1995 to assess anthropogenic changes and their impact on the basin's hydrology. The progressive modification of the likelihood of a flooding event over the same period was then investigated using homogeneity and statistical tests on available hydrometric data. The evolution of the annual expected flooding damage was then evaluated using a coupled hydrologic/hydraulic simulator linked to a damage analysis model. The simulator and model were used to estimate flooding damage over a wide range of flooding return periods, for conditions prevailing in 1963 and 1995. Results of the analysis reveal the absence of any increasing or decreasing trend in the historical occurrence of flooding events. However, a general increase in the annual expected flooding damage was observed for all studied river sections. This increase is linked to an historical increase in damages for a given flooding event, and is the result of unbridled construction and development within the flood zone. To assess for future trends, this study also examined the potential impacts linked to the anticipated global warming. Results indicate that a significant increase in seasonal flooding events and annual expected flooding damage is possible over the next century. In fact, what is now considered a 100‐year flooding event for the summer/fall season could become a ten‐year event by the end of this century. This shows that potential future impacts linked to climate change should be considered now by engineers, land planners, and decision makers. This is especially critical if a design return period is part of the decision making process.     

An Analysis of the Effects of Land Use and Land Cover on Flood Losses along the Gulf of Mexico Coast from 1999 to 2009

Major coastal flooding events over the last decade have led decision makers in the United States to favor structural engineering solutions as a means to protect vulnerable coastal communities from the adverse impacts of future storms. While a resistance‐based approach to flood mitigation involving large‐scale construction works may be a central component of a regional flood risk reduction strategy, it is equally important to consider the role of land use and land cover (LULC) patterns in protecting communities from floods. To date, little observational research has been conducted to quantify the effects of various LULC configurations on the amount of property damage occurring across coastal regions over time. In response, we statistically examine the impacts of LULC on observed flood damage across 2,692 watersheds bordering the Gulf of Mexico. Specifically, we analyze statistical linear regression models to isolate the influence of multiple LULC categories on over 372,000 insured flood losses claimed under the National Flood Insurance Program per year from 2001 to 2008. Results indicate that percent increase in palustrine wetlands is the equivalent to, on average, a $13,975 reduction in insured flood losses per year, per watershed. These and other results provide important insights to policy makers on how protecting specific types of LULC can help reduce adverse impacts to local communities.     

Cyberinfrastructure and Web Apps for Managing and Disseminating the National Water Model

Hydrologic modeling can be used to provide warnings before, and to support operations during and after floods. Recent technological advances have increased our ability to create hydrologic models over large areas. In the United States (U.S.), a new National Water Model (NWM) that generates hydrologic variables at a national scale was released in August 2016. This model represents a substantial step forward in our ability to predict hydrologic events in a consistent fashion across the entire U.S. Nevertheless, for these hydrologic results to be effectively communicated, they need to be put in context and be presented in a way that is straightforward and facilitates management‐related decisions. The large amounts of data produced by the NWM present one of the major challenges to fulfill this goal. We created a cyberinfrastructure to store NWM results, “accessibility” web applications to retrieve NWM results, and a REST API to access NWM results programmatically. To demonstrate the utility of this cyberinfrastructure, we created additional web apps that illustrate how to use our REST API and communicate hydrologic forecasts with the aid of dynamic flood maps. This work offers a starting point for the development of a more comprehensive toolset to validate the NWM while also improving the ability to access and visualize NWM forecasts, and develop additional national‐scale‐derived products such as flood maps.     

Nonparametric Monte Carlo Simulation for Flood Frequency Curve Derivation: An Application to a Korean Watershed1

Abstract: A mix of causative mechanisms may be responsible for flood at a site. Floods may be caused because of extreme rainfall or rain on other rainfall events. The statistical attributes of these events differ according to the watershed characteristics and the causes. Traditional methods of flood frequency analysis are only adequate for specific situations. Also, to address the uncertainty of flood frequency estimates for hydraulic structures, a series of probabilistic analyses of rainfall‐runoff and flow routing models, and their associated inputs, are used. This is a complex problem in that the probability distributions of multiple independent and derived random variables need to be estimated to evaluate the probability of floods. Therefore, the objectives of this study were to develop a flood frequency curve derivation method driven by multiple random variables and to develop a tool that can consider the uncertainties of design floods. This study focuses on developing a flood frequency curve based on nonparametric statistical methods for the estimation of probabilities of rare floods that are more appropriate in Korea. To derive the frequency curve, rainfall generation using the nonparametric kernel density estimation approach is proposed. Many flood events are simulated by nonparametric Monte Carlo simulations coupled with the center Latin hypercube sampling method to estimate the associated uncertainty. This study applies the methods described to a Korean watershed. The results provide higher physical appropriateness and reasonable estimates of design flood.     

Flood Forecasting Using Neural Computing Techniques and Conceptual Class Segregation

Various neural networks models are developed and applied for flood forecasting at Sangye station (no. 1) of the Bocheong Stream catchment, which is one of the International Hydrological Program's representative catchments, Republic of Korea. The neural networks models (NNMs) are multilayer perceptron‐neural networks model (MLP‐NNM), generalized regression neural networks model (GRNNM), and Kohonen self‐organizing feature maps neural networks model (KSOFM‐NNM). Data used for model training and testing are divided into two groups: such as floods and typhoon events. Single conventional application and class segregation implementation are applied to evaluate the neural networks models. KSOFM‐NNM forecasts flood discharge more accurately than do MLP‐NNM and GRNNM for the testing data of Methods I and II for single conventional application and class segregation implementation. This study shows that class segregation can capture the dynamics of different physical processes and overcome the difficulties using single conventional application of neural networks models.     

Flash Flood Occurrences Since the 17th Century in Steep Drainage Basins in Southern Italy

The historical floods that have occurred since the seventeenth century were collected for a study area in southern Italy. Damages caused by floods, rainfall and the main anthropogenic modifications are discussed all together. The aim was to assess whether the frequency of floods is changing and, if so, whether these changes can be attributed to either rainfall and/or anthropogenic modifications. In 4?% of cases, mainly occurred in past centuries, floods damaged people. Hydraulic works, roads and private buildings were the more frequently damaged elements (25, 18 and 14?% of the cases, respectively). The annual variability of rainfall was discussed using an annual index. Short duration-high intensity rainfalls were characterized considering time series of annual maxima of 1, 3, 6, 12, and 24?h and daily rainfall. The rainfall shows a decreasing trend, in terms of both the annual maximum of short duration and the annual amount. The population has been progressively increasing since the sixteenth century, except during the years following the catastrophic 1908 earthquake. The rate of population growth has been very high since the second half of the twentieth century; the urbanized areas greatly increased, especially following the second half of the twentieth century. At the same time, the trend of damaging floods has been increasing, especially since the seventies. The analysis indicates that, despite a rainfall trend favourable towards a reduction in flood occurrence, floods damage has not decreased. This seems to be mainly the effect of mismanagement of land use modifications.     

A Probabilistic,Parcel‐Level Inundation Prediction Tool for Medium‐Range Flood Forecasting in Large Lake Systems

This study contributes a bathtub‐style inundation prediction model with abstractions of coastal processes (i.e., storm surge and wave runup) for flood forecasting at medium‐range (weekly to monthly) timescales along the coastline of large lakes. Uncertainty from multiple data sources are propagated through the model to establish probabilistic bounds of inundation, providing a conservative measure of risk. The model is developed in a case study of the New York Lake Ontario shoreline, which has experienced two record‐setting floods over the course of three years (2017–2019). Predictions are developed at a parcel‐level and are validated using inundation accounts from an online survey and flyover imagery taken during the recent flood events. Model predictions are compared against a baseline, deterministic model that accounts for the same processes but does not propagate forward data uncertainties. Results suggest that a probabilistic approach helps capture observed instances of inundation that would otherwise be missed by a deterministic inundation model. However, downward biases are still present in probabilistic predictions, especially for parcels impacted by wave runup. The goal of the tool is to provide community planners and property owners with a conservative, parcel‐level assessment of flood risk to help inform short‐term emergency response and better prepare for future flood events.     

Runoff Curve Numbers at the Agricultural Field‐Scale and Implications for Continuous Simulation Modeling

This study used monitoring in the waterways of agricultural fields to understand the use of the runoff curve number (CN) in continuous simulation models. The CN has a long history as a design tool for estimating runoff volumes for large, single storms on small watersheds, but its use in continuous simulation models to describe runoff from smaller storms and relatively small areas is more recent and controversial. We examined 788 nonwinter rainfall events on four agricultural fields over five years (2004‐2008) during which runoff was generated in 87 events. The largest 20 runoff events on each field generated approximately 90% of the total runoff volume. The runoff event CNs showed an inverse correlation with storm depth that could not consistently be explained by previous precipitation. We review how small areas of higher runoff generation within larger areas will systematically increase the apparent CN of the larger area as the storm size decreases. If this variation is not incorporated into a model explicitly, continuous simulation modelers must understand that when source areas are aggregated or when runoff generation is spatially variable, the overall CN is not unique when smaller storms are included in the calibration set.     

Climate Change,Atmospheric Rivers,and Floods in California – A Multimodel Analysis of Storm Frequency and Magnitude Changes1

Dettinger, Michael, 2011. Climate Change, Atmospheric Rivers, and Floods in California – A Multimodel Analysis of Storm Frequency and Magnitude Changes. Journal of the American Water Resources Association (JAWRA) 47(3):514‐523. DOI: 10.1111/j.1752‐1688.2011.00546.x Abstract: Recent studies have documented the important role that “atmospheric rivers” (ARs) of concentrated near‐surface water vapor above the Pacific Ocean play in the storms and floods in California, Oregon, and Washington. By delivering large masses of warm, moist air (sometimes directly from the Tropics), ARs establish conditions for the kinds of high snowlines and copious orographic rainfall that have caused the largest historical storms. In many California rivers, essentially all major historical floods have been associated with AR storms. As an example of the kinds of storm changes that may influence future flood frequencies, the occurrence of such storms in historical observations and in a 7‐model ensemble of historical‐climate and projected future climate simulations is evaluated. Under an A2 greenhouse‐gas emissions scenario (with emissions accelerating throughout the 21st Century), average AR statistics do not change much in most climate models; however, extremes change notably. Years with many AR episodes increase, ARs with higher‐than‐historical water‐vapor transport rates increase, and AR storm‐temperatures increase. Furthermore, the peak season within which most ARs occur is commonly projected to lengthen, extending the flood‐hazard season. All of these tendencies could increase opportunities for both more frequent and more severe floods in California under projected climate changes.     

Analysis of Damaging Hydrogeological Events: The Case of the Calabria Region (Southern Italy)

A period of bad weather conditions due to prolonged intense rainfall and strong winds can trigger landslides, floods, secondary floods (accumulation of rain on surfaces with low permeability), and sea storms, causing damage to humans and infrastructure. As a whole, these periods of bad weather and triggered phenomena can be defined as damaging hydrogeological events (DHEs). We define a methodological approach based on seven simple indexes to analyze such events. The indexes describe the return period (T) and trend of rainfall, the extent of hit areas, and the level of damages; they can be considered attributes of georeferenced features and analyzed with GIS techniques. We tested our method in an Italian region frequently hit by DHEs. In a period of 10 years, 747 damaging phenomena (landslides, 43%; floods, 38%) and 94 DHEs have been classified. The road network and housing areas are the most frequently damaged elements, threatened by all types of damaging phenomena. T classes are almost in accordance with the level of damage. These results can be used to outline warning levels for civil protection purposes, to forecast the areas most likely to be hit and the potential ensuing damage, to disseminate information concerning vulnerable areas, and to increase people’s awareness of risk.     

Time‐Dependent Health Risk from Contaminated Groundwater Including Use of Reliability,Resilience, and Vulnerability as Measures

Traditionally, assessment of human health risk caused by contamination of a water supply focuses on the maximum risk to an individual. Here, we introduce a time‐dependent risk assessment method and adapt and explore the reliability, resilience, and vulnerability (RRV) criteria from the surface‐water literature as possible tools for assessing this risk. Time‐dependent risk assessment, including RRV, is applied to two synthetic examples where water quality at a well varies over time. We calculate time‐dependent health risks for discrete periods of exposure to the contaminated water for a variable population. The RRV criteria provide information about time‐dependent risk: probability of an acceptable risk, probability of system recovery, maximum risk, and average exceedance of a prescribed risk threshold. The results demonstrate that episodic contamination events produce fundamentally different time‐dependent risks than long‐term events: these differences, such as generally lower risks for the episodic contamination, can be captured via plots of the risk and the RRV criteria. Furthermore, the evaluation of time‐dependent health risk and the RRV criteria demonstrates significant sensitivity to the shape of the contaminant breakthrough curve, length of exposure, and variability within the population. Overall, analysis of time‐dependent health risks provides substantial insight into the structure of risk, with RRV providing a reasonable framework for the evaluation of these risks.     

Flood problems in Bangladesh: Is there an indigenous solution?

Bangladesh, situated on the delta of the Ganges, the Brahmaputra, and the Meghna rivers, experiences two distinct types of inundations: (a) river floods resulting from excessive runoff contributed by monsoon precipitation and (b) coastal floods induced by storm surges of tropical cyclones. The river floods are normal annual events and human settlements and agricultural practices have adapted admirably well to their regimes. Abnormal floods that occur once in every few years cause serious damage to crops and properties. To minimize flood losses, a number of modern engineering projects have been constructed within Bangladesh. However, the successful solution of the problem would probably require some international collaboration for basinwide unified systems planning, since large parts of the drainage basins of Bangladesh lie beyond its borders. In the absence of such collaboration, internal resources should be utilized for the construction of smaller public projects, such aspolders, and for encouraging and reinforcing various types of indigenous adjustments to floods. There are very few successful indigenous adjustments to coastal floods. Most of the structural solutions, such as community shelters and higher embankments, are expensive public projects that are probably beyond the means of the internal resources of the country.     

History and Development of the NRCS Lag Time Equation1

Abstract: Many of the hydrologic methods that are used in engineering practice today resulted from the Spring Flood of 1936, which blanketed the Northeastern portion of the United States. Because of the flood damage that was caused by this rainfall‐snowmelt event, many federal agencies including the U.S. Army Corp of Engineers and the Soil Conservation Service (SCS) implemented the hydrologic theories that were available in the literature at this time and developed hydrologic procedures for design flow estimation. Sherman had recently published his unit hydrograph theory in 1932, and later in 1938 Snyder, who had been charged by the Water Resource Council to develop a synthetic unit hydrograph, published his famous paper. The SCS unit hydrograph theory was developed by Victor Mockus in the late 1950s. Most if not all of the theories at that time reported the rainfall‐runoff process for floods as a surface phenomenon, and as such those theories all required some type of a timing parameter to estimate watershed response time. This article documents the development of the SCS lag equation.     

Predicting Debris Yield From Burned Watersheds: Comparison of Statistical and Artificial Neural Network Models1

Abstract: Alluvial fans in southern California are continuously being developed for residential, industrial, commercial, and agricultural purposes. Development and alteration of alluvial fans often require consideration of mud and debris flows from burned mountain watersheds. Accurate prediction of sediment (hyper‐concentrated sediment or debris) yield is essential for the design, operation, and maintenance of debris basins to safeguard properly the general population. This paper presents results based on a statistical model and Artificial Neural Network (ANN) models. The models predict sediment yield caused by storms following wildfire events in burned mountainous watersheds. Both sediment yield prediction models have been developed for use in relatively small watersheds (50‐800 ha) in the greater Los Angeles area. The statistical model was developed using multiple regression analysis on sediment yield data collected from 1938 to 1983. Following the multiple regression analysis, a method for multi‐sequence sediment yield prediction under burned watershed conditions was developed. The statistical model was then calibrated based on 17 years of sediment yield, fire, and precipitation data collected between 1984 and 2000. The present study also evaluated ANN models created to predict the sediment yields. The training of the ANN models utilized single storm event data generated for the 17‐year period between 1984 and 2000 as the training input data. Training patterns and neural network architectures were varied to further study the ANN performance. Results from these models were compared with the available field data obtained from several debris basins within Los Angeles County. Both predictive models were then applied for hind‐casting the sediment prediction of several post 2000 events. Both the statistical and ANN models yield remarkably consistent results when compared with the measured field data. The results show that these models are very useful tools for predicting sediment yield sequences. The results can be used for scheduling cleanout operation of debris basins. It can be of great help in the planning of emergency response for burned areas to minimize the damage to properties and lives.     

The florence floods—What the papers said

The floods of 1966 in Northern Italy provoked varying reactions from officialdom and the press. Political and administrative problems received as much coverage as the environmental effects of the disaster, but learned opinion gained a new, if rather temporary, status in the newspapers of the time. In retrospect, economic recovery seems to have occurred more rapidly than predicted in the gloomy forecasts of the time, although a clear picture of the disruption caused by the floods     

AutoRAPID: A Model for Prompt Streamflow Estimation and Flood Inundation Mapping over Regional to Continental Extents

This article couples two existing models to quickly generate flow and flood‐inundation estimates at high resolutions over large spatial extents for use in emergency response situations. Input data are gridded runoff values from a climate model, which are used by the Routing Application for Parallel computatIon of Discharge (RAPID) model to simulate flow rates within a vector river network. Peak flows in each river reach are then supplied to the AutoRoute model, which produces raster flood inundation maps. The coupled tool (AutoRAPID) is tested for the June 2008 floods in the Midwest and the April‐June 2011 floods in the Mississippi Delta. RAPID was implemented from 2005 to 2014 for the entire Mississippi River Basin (1.2 million river reaches) in approximately 45 min. Discretizing a 230,000‐km² area in the Midwest and a 109,500‐km² area in the Mississippi Delta into thirty‐nine 1° by 1° tiles, AutoRoute simulated a high‐resolution (~10 m) flood inundation map in 20 min for each tile. The hydrographs simulated by RAPID are found to perform better in reaches without influences from unrepresented dams and without backwater effects. Flood inundation maps using the RAPID peak flows vary in accuracy with F‐statistic values between 38.1 and 90.9%. Better performance is observed in regions with more accurate peak flows from RAPID and moderate to high topographic relief.